Tag: AI

  • State of the Profession 2026 by CIPR

    State of the Profession 2026 by CIPR

    About the paper

    CIPR’s State of the Profession 2026 is original survey research on working conditions, activities, AI use, recruitment, pay, professional challenges, polarisation and inclusion in public relations.

    Chalkstream conducted an online survey for CIPR from 13 April to 4 May 2026, receiving 738 responses, of which 573 were complete; 88% of respondents were UK-based and 12% were based outside the UK.

    The 2026 sample is older, more senior and contains more agency, consultancy and independent practitioners than previous waves, so year-on-year comparisons, particularly on income, activities and AI, should be treated with caution.

    Length: 75 pages

    More information/download: https://newsroom.cipr.co.uk/organisations-more-cautious-about-taking-public-positions-compared-to-five-years-ago-ciprs-2026-state-of-the-profession-report-reveals/

    Core Insights

    1. What overall picture does the report paint of the PR profession in 2026?

    The central picture is not of a profession moving uniformly in one direction, but of one experiencing the same pressures very differently depending on sector, seniority, age and geography.

    Employment conditions illustrate this split clearly. Agencies and consultancies appear comparatively buoyant: 49% say they are growing, up from 38% in 2024. In-house teams look much weaker, with only 21% reporting growth, down from 33%, while 65% describe their teams as stable and 13% as shrinking. Confidence in future growth is correspondingly low. Among in-house respondents, 42% are not at all confident that their team will grow over the next 12 months, rising to 46% in the public sector.

    The profession’s day-to-day work combines continuity with some movement towards more strategic responsibilities. Copywriting and editing remains the most commonly reported activity, cited by 52% of practitioners, as it has been throughout the series since at least 2018. Strategic planning has moved into second place at 41%, followed by PR programmes and campaigns at 38%, community and stakeholder relations and media relations at 36% each. The report cautions, however, that the apparent rise of strategic planning may partly reflect the more senior 2026 sample.

    The perceived challenges facing the profession have also shifted. Fake news and disinformation is now the most commonly identified challenge, cited by 54%, replacing AI, which falls to second place at 49%. The expanding skill set expected of individual practitioners comes third at 39%. Concerns about professional status have also re-emerged: 34% cite PR not being seen as a professional discipline, returning the issue to the top five.

    Taken together, the report portrays a profession whose traditional work has not disappeared, but whose operating environment has become more difficult. Practitioners are simultaneously dealing with technological change, disinformation, expanding skills expectations, polarised public debate and uneven organisational investment in communication.

    2. What does the study reveal about the adoption of AI, and where are the largest gaps between use, confidence and governance?

    AI has become a normal part of professional practice, but the report finds a striking gap between widespread adoption and much more ambivalent attitudes towards its value.

    Eighty-two percent of practitioners use AI at least occasionally for work. Regular use remains unchanged from 2024 at 45%, but occasional use has risen from 28% to 37%, while the proportion saying they have never used AI has fallen from 8% to just 3%. Use is therefore close to universal, even though intensive use has not increased.

    Practitioners also report substantial practical benefits. Eighty-six percent say AI has improved the speed with which they complete tasks, and 75% say it has improved their ability to concentrate on higher-value or more strategic work. The evidence is more mixed when it comes to quality: 55% report a positive effect on the quality of their work, 48% on consistency and personalisation, and only 37% on the quality of decision-making.

    The attitudes data reveals an important tension. Seventy-one percent feel confident in their ability to adapt to AI-related changes, and 81% believe the skills that make PR professionals valuable cannot be replicated by AI. Yet only 47% believe AI will improve the overall quality of PR work. Thirty-four per cent regard AI as a threat to the profession, while 76% believe AI-generated content risks damaging trust in public relations.

    Governance has not fully caught up with adoption. Although 66% say their organisation has an AI policy, 24% of users report having no formal policy or guidance on which tools may be used. A third say they have used unapproved AI tools at work. Sixty percent have received AI training.

    There is also a weak link between organisations and their external suppliers. Among in-house practitioners who use agencies or independents, half have never asked them about their use of AI. Looking from the supplier side, 49% of agencies and independents say clients have not asked them about AI use at all, while only 5% say most or all clients ask.

    Sector differences are pronounced. Private-sector and agency practitioners tend to be the most positive and confident about AI. The public sector has the strongest policy and training structures: 79% report an organisational AI policy and 67% have received training. Not-for-profit and NGO practitioners appear comparatively exposed, with lower confidence, weaker policy coverage and greater concern about AI as a threat.

    The report therefore suggests that PR has moved beyond the question of whether AI will be adopted. The more important issue is now whether organisations can develop governance, skills and professional standards quickly enough to match the level of actual use.

    3. What does the report say about employment, recruitment and income, and how reliable are the apparent changes?

    The labour-market findings point to a growing divide between agencies and in-house communication functions.

    Nearly half of agencies and consultancies report that they are growing, compared with just over one-fifth of in-house teams. Agency respondents are also substantially more optimistic about the future: 46% are moderately or extremely confident that their organisation will grow over the next year, compared with only 21% of in-house practitioners.

    The differences are sharpest in the public sector. Forty-six percent of public-sector respondents are not at all confident about team growth over the next 12 months, compared with 34% in the private sector and 44% in not-for-profits and NGOs.

    The survey also finds changes in working patterns. Thirteen percent of respondents work part-time, compared with 7% in 2024. CIPR cautions against interpreting this as evidence of a profession-wide move towards part-time employment because independent practitioners, who are particularly likely to work part-time, make up a larger share of the 2026 sample.

    The same caveat matters considerably for salaries. Average full-time income is reported at £64,817, 22% above the 2024 figure of £53,052 and the highest recorded in the series. But the report explicitly attributes much of this rise to the greater seniority of the 2026 respondents. Directors, partners, founders and managing directors average £102,887, compared with £69,930 for heads of communication and associate directors, £56,311 for managers and £38,774 for interns, trainees, assistants and executives. A sample containing more senior practitioners will therefore mechanically produce a higher overall average.

    Organisation type also matters. Private-sector in-house practitioners report the highest average full-time income at £74,419, followed by agency and consultancy practitioners at £68,675 and independents at £66,900. Public-sector practitioners average £59,156 and not-for-profit and NGO practitioners £56,714.

    The estimated gender pay gap remains unresolved. After controlling statistically for age and seniority, the report calculates a gap of £5,539, compared with £3,894 in 2024. The 2026 estimate falls just short of statistical significance because fewer respondents provided income data, so the apparent increase should not be treated as definitive. The report’s more defensible conclusion is that there is no evidence that the gap has disappeared or clearly narrowed.

    Recruitment also contains a diversity concern. Among senior in-house respondents involved in recruitment, non-White practitioners are less likely than White practitioners to participate in recruitment decisions, 59% compared with 70%. The same pattern appeared in the 2022 and 2024 studies, making it more than a one-year anomaly within this survey series.

    4. How is growing social and political polarisation affecting the work and responsibilities of PR practitioners?

    This is one of the report’s most distinctive additions. For the first time, the State of the Profession study includes a substantial section specifically devoted to polarisation and controversial issues.

    The findings suggest that dealing with contested issues is increasingly becoming part of normal PR work. Sixty-four percent say navigating polarised opinions is becoming a larger part of their job, while 54% say polarisation in society has made their work more difficult. Ninety-one percent regard keeping up with current affairs as necessary for their job, yet 38% say doing so negatively affects their mental health.

    Personal and professional views also regularly come into conflict. Forty-three percent say they are sometimes asked to communicate messages with which they personally disagree, although 59% believe representing views one may personally disagree with is part of the professional role.

    More broadly, 82% say they encounter ethically or politically difficult topics at least occasionally: 40% rarely, 28% sometimes, 9% often and 4% very often. Younger practitioners report such experiences considerably more frequently. Among those aged 16–34, 53% say they encounter them sometimes, often or very often, compared with 21% among those aged 55 and over. The report explicitly says it cannot determine whether this reflects differences in the work given to younger employees or differences in the attitudes of the generations themselves.

    These issues are not merely theoretical. Practitioners report changing employment or client decisions because of organisations’ positions on social, political or ethical issues. Thirty percent have raised concerns internally about an employer’s stance, 24% have started looking for another job, and 22% have decided not to apply for a job they would otherwise have considered. On the agency and consultancy side, 39% have decided not to pitch for work because of a potential client’s position, 37% have declined a client or project and 22% have ended or attempted to end a client relationship.

    Organisations themselves appear to have become more cautious. Fifty-two percent of respondents say their organisation is more reluctant to take public positions than five years ago, while 48% believe the risks of taking positions on controversial issues outweigh the benefits. At the same time, 47% say organisations face greater pressure to comment, and 46% say employees increasingly expect them to take positions.

    The crucial governance finding is that fewer than half have meaningful organisational guidance. Only 21% report a formal policy on controversial issues and another 24% informal guidance; 39% have neither.

    When asked what would help, the most popular answer is not communications training. Sixty-one per cent want senior leadership to take responsibility for decisions on controversial issues, ahead of training in navigating polarised audiences at 53% and clearer organisational policy at 52%.

    The implication is that polarisation is increasingly a corporate governance issue rather than simply a communication challenge. Practitioners appear to be asking senior leaders to make the fundamental decisions about whether an organisation should speak, remain silent or take a particular position, rather than leaving communicators to carry that responsibility themselves.

    5. What does the study show about diversity and inclusion, and where are the tensions between the profession’s overall picture and individual experiences?

    The headline picture of workplace inclusion is comparatively positive, but the aggregate figure conceals substantial differences between groups.

    Sixty-eight percent of respondents describe their work environment as very or extremely inclusive, including 29% who call it extremely inclusive. Only 8% describe their workplace as not at all or only slightly inclusive.

    Organisation type matters considerably. Eighty-one percent of agency and consultancy respondents describe their workplace as very or extremely inclusive, compared with 62% in private-sector in-house teams, 61% in the public sector and 67% in not-for-profit organisations and NGOs.

    The most significant difference, however, concerns ethnicity. Among White respondents, 72% describe their workplace as very or extremely inclusive and only 5% as not at all or slightly inclusive. Among non-White respondents, the corresponding figures are 57% and 18%. Non-White practitioners are therefore more than three times as likely to report low workplace inclusivity.

    Representation also changes markedly with seniority. White respondents account for 58% of interns, trainees, assistants and executives, but their proportion rises progressively to 87% among directors, partners and managing directors. The report notes that this pattern has appeared in the data for several years.

    At the same time, diversity itself has fallen as a perceived industry challenge. Only 17% now identify lack of diversity among PR practitioners as one of the profession’s major challenges, placing it tenth, compared with fifth in 2024. This creates an important tension within the findings: concern about diversity has declined among practitioners as a whole even though the data continues to show substantial differences in representation, recruitment involvement and perceived inclusion.

    The report also broadens the diversity picture beyond ethnicity and gender. Fourteen percent of respondents report a long-term physical or mental health condition affecting their daily activities, while 16% identify as neurodivergent. Both figures vary considerably by employment type. Nineteen percent of independent practitioners identify as having a disability and 32% as neurodivergent, compared with 6% and 12% respectively among agency and consultancy respondents. The report does not establish why independent practice attracts or accommodates these groups at higher rates, so causal interpretations would go beyond the evidence.

    Overall, the report suggests that the profession should be cautious about treating a generally positive average assessment of inclusion as evidence that inclusion problems have been resolved. The experience of the average respondent is not necessarily the experience of practitioners from less well represented groups, and some of the strongest inequalities become visible only when the data is broken down by ethnicity, seniority or organisation type.

  • Global Corporate Affairs Survey 2026 by Oxford-GlobeScan

    Global Corporate Affairs Survey 2026 by Oxford-GlobeScan

    About the paper

    The 2026 Oxford-GlobeScan Global Corporate Affairs Survey is an original expert survey examining the risks, opportunities and evolving role of Corporate Affairs.

    The online survey was fielded from 4 February to 27 March 2026 among 294 senior Corporate Affairs and related professionals in 51 countries; 83 percent had more than ten years’ experience and 79 percent reported to Board, CEO or Group Executive level.

    The sample spans Europe, South America, North America, Africa and Asia-Pacific, although it is unevenly distributed geographically, with Europe accounting for 44 percent and South America 26 percent of respondents.

    Length: 28 pages

    More information/download: https://globescan.com/2026/07/20/oxford-globescan-global-corporate-affairs-survey-2026/

    Core Insights

    1. What is the report’s central argument about how the Corporate Affairs environment is changing?

    The report argues that Corporate Affairs is being reshaped by the convergence of geopolitical volatility, geoeconomic fragmentation and rapid technological change, particularly AI. Its central message is not simply that the external environment has become more difficult, but that the traditional Corporate Affairs model is becoming inadequate for dealing with it.

    Geopolitical risk remains by far the dominant short-term concern, cited by 76 percent of respondents. However, the report argues that the nature of this risk is becoming more concrete and economically consequential. Concerns about tariffs, supply chains, economic uncertainty, energy costs and changing trade relationships indicate what the authors describe as a return to “geoeconomics”: political objectives increasingly being pursued through economic instruments.

    At the same time, AI has moved dramatically up the agenda. AI and technology were identified as a major business risk by 44 percent of respondents, compared with only 17 percent in 2025. Yet the same technological transformation is also seen as the largest opportunity available to business, with 71 percent naming innovation, digitalisation and AI as a major opportunity.

    This combination of geopolitical and technological disruption creates a more complex role for Corporate Affairs. The function is increasingly expected to interpret external developments, anticipate political and reputational risks, coordinate responses across the organisation and translate external intelligence into advice for senior management.

    The report therefore portrays Corporate Affairs as moving away from being primarily a communications or stakeholder-relations function towards becoming a strategically embedded organisational capability. Its emerging role is closer to that of a navigation function: helping leadership understand an increasingly volatile environment and decide how the organisation should respond.

    This transformation is not presented simply as an aspiration from the authors. Seventy-four percent of respondents themselves believe that their existing Corporate Affairs approach requires some form of revision, including 17 percent who believe it requires radical revision.

    2. What do Corporate Affairs leaders see as the most important risks and opportunities for global business?

    The risk landscape is dominated by geopolitics. Seventy-six percent of respondents identify geopolitical risk as one of the biggest risks facing global business over the next two years, making it the leading concern for the seventh consecutive year.

