How 92 business professionals are navigating AI acceptance and trust, revealing pragmatic attitudes but clear conditions around consent, human oversight, and shared responsibility for managing workforce disruption.
The research shows that public attitudes toward