Tag: reputation

2026 Global RepTrak 100 by RepTrak

About the paper

The report argues that corporate reputation has entered a “multiplayer” era in which it is shaped less by top-down corporate messaging and more by communities, employees, creators and, now, AI systems.

It is a mixed-methods report built primarily on RepTrak’s proprietary survey-based reputation dataset, using responses from the informed general public across 14 major economies collected at the end of 2025, with additional case-based interpretation; the precise number of survey respondents is not clearly specified in the report, although the overall panel reach is described as 70 million consumers and business professionals globally.

Length: 11 pages

More information / download:
https://www.reptrak.com/globalreptrak/

Core Insights

1. What is the report’s central argument about how corporate reputation is changing?

The core argument is that reputation no longer belongs mainly to companies or to the communication channels they control. Instead, it is increasingly co-created by stakeholder networks: communities, employees, creators, cultural participants, and AI systems that synthesise what those groups are already saying. The report calls this shift “multiplayer” reputation. In practical terms, that means the old model of corporate-heavy storytelling through press releases, media relations and investor relations is losing relative power, while reputation increasingly depends on whether external stakeholders choose to carry and reinforce a company’s narrative.

The report is careful not to say that corporate communication has become irrelevant. Rather, it says companies still set inputs and core storylines, but they are no longer the “final shapers” of perception. The decisive difference is whether others pick up that story and validate it through their own voices and experiences. The conclusion on page 11 is especially clear: reputation now “belongs to the communities that choose to carry it”.

That framing also reveals the author’s perspective. RepTrak is not merely describing a media trend; it is arguing for a strategic reorientation in corporate communications. The implied advice is that companies should spend less energy trying to dominate the narrative and more energy creating the conditions for genuine third-party advocacy.

2. How does the report support its “multiplayer” thesis with methodology and evidence?

The report’s main evidence comes from RepTrak’s global reputation model. It says the 2026 ranking is based on survey responses collected across 14 major economies at the end of 2025 from what it calls the informed general public, meaning people who know a company and have formed an opinion about it. Companies had to meet revenue, familiarity and reputation-score thresholds to qualify for inclusion. RepTrak then applies its proprietary “Feel, Think, Do” model, linking emotional response, rational drivers and behavioural outcomes.

The report uses this framework to show that headline reputation remains strong while the underlying mechanics are changing. Average global Reputation Scores rose for a third straight year to 74.6, suggesting that the system is not collapsing. But beneath that stable surface, channel effectiveness is shifting and the companies rising fastest tend to be those whose reputations are sustained by broader stakeholder networks rather than by owned channels or tightly controlled brand ecosystems.

The evidence is therefore both quantitative and interpretive. Quantitatively, the report cites changes in scores, rank movements, and channel impact. Interpretively, it adds company case examples such as LEGO, adidas, Nike and NVIDIA to show how those shifts may work in practice. That makes this less a pure data pack and more a research-led argument built from proprietary survey analysis plus illustrative case reading. The methodology is fairly clear on the survey structure and qualification rules, but the exact respondent count for this specific edition is not clearly specified in the report.

3. Which companies best illustrate the report’s view of winners and losers in the new reputation environment?

The clearest winner is LEGO. It retained the number one position with a Reputation Score of 78.2, and the report treats it as the model multiplayer brand. Its importance lies not only in rank but in the source of its resilience: even when its Products & Services driver slipped slightly, overall reputation held because a wider network of fans, educators, builders and communities continued to advocate for it. The LEGO case on page 6 deepens this argument by showing how product strength fuels community expansion through the Botanical Collection, Fortnite participation, and collaborations with Crocs and adidas. The message is that strong products recruit new communities, and those communities then do reputational work that product quality alone cannot achieve.

Adidas is the other flagship winner. It rose from #16 in 2024 to #2 in 2026, with the report highlighting gains in Conduct, Workplace and “Fair in business”. The argument is that adidas rebuilt and broadened its reputation not through classic controlled brand campaigns, but through creator ecosystems, cultural collaborations and participation in spaces such as Roblox. In the report’s logic, those communities did not merely consume the brand story; they helped author it.