    The second-largest risk is the impact of AI and technology at 44 percent, up 27 percentage points from 2025. This is one of the most striking changes in the survey. Macroeconomic conditions rank third at 34 percent, followed by climate change at 19 percent. Data privacy and cyber risk, populism and social division, reputation risk, regulatory pressures, supply-chain problems and misinformation also feature among the principal concerns.

    The rankings vary by sector and region. Geopolitics leads across every region, but macroeconomic risks rank particularly highly in Africa and North America. Sector differences are also evident: climate change remains especially important in food and agriculture, while data privacy and cyber issues are prominent in ICT, media and financial services.

    The opportunity landscape looks very different but is driven by some of the same forces. Innovation, digitalisation and AI dominate at 71 percent, substantially ahead of stakeholder engagement and trust building at 23 percent and sustainable growth and ESG strategies at 17 percent. Economic growth and upliftment, economic and political stability, collaboration, talent and skills, and the energy transition are also identified as opportunities.

    This creates one of the report’s central paradoxes. AI is simultaneously regarded as one of the largest threats and the single largest opportunity facing business. The report interprets this as evidence that Corporate Affairs organisations must learn both to exploit new technology and to manage the disruption it creates.

    The increasing importance of stakeholder engagement is another notable finding. Mentions of stakeholder engagement and trust building as an opportunity rose from 11 percent in 2025 to 23 percent in 2026. The report therefore sees technological capability and stakeholder legitimacy as complementary rather than competing priorities.

    Human capability is also important. Respondents connect successful technological adoption with skills, leadership capacity and organisational readiness. The opportunity presented by AI is therefore not regarded simply as a matter of acquiring technology; organisations also need people capable of integrating it into decision-making and business processes.

    3. How are governance, ESG, corporate purpose and political advocacy changing?

    The report describes a substantial recalibration of the ESG agenda rather than its disappearance.

    Climate change remains the most frequently cited ESG issue, mentioned by 40 percent of respondents, but this is down from 51 percent in 2025. Governance and ethics have risen sharply to 34 percent, while regulation and policy uncertainty rank third at 16 percent.

    The shift becomes even clearer when respondents are asked which component of ESG represents the greatest reputational risk. Governance ranks first globally at 45 percent, ahead of environmental risk at 27 percent and social risk at 26 percent. Governance has therefore moved from the lowest-ranked of the three dimensions in 2024 to the highest in 2026.

    The report interprets governance broadly. It includes ethics, accountability, board oversight, transparency, disclosure, regulatory compliance and the organisation’s ability to demonstrate effective control in volatile circumstances. The growing prominence of governance therefore connects directly with the wider geopolitical and regulatory environment described elsewhere in the report.

    Another striking change concerns diversity and inclusion. Internal inclusivity and diversity falls to 7 percent among respondents’ leading ESG issues, compared with 21 percent in 2025. The authors interpret this not as evidence that social issues have become irrelevant, but as a more cautious corporate response to political and cultural controversy, with organisations concentrating attention on governance, regulation and compliance.

    Political advocacy remains significant despite these pressures. Thirty-three percent of respondents say their organisation increased its political advocacy during the previous year. Increased engagement is particularly common in Asia-Pacific, North America and Europe. The report also notes that the same external pressures can produce opposite responses: some organisations increase advocacy to shape policy and manage uncertainty, while others reduce their visibility because of concerns about politicisation or reputational exposure.

    Corporate purpose shows a similar tension. Eighty-two percent of respondents say their company has a stated corporate purpose, but only 32 percent report increased executive-level discussion of purpose during the previous year, down from 45 percent in 2025. The biggest obstacles to implementing purpose include the political environment, lack of an integrated implementation plan, organisational complexity and inadequate measurement.

    Yet respondents do not expect expectations of corporate purpose to disappear. Thirty-six percent believe societal expectations for purposeful organisational leadership will be significantly higher in three years. The report therefore identifies a growing tension between organisations becoming more cautious internally and stakeholders continuing to expect businesses to demonstrate a broader societal role.

    4. How much value does Corporate Affairs believe it creates, and how effectively is that value being measured?

    Corporate Affairs practitioners remain confident that their function contributes substantially to business value, but the report exposes a significant gap between perceived value and the ability to demonstrate it systematically.

    Seventy-nine percent of respondents believe Corporate Affairs has a high or significant impact on creating and protecting business value. This remains a strong result, although it has declined from 84 percent in 2025 and 91 percent in 2023.

    Respondents identify forecasting around risk, crises and political developments as the area in which Corporate Affairs is most effectively creating business value, mentioned by 32 percent. Reputation management follows at 30 percent, while building internal alignment behind strategic objectives reaches 21 percent.

    When asked more broadly where Corporate Affairs has contributed during the previous year, trust and reputation dominate. The report groups 94 percent of responses around reputation capital and trust, 69 percent around policy and risk, and 46 percent around growth and people-related contributions such as market access, revenue enablement and talent.

    The measurement evidence, however, is considerably weaker. Only 49 percent say that they formally measure the impact or success of Corporate Affairs, while 41 percent say that they do not.

    Even among the 143 respondents who use formal metrics, measurement remains heavily concentrated on relatively traditional indicators. Media performance is used by 75 percent, formal reputation or trust tracking by 65 percent, and informal stakeholder sentiment or engagement by 65 percent. Employee engagement and internal alignment indicators are used by 60 percent.

    More outcome-oriented measures are less widespread. Public-policy and advocacy outcomes are used by 45 percent, risk-mitigation indicators by 39 percent, and investor perceptions or valuation signals by only 31 percent.

    The report consequently identifies measurement as one of the function’s significant weaknesses. Corporate Affairs practitioners themselves say that impact measurement, data and AI capabilities require improvement, while the authors argue that organisations need to move beyond media monitoring and informal stakeholder feedback towards more structured assessments of reputation, trust, risk reduction and business value.

    A second capability gap concerns AI-generated misinformation. Only 18 percent of respondents consider their Corporate Affairs function fully prepared to handle a deepfake or AI-driven misinformation incident, while 43 percent consider themselves not very prepared. The report therefore depicts a function that recognises both its strategic importance and the scale of the capability development still required.

    5. What does the report say Corporate Affairs must change to become future-fit?

    The strongest conclusion is that maintaining the existing Corporate Affairs model is no longer considered sufficient. Seventy-four percent of respondents believe their approach requires revision: 57 percent favour modest revision and 17 percent favour radical change.

    The two major forces behind this perceived need are geopolitical volatility and AI. Respondents identify the need to respond more effectively to geopolitical conditions as one of the principal drivers of change, alongside the need to manage and exploit AI and communications technology.

    When respondents specify what needs to change, clearer strategy, focus and prioritisation come first at 35 percent. Operating models, governance and internal alignment follow at 30 percent. Other priorities include resource allocation, stronger stakeholder and leadership influence, specialised skills, improved measurement and data capabilities, and stronger brand and reputation management.

    Asked for the single most important action that would make Corporate Affairs future-fit, respondents place proactive, insight-led and adaptive strategic planning first at 26 percent. Adoption and integration of AI and technology follows closely at 24 percent, while talent acquisition and upskilling ranks third at 21 percent. Anticipatory governance, including foresight and risk preparedness, reaches 13 percent.

    The report ultimately condenses its findings into four broad requirements.

    First, Corporate Affairs strategy needs to be embedded within overall business strategy. The function should have close access to the CEO, Board and senior leadership because its ability to interpret external developments is increasingly relevant to strategic decision-making.

    Second, organisations need operating models that connect headquarters, regions and business units. Geopolitical differences, trade policies and rapidly changing stakeholder expectations make internal alignment and coordinated execution increasingly important.

    Third, Corporate Affairs needs new capabilities. AI expertise is one component, both for exploiting technology and responding to threats such as misinformation. But the report also emphasises foresight, political and economic analysis, international trade knowledge, forecasting and specialist talent.

    Fourth, Corporate Affairs needs stronger evidence of its impact. Demonstrating contributions to reputation, trust, risk management and long-term business value becomes increasingly important if the function is to justify its more strategic organisational position.

    Taken together, these findings portray a profession moving from communication and reputation management towards a broader strategic role centred on intelligence, anticipation, organisational alignment and external navigation. The report’s core implication is that reputation and stakeholder engagement remain fundamental capabilities, but they are no longer sufficient on their own. Future Corporate Affairs teams are expected to combine them with geopolitical intelligence, technological competence, foresight, measurement and much closer integration with business strategy.

  • World PR Report 2025-2026 by ICCO

    World PR Report 2025-2026 by ICCO

    About the paper

    The report ‘The Era of Relationship Capital – Consultancy Views of the Path Ahead’ examines the state and future direction of the global PR consultancy industry, combining original survey research with industry expert commentary.

    Its core research is an online survey conducted from September 2025 to March 2026 among 244 PR professionals across Africa, Asia-Pacific, Eastern Europe, Western Europe, the United Kingdom, North America, Latin America and the Middle East.

    Country-level coverage, recruitment method and weighting are not clearly specified, and the UK, North America, Latin America and Middle East each have fewer than 30 respondents. There is also a methodological inconsistency: at least one chart gives a base of 291 respondents rather than the stated 244, so detailed figures and regional comparisons should be treated with some caution.

    Length: 61 pages

    More information/download: https://www.iccoglobal.com/icco-world-pr-report/

    Core Insights

    1. What overall picture does the report paint of the global PR industry in 2025–2026?

    The report depicts a profession that remains broadly confident about its future but is operating under significantly greater pressure. The global optimism score for PR market growth is 6.2 out of 10, down from 6.6 the previous year, while 63 percent of respondents say they are optimistic about growth in their market. The result is not a story of contraction so much as one of more cautious confidence.

    More importantly, the report argues that the nature of PR is changing. Traditional execution is becoming less central to where agencies expect value and growth to come from. Strategic consulting was the leading growth area over the previous year, cited by 37 percent of respondents, followed by corporate reputation at 35 percent. Looking ahead, corporate reputation is expected to be the largest growth area at 41 percent, followed by strategic consulting at 39 percent. Strategic consulting also leads expected investment at 39 percent.

    Technology, healthcare and financial and professional services are the strongest client sectors. Technology is particularly prominent, reflecting both the wider AI boom and organisations’ need to communicate technological change.

    The report therefore presents PR as moving further “upstream” into advisory work. Its authors and contributors repeatedly describe a shift away from communications as primarily an execution or amplification function and towards judgement, interpretation, risk management, reputation and organisational decision-making.

    However, that development is taking place amid substantial economic and organisational pressure. The leading challenges include effectively integrating AI, cited by 34 percent; clients unwilling to commit sufficient funds, 31 percent; economic conditions, 29 percent; and pressure to achieve profit and margin targets, 25 percent.

    The central tension is consequently clear: PR agencies see growing demand for higher-value strategic advice at the same time that budgets, business models, technology and talent are all under pressure.

    2. How is AI changing PR practice, skills and professional expectations?

    AI is the report’s most powerful technological theme. It has moved from experimentation towards normal professional infrastructure.

    Eighty-nine percent of respondents say their organisations have integrated AI tools into everyday processes, up from 74 percent the previous year. Ninety-one percent identify AI as the technology most likely to be relevant to PR practitioners over the next five years, far ahead of cybersecurity at 38 percent and data science at 37 percent.

    The skills agenda reflects that transformation. Mastery of AI tools is identified by 45 percent as one of the most important skill sets for the coming decade, narrowly ahead of strategic consulting at 43 percent. Another 32 percent highlight the ability to critically assess and ethically deploy AI-generated content.

    The report does not, however, portray technological capability as sufficient by itself. Its contributors consistently argue that AI increases rather than eliminates the importance of human judgement. AI can accelerate research, pattern recognition, content creation and analysis, but the professional still has to provide context, interpretation, cultural understanding and strategic judgement.

    This creates a second dimension to AI adoption: governance and ethics. Eighty-four percent believe ethical adoption and use of AI is important for the PR industry, and 78 percent regard cultural intelligence as important as AI becomes embedded in communications.

    There is consequently a gap between adoption and maturity. AI is already widespread, yet effectively integrating it remains the single most frequently cited business challenge. The report suggests that the next competitive divide will not simply be between agencies that use AI and those that do not. It will be between organisations that combine AI with strong data, governance, professional judgement and strategic capability and those that merely use it to accelerate existing production.

    3. How are client demand and agency value shifting, and what does this mean for the traditional PR business model?

    The report provides considerable evidence that clients increasingly expect PR agencies to contribute at a more strategic level.

    Strategic consulting, corporate reputation and public affairs are all significant growth areas, while influencer communications, measurement and analytics and ESG also attract investment. Expected investment is led by strategic consulting at 39 percent and influencer communications at 38 percent, with measurement and analytics at 27 percent and ESG at 23 percent.

    Corporate reputation is especially important to the report’s argument. Sixty-three percent identify proactively improving corporate reputation as an important client objective, considerably ahead of issues and crisis management at 41 percent and increasing sales at 38 percent. Corporate reputation is also the service area most widely expected to grow during the coming year.

    At the same time, AI is reducing the scarcity value of routine production. Several contributors argue that content creation and other executional activities will increasingly become commoditised as technology lowers their cost. This makes expertise, interpretation, evidence and trusted counsel relatively more valuable.

    Yet there is an important contradiction in the data. Although the industry aspires to become more strategic, client measurement requests remain heavily oriented towards traditional communications outputs. Media clippings are the most commonly requested item, cited by 56 percent, while 36 percent still report client requests for Advertising Value Equivalency and another 36 percent for sentiment metrics.

    The industry’s own use of measurement also remains disproportionately focused on reporting. Eighty-two percent say measurement and evaluation metrics are used for reporting, compared with 52 percent for planning, 50 percent for justifying fees and budgets, 44 percent for decision-making and 40 percent for advising.

    The report therefore describes an industry caught between two models. Its growth ambitions centre on advisory work, reputation and strategic influence, but important parts of its measurement and client relationship practices remain rooted in traditional reporting and communications outputs.

    4. What structural challenges could prevent PR firms from making this transition successfully?

    Talent is one of the most persistent constraints. Retaining key talent is the most commonly identified talent challenge at 41 percent, followed by developing junior and mid-level staff at 35 percent and motivating younger executives at 34 percent.

    The industry’s recruitment patterns may reinforce the problem. Rival agencies remain the leading expected source of talent at 61 percent, followed by journalism at 54 percent and graduate programmes at 49 percent. Several expert contributors argue that this is poorly aligned with the industry’s emerging needs for expertise in data, technology, regulation, policy and specialised industries.