NVIDIA is the most interesting new entrant. It debuts at #14 and is described as a company whose reputation was built “almost entirely without traditional corporate communications infrastructure”. The report attributes that to its developer community, the broader cultural moment around AI, and consistent execution over time. That makes NVIDIA an important proof point for the claim that a company can accumulate reputational strength through network effects well beyond formal communications.

On the losing side, Nike is the key contrast case. It fell to #50, and the report presents this as the result of narrowing its ecosystem through a direct-to-consumer strategy centred on owned channels. In the report’s reading, Nike pursued control in the name of community, but ended up weakening the independent stakeholder network that could have supported it when performance and conduct issues emerged. Spotify and Harley-Davidson are also presented as declines that support the broader thesis, while Disney is cited as an example of a once-strong reputation falling out of the rankings entirely.

4. What does the report say about channels, and why is AI such an important development?

One of the report’s most important findings is that channel impact is compressing. It says impact, defined as the gap in Reputation Score between those exposed to a channel and those not exposed, is now at its lowest recorded level across every channel. But the report insists this should not be misread as simple decline. The exposed score has mostly stayed flat or risen; the big change is that the non-exposed score has risen by 2 to 4 points across nearly every touchpoint since 2017. In other words, strong perceptions formed in one place now travel across the wider ecosystem, lifting the baseline everywhere else.

This is where the report introduces its “co-authorship effect”. People reached through a channel do not remain passive recipients. They carry their interpretations into other spaces, interactions and communities. That helps explain why individual channels appear less decisive on their own: they now operate inside a networked environment where meaning spills across boundaries.

AI matters because it is presented as a fundamentally different kind of channel. Unlike other channels that people encounter passively or habitually, generative AI is used in response to explicit questions. That makes it an answer engine rather than just a distribution channel. The report says AI reaches only 10% of stakeholders, ranking 11th out of 14 channels by reach, but already ranks 7th in impact with a score of 6.6. Its exposed score is 80.4 versus a non-exposed score of 74, giving it one of the largest impact gaps in the dataset.

The implication is striking: AI does not simply repeat corporate messaging. It synthesises what everyone else has said about a company. For firms with genuine stakeholder alignment, AI amplifies that positive signal. For firms with a gap between claimed identity and lived reality, AI becomes, in the report’s phrase, an “unsparing auditor”. That is one of the report’s strongest strategic warnings for communications leaders.

5. What broader implications does the report draw for corporate communications and reputation strategy?

The report’s biggest implication is that communications strategy must move from message control to stakeholder alignment. It suggests that the companies that will perform best are not the loudest broadcasters but those that provide a clear, consistent and credible core story that others are willing to adapt and carry in their own voice. That requires communicators to think less like message managers and more like stewards of an ecosystem.

A second implication is that fundamentals still matter. The report does not celebrate decentralised storytelling for its own sake. It repeatedly argues that community amplification only works when there is something worth amplifying: strong products, credible leadership, visible values, and employee cultures that can withstand scrutiny. This is why LEGO’s product strength is so central, and why firms under pressure on performance, conduct or leadership lose ground even in a networked environment.

A third implication comes from the “Feel, Think, Do” data. Reputation appears to be splitting between short-term transaction behaviour and longer-term commitment. Buy and Recommend both fell by one percentage point, while Invest rose by one point. The report interprets this as stakeholders becoming more selective in immediate transactions but more willing to back companies they believe in over time. That suggests reputation may increasingly function as a buffer and a trust reserve, not just a demand driver.

Taken together, the report’s conclusion is quite pointed: the architecture of trust has not changed, but the environment in which trust is formed has. More channels, more peer influence and AI synthesis mean that companies cannot rely on communications volume or channel control alone. They have to earn consistency across products, behaviour, culture and stakeholder experience, because that is what communities and AI will now reflect back to the market.

This post’s ‘featured image’ was constructed with A.I. to fit the entire paper’s title into a 3:2 image.