    Their interpretation is that communications firms need more multidisciplinary recruitment from areas such as analytics, law, science, public policy and professional services rather than repeatedly competing for the same conventional PR talent.

    Measurement creates another structural challenge. Twenty percent identify the inability to measure PR impact effectively as a major business challenge. The report’s measurement commentary argues that AI makes rigorous evaluation more important because technology can scale poor assumptions and weak analysis just as efficiently as it can scale good analysis.

    Ethics is similarly becoming more consequential. Misinformation and disinformation are consistently identified among the profession’s leading ethical challenges. Eighty-six percent believe agencies should advise clients against behaviour they regard as unethical, and 73 percent say they have turned down a client or assignment for ethical reasons. However, only 44 percent believe PR is more ethical than other industries, indicating a considerable gap between professional aspirations and perceptions of industry-wide behaviour.

    Workforce sustainability presents an additional problem. Thirty-four percent of respondents report experiencing mental health problems during the previous 12 months, while formal organisational support is far from universal.

    Taken together, the findings suggest that PR’s move towards strategic advisory work requires more than adding new technologies or services. Agencies need deeper talent pools, stronger measurement capability, credible ethics and governance, and organisational models capable of retaining and developing professionals with increasingly complex skill sets.

    5. What does the report mean by “relationship capital”, and why does it see this as the profession’s central strategic opportunity?

    “Relationship capital” is the organising idea used by ICCO to connect many of the report’s otherwise separate findings. It describes the accumulated trust, goodwill, credibility and access that allow an organisation to be heard and believed by important stakeholders, particularly during uncertainty or crisis.

    The concept reflects the report’s view of the environment in which organisations now operate. Geopolitical instability, economic uncertainty, social polarisation, fragmented media, misinformation and AI-generated content have all made reputation and trust more difficult to establish and maintain.

    ICCO’s argument is that attention alone therefore becomes less strategically valuable. Attention can be purchased or generated quickly, whereas relationships and credibility have to be accumulated through consistent behaviour and engagement over time.

    This helps explain why corporate reputation, strategic consulting, crisis counsel, public affairs, insight and evaluation feature so prominently throughout the report. They all contribute to an advisory model in which communications professionals help organisations understand stakeholders, anticipate risk, make decisions and maintain legitimacy rather than simply generate visibility.

    It also reveals the report’s underlying perspective. As an organisation representing PR consultancies, ICCO clearly has an interest in presenting communications as a strategically important management discipline rather than a commodity service. The survey findings provide some support for that interpretation, particularly the growth of strategic consulting and corporate reputation. However, other findings, including continued budget pressure, traditional measurement practices and strong demand for executional services such as influencer communications, show that the transition is incomplete.

    The report’s broader conclusion is therefore not that traditional PR activities are disappearing. It is that their relative value is changing. As AI makes communications production cheaper and information environments more fragmented, the report expects competitive advantage to shift towards the capabilities that are harder to automate: trusted relationships, judgement, strategic interpretation, ethical counsel and evidence-based advice.

  • What is A.I. reading – May 2026 Edition by Muck Rack

    What is A.I. reading – May 2026 Edition by Muck Rack

    About the paper

    What is AI Reading? by Muck Rack’s Generative Pulse examines which sources generative AI systems cite when answering realistic consumer prompts.

    The report is a modelling/data-pack style citation analysis based on a large prompt set submitted to ChatGPT, Claude and Gemini, with more than 25 million cited links analysed across multiple industries; the geographic scope is not clearly specified in the report.

    Length: 43 pages

    More information / download:
    https://generativepulse.ai/report/

    Core Insights

    1. What is the central finding of the report about the sources AI systems cite?

    The report’s central argument is that generative AI citations are overwhelmingly shaped by non-paid, earned, third-party sources rather than paid media or advertising. About 99% of links cited by AI come from non-paid media, while paid and advertorial content accounts for only 0.3% of all citations. Press releases account for 1.1%.

    Within that non-paid universe, journalism remains a major foundation of AI visibility. The report finds that about 27% of all links cited by AI are journalistic. This is framed as a consistent pattern across Muck Rack’s previous studies, where journalism has generally accounted for 20–30% of AI citations.

    However, journalism is not the only important source category. The report’s pie charts on pages 5–7 show a broader mix: corporate blogs and content at 24%, aggregators and encyclopedic sources at 17.4%, owned media at 13.7%, government/NGO sources at 8.6%, academic/research sources at 4%, social/UGC at 2.9%, tech platforms at 0.9%, press releases at 1.1%, and paid/advertorial at 0.3%.

    The implication is clear: visibility in AI-generated answers is not mainly bought through advertising. It is earned through the kinds of sources AI systems treat as credible, relevant or useful: journalism, reference sources, third-party content, government data, academic material, user-generated platforms and some owned content.

    2. How do ChatGPT, Claude and Gemini differ in their citation behaviour?

    The report argues that each AI provider has a distinct citation ecosystem. ChatGPT, Claude and Gemini do not simply cite the same sources at different volumes; they appear to rely on meaningfully different source environments.

    ChatGPT is described as a “near-universal citer”. It includes citations in 96% of responses, but averages only about five sources per response. Claude is more selective: only 55% of its responses include citations, but when it does cite, it averages 13 sources per response. Gemini sits between the two, citing in 82% of responses and averaging eight sources per response.

    The differences become even sharper when looking at the actual domains. ChatGPT’s top cited domain is Wikipedia, followed by Axios, YouTube, Kiplinger and Forbes. Claude’s top domain is PubMed Central, followed by Wikipedia, Quora, ScienceDirect and NerdWallet. Gemini’s top cited domain is Reddit, followed by YouTube, Quora, Wikipedia and NIH.

    The report’s interpretation is that these are “three effectively separate information environments”. For PR and communications teams, this matters because AI visibility cannot be reduced to a single generic “AI search” strategy. A brand, journalist, outlet or source may matter a great deal in one model and be nearly invisible in another.

    3. What determines whether journalism, press releases or other content gets cited?

    The report finds that the type of question asked strongly shapes the type of source cited. Industry trend queries are especially likely to draw on journalism: 46% of industry trend responses cite journalistic sources, more than twice the rate for how-to and comparative queries.

    By contrast, how-to queries are less journalism-driven. The report says AI tends to rely more on reference content and brand-owned material when users ask how to do something. Comparative evaluation and best-of queries also behave differently, often pulling in review content, platforms, maps, rankings or consumer advice sources depending on the category.

    Press releases are most likely to appear in industry trend responses, but even there they remain a relatively small part of the citation mix. Around 1.16% of industry trend citations are press releases, compared with 0.33% for best-of queries, 0.27% for risk/due diligence, 0.25% for problem/discovery, 0.13% for comparative evaluation and 0.09% for how-to.

    Recency also matters, especially for journalism. Among journalism citations with known publish dates, 57% were published within the previous 12 months. The report notes a sharp peak in the first month after publication, followed by a decline through month six and then a long tail. Older articles still matter, but the bias toward recent coverage is clear.

    4. Which sources, platforms and outlets stand out most in the report?

    Several sources stand out because they behave differently from the broader pattern.

    Wikipedia is especially important for ChatGPT and Claude. It appears among the top three cited domains in 12 of 17 industries for ChatGPT and 8 of 17 industries for Claude. For Gemini, it appears in the top three in only 3 of 17 industries, because Gemini is more strongly shaped by Reddit, brand domains and Q&A platforms such as Quora.

    Reddit is particularly important for Gemini. The report says Reddit is Gemini’s single most-cited domain, accounting for about 2.4% of all Gemini citations. By contrast, ChatGPT cited Reddit only 16 times and Claude cited it zero times in the study.

    YouTube also differs by provider. It accounts for about 2.1% of Gemini citations and about 2.0% of ChatGPT citations, while Claude returned zero YouTube citations across the study. Claude does cite other video platforms such as TikTok and Vimeo, but not at the same level.

    Among journalism outlets, the standout is Axios. The report says journalism citations are spread across more than 20,000 distinct outlets, with no single publication generally dominating. Axios is the exception: it appears in ChatGPT’s top three cited domains across 13 of 17 industries. The Associated Press appears in the top three for one industry, while The New York Times and Reuters do not appear in the top three most cited sources for any industry in the report.

    5. What are the main implications for PR and communications teams?

    The report’s main implication is that AI visibility is increasingly connected to earned authority across multiple information environments. Traditional media relations still matters, but not in a simple “get mentioned in top-tier media” way.

    First, journalism remains important because it accounts for about 27% of all AI citations and is especially influential for industry trend queries. For brands that want to shape how AI explains what is happening in a sector, credible media coverage appears to be particularly valuable.

    Second, the report suggests that citation strategy must be model-specific. A communications team optimising for ChatGPT would pay close attention to Wikipedia, Axios, YouTube and sector-specific sources. A team concerned with Gemini would need to understand Reddit, YouTube, Quora and other user-generated or community-driven environments. For Claude, academic, research, personal finance and reference-style sources appear more prominent.

    Third, owned media still matters, but it is not enough on its own. Owned media accounts for 13.7% of citations, while corporate blogs and content account for 24%. The distinction in the report is important: third-party corporate/blog content is categorised as earned when it is not owned by the company or product targeted in the query, while first-party corporate/blog content is owned media. This suggests that corporate content can influence AI, but third-party validation remains highly significant.

    Fourth, communications teams need to think beyond classic media lists. Depending on the sector and query type, AI may cite Google Maps, TripAdvisor, Reddit, Quora, YouTube, PubMed Central, NIH, government websites, academic databases, review sites, ranking platforms or trade publications. The relevant source ecosystem changes by industry.

    Finally, the report implies that AI visibility is dynamic. The methodology section explicitly notes that generative AI systems are rapidly evolving and opaque, and that observed behaviours may shift as models are updated or retrained. So the findings should be treated as a snapshot of AI citation behaviour in May 2026, not as a permanent rulebook.

  • 2026 Work Trend Index by Microsoft

    2026 Work Trend Index by Microsoft

    About the paper

    Microsoft’s 2026 Work Trend Index Annual Report examines how A.I. agents are changing work, arguing that as agents take on more execution, human agency shifts towards intent-setting, judgement, orchestration and accountability.

    It is a mixed-methods report based on Microsoft 365 telemetry, a 20,000-person online survey of A.I.-using knowledge workers across 10 markets, a separate 1,800-person global survey on managers and agentic A.I., and expert perspectives; the main survey covers Australia, Brazil, France, Germany, India, Italy, Japan, the Netherlands, the UK and the US.

    Length: 29 pages

    More information / download:
    https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization

    Core Insights

    1. What is the central argument of the report?

    The report’s central argument is that AI and agents are not merely productivity tools; they are forcing a redesign of the operating model of work. Microsoft frames this as a shift in which agents take on more execution, while humans gain more “agency”: more capacity to define intent, direct work, exercise judgement and own outcomes.

    The report is explicit that the key question is no longer simply whether organisations are adopting AI. The larger question is whether they are structurally capable of capturing its value. Its thesis is that many workers are already using AI in advanced and resourceful ways, but their organisations have not yet redesigned the surrounding systems — leadership alignment, incentives, governance, performance evaluation, culture and management practices — to match what AI now makes possible.

    A useful way to express the report’s logic is this: AI raises individual capability, but organisational design determines whether that capability becomes institutional advantage. The report repeatedly contrasts “AI adoption” with “AI absorption”. Adoption means people or teams use tools. Absorption means the organisation changes how work is designed, evaluated, governed, learned from and scaled.

    This is why the report’s three-part structure matters. At the employee level, AI expands what individuals can do. At the leader level, the task becomes rearchitecting work rather than simply deploying tools. At the organisational level, the most advanced firms become “Learning Systems” that capture lessons from AI-enabled work and turn them into repeatable practices.

    2. How does AI change the role and value of individual employees?

    The report argues that AI lifts the ceiling on individual potential by helping people do more complex, higher-value work. In Microsoft’s privacy-preserving analysis of more than 100,000 Microsoft 365 Copilot chats, 49% of classified conversations supported cognitive work such as analysing information, solving problems, evaluating and thinking creatively. The rest were split between working with people, finding information and producing work.

    Survey findings reinforce this. According to the report, 66% of AI users say AI has allowed them to spend more time on high-value work, and 58% say they are producing work they could not have produced a year earlier. Among “Frontier Professionals” — the most advanced AI users in the research — that latter figure rises to 80%.

    However, the report does not present this as simple automation. Its stronger claim is that human value moves. As AI takes on more execution, the employee’s differentiating contribution becomes judgement, quality control, critical thinking, intent-setting and the design of the human-AI workflow. The report says AI users themselves recognise this: 50% name quality control of AI output as an increasingly important human skill, while 46% name critical thinking. A striking 86% say they treat AI output as a starting point, not a final answer, and that they remain responsible for the thinking.

    The report’s page 9 framework is particularly useful. It describes four modes of working with AI: asking, delegation, collaboration and exploration. The key point is not that one mode is superior. The most advanced users know which mode a task requires. Quick factual queries may be “asking”; recurring reports or structured summaries may be “delegation”; proposals and judgement-heavy communication may require “collaboration”; and unfamiliar workflows may call for “exploration”. This is an important conceptual distinction because it moves the discussion beyond “prompting” towards work design.

    3. What is the “Transformation Paradox” identified by the report?

    The “Transformation Paradox” is the report’s term for the gap between workers’ AI readiness and organisations’ ability to support it. Microsoft maps respondents across two dimensions: individual AI capability and organisational readiness. The result is a five-zone model.

    Only 19% of AI users fall into the “Frontier” zone, where both individual capability and organisational readiness are high. Another 10% are in “blocked agency”: they have strong individual AI capability, but their organisations are not ready to support or absorb it. A further 5% represent “unclaimed capacity”, where the organisation is more ready than the individual. Sixteen per cent are “stalled”, with both low individual capability and low organisational readiness. The largest group, 50%, sits in the “emergent” zone, where both individual practice and organisational conditions are still forming.

    The paradox is that the pressure to use AI is rising, but the organisational systems still reward old ways of working. The report says 65% of AI users fear falling behind if they do not adapt quickly with AI, yet 45% say it feels safer to focus on current goals than to redesign work with AI. Only 13% say they are rewarded for reinventing work with AI even when results are not immediately achieved.