8 April 2026
Leading at the Intersections 2026 by Weber Shandwick
About the paper

Weber Shandwick’s Leading at the Intersections 2026 is a short corporate affairs trends report about the strategic shifts reshaping modern corporate affairs, especially in the U.S. and for U.S. multinationals.

It is primarily an expert commentary / advisory perspective, with one referenced survey of Fortune 1000 communications and corporate affairs executives conducted by Weber Advisory and Gravity Research; the sample size, fieldwork method and timeframe are not clearly specified in the report.

The geographic focus is mainly the United States, with some attention to global stakeholder expectations around U.S. companies abroad.

Length: 13 pages

More information / download:
https://webershandwick.com/news/the-five-shifts-redefining-the-c-suite-agenda-in-2026

Core Insights

1. What is the central argument of the report?

The report argues that corporate affairs leaders are now operating “at the intersections” of several forms of disruption:
- geoeconomic instability
- polarised U.S. politics
- reputational volatility
- AI-driven transformation
- workforce anxiety
- cultural fragmentation
- and changing expectations around responsible business.
Its core message is that corporate affairs can no longer be treated as a reactive communications function. The authors frame it as a strategic leadership capability that must help organisations make sense of complexity, protect licence to operate, create stakeholder value and support business resilience.

The report’s strongest underlying assumption is that the operating environment has become too volatile for narrow, bottom-line-only communication. Companies need to understand how business value, stakeholder expectations, culture, politics, technology and social impact now interact. In that sense, the report positions modern corporate affairs as a form of integrated strategic intelligence.

2. How does the report suggest companies should think about value in a time of disruption?

The report warns that in uncertain times, leaders may be tempted to focus narrowly on economic value and the bottom line. Weber Shandwick argues the opposite: disruption is precisely when companies need to broaden their understanding of value.

It identifies several value dimensions beyond financial performance:
- functional value
- emotional value
- and societal value.
The point is not that profit becomes irrelevant, but that companies’ licence to operate depends on more than profit. Trust, relevance, purpose, stakeholder relationships and perceived contribution to society all become part of the value equation.

The report links this especially to the 2026 U.S. midterm environment. Affordability, cost of living, inequality, trade, healthcare, housing, immigration and AI regulation are all described as issues shaping public expectations. In this environment, companies face reputational risk if they are seen as detached from ordinary stakeholder concerns.

One particularly useful insight is that “corporate speak” is no longer neutral. The report frames over-polished, generic language as a credibility risk. Companies are advised to communicate with more emotion, empathy and candour — not as a stylistic preference, but as a trust-building necessity.

3. What new expectations does the report identify for corporate diplomacy?

The report argues that U.S. multinationals will be pushed into more explicit forms of corporate diplomacy in 2026. The key issue is that foreign stakeholders may increasingly expect U.S. companies to show that they are not simply proxies for U.S. foreign policy.

This is an important distinction. The report suggests that U.S. brands have, so far, retained some independence from declining perceptions of U.S. political leadership. But that separation may become harder to maintain if U.S. government actions become more confrontational or less aligned with international norms.

Three expectations stand out. First, companies must demonstrate local accountability: where decisions are made, how local interests are protected and which commitments endure despite political shifts in Washington. Second, they need deeper local relationships across government, business and civil society, because these relationships become a form of reputational defence. Third, executives may need to speak more visibly and carefully abroad, because silence can increasingly be interpreted as alignment.

The report also connects this to B2G strategy in an “America First” context. For tech companies in particular, it recommends local storytelling around outcomes governments already care about: workforce upskilling, manufacturing, energy resilience, defence readiness and public-sector efficiency.

4. Why does the report treat cultural intelligence as a leadership capability?

The report presents cultural intelligence as a core leadership currency because companies are increasingly pressured to respond to cultural flashpoints in real time. Digital discourse, ideological polarisation, influencer dynamics, bots and platform algorithms all make it harder for organisations to remain silent or generic without others filling in the blanks.

The report’s argument is not that companies should comment on everything. Rather, leaders need to know their organisation’s “true north” and make sharper decisions about when to engage, how to engage and when not to engage. Cultural intelligence is therefore both an external sensing capability and an internal decision-making discipline.

The report highlights three ways to build cultural adaptation fluency. Companies should internalise organisational values so they function as an operating system rather than decorative statements. They should dig deeper into the “why” behind cultural signals, not just track what is trending. And they should make scenario planning a routine practice, using AI and other tools to anticipate how cultural communities and influencers may react.

A useful nuance here is the distinction between audiences and algorithms. The report notes that meaning still comes from human belief, but reach is shaped by platforms. Leaders therefore need to design communication for both human interpretation and algorithmic circulation.

5. What implications does the report draw for responsible business and AI transformation?

The report argues that responsible business is not disappearing, even if the language around ESG, sustainability or social impact changes for political and practical reasons. The fundamentals remain important because companies still need to balance material business pressures, stakeholder tensions and reputational risk.

Three responsible business challenges are highlighted.
1. First, companies must define the future of human work as AI integration accelerates. Stakeholders will expect human-first integration plans, workforce readiness and credible opportunities for future talent.
2. Second, the report argues that climate action is becoming more fragmented because coordinated multilateral action is weakening, while “China First” green tech and “America First” energy politics reshape the context.
3. Third, companies must separate values from “vibes”: in a fragmented information environment, responsibility strategies must be anchored in the business model rather than broad, consensus-seeking purpose claims.
On AI, the report’s position is pragmatic rather than utopian. AI transformation is treated as unavoidable, but the authors warn against simplistic winner/loser narratives. Companies need a transformation narrative that proves the business case while addressing the human side of change.

The report identifies three AI-related communication challenges:
- real-time stakeholder insight
- machine readability intelligence
- and human-centred generative creativity.
The most distinctive point is “machine readability”: companies now need to understand how they appear in AI search and AI-generated summaries, which sources shape those outputs, and how to correct misinformation or poor representations.

The final message is: be AI-enabled, not AI-enthralled. For B2B marketing in particular, AI should not replace the entire martech stack or become a reason to defund other vital technologies. The stronger argument is for deliberate integration: clear use cases, regulatory awareness, privacy safeguards and attention to workforce impact.
5 March 2026
The Global Reputation Economy by Burson

About the paper

This Burson report argues that reputation should be treated as a measurable corporate asset class, which it calls “Reputation Capital”.

It is a mixed-methods, proprietary modelling report combining stakeholder input, brand tracking, media analysis and stock-market modelling; the methodology is more fully described than many agency papers, though some elements remain proprietary.

The analysis covers 66 publicly traded companies, drawing on tens of thousands of stakeholders, and spans October 2024 to October 2025 across companies headquartered in the United States and other countries.

Length: 30 pages

More information / download:
https://www.bursonglobal.com/p/reputation-economy

Core Insights

1. What is the report’s central argument about reputation, and why does Burson believe it matters now?

The core argument is that reputation is no longer a soft, retrospective PR concept but a quantifiable form of capital that can be measured, managed and used as a strategic business asset. Burson argues that in a hyper-connected environment, where companies are exposed to constant scrutiny from legacy media, social platforms, activists and independent publishers, traditional reputation tools are too slow and too shallow. In that context, companies need something closer to near real-time intelligence rather than annual surveys or periodic brand tracking.

The report therefore reframes reputation as “Reputation Capital”: an asset that can create competitive advantage, build resilience, support bolder decision-making and fuel sustainable growth. It also claims this asset matters not just to communications teams, but to investors, boards and corporate leaders because it can be linked directly to upside and downside in business performance.

The deeper point is that reputation affects strategic freedom. Companies with strong reputations can absorb setbacks more easily, take bigger risks and retain stakeholder trust. Companies with weak reputations are more exposed: a single misstep is interpreted not as an exception, but as proof of a broader pattern. In that sense, the report presents reputation as a buffer, an enabler and a financial driver all at once.