    This is one of the report’s most important findings because it reframes AI transformation as a management and systems problem. The obstacle is not simply lack of tools or lack of individual skill. It is the mismatch between what employees are capable of doing and what their organisations measure, encourage, permit and reward.

    4. What role does leadership play in turning AI use into organisational value?

    The report places substantial responsibility on leaders and managers. It argues that the job of every leader is now to “rearchitect work” — not just introduce AI tools, but redesign workflows, roles, incentives, metrics, governance and expectations around what humans and agents should each do.

    Leadership alignment appears to be weak. Only 26% of AI users say their leadership is clearly and consistently aligned on AI. The report also finds a perception gap between leaders and employees: leaders are more likely than employees to say AI-driven reinvention feels safe and rewarded. That suggests that senior leaders may believe the organisation is more supportive of AI transformation than employees experience in practice.

    Managers are presented as especially important because they translate strategy into everyday behaviour. A separate Microsoft-led study of 1,800 employees globally found that when managers actively model AI use, employees report a 17-point lift in AI value, a 22-point lift in critical thinking about AI use and a 30-point lift in trust in agentic AI. When managers create psychological safety around experimentation, employees report higher AI readiness and value, and are more likely to be high-frequency users of agentic AI.

    The report also shows that Frontier Professionals tend to work in stronger managerial environments. Compared with non-Frontier Professionals, they are more likely to say their manager openly uses AI, sets quality standards for AI-assisted work, creates space for experimentation and encourages more ambitious work redesign. This supports the report’s broader argument: individual AI skill matters, but it compounds only when leaders create the right organisational conditions.

    5. What does the report mean by saying every firm must become a “Learning System”?

    The report uses “Learning System” to describe an organisation that captures what AI-enabled work is teaching it and turns those insights into shared, repeatable and improving practices. This is the report’s organisational endgame: the firms that win are not simply those with the most AI tools, but those that learn fastest from their own work.

    The evidence behind this claim comes from Microsoft’s AI Impact Analysis. The report finds that organisational factors — culture, manager support and talent practices — account for more than twice the reported AI impact of individual mindset and behaviour: 67% versus 32%. The top single factor is organisational AI culture, followed by talent practices and manager support. The methodology is careful to state that these are statistical associations, not causal effects, because the variables are self-reported at the same moment.

    The report argues that as agents become more active, they generate valuable signals: what worked, what failed, where quality drifted, which hand-offs broke down and where workflows need redesign. In weaker organisations, these signals stay local. In stronger ones, they are captured, shared and encoded into routines. This is what Microsoft calls “Owned Intelligence”: institutional know-how that compounds over time, is specific to the firm and is difficult for competitors to copy.

    The report identifies three questions every advanced organisation must answer:

    1. who reviews agent performance
    2. who has authority to update agent workflows
    3. and how local wins are captured and scaled.

    It also argues that this requires coordination across four roles: employees who redesign their work around intent and review; leaders who redesign processes around outcomes and agent autonomy; IT teams that manage agents as entities with identities, permissions and lifecycle controls; and security teams that build monitoring, auditability and policy enforcement into the system.

    The implication is clear: AI transformation is not finished when employees start using AI. It becomes strategically meaningful only when the organisation builds the infrastructure to learn from AI-enabled work, codify that learning and continuously improve how humans and agents work together.

  • The Wharton Blueprint for A.I. Agent Adoption by Wharton

    The Wharton Blueprint for A.I. Agent Adoption by Wharton

    About the paper

    The report is a mixed-methods synthesis rather than a single original study: it combines recent academic research on human-AI interaction, practitioner perspectives from executives at firms deploying AI agents, and recommendations from Wharton faculty.

    It does not present one unified sample or fieldwork design; instead, it draws on multiple experiments, surveys, working papers, and case-informed expert contributions, with sample sizes and geographies varying by cited study and not clearly specified at the report level.

    Length: 47 pages

    More information / download:
    https://knowledge.wharton.upenn.edu/special-report/wharton-blueprint-ai-agent-adoption/

    Core Insights

    1. What is the central argument of the report?

    The report’s core argument is that AI agent adoption is no longer mainly constrained by technology, but by psychology. The authors argue that many people are still unwilling to let AI agents perform meaningful tasks on their behalf, not because the systems cannot do anything useful, but because users hesitate to believe that the agent is competent, to trust it, and to hand over control. The report therefore frames adoption as a behavioural and design challenge rather than a purely technical one.

    It organises this adoption problem into three “psychological frictions”:

    • perceived competence
    • trust
    • and delegation of control.

    These are presented as the three core barriers that developers and organisations must overcome if they want broader acceptance of AI agents. In other words, the report is not asking only whether agents work; it is asking what makes people willing to let them work on their behalf in real settings.

    The report is also quite explicit that this is a practical blueprint. It is designed for people building or deploying AI agents and aims to translate behavioural science into design recommendations. That makes its purpose strongly applied: it is less about theorising AI adoption in the abstract and more about showing how organisations can reduce resistance and increase real-world uptake.

    2. How does the report explain the first friction, perceived competence, and what makes users believe an AI agent can do the job?

    The first friction is perceived competence, which the report defines as the user’s subjective belief in the agent’s ability to perform desired actions. The key point here is that perceived competence is not the same as technical capability. An agent may be highly capable in a technical sense, but if users do not experience it as capable, they will still hesitate to adopt it.

    The report argues that users prefer agents that appear competent rather than warm. Across cited experiments, people were less willing to use AI that sounded cheerful or friendly than AI that signalled expertise, consistency, and reasoning. Competence cues included explaining criteria, showing reasoning, and making recommendations in a way that felt rigorous rather than overly personable. In practice, the report suggests that in serious domains such as health, finance, law, and professional work, a warm personality can actually undermine adoption if it makes the system seem less capable.

    Another major idea is that people judge whether the agent adds value. In the report’s discussion of AI-enabled travel agents, four factors shaped that perception:

    • convenience
    • personalisation
    • ubiquity
    • and superior functionality.

    At the same time, benefits alone are not enough. Privacy concerns, technology anxiety, and the desire for human interaction can still depress adoption even when users recognise obvious advantages. That means perceived competence depends both on positive cues about usefulness and on reducing perceived reasons not to engage.

    The report also places strong weight on explanations. In high-stakes contexts, users felt AI was more reliable and safer when it explained its process in detail, including the steps taken, data considered, or method used. Explanations therefore function not only as transparency, but as a signal of seriousness and quality. Finally, the report argues that agents can borrow competence from humans: when AI is presented as supporting a credible human expert rather than acting as an equal or rival, resistance falls and competence rises. This is one of the clearest examples of the report’s broader logic that adoption depends heavily on perception and framing.

    3. What does the report say about trust, and which factors most strongly increase or weaken trust in AI agents?

    The second friction is trust, defined as the user’s willingness to rely on the AI agent despite uncertainty. The report presents trust as central because AI agents are not just offering information; they are potentially taking action. That raises the stakes, since users must feel confident not only in what the system says but in what it might do.

    One of the strongest findings is that trust improves when users understand the agent’s limitations. The report cites experiments showing that people trusted AI more, and worked more effectively with it, when they were explicitly told where it was likely to fail. Rather than undermining confidence, acknowledging weaknesses helped users feel that they understood the system’s boundaries and therefore knew when to rely on it and when to be cautious. This is an important insight because it runs against the instinct to present AI as broadly capable and seamless.

    The report also argues that proof of successful outcomes often matters more than technical explanations. Users were more persuaded by evidence that the agent had successfully performed similar tasks before than by detailed accounts of how the system worked internally. The implication is that many users do not primarily want interpretability in a technical sense; they want reassurance that the system delivers results. Similarly, trust rises when agents reduce uncertainty before, during, and after use by making goals explicit, showing steps as they happen, and demonstrating how feedback improves future actions.

    Other trust-building mechanisms in the report include making the agent seem as though it understands the user’s goals, labelling it as “learning” or “improving,” using precise rather than rounded numbers, and tailoring outputs to specific user criteria rather than generic averages. The report repeatedly warns against the opposite pattern: trust falls when AI feels generic, when it seems to optimise for “most users,” or when it makes the process so effortless that users feel detached from the outcome. That last point is especially interesting, because the report suggests that convenience alone does not guarantee trust; too much automation can reduce psychological ownership and make people less willing to accept the result.

    4. How does the report understand delegation of control, and what level of autonomy does it recommend?

    The third friction is delegation of control, which the report defines as the user’s willingness to grant the AI the autonomy required to act on their behalf. This is where adoption becomes most sensitive, because an AI agent can move from being a helpful assistant to something that feels intrusive or disempowering.

    The report’s clearest conclusion is that people prefer a moderate level of autonomy. Too little autonomy makes the agent feel burdensome and not worth using, because the human has to micromanage it. Too much autonomy makes people feel that their freedom and control are being taken away. The recommended design pattern is therefore “human in the loop”: the agent should do meaningful preparatory or analytical work, but final decisions or key approvals should remain visible and accessible to the user.

    Control is not just about actual permissions; it is also about felt control. The report highlights research showing that concerns about control account for a substantial share of people’s decision whether to adopt AI. It therefore recommends making edit, pause, stop, reverse, and review options highly visible. Users need to know not only that controls exist, but where they sit and how easily they can be used. This is consistent with the report’s wider emphasis on transparency, checkpoints, and reversibility.

    The report adds two further nuances. First, adoption increases when users feel the agent is “theirs,” for example through naming, setup choices, or preferences, because ownership increases willingness to invest attention and effort. Second, the report notes that people rely more on AI under pressure, because repeated evaluation is cognitively costly. Yet it does not recommend manufacturing urgency; instead, it suggests using real workflow pressures to make AI the easier default. The overall message is that delegation works best when autonomy is earned gradually, bounded clearly, and embedded in a structure where the human still feels like the principal actor.

    5. What are the report’s wider implications for organisations, and what limitations does it acknowledge?

    For organisations, the report implies that successful AI agent deployment depends as much on behavioural design, workflow design, and communication as on model performance. The most important practical lesson is that adoption is unlikely to follow automatically from capability. Even if agents can perform useful tasks, users may resist them unless systems are positioned as competent, transparent, bounded, and supportive rather than all-powerful or frictionless. In organisational terms, this means deployment strategies must address psychology deliberately.

    The report’s recommendations point towards a fairly specific organisational philosophy. Agents should be framed as assistants, not authorities; they should explain their reasoning in business terms; they should show users what they are doing before, during, and after action; and they should operate within structures resembling job descriptions, permissions, escalation paths, and measurable outcomes. This effectively treats AI agents less like magical automation and more like junior or specialist collaborators who require oversight, governance, and staged trust-building.

    At the same time, the report is careful to acknowledge limitations. It explicitly states that this is based on the current best scientific evidence in a rapidly evolving field, and that many insights are extrapolated from adjacent AI and organisational behaviour research because AI agents themselves are still relatively novel. The report also notes limitations study by study: many cited findings come from experiments, hypothetical scenarios, specific sectors such as travel, healthcare, e-commerce, or financial advice, or controlled lab environments rather than long-term real-world deployments. That means the blueprint is evidence-informed, but not final or universally settled.

    So the deeper implication is twofold. First, organisations should not wait for perfect certainty before designing for adoption, because the report offers a strong behavioural framework already. Second, they should treat these recommendations as a living playbook, to be tested, adapted, and updated as both the science and the technology mature. That combination of confidence and caution is one of the report’s defining features.

  • The 2026 AI Index Report by Stanford University

    The 2026 AI Index Report by Stanford University

    About the paper

    The Stanford AI Index Report 2026 is a broad, global data-pack and secondary-analysis report on the state of artificial intelligence across research, technical performance, responsible AI, economy, science, medicine, education, policy and public opinion.

    It is based on multiple datasets and contributors, including sources such as Epoch AI, GitHub, Lightcast, LinkedIn, Quid, Zeki and McKinsey; it is not a single original survey, and sample sizes vary by section.

    The report’s geographic scope is global, though some chapters rely heavily on U.S., China, Europe and selected country-level data; some methodology details are chapter-specific rather than contained in one unified method statement.

    Length: 425 pages

    More information / download:
    https://hai.stanford.edu/ai-index/2026-ai-index-report

    Core Insights

    1. What is the central argument of the AI Index Report 2026?

    The central argument is that AI is scaling faster than the surrounding systems can adapt. The report frames 2025–26 as the period “after arrival”: AI is no longer an emerging technology sitting on the margins, but a mainstream force moving through work, education, science, medicine, infrastructure and policy. Generative AI reached roughly 53% population-level adoption within three years, organisational adoption reached 88%, and AI companies are scaling revenue and investment faster than previous technology waves.

    But the report repeatedly stresses that capability growth is outpacing governance, measurement and institutional readiness. Benchmarks are saturating, leading models are becoming harder to distinguish, frontier labs are disclosing less, and independent testing does not always confirm developer-reported performance. The result is not a simple story of progress or danger, but a more complex pattern: AI capabilities, adoption and investment are accelerating, while evaluation, regulation, education, labour-market adaptation and responsible AI practice are struggling to keep up.

    2. What does the report say about AI capability and model competition?

    The report argues strongly against the idea that AI capability is plateauing. Frontier systems continued to improve in 2025 across reasoning, coding, mathematics, multimodal understanding and agentic task execution. On SWE-bench Verified, performance rose from around 60% to near the human baseline in a single year. Some models now meet or exceed human baselines on PhD-level science questions, multimodal reasoning and competition mathematics.

    At the same time, the report highlights a “jagged frontier”. AI systems can produce astonishing results in some domains while remaining surprisingly weak in others. One striking example is that Gemini Deep Think achieved gold-medal performance at the International Mathematical Olympiad, while the top model could read analogue clocks correctly only about half the time. Similarly, AI agents improved dramatically on OSWorld, from around 12% to roughly 66% task success, but still fail about one in three attempts on structured computer-use tasks. Robots also remain far from general competence in the physical world, succeeding in only 12% of household tasks despite strong performance in controlled simulation environments.

    The competitive landscape is also changing. The U.S.–China model performance gap has effectively closed, with U.S. and Chinese models trading the lead multiple times since early 2025. As of March 2026, the top U.S. model led the top Chinese model by only 2.7%. At the same time, top frontier models from Anthropic, xAI, Google, OpenAI, Alibaba and DeepSeek are tightly clustered, making raw benchmark performance less useful as a differentiator. The report suggests that competition may increasingly shift towards cost, reliability, latency, usability and domain-specific performance.