2. How does the report measure reputation, and what kind of evidence does it use?

Burson presents this as a mixed model connecting three domains: stakeholder belief, media signals and financial outcomes. It says the model draws on tens of thousands of stakeholders, including consumers, business decision-makers and opinion leaders, using a comprehensive question set alongside daily brand tracking. It then combines that with large-scale monitoring of traditional and social media, described as terabytes of daily mentions, to build a media profile for each company. Finally, it links those inputs to stock performance by isolating the “unexpected return” in share price movement that cannot be explained by normal market trends or financial fundamentals alone.

That is important because the report is not presenting original survey data alone, nor a pure media audit, nor a simple financial analysis. It is explicitly a mixed-methods modelling exercise designed to connect perception to business value. Burson says this is what allows reputation to move from an attitudinal concept to a leading indicator of performance. The model was also externally validated, according to the report, by Dr Felipe Thomaz of Oxford/Saïd Business School.

At the heart of the model is an eight-lever framework: Products, Innovation, Financial Performance and Creativity on one side, and Leadership, Governance, Workplace and Citizenship on the other. Burson treats these as the building blocks of reputation and claims the framework can show not just how a company is perceived overall, but which levers are driving strength, weakness and business impact.

3. What does the report claim about the financial value of reputation?

The headline claim is that reputation produced an average of 4.78% in added, unexpected annual shareholder returns across the companies studied. Burson defines these returns as “unexpected” because they sit beyond what standard financial indicators such as revenue or margins would predict, and “additional” because the model attributes them directly to reputation.

From that base, the report extrapolates to the wider market and estimates the global “Reputation Economy” at just over $7 trillion. It also says that, within its sample, the value of reputation varied widely by company, ranging from $2 million to as much as $202 billion. These numbers are used to support the broader thesis that reputation is not merely symbolic or narrative-based, but economically consequential.

The report also argues that the biggest opportunity is not simply to maintain a good reputation, but to move from “Reputation Stagnation” into “Reputation Cultivation”. It groups companies into three categories: cultivation, stagnation and erosion. Roughly 60% of firms fall into stagnation, according to the report, suggesting that many companies are leaving value on the table because they lack a deliberate, data-driven reputation strategy.

That framing reveals Burson’s perspective clearly: reputation management should be treated as value creation, not just risk mitigation. The report is effectively making the case for boards and leaders to think about reputation in capital-allocation terms.

4. According to the report, what separates reputation leaders from laggards?

Burson’s argument is that there is no single silver bullet. The biggest difference between leaders and laggards is not one lever but broad, disciplined strength across all eight. The report says the top quartile scores 11 to 15 points higher on every lever and that the gap between top and bottom performers is 13.8 points on a 100-point scale.

Even so, three gaps stand out most strongly in the report’s description of a modern reputation leader: visionary innovation, excellence in product delivery and unimpeachable governance. Those are presented as the clearest markers of the firms that convert reputation into strategic advantage.

What matters here is the report’s underlying assumption that reputation is systemic. Leaders do not just communicate better; they manage the organisation more coherently. Workplace culture supports innovation, governance underpins product quality, and credibility across the system gives leaders resilience. That resilience then changes the CEO’s risk calculus: strong-reputation firms can launch ambitious products, enter complex markets and recover from setbacks more easily because stakeholders grant them the benefit of the doubt. Laggards, by contrast, become defensive, incremental and strategically paralysed.

So the report’s model of leadership is not glamour-driven. It is operational. It suggests that reputation leadership comes from sustained excellence, cross-functional consistency and the ability to avoid weak links.

5. What are the report’s most important conclusions for sector strategy, workplace investment and AI?

One of the most interesting parts of the report is its claim that industries have different “reputational centres of gravity”. Tech, for example, remains highly valuable in Reputation Capital terms and scores strongly on Products and Innovation, but its growth is now almost flat. Burson argues that tech’s future reputation gains will depend less on disruptive launches and more on Governance, Leadership and Citizenship, especially as AI raises broader social concerns.