    3. How are AI development, infrastructure and talent distributed globally?

    The report shows a field that is both concentrated and dispersing. Frontier model development remains heavily concentrated in industry and in a small number of countries. Industry produced more than 90% of notable AI models in 2025, while academia produced only two notable models. The United States still leads in notable model production, with 59 notable models in 2025 compared with China’s 35 and South Korea’s 8.

    However, China leads in several research and innovation indicators: publication volume, citations, patent output and industrial robot installations. The U.S. still retains advantages in top-tier model production, higher-impact patents and private investment, but China’s scale in research and patenting is now central to the global AI landscape. South Korea stands out for innovation density, leading the world in AI patents per capita.

    Infrastructure is even more concentrated. The United States hosts 5,427 data centres, more than ten times any other country, and global AI compute capacity has grown roughly 3.3 times per year since 2022. Yet the hardware supply chain has a critical dependency: TSMC in Taiwan fabricates almost every leading AI chip. This makes AI sovereignty difficult for most countries because the underlying compute, chips, data centres and talent are unevenly distributed.

    Talent patterns are also shifting. The U.S. remains home to more AI talent than any other country, but the number of AI researchers and developers moving to the U.S. has dropped 89% since 2017, including an 80% decline in the last year alone. Switzerland and Singapore lead in AI researchers and developers per capita, while the report notes that gender gaps in AI talent remain deeply entrenched, with no country approaching parity.

    4. What economic, labour-market and environmental consequences does the report identify?

    Economically, the report depicts AI as both a major investment boom and an uneven productivity story. U.S. private AI investment reached $285.9 billion in 2025, more than 23 times the $12.4 billion invested privately in China, though the report notes that private investment alone may understate China’s total AI spending because of government guidance funds. The U.S. also led in entrepreneurial activity, with 1,953 newly funded AI companies in 2025.

    The productivity evidence is promising but not universal. The report cites productivity gains of 14% to 26% in customer support and software development, while finding weaker or even negative effects in tasks requiring more judgement. This is important because some of the clearest productivity gains appear in fields where entry-level employment is beginning to decline. In U.S. software development, developers aged 22 to 25 saw employment fall nearly 20% from 2024, while headcount for older developers continued to grow.

    The environmental footprint is becoming much harder to ignore. Grok 4’s estimated training emissions reached 72,816 tons of CO₂ equivalent. AI data centre power capacity rose to 29.6 GW, roughly comparable to New York state at peak demand. The report also estimates that annual GPT-4o inference water use may exceed the drinking water needs of 1.2 million people. The implication is that AI’s economic value and social utility must increasingly be weighed against energy, water, infrastructure and supply-chain constraints.

    5. What does the report imply for governance, education, science, medicine and public trust?

    The report’s broader implication is that AI is moving into high-stakes domains before institutional systems are fully ready. Responsible AI is not keeping pace with capability: leading frontier developers commonly report capability benchmarks, but responsible AI benchmark reporting remains inconsistent. Documented AI incidents rose from 233 in 2024 to 362, and the report notes that improving one responsible AI dimension, such as safety, can sometimes degrade another, such as accuracy.

    In education, formal systems are lagging behind actual use. More than 80% of U.S. high school and college students now use AI for school-related tasks, but only half of middle and high schools have AI policies, and just 6% of teachers say those policies are clear. This suggests a growing gap between everyday AI use and the institutional guidance needed to use it well.

    In science and medicine, the report is cautiously optimistic but evidence-conscious. AI models for science can outperform human scientists on some benchmarks, such as ChemBench, but they remain weak in areas such as astrophysics replication and Earth observation. The report also notes that smaller, specialised models can outperform much larger ones in scientific domains, challenging the assumption that bigger is always better. In medicine, AI scribes and clinical note-generation tools saw substantial adoption, with some physicians reporting up to 83% less time spent writing notes and reduced burnout. But the broader evidence base remains thin: a review of more than 500 clinical AI studies found that nearly half relied on exam-style questions rather than real patient data, and only 5% used real clinical data.

    Policy is becoming more active but also more fragmented. AI sovereignty is emerging as a defining policy principle, especially as countries seek greater control over infrastructure, data, models, applications and talent. National AI strategies are expanding, particularly among developing economies, but actual capabilities remain uneven. The EU, U.S. and Asian countries are also moving in different policy directions, with the EU AI Act’s first prohibitions taking effect while the U.S. shifted towards deregulation.

    Finally, public opinion is divided. Experts and the public view AI’s future very differently: 73% of experts expect AI to have a positive impact on how people do their jobs, compared with only 23% of the public. Trust in institutions to regulate AI is fragmented, with the United States reporting the lowest level of trust in its own government to regulate AI among surveyed countries, at 31%. The report therefore closes on a central tension: AI is becoming more capable, more widely adopted and more economically valuable, but trust, governance and social readiness remain far less mature.

  • Want RoI from A.I.? Go for growth by PwC

    Want RoI from A.I.? Go for growth by PwC

    About the paper

    PwC’s AI performance study examines why some large companies generate measurable ROI from AI while others remain stuck in pilot activity.

    It is original proprietary survey research based on 1,217 senior executives, all director-level or above, across 25 sectors and across Africa, Asia, Europe, the Middle East, North America and South America; fieldwork took place in October and November 2025.

    The sample is heavily weighted towards large publicly listed companies: 91% were publicly listed and 76% had revenue of US$1 billion or more.

    Length: 30 pages

    More information / download:
    https://www.pwc.com/gx/en/issues/technology/ai-performance/want-ai-roi-go-for-growth.html#the-takeaways

    Core Insights

    1. What is the central argument of the report?

    The report argues that AI ROI is not mainly a function of how many AI pilots a company launches. It comes from what PwC calls “AI fitness”: the ability to aim AI at important business outcomes, build the foundations that make AI scalable and reliable, and embed AI deeply across the enterprise.

    PwC’s headline finding is that the most AI-fit companies achieve AI-driven financial performance that is 7.2 times higher than other companies. The study defines this performance as revenue and efficiency gains attributable to AI, adjusted against sector medians. According to the report, 20% of the surveyed companies capture 74% of AI-driven returns, showing that value is highly concentrated among a relatively small group of leaders.

    The core message is therefore: companies should stop counting pilots and start building the organisational machinery that turns AI activity into measurable business impact. The strongest performers treat AI not as a tool for scattered productivity gains, but as a reinvention engine that can reshape products, business models, operating models and competitive positioning.

    2. What does PwC mean by “AI fitness”?

    PwC defines AI fitness as a combination of six foundational capabilities and three measures of AI use.

    The six foundations are:

    1. Innovation — dedicated infrastructure, business-unit ownership and disciplined portfolio reviews.
    2. Governance and risk — Responsible AI frameworks, access controls, compliance processes and oversight bodies.
    3. Data and technology — modern platforms, trusted data, reusable components and redesigned workflows.
    4. Strategy — a clear connection between AI deployment and corporate strategy.
    5. Investment — sufficient funding and the ability to reallocate resources as priorities shift.
    6. Workforce — skills, incentives, collaboration models and trust in AI-generated insights.

    The three measures of AI use are:

    1. Breadth and depth — how widely AI is used across the value chain and how deeply it is embedded into workflows.
    2. Sophistication — whether AI is used merely to assist and summarise, or whether it executes tasks, operates within guard rails or becomes autonomous.
    3. Capturing value from industry convergence — whether AI helps the company identify and exploit new value pools across traditional sector boundaries.

    The important point is that PwC does not define AI leadership as “using more AI” in a generic sense. Leadership comes from combining AI use with the organisational conditions that make AI repeatable, trusted and commercially meaningful.

    3. Why does the report say companies should aim AI at growth and reinvention, not just efficiency?

    The report acknowledges that many companies use AI to improve efficiency: faster claims processing, code generation, customer support automation, productivity improvements and cost reduction. But PwC argues that the biggest returns come when AI changes what the company sells, how it creates value and where it competes.

    AI leaders are 2.6 times as likely as other companies to say AI has improved their ability to reinvent their business model. They are also more likely to use AI to identify emerging value pools, respond to changing customer needs, collaborate across sectors and compete outside their traditional industry boundaries.

    The strongest individual AI fitness factor in the study is the ability to capture growth from industry convergence. PwC’s logic is that AI allows companies to detect and act on new combinations of customer needs, data, services and partnerships that cut across old sector lines.

    The John Deere example illustrates this argument. The company used AI-powered precision spraying not simply to make an existing machine smarter, but to support a more service-like model linked to verified outcomes. According to the report, See & Spray was used on more than 1 million acres in the 2024 growing season and saved an estimated 8 million gallons of herbicide mix, while also positioning John Deere for recurring services revenue rather than one-off hardware differentiation.

    4. What foundations do AI leaders build differently from other companies?

    PwC’s argument is that foundations matter because they improve the “conversion rate” from AI use to measurable results. Companies with stronger foundations reportedly see nearly double the improvement in AI-driven performance when they increase AI use, compared with companies with weaker foundations.

    The report highlights five especially important foundation-building practices.

    First, AI leaders fund and flex the portfolio like investors. They invest materially more in AI — PwC says leaders invest 2.5 times as much of revenue as other companies — and they are better at reallocating financial and human resources towards higher-value AI opportunities.

    Second, they create infrastructure for innovation. They are more likely to provide sandbox environments and dedicated technical infrastructure for experimentation, and more likely to place accountable innovation owners inside business units.

    Third, they build workforce trust. Employees in AI-leading organisations are 2.1 times as likely to trust AI-generated insights and act on them in daily decisions. The report links this trust to cross-functional co-creation, role-based learning, incentives to experiment and clear guard rails.

    Fourth, they use governance to accelerate rather than obstruct. Leaders are more likely to have a documented Responsible AI framework and a cross-functional governance board, but the report frames governance as a way to move routine use cases faster while escalating only higher-risk cases.

    Fifth, they remove data and technology friction. AI leaders are much more likely to have reusable centrally catalogued AI components, high-quality accessible data, modern cloud-based platforms, and redesigned workflows that incorporate AI rather than merely adding AI tools on top of existing processes.

    The overall message is pragmatic: companies should not try to modernise everything at once. They should build only the foundations needed to scale the AI initiatives most closely tied to strategic outcomes.

    5. What are the report’s main implications for executives?

    The main implication is that executives need to move from an “AI activity” mindset to an “AI performance” mindset.

    PwC repeatedly warns against treating AI pilots as proof of progress. The report suggests that many companies have visible AI activity but little evidence of revenue growth, cost reduction, better decisions or improved customer outcomes. The practical challenge is therefore to connect AI to specific business objectives, measure impact, and stop or scale initiatives based on evidence.

    The report’s recommended executive agenda has four parts.

    First, aim AI at strategic value, especially growth, reinvention and industry convergence, rather than only internal productivity.

    Second, assign ownership and metrics. Every important AI initiative should have a business owner, success measures and accountability for outcomes.

    Third, industrialise what works. Companies should take proven use cases and replicate them across functions, regions, workflows and decision points, rather than leaving value trapped in isolated pockets.

    Fourth, move carefully towards greater autonomy. PwC argues that automation of routine, high-frequency decisions is strongly linked to AI-driven performance, but it should happen within explicit guard rails, with decision quality measured and trust thresholds met before expansion.

    The conclusion is that AI advantage may compound. The leading companies are learning faster, redeploying solutions faster and safely automating more decisions. For laggards, the risk is not just missing short-term efficiency gains, but falling behind as AI-fit competitors turn their foundations into a widening performance premium.

  • State of the Global Workplace 2026 by Gallup

    State of the Global Workplace 2026 by Gallup

    About the paper

    The paper examines employee engagement, wellbeing, daily emotions, job-market sentiment and the human side of AI adoption, arguing that management quality is a decisive factor in whether AI translates into organisational value.

    It is an original survey-based research report built primarily on Gallup World Poll data, supplemented by additional random U.S. workforce web samples; the report draws on 263,810 respondents in 2025, including 141,444 employed adults, and on a full 2009–2025 trend base of 5,754,327 respondents, covering more than 160 countries and areas worldwide.

    Length: 251 pages

    More information / download:
    https://www.gallup.com/workplace/349484/state-of-the-global-workplace.aspx

    Core Insights

    1. What is the report’s central argument about the global workplace in 2026?

    The report’s central argument is that the workplace challenge is not simply technological change, but whether organisations are managed well enough to convert change into performance. Gallup explicitly frames the report around “the human side of the AI revolution” and says the manager is the strongest predictor of employee AI adoption apart from technical integration. In other words, the report treats management effectiveness, employee engagement and organisational readiness as the real bottlenecks in turning AI into measurable gains.

    That argument rests on a broader point: workplaces are entering the AI era while employee engagement is weakening rather than strengthening. Global engagement fell again in 2025, to 20%, its lowest level since 2020, and Gallup argues this matters because engagement is a practical indicator of readiness for disruption and change. Low engagement, in this framing, is not just a morale issue but an economic and strategic one.

    2. What are the report’s most important global findings on engagement, wellbeing, emotions and the job market?

    The most striking headline is that global employee engagement declined for a second consecutive year, falling to 20% in 2025 from a 2022 peak of 23%. At the same time, 64% of employees are classified as not engaged and 16% as actively disengaged. Gallup estimates that low engagement cost the global economy about $10 trillion in lost productivity, or 9% of global GDP.

    By contrast, wellbeing improved slightly. Global thriving rose from 33% to 34% in 2025, the first improvement in three years. But that modest recovery sits alongside persistently high negative emotions: 40% reported stress, 22% anger, 23% sadness and 22% loneliness. The report makes clear that these emotional burdens remain above pre-pandemic levels, suggesting that the workplace has not returned to its earlier psychological baseline.

    Job-market sentiment also improved, but only slightly. Globally, 52% of employees said it was a good time to find a job, up one point from the previous year, though still below the 2019 peak. So the overall picture is mixed: modest improvement in wellbeing and job confidence, but continued weakness in engagement and continued elevation in emotional strain.

    3. Why does Gallup place so much emphasis on managers?

    Gallup’s answer is that the recent global deterioration in engagement is largely a management story. The report says lower engagement among managers accounts for most of the recent downturn, and manager engagement has dropped by nine points since 2022. On the chart on page 7, managers fall from 31% engaged in 2022 to 22% in 2025, while non-managers move only marginally, from 20% to 19%. That is a significant loss of what Gallup calls the former “engagement premium” of being a manager.

    The report also argues that managers are pivotal to meaningful AI adoption. In Gallup’s Q1 2026 U.S. workforce survey, the top two drivers of frequent AI use are integration with existing systems and manager-led adoption. Employees in AI-investing organisations who strongly agree that their manager actively supports AI are 98.7 times as likely to strongly agree that AI has transformed how work gets done, and 97.4 times as likely to strongly agree that AI gives them more opportunities to do what they do best every day.

    Gallup’s perspective is therefore not that AI will bypass human leadership, but almost the reverse: the better the management layer, the more likely AI is to become valuable. The report even suggests AI could improve global engagement if it helps managers apply stronger people-management practices more consistently.

    4. What patterns does the report reveal across regions, and what do they imply?

    The regional data show a world that is far from uniform. On engagement, the United States and Canada rank first at 31%, followed closely by Latin America and the Caribbean at 30%, while Europe ranks last at just 12%. That makes Europe the lowest-engagement region globally despite relatively strong life evaluation and job-climate scores. This suggests that a region can look comparatively strong on broader life outlook without generating much attachment to work itself.

    On wellbeing, Latin America and the Caribbean lead at 56% thriving, closely followed by Australia and New Zealand at 55%, whereas South Asia is lowest at 16%. On job climate, Southeast Asia leads at 64%, while the Middle East and North Africa is lowest at 36%. The United States and Canada stand out for a sharp deterioration in job confidence, dropping 10 points year on year to 47%, making the region second-to-last on that measure.

    Emotionally, the picture is equally uneven. South Asia has the highest anger and sadness, Sub-Saharan Africa and South Asia the highest loneliness, while Post-Soviet Eurasia records the lowest stress. The report’s implication is that there is no single global workplace condition. Instead, different regions combine engagement, wellbeing, stress and job optimism in distinct ways, which matters for both management practice and interpretation.

    5. What does the report suggest about AI, jobs and the future of work?

    Gallup presents AI as a force that is already boosting individual productivity for many workers but has not yet reliably produced organisation-level gains. The report notes that in U.S. organisations that have implemented AI, 65% of workers say AI has had a somewhat or extremely positive effect on their productivity, yet only 12% strongly agree that AI has transformed how work gets done in their organisation. This gap between personal efficiency and institutional transformation is one of the report’s core tensions.

    The report also shows rising concern about AI-related job loss. In Q1 2026, 18% of U.S. employees said it was somewhat or very likely their job would be eliminated within five years because of technological innovation, rising to 23% in organisations where AI had been implemented. In finance, insurance and technology, the figures are higher still. At the same time, Gallup says the employment effects are not uniformly negative: larger AI-implementing employers are more likely to be reducing headcount, while smaller ones are more likely to be expanding.

    The broader conclusion is that AI is reconfiguring work, but outcomes depend heavily on context, especially leadership, workforce choice and upskilling. Gallup repeatedly links optimism and wellbeing to employees feeling they have choices in the work they do. So the report’s future-of-work message is not deterministic. AI may intensify pressure, flatten structures and increase job anxiety, but it may also improve management and expand capability, depending on how organisations lead the transition.

  • Crossing the Rubicon by the Scriptorium Initiative

    Crossing the Rubicon by the Scriptorium Initiative

    About the paper

    This is a conceptual whitepaper about how brand and communications functions should evolve into AI-native operating models, arguing that AI changes not just the tools of communication but the logic of the function itself.

    It reads as a practitioner whitepaper grounded in secondary analysis, cited literature, and practice-based reflection rather than original empirical research; no respondent base, interview sample, fieldwork period, or defined geographic dataset is clearly specified in the report.

    Length: 28 pages

    More information / download:
    https://scriptorium-initiative.ai/follow-us

    Core Insights

    1. What is the report’s central argument about AI and the future of the communications function?

    The report’s central argument is that AI represents a structural break for communications, not a simple productivity tool. It says previous technologies expanded reach and speed, but AI changes the function more fundamentally because it can read, write, interpret and increasingly act. In that sense, AI is described not just as another channel, but as “the medium, the message, and the messenger”.

    That leads to the paper’s key claim: communications leaders must decide whether to use AI merely to do old work faster, or to redesign the function around intelligence itself. The report repeatedly frames this as “crossing the Rubicon” — an irreversible leadership choice to rebuild workflows, governance, measurement and roles around human–machine collaboration.

    Importantly, the paper does not argue for replacing communicators. It argues that the purpose of communications remains the same — building trust and shaping behaviour — but that the operating model must change. In the author’s framing, the communicator moves from being chiefly a producer of messages to becoming a steward of intelligence, coherence and meaning.

    2. According to the report, what stays constant even as AI transforms communications?

    The report is very clear that the fundamentals do not change even when the tools do. Across different chapters, it returns to two enduring anchors: trust and behaviour. Communications, in the author’s view, has always been about credibility and action — earning belief and shaping what people do. In the AI era, these anchors become more rather than less important because synthetic content, deepfakes and machine-generated noise make trust scarcer and therefore more valuable.

    The paper also identifies five timeless principles that should still guide the profession: truth and transparency, audience understanding, narrative coherence, reciprocity and feedback, and governance and accountability. These are presented almost as first principles for navigating AI disruption. The implication is that while AI may transform production, distribution and optimisation, it does not remove the need for human honesty, empathy, judgment and responsibility.

    That continuity matters because it gives the report its normative centre. The author is not celebrating automation for its own sake. The paper argues that AI-native communications should be assessed by whether intelligence is aligned with integrity, whether meaning remains coherent, and whether human accountability is preserved.

    3. How does the report say the work of communicators will change in practice?

    The report says communicators will increasingly work inside hybrid systems made up of humans and intelligent agents. In these systems, AI will handle more of the executional load: drafting, monitoring, summarising, simulating reactions, spotting reputational risks and supporting decisions in real time. Humans, meanwhile, will focus more on interpretation, ethical judgment, tone, legitimacy and connection.

    One of the strongest ideas in the whitepaper is the shift from “messages” to “meaning systems”. In the old model, communications teams created messages and distributed them through chosen channels. In the new model, messages are filtered, rewritten, summarised and ranked by algorithms before they reach audiences. That means communicators can no longer assume control over the final expression of what they say. Their job becomes designing the system around intent, boundaries, tone and ethics so that AI-generated outputs still cohere over time.

    The paper also says roles will become less rigid. Traditional job boundaries such as press officer, content manager or speechwriter weaken as AI absorbs more production work. What becomes valuable are higher-order capabilities: sensemaking, narrative judgment, ethical discernment and orchestration. Measurement changes too: instead of counting outputs, speed or reach, the report says the function should focus on trust, credibility and behavioural effect.

    A further practical change concerns career development. The report worries that entry-level apprenticeship work may erode if AI takes over research, drafting and scheduling. That creates a paradox: short-term productivity may rise while long-term human capability weakens. So the future communicator is imagined not as someone learning by doing repetitive tasks, but as someone learning to supervise, question and guide intelligent systems.

    4. What leadership model does the report propose for communications chiefs and senior teams?

    The report argues that leadership must shift from control to coherence. Because AI systems are probabilistic rather than fully predictable, leaders cannot simply rely on traditional command-and-control models. Instead, they need to create clear intent, shared values, ethical boundaries and governance structures that keep human judgment at the centre.

    A core principle here is the “human communicator-in-the-loop”. The report insists that AI can assist, accelerate and act, but cannot be accountable. Responsibility for truth, tone and trust must remain human. This is not framed as a minor safeguard but as a foundational leadership doctrine for the AI-native communications function. Humans do not need to approve everything, but they must intervene wherever legitimacy, emotion, trust or significant consequences are involved.

    For chief communications officers in particular, the report outlines a changing role across three phases. In Phase I, the CCO is a learner who builds fluency, shared language and psychological safety around AI. In Phase II, the CCO becomes an architect who integrates AI into workflows, governance and cross-functional collaboration. In Phase III, the CCO becomes a steward of intelligence, acting as the moral and narrative compass for a function whose “Comms Cortex” sits at the centre of the operating model.

    This leadership model is as cultural as it is technical. The paper stresses that people do not simply need tools; they need readiness, trust and inclusion. Leaders must explain why AI is being adopted, what will change, and what must not change. In that sense, the paper presents AI transformation not as a software implementation exercise, but as an exercise in organisational meaning-making and ethical design.

    5. What roadmap does the report offer, and what are its main implications for organisations?

    Rather than promising a fixed end state, the report offers what it calls the “Scriptorium Journey”, a three-phase path towards an AI-native communications function. Phase I, “Wake Up and Skill Up”, is about literacy, orientation and ethical grounding. Phase II, the “Agentic Foundry”, is where experimentation becomes structured integration and hybrid workflows begin to take shape. Phase III, “AI Native and Hybrid Teams”, is the point at which intelligence becomes the organising logic of the function and the Comms Cortex becomes its central cognitive infrastructure.

    One of the report’s most interesting assumptions is that organisations should stop thinking in terms of static target operating models. Because AI is evolving too quickly, the author argues that success should not be defined as reaching a final destination. Instead, organisations need a quarterly rhythm of foresight, experimentation, leadership alignment, team immersion and ongoing evolution. Readiness and relevance over time matter more than finishing a transformation programme.

    The wider implication is that hesitation also carries risk. The report warns that organisations that delay may find their voice increasingly shaped by systems they do not govern. By contrast, those that redesign communications deliberately around intelligence, while retaining human accountability, can preserve trust, relevance and strategic influence. In the paper’s closing logic, the future function may be smaller in structure but deeper in purpose: less focused on output volume, more focused on aligning purpose, perception and behaviour at scale.

    Overall, the report is less a research study than a strategic manifesto for senior communications leaders. Its value lies in the conceptual framework it offers: AI as a structural shift, trust and behaviour as constants, human accountability as non-negotiable, and transformation as a continuing leadership rhythm rather than a one-off change project.

  • Future of Work 2026 – Industry Report by LexisNexis

    Future of Work 2026 – Industry Report by LexisNexis

    About the paper

    This sector-specific industry analysis draws on the LexisNexis Future of Work 2026 global survey of 1,400 professionals across more than 20 industries, conducted online through Sago using random sampling from 8–17 September 2025.

    The report focuses on PR, media and communications professionals worldwide, but the size and geographic composition of this sector-specific subsample are not clearly specified in the report.

    It is best characterised as a secondary sector analysis of original survey research, supplemented by LexisNexis interpretation and recommendations.

    Length: 7 pages

    More information/download: https://www.lexisnexis.com/en-us/industries/public-relations-communication/ai-pr-comms-report.page

    Core Insights

    1. What is the report’s central finding about generative AI adoption in PR, media and communications?

    The central argument is that PR, media and communications professionals face an unusually difficult combination of high potential value from generative AI and high reputational exposure when it goes wrong.

    Content creation is the sector’s leading use case for genAI, reported by 55% of respondents. This makes the technology highly relevant to everyday professional work because producing written and other communicative content is central to the sector. At the same time, adoption is relatively cautious: only 32% say they use genAI frequently or always, compared with 52% across all industries, while 45% remain at an early experimental stage.

    LexisNexis interprets this not simply as slow adoption, but as a consequence of the particular risks facing communicators. Errors in many other professions may remain internal, whereas inaccurate, fabricated or misattributed information in PR or media can immediately become public. The report therefore presents the sector as caught between the efficiency benefits of AI and the requirement to protect organisational and professional credibility.

    Its broader thesis is that the sector now needs to move from asking what AI can do towards establishing whether AI-generated information can be trusted. The transition is characterised as a shift from AI “capability” towards AI “credibility”.

    2. Why does the report consider misinformation and reputation particularly serious risks for this sector?

    Misinformation is the standout concern among PR, media and communications professionals. Some 59% identify misinformation as a leading concern, compared with 40% across the full cross-industry sample.

    The report connects this anxiety directly with the nature of communications work. Published material can be scrutinised by journalists, customers, clients and wider public audiences, and errors can become permanent parts of the public record. In media particularly, credibility is based heavily on accuracy, so hallucinations, incorrect sourcing or misattribution can undermine trust in both the individual communicator and the organisation.

    There is also a practical verification problem. The report says that 36% struggle to determine whether AI outputs are based on high-quality sources, while 32% cannot identify hallucinations or incorrect outputs. These weaknesses are especially consequential when AI is used to produce press releases, articles, features and other externally published materials.

    Consequently, PR and communications professionals appear to place greater value than many other sectors on mechanisms that make AI output auditable. Transparent data sources are identified by 45% as the leading way to increase trust, while 41% say human oversight increases trust.

    The report therefore treats misinformation not merely as an AI accuracy problem but as a reputation-management problem. For communications functions, uncertainty about where information comes from and whether it can be verified becomes an organisational risk.

    3. How serious is the gap between AI use and AI competence in PR, media and communications?

    The report portrays the competence gap as one of the sector’s most important weaknesses.

    Only 50% of PR, media and communications professionals describe themselves as very or extremely confident in their ability to use genAI, compared with 64% across industries generally. More strikingly, only 5% say they can confidently explain large language models, compared with 29% across the overall sample.

    The report therefore distinguishes between being able to operate an AI tool and understanding enough about the technology to assess its limitations. This distinction becomes critical when professionals need to judge source quality, identify hallucinations or decide whether an AI-generated claim is reliable enough to publish.

    Training alone does not appear to have solved the problem. Across the entire survey, 82% of professionals receive some form of AI training, yet 41% of PR, media and communications respondents still cite lack of training as a leading barrier. LexisNexis consequently argues that organisations need to move beyond basic AI awareness towards specialised training in verification, source assessment and recognition of situations requiring human checking.

    The report’s underlying concern is therefore not simply that communications professionals need more experience with AI. They need a different kind of competence: the ability to evaluate AI-generated information safely in contexts where mistakes can create public consequences.

    4. What contradiction does the report identify between cautious AI adoption and unauthorised AI use?

    One of the most interesting patterns is that relatively cautious overall adoption coexists with substantial unauthorised use.

    Only 32% of PR, media and communications professionals report using genAI frequently or always, suggesting comparatively restrained adoption. Yet the report also says that large numbers use AI tools without organisational approval.

    There is an internal numerical inconsistency in the report on this point. The narrative states that 63% engage in unauthorised usage compared with 53% overall, while the accompanying chart gives the PR, media and communications figure as 59%. The report does not explain the discrepancy, so the exact sector figure cannot be established confidently from the document.

    Regardless of whether the figure is 59% or 63%, the underlying pattern is clear: formal adoption may be cautious while informal or unsanctioned use is widespread. Among unauthorised users, 62% reportedly say deadlines will not wait, while 61% point to the need for efficiency.

    This creates what the report effectively presents as a governance paradox. Reputational anxiety makes professionals wary of AI, but everyday workload and deadline pressures simultaneously encourage them to use it outside approved systems.

    The implication is that restrictive policies alone may not control AI use. If employees perceive AI as necessary to complete their work efficiently, banning or delaying access can push usage outside organisational governance rather than eliminating it.

    5. What does the report recommend leaders should do, and what perspective underlies those recommendations?

    LexisNexis proposes five main actions. Organisations should provide purpose-built AI tools for high-stakes content work; invest in specialised training focused on quality verification; embed human validation checkpoints into AI-supported workflows; create reputation-protection frameworks defining permitted and prohibited use cases; and accelerate the transition from experimentation towards properly governed integration.

    These recommendations follow logically from the report’s diagnosis. If source verification is difficult, tools should make sourcing more transparent. If users struggle to detect hallucinations, training should concentrate on validation rather than simply prompting or basic AI literacy. If deadline pressures encourage unauthorised adoption, organisations need sanctioned tools that employees can realistically use in everyday workflows.

    The report also argues that human oversight should be designed into processes rather than treated as an abstract principle. Suggested measures include spot-checking AI-generated material and mandatory human review for particularly consequential outputs.

    At the same time, the recommendations should be understood within the report’s commercial perspective. LexisNexis is not an independent academic institution but a provider of information and AI-enabled services. The report explicitly recommends purpose-built tools, reputable technology vendors and transparent source environments, and concludes by inviting readers to explore LexisNexis AI solutions and its content resources.

    The report therefore serves two purposes simultaneously: it presents survey-based evidence about genuine challenges surrounding AI adoption in communications, while also framing those challenges in ways that support LexisNexis’s proposition around trusted data, verifiable sources and enterprise AI tools.

  • The Unofficial Linkedin Algorithm Guide by Trust Insights

    The Unofficial Linkedin Algorithm Guide by Trust Insights

    About the paper

    This report is an independent secondary analysis of LinkedIn’s feed and recommendation architecture in Q1 2026, produced by Trust Insights rather than LinkedIn itself.

    It synthesises public LinkedIn engineering research, academic papers, conference material and some verified news reporting, and the report says it draws on more than 30 current LinkedIn engineering publications, with 31 primary sources consulted overall; there are no survey respondents or interview participants because this is not original field research.

    Its scope is platform-specific rather than geographic: it focuses on LinkedIn’s global product and AI systems as documented in public sources.

    Length: 138 pages

    More information / download:
    https://www.trustinsights.ai/insights/whitepapers/the-unofficial-linkedin-algorithm-guide-for-marketers/

    Core Insights

    1. What is the central argument of the guide?

    The guide’s core argument is that LinkedIn’s feed no longer works as a largely signal-counting system that rewards simplistic “algorithm hacks”. Instead, it argues that LinkedIn now operates through a more sophisticated two-stage AI architecture built around retrieval and ranking, and that success depends on aligning with how those systems understand relevance.

    More specifically, the report says the old game of sending numerical signals to a mechanical system is over. In its place, LinkedIn now uses two complementary systems: an LLM-powered retrieval system that reads language and a sequential ranking system that learns from behaviour over time. That leads the authors to a new premise: language quality determines retrieval, while engagement patterns determine ranking.

    The guide compresses this into three main propositions. First, relevance has displaced recency as the main logic of feed ranking, within the 30-day window of connection-based retrieval. Second, LinkedIn’s content distribution works as a two-stage pipeline, meaning content has to be retrieved before it can be ranked. Third, both retrieval and ranking depend on semantic embeddings, so coherence across profile, content and engagement shapes who sees your posts.

    In practical terms, the guide is arguing against superstition and in favour of structural understanding. It wants readers to stop obsessing over posting-time folklore and instead focus on profile clarity, topical consistency, high-quality writing and engagement that creates a coherent professional signal.

    2. What evidence and methodology does the report use to support that argument?

    This is not a survey report or interview-based study. It is an evidence synthesis built from public technical and engineering material about LinkedIn. The report states that it synthesises engineering research, academic papers and conference presentations from LinkedIn’s own AI researchers, and that it uses generative AI to help synthesise the material.

    In the methodology section, Trust Insights says it used Google Gemini 2.5 Pro and Anthropic’s Claude to synthesise roughly 400,000 words of source material across the primary sources. It also says that technical claims are traced back to official LinkedIn publications, peer-reviewed research or verified news sources, and that future-facing claims are labelled as goals rather than confirmed deployments.

    The source base is fairly substantial for a document of this kind. The guide says it draws on more than 30 current LinkedIn engineering publications, and elsewhere specifies that 31 primary publications were consulted overall, including 20 current 2025–2026 LinkedIn engineering publications that form the main evidentiary base.

    The report is also reasonably careful about limitations. It says LinkedIn has not endorsed or reviewed the guide, that LinkedIn’s systems change continuously, that some architectural claims are inferences where public documentation is incomplete, and that published sources do not cover everything LinkedIn uses in production. That makes the methodology stronger than a typical “guru” explainer, but it still remains an independent interpretation of partial public evidence rather than a definitive inside account.

    3. What does the guide say actually changed in LinkedIn’s architecture in 2026?

    According to the report, the big change publicly confirmed in March 2026 was that LinkedIn had replaced a fragmented older feed infrastructure with two complementary AI systems: a unified LLM-based retrieval system and a sequential ranking model. The older setup had relied on multiple separate retrieval sources and a ranking model that treated impressions more independently. The newer design is meant to understand both semantic relevance and behavioural sequences more effectively.

    On the retrieval side, the guide says LinkedIn fine-tuned LLaMA-3 as a dual encoder that generates embeddings for members and content, enabling semantic matching at scale. It describes this as a unification of several previously separate retrieval systems. The model uses profile information and positive engagement history to generate a member embedding, and it can retrieve relevant content even for users with sparse connection histories.

    On the ranking side, the report says LinkedIn’s production ranker is now the Generative Recommender, a sequential transformer that processes more than 1,000 historical interactions to identify behavioural patterns. Rather than “reading” posts in the way many users imagine, it learns from the sequence of what a member viewed and how they interacted. The report emphasises that recent engagement carries more weight because of how the sequence model works.

    The broader implication, in the guide’s telling, is that LinkedIn is no longer mainly about gaming simple engagement weights. It is about becoming legible to two AI systems at once: one that reads your professional language and one that reads your professional behaviour.

    4. What are the most important practical implications for creators, marketers and professionals?

    The guide’s practical message is that profile quality, content quality and engagement quality now reinforce one another. Since retrieval depends on semantic clarity, users need profiles and posts that clearly express expertise, use relevant professional language and stay topically coherent. Since ranking depends on behavioural patterns, users also need consistent engagement histories that reflect the professional domains they want to be associated with.

    This makes the profile much more important than in many older LinkedIn playbooks. The guide describes the profile as a foundational input to both stages of the system: it feeds the retrieval model directly, and a separate profile embedding also feeds the ranking model. In the guide’s framing, your headline, About section and experience descriptions are not just presentation devices for humans; they are inputs into machine understanding.

    The same logic applies to content. The guide argues that high-performing posts are not merely those that generate quick reactions, but those that have clear topical focus and generate sustained, meaningful engagement from the right professional communities. That is why it repeatedly stresses semantic coherence, useful content and deliberate audience alignment over tricks such as timing obsession or comment-baiting.

    It also argues that engagement should be treated strategically. Because likes, comments, shares and dwell patterns become part of the behavioural sequence the ranking model processes, what you engage with influences what you are shown and how LinkedIn understands your professional identity. In other words, your interactions do not merely boost others; they also train the platform’s understanding of you.

    5. What are the report’s biggest conclusions and limitations?

    Its biggest conclusion is that durable LinkedIn success now comes from clarity, coherence and consistency rather than from short-term “algorithm hacks”. The authors say the only sustainable strategy is to maintain a strong professional profile, create valuable content and build a legible engagement pattern, because the underlying system is continuously evolving and increasingly hard to game.

    A second conclusion is that discoverability and distribution are no longer simple matters of network size or posting frequency alone. Because the retrieval system can infer interests semantically, newer users and low-connection users may benefit more than before, provided their profile and content are clear enough. The report cites improved interaction metrics for these users as evidence of that shift.

    At the same time, the guide is careful to flag its own limits. It is not endorsed by LinkedIn, it relies on public disclosures that are necessarily incomplete, and some of its interpretation involves architectural inference. It also notes that LinkedIn updates its systems continuously, so details may change after publication.

    So the fairest reading is this: the report is a strong, well-documented interpretive guide to LinkedIn’s public technical record, not a final or official specification. Its value lies in translating scattered engineering evidence into a strategic model that creators and professionals can actually use.

  • Global Foresight 2036 by the Atlantic Council

    Global Foresight 2036 by the Atlantic Council

    About the paper

    The report is a mixed-methods strategic foresight publication from the Atlantic Council that combines original survey research, expert commentary, six “snow leopard” horizon-scanning essays, and a shorter AI discussion section.

    Its core empirical base is the organisation’s fourth annual survey of 447 geostrategists and foresight practitioners from 72 countries, fielded in November and December 2025; the respondent pool is global, though roughly half are US citizens and the sample is drawn from the Atlantic Council’s network rather than a general population sample.

    Length: 76 pages

    More information / download:
    https://www.atlanticcouncil.org/content-series/atlantic-council-strategy-paper-series/global-foresight-2036/

    Core Insights

    1. What is the report’s central argument about the world of 2036?

    The report’s central argument is that the decade ahead is likely to be more unstable, more fragmented, and more dangerous than the present, even though the exact future cannot be predicted with certainty. Rather than offering a single forecast, the report uses foresight to map the pressures, risks, and directional shifts that experts think are most likely to shape 2036.

    Its overall tone is notably pessimistic. The opening findings state that 63 percent of respondents expect the world in 2036 to be worse off than it is now, while only 37 percent think it will be better. The report frames this darker mood through a cluster of reinforcing trends:

    • intensifying US-China rivalry
    • a possible hot conflict over Taiwan
    • weakening multilateral institutions
    • democratic decline
    • nuclear proliferation
    • climate stress
    • and rapid AI advances whose benefits are matched by growing concern.

    At the same time, the report is not purely apocalyptic. It argues that foresight is valuable precisely because it helps policymakers and readers prepare for multiple possible futures, including surprising ones. That is why the publication pairs survey findings with under-the-radar “snow leopards” and a separate AI section: the aim is not just to describe probable big trends, but to widen the reader’s field of vision.

    2. What are the report’s main findings from the expert survey?

    The report organises its main survey results into ten headline findings. The most prominent is that respondents broadly expect China to surpass the United States economically by 2036, even if they do not think China will simply replace the US as an uncontested global hegemon. Instead, most foresee either bipolar competition or a more diffuse multipolar system. They also increasingly expect China to attempt to take Taiwan by force, and more than 40 percent foresee another world war, most likely sparked in Taiwan or surrounding waters.

    A second major finding is that NATO is expected to survive, but not unchanged. Respondents are divided on whether it will become more or less influential, yet 44 percent think it will no longer exist in its current form by 2036. The survey also suggests growing doubts about whether the United States will still play the same commanding role within the alliance.

    On Russia and Ukraine, the survey points away from a decisive Russian victory and toward a frozen conflict. Respondents also see Russia as a diminished power by 2036, though still potentially dangerous, especially in nuclear terms. On AI, a majority believe artificial general intelligence could emerge within the decade, and more respondents still see AI as a net positive than a net negative, though the gap has narrowed as concern rises.

    Other major findings include:

    • expectations of wider nuclear proliferation, especially involving Iran and possibly Saudi Arabia, South Korea, Japan, and some NATO countries
    • a more autonomous but still strategically limited Europe
    • declining climate cooperation even as warming worsens
    • weakening global institutions alongside democratic erosion
    • and continued dollar dominance, though with crypto seen as the biggest challenger rather than another national currency.

    3. What evidence and patterns in the report best reveal how experts think power is shifting?

    One of the clearest patterns is that respondents do not think the future belongs to a single dominant actor in the way the post-Cold War era was often understood. The report repeatedly points to diffusion, contestation, and erosion of established forms of leadership. China is seen as rising strongly in economic power, nearly matching the US in technology and diplomacy, while the United States is still expected to remain militarily pre-eminent. That split itself is telling: respondents appear to be imagining a world where different forms of power are no longer concentrated in one state.

    Another pattern is institutional weakening.

    Respondents expect the United Nations, UN Security Council, WTO, World Bank, and IMF all to lose influence over the next decade.

    That suggests not merely dissatisfaction with current institutions, but a broader expectation that the post-1945 order is fraying. The report explicitly connects this decay with democratic recession, arguing that respondents who foresee deeper democratic decline are especially likely to expect institutional weakening and a worse overall world.

    A third pattern concerns regional and bloc-level reconfiguration. Europe is not expected to become the world’s leading military, economic, or tech power, yet respondents increasingly think it will achieve greater strategic autonomy. NATO may endure, but in altered form. The Global South section then adds another layer by showing that respondents from those countries often expect even sharper shifts away from US primacy and are more inclined to see China rising, Russia doing better in Ukraine, and even internal US breakdown.

    Together, these patterns show that the report is less about simple replacement of one superpower by another and more about a messy redistribution of influence across states, blocs, technologies, markets, and non-state actors.

    4. What does the report suggest about the role of technology and underappreciated trends in shaping the future?

    The report treats technology as both a direct force of change and a lens that reshapes how other global risks unfold. AI is the most obvious example. Respondents expect major advances, including the possible arrival of AGI within a decade, and the report presents AI as a technology with systemic implications for economics, geopolitics, knowledge production, and everyday life. But the accompanying expert discussion is more cautious than the survey toplines: Atlantic Council specialists stress that today’s AI is not good at truly forecasting the future, that AGI is far from certain, and that trust, energy demands, and market instability could constrain progress.