Other sectors reveal different lessons. Aerospace is presented as a comeback story built not just on better products, but on fixing foundational levers such as Governance and Workplace. Automotive is described as facing a “Citizenship Challenge”, where EV narratives are not enough if stakeholders see gaps in labour practices, safety, supply-chain ethics or broader societal impact. Finance is shown as especially vulnerable because it is declining across three protective levers at once: Leadership, Governance and Citizenship.

The report’s strongest practical recommendation, however, is about the Workplace lever. Burson calls this the highest-ROI reputation investment because it is under-valued and under-invested, despite showing a large performance gap between best and worst performers. The argument is that employees are now the most credible carriers of company culture, so internal culture becomes a driver of external trust. In other words, the hidden engine of reputation is not the flashiest campaign, but the quality of the employee experience.

That leads directly to the AI section. Burson says most companies discuss AI in terms of innovation and efficiency, but the more important reputational question is people. The report argues that a company’s AI strategy is effectively a statement about how it values employees. Firms that use AI for augmentation, reskilling and transparent co-creation with staff may gain a “reputation dividend”. Firms that use it mainly for opaque top-down cost cutting may pay a “reputation tax” through backlash, talent loss and weaker Workplace scores. The key question, in Burson’s words, is no longer whether a company has an AI strategy, but whether it has an “AI people strategy”.

14 January 2026
The Ipsos AI Monitor 2025 by Ipsos

About the paper

The paper is a 30-country survey about public understanding of AI, trust, perceived risks, and expectations for AI’s impact on work, content, brands, economies and everyday life.

It is original survey research conducted by Ipsos via its Global Advisor online platform and, in India, its IndiaBus platform, between 21 March and 4 April 2025, with 23,216 adults across 30 countries; India used a mixed face-to-face and online approach.

The methodology is clear, but Ipsos notes that some country samples are more “connected” than nationally representative, and that the 30-country average is an unweighted average across markets rather than a population-adjusted global figure.

Length: 57 pages

More information / download:
https://www.ipsos.com/en-dk/ipsos-ai-monitor-2025

Core Insights

1. What is the central tension in public attitudes towards AI?

The report’s central argument is that public opinion on AI is defined by a tension Ipsos calls the “Wonder and the Worry of AI”. People recognise AI’s potential and expect it to become embedded in many areas of life, but they also feel nervous about its consequences.

At the 30-country average level, 52% say AI products and services make them excited, while 53% say they make them nervous. That means excitement and anxiety are not opposing camps so much as overlapping reactions: many people appear to hold both views at once.

This tension is also geographically uneven. The Anglosphere — the US, Great Britain, Canada, Ireland and Australia — is described as more nervous than excited. European markets sit in a middle zone, with moderate excitement and less intense nervousness. Several South-East Asian markets are much more positive, while Japan is presented as an outlier: neither especially excited nor especially nervous.

The broader meaning is that AI is not being received as a simple “innovation story”. People expect progress, but they are not automatically confident that the benefits will be fairly distributed, responsibly governed, or socially benign.

2. How much do people understand AI, and how does knowledge vary by country?

A majority say they understand AI at a general level, but fewer say they understand where AI is actually being used.

Across the 30 countries, 67% agree that they have a good understanding of what artificial intelligence is. However, only 52% say they know which types of products and services use AI. That gap matters: people may feel familiar with AI as a concept while still being unsure where it is embedded in everyday services.

There are large country differences. Indonesia, Thailand and South Africa are among the highest on claimed understanding of AI, while Japan is lowest. For knowing which products and services use AI, Indonesia and Thailand again rank high, while Belgium, Japan and Canada are at the lower end.

This suggests that “AI literacy” is not just a question of awareness. The public may know the term, recognise the general idea, and still lack practical understanding of where AI is operating in search, marketing, recruitment, news, advertising, disinformation, customer service or workplace tools.

3. What does the report reveal about trust in AI, companies and governments?

Trust is one of the report’s most important fault lines. People are not simply asking whether AI is useful; they are asking who controls it, who regulates it, and whether organisations using it can be trusted.