    The six “snow leopards” deepen this technological and horizon-scanning emphasis. These essays focus on phenomena that may be easy to overlook now but could become highly consequential. They include:

    1. Private tech firms shaping conflict outcomes
    2. Circular rather than one-way migration
    3. Kelp forests as climate and economic assets
    4. The erosion of the human rights order
    5. AI-driven cultural erasure
    6. Neurotechnology capable of decoding thought.

    What unites these cases is the report’s belief that future disruption will not come only from the usual headline issues. Some of the biggest shifts may emerge from domains that sit between established categories: companies behaving like geopolitical actors, migration functioning as an innovation loop, environmental restoration becoming industrial strategy, or data bias turning into cultural loss. This part of the report broadens the frame beyond interstate competition and argues for paying attention to early signals and second-order effects.

    5. What are the report’s biggest implications for policymakers, strategists, and communicators?

    The report’s main implication is that leaders should prepare for a world defined less by stability and rule-bound cooperation than by rivalry, fragmentation, and institutional stress. For policymakers, this means planning for deterrence, alliance adaptation, nuclear risk, democratic erosion, and climate-related conflict at the same time rather than treating them as separate silos. The report’s structure itself makes that point: geopolitics, democracy, climate, finance, and technology are deeply entangled.

    A second implication is that strategic assumptions inherited from the post-Cold War period look increasingly fragile. The report suggests that US leadership can no longer be taken for granted, NATO may need redesign rather than maintenance alone, and multilateral bodies may not be capable of managing future crises in the way they were once expected to. That pushes strategists toward resilience, contingency planning, and coalition-building under less favourable conditions.

    For communicators, the report is especially useful as a map of narratives that may dominate the coming decade:

    • democratic decline
    • technological disruption
    • geopolitical fragmentation
    • and competing visions of order.

    It also shows that audiences are unlikely to share a single worldview.

    The section on Global South respondents is particularly important here, because it demonstrates that expectations about the future vary significantly by geography and political vantage point. In practical terms, this means communication about global risk, strategy, or public policy will increasingly need to account for fragmented perceptions rather than assuming one shared interpretive frame.

    The final implication is methodological: the report argues implicitly for foresight as a discipline of disciplined imagination rather than prediction. Its value lies not in claiming certainty, but in helping readers test assumptions, notice emerging signals, and think more seriously about consequences before they fully arrive.

  • State of AI in PR 2026 by Muck Rack

    State of AI in PR 2026 by Muck Rack

    About the paper

    The report is an original research survey about how PR professionals are using, governing and judging generative AI and emerging AI agents in their work.

    It is based on a survey of 564 PR professionals fielded from 5 December to 24 December 2025 and distributed primarily via email; responses were reviewed for low-effort patterns and outliers.

    The geographic scope is not clearly specified in the report, and the methodology is useful but relatively thin on sampling detail beyond distribution method and data cleaning.

    Length: 28 pages

    More information / download:
    https://muckrack.com/resources/research/state-of-ai-in-pr

    Core Insights

    1. What is the report’s central argument about the current state of AI in PR?

      The core argument is that generative AI has moved from novelty to normal practice in PR, but that adoption has now largely plateaued. The report says AI use has “peaked” at around three-quarters of PR professionals, with 76% already using generative AI in their workflow and only modest shares still undecided or resistant. In other words, the market now appears split between a large majority who have already incorporated AI and a smaller minority who are unlikely to change their minds soon.

      The report also argues that this is no longer just an individual experimentation story. Organisations are adapting to AI institutionally: 51% of respondents say their company has an AI use-case policy, and 43% say their workplace offers AI training. That suggests AI in PR is becoming formalised, governed and embedded in workplace practice rather than remaining a purely ad hoc tool used by curious individuals.

      At the same time, the report draws a clear line between generative AI and agentic AI. While text-generation and workflow support are mainstream, AI agents have not yet crossed into broad professional adoption. Only 12% say they use AI agents in their work, and most respondents remain uncomfortable with autonomous action without human review. So the report’s bigger message is not “AI is coming”; it is “AI is here, but autonomy is not yet trusted.”

      2. How are PR professionals actually using AI, and where do they see the most value?

      The report shows that PR professionals are using AI mainly in mainstream knowledge and writing tasks, not in highly specialised or fully automated ways. The most common use case is editing and refinement, cited by 86%, followed by research and insights at 76%, writing and content creation at 74%, and strategy and planning at 68%. Administrative tasks are also significant at 51%, whereas media outreach, measurement and creative asset production remain much less central.

      That distribution matters because it shows where AI currently fits the profession best: it acts primarily as a cognitive and editorial assistant. It helps polish drafts, speed up background work, support ideation and assist with planning. The report says PR pros use AI in an average of four distinct work areas, which reinforces the idea that adoption is broad across tasks even if it is not yet deep in every part of the workflow.

      Perceived value is also very strong. Eighty-two per cent say AI has improved the quality of their work, while 93% say it helps them complete projects more quickly. When asked where the greatest time savings occur, respondents again point to core communication work: editing and refinement, research and insights, and writing and content creation. This suggests AI’s practical value in PR is currently less about replacing judgement and more about compressing routine labour around producing and shaping communication.

      3. What does the report suggest about trust, oversight and the limits of AI in PR practice?

      A key theme running through the report is that PR professionals are using AI extensively, but they do not trust it enough to leave it unsupervised. The clearest sign of this is editing behaviour: 98% say they always or often edit AI-generated text before using it. Although the extent of editing has decreased over time, the human review step remains almost universal. That implies AI is accepted as a draft partner, not as a final author.

      The report also reveals a selective approach to data input. Large majorities say they avoid entering financial data, personally identifiable information and proprietary or strategic material into AI systems. Yet far fewer avoid entering client or brand names and internal communications. This points to a practical but uneven data-risk mindset: many PR professionals recognise serious information-governance risks, but boundaries around what is safe to share with AI are still inconsistent.

      Trust drops even further when the report turns to AI agents. Only 7% say they would be comfortable allowing an AI agent to send messages or publish updates without human review, while about nine in ten are uncomfortable. The main conditions for greater trust are strong evidence of reliability, explicit human approval before publishing or sending, strong privacy and security safeguards, and transparent records of what the agent did. The report therefore implies that the profession is not rejecting automation outright, but it wants clear guardrails, accountability and a human in the loop.

      4. What risks, concerns and professional tensions does the report identify?

      The biggest concern in the report is not that AI will immediately destroy PR jobs, but that it could erode professional formation. Seventy-seven per cent say the main risk is that younger or newer PR professionals will fail to learn the principles of the profession and rely too heavily on tools. That is a profound concern because it goes to capability-building, judgement and the long-term health of the field.

      Other major concerns are also closely tied to quality and professional standards. Sixty-three per cent worry about unscrutinised AI output lowering the quality of communication, while 61% fear content becoming less original or creative and 61% think audiences may be overwhelmed by rising volumes of content. There is also concern that clients or firms may conclude they no longer need content creators. Together, these findings show that respondents fear AI may devalue craft, raise noise levels and weaken the human distinctiveness of communication work.

      Among non-users, the resistance is often principled rather than merely practical. The report notes that the 7% who do not plan to explore generative AI often cite environmental impact, plagiarism and broader ethical concerns. Many of them do not appear persuadable: 80% say they do not see themselves using AI in the future no matter what. That is important because it means non-adoption is not simply a training gap for everyone; for some, it reflects a deeper values-based objection.

      5. What are the report’s main implications for the future of PR work, capability-building and organisational practice?

      The report points towards a future in which the competitive edge in PR will come not from whether someone uses AI, but from how well they use it under proper governance. Since adoption has already stabilised at a high level, the next differentiator is likely to be capability. Respondents say the most important new skills are prompt writing or engineering, ethical decision-making, AI tool evaluation and selection, and data literacy. That suggests the profession is shifting from simple tool familiarity to a broader blend of technical fluency, judgement and governance awareness.

      There is also a strong implication for employers. Training and policy appear to matter. Among those open to AI but not yet using it, 60% say training or proven examples would help them start. At the same time, the growth in workplace policies and training since 2024 suggests companies are beginning to respond to that need. So the report implies that organisational maturity around AI is no longer optional; it is becoming part of professional infrastructure.

      Finally, the report suggests that PR is heading towards a hybrid future rather than a fully automated one. Paid AI use is rising rapidly, and everyday AI-supported work is now common, but agentic AI remains marginal because trust, accountability and reputational risk still demand human control. The underlying conclusion is that PR professionals are prepared to augment their work with AI, but not to surrender authorship, responsibility or final judgement to it. That is probably the report’s most important strategic implication.

    1. A.I. Radar 2026 by BCG

      A.I. Radar 2026 by BCG

      About the paper

      BCG AI Radar 2026 is a survey-based report on corporate AI investment, CEO ownership of AI transformation, and the rise of agentic AI.

      It is based primarily on BCG’s 2026 AI Radar Survey of 2,360 executives, including 640 CEOs, across multiple industries and markets including the US, Europe, India, Japan, Greater China, the Middle East and Africa.

      The methodology is original survey research, supplemented in places by BCG analysis, BCG client experience, and a separate BCG–MIT Sloan Management Review dataset.

      Length: 29 pages

      More information / download:
      https://www.bcg.com/publications/2026/as-ai-investments-surge-ceos-take-the-lead

      Core Insights

      1. What is the central argument of the report?

      The report argues that AI has moved from experimental technology investment to a strategic, CEO-level transformation agenda. BCG’s core claim is that corporate AI investment is not only increasing sharply, but is also becoming more durable: organisations are planning to keep investing even if near-term financial returns disappoint.

      The clearest evidence is that projected AI investment has roughly doubled from 2025 to 2026, rising from around 0.8% to 1.7% of organisational revenue. At the same time, 94% of organisations say they will continue investing even if their AI initiatives do not pay off in 2026. Only 6% say they would pull back.

      The deeper message is that BCG sees AI no longer as a CIO-led technology programme, but as a broad business transformation. This is why the report places so much emphasis on CEOs: 72% of CEOs surveyed say they are now the main decision maker on AI, twice the share reported the previous year.

      2. What evidence does BCG provide that AI investment is becoming more serious and sustained?

      BCG provides three main pieces of evidence.

      First, AI investment as a share of revenue is projected to double in 2026. The report shows AI investment rising from about 0.6% of revenue in 2024, to 0.8% in 2025, and then to 1.7% in 2026. That is a significant escalation, especially because BCG notes that the average base revenue of the underlying companies has remained almost the same.

      Second, the increase is broad-based across industries. The chart on page 7 shows all industries planning higher AI investment in 2026. Technology leads at 2.1% of annual revenue, followed by financial institutions at 2.0%, insurance and energy/utilities at 1.9%, consumer at 1.7%, and healthcare at 1.6%. Industrials and real estate are lower at 0.8%, but still show an increase.

      Third, the commitment appears resilient. On page 5, BCG reports that 70% of respondents would “stay the course” or make strategic changes if AI does not deliver the desired financial impact in the next 12 months, while 24% would ramp up resourcing or invest in outside experts. That means disappointment would not necessarily reduce investment; for many organisations, it could trigger more disciplined or more aggressive implementation.

      3. How is leadership of AI changing inside organisations?

      The report’s second major argument is that AI transformation is becoming CEO-led rather than CIO-led. This is one of the most important shifts in the deck.

      BCG reports that 72% of CEOs say they are the main decision maker on AI in their organisation, twice the level from the previous year. It also reports that 82% of CEOs are more optimistic about AI’s ability to deliver ROI than they were 12 months earlier.

      The report goes further by linking AI success to CEO job security. Half of surveyed CEOs believe their job stability depends on getting AI investments and strategy right by 2026. That is a striking framing: AI is not presented merely as a productivity tool, but as a defining test of executive leadership.

      BCG also shows that CEOs express stronger conviction than other executive groups. On page 13, CEOs are slightly ahead of CIOs/CTOs in saying they are ready to lead an AI transformation, confident AI will pay off, and expecting major role disruption by 2030. The implication is that AI is becoming a top-management issue because it affects operating models, workforce design, competitive advantage, and future business models.

      4. What role does agentic AI play in the report’s argument?

      Agentic AI is presented as the next major mechanism through which organisations expect to see measurable value from AI. BCG reports that around 90% of CEOs believe AI agents will enable their organisations to report measurable ROI in 2026, and that CEOs have committed more than 30% of their organisation’s 2026 AI investment to agentic AI.

      The report describes agentic AI as both an opportunity and a risk. On cybersecurity, for example, 59% of leaders see AI agents as both a threat and an opportunity. The same capabilities that make agents useful — automation, scale, system access, continuous learning — can also make them dangerous if misused, hacked, or poorly governed.

      The report also uses BCG–MIT Sloan Management Review data to show that AI applications are expected to take on broader roles in organisations. Currently, 26% of organisations say AI acts as an assistant; in three years, that rises to 61%. The expected role of AI as colleague, coach, mentor, rival, and even boss also increases substantially. This supports BCG’s point that agentic AI is not just a software upgrade; it changes how companies organise work, decision-making, and governance.

      5. What does BCG believe separates leading CEOs from the rest?

      BCG identifies three CEO archetypes: Followers, Pragmatists, and Trailblazers.

      Followers, around 15% of CEOs, recognise AI’s potential but lack full conviction and make cautious early investments. Pragmatists, around 70%, are excited and confident but invest when they see clear value and lower risk. Trailblazers, around 15%, are the most committed group: they invest more heavily, upskill more of their workforce, spend more time deepening their own AI expertise, and are more confident that AI will deliver ROI.

      The report’s most important distinction is that Trailblazers create a “positive flywheel”. They make AI and agentic AI a top priority, deepen their own AI literacy, commit capital at scale, upskill the organisation, and track measurable ROI. For example, Trailblazers spend around 60% of their AI budget on agentic AI, compared with 25% for Pragmatists and Followers. They also allocate around 60% of their AI budget to upskilling and retraining the current workforce, and report that around 70% of their workforce has been upskilled or reskilled on AI.

      BCG’s practical conclusion is therefore quite direct: CEOs must act decisively. The final recommendation is a five-part agenda:

      1. Make AI a key priority
      2. Deepen AI literacy
      3. Commit investments at scale
      4. Upskill the organisation
      5. Track measurable ROI.

      The report’s underlying assumption is that AI advantage will not come mainly from adopting tools, but from executive commitment, organisational redesign, workforce capability, and disciplined measurement.