Only 48% across the 30-country average say they trust companies using AI to protect their personal data. Trust is much higher in countries such as Indonesia, Thailand and India, while Sweden, Canada, Japan, France and the United States sit much lower. The net trust measure is only slightly positive at the global country average level, which signals a fragile trust environment for brands and platforms.

Governments are trusted somewhat more than companies in this context: 54% say they trust their government to regulate AI responsibly. But this also varies dramatically. Singapore, Indonesia, Malaysia and Thailand are high-trust markets, while the United States, Japan, Hungary, Great Britain and Canada are much lower. Ipsos suggests that low trust in government regulation may help explain higher nervousness in some markets, especially the US.

One striking finding is that people say they trust AI more than people not to discriminate or show bias. At the 30-country average, 54% trust AI not to discriminate or show bias, compared with 45% who trust people not to discriminate or show bias. That does not mean people think AI is neutral; rather, it suggests that public trust in human fairness is also weak.

The strongest trust-related consensus is disclosure. Seventy-nine per cent agree that products and services using AI should have to disclose that use. This is one of the clearest implications for organisations: transparency is not a niche concern but a mainstream expectation.

4. How do people feel about AI-generated content, advertising and brand use?

The report shows a clear public distinction between expecting AI-generated content and preferring it. People believe AI will be widely used, but they still prefer human-created content in most cases.

For example, 79% think AI is likely to be used for online search results, and only 28% say they are uncomfortable with that use. That suggests search may be one of the more socially acceptable AI applications. By contrast, people are much more uncomfortable with AI-generated political ads, AI-written news stories, AI screening job applicants, and AI used to create or target disinformation.

When asked about content preferences, the public consistently favours human-driven content. Seventy per cent prefer human-driven online news articles or websites; 71% prefer human-driven photojournalism; 67% prefer human-driven movies; 62% prefer human-driven advertising; and 60% prefer human-driven customer marketing websites.

For brands, the picture is mixed and potentially risky. People are split on whether AI use would make them trust companies more or less. At the 30-country average, AI-enhanced product images produce 34% more trust and 38% distrust; AI-written product descriptions produce 33% more trust and 42% distrust; AI-created advertising images or video produce 30% more trust and 38% distrust; and AI-written product reviews produce 29% more trust and 36% distrust.

The implication is that AI use in marketing is not automatically reputationally damaging, but it is not automatically efficiency-positive either. Brands may gain from AI where it improves usefulness, speed or relevance, but they risk distrust when AI is perceived as deceptive, synthetic, manipulative or insufficiently disclosed.

5. What future impact do people expect AI to have on jobs, economies and everyday life?

People expect AI to become more important in daily life, but their expectations are uneven across domains.

A majority already feel AI has affected them: 52% say AI products and services have profoundly changed their daily life in the past three to five years. Looking ahead, 67% say AI will profoundly change their daily life in the next three to five years. So AI is not viewed as speculative; it is already part of people’s lived experience and expected to intensify.

On work, the findings are ambivalent. Globally, 59% think AI is likely to change how they do their current job in the next five years, but only 36% think it is likely to replace their current job. Even more importantly, people are more optimistic about their own job than about the wider labour market. Among those with a job, 38% think AI will make their own job better, while 16% think it will make it worse. But for the job market overall, only 31% think AI will make it better, while 35% think it will make it worse.

This “my job versus the job market” distinction is one of the report’s most useful insights. People may believe they personally can adapt, benefit or remain protected, while still worrying about broader labour disruption.

The same pattern appears in other future-facing areas. People are optimistic that AI will improve efficiency: 55% say it will make the amount of time it takes to get things done better, compared with only 10% who say worse. They are also more positive than negative about entertainment options and health. But they are much more concerned about disinformation: only 29% think AI will make the amount of disinformation on the internet better, while 40% think it will make it worse.

Economically, the global country average is cautiously positive: 34% think AI will improve their country’s economy, while 23% think it will worsen it. Ipsos argues that countries most excited about AI tend to be countries where people are also more likely to believe AI will benefit the economy. In other words, enthusiasm appears tied not only to technology itself, but to whether people believe AI will produce visible, shared economic benefits.

11 June 2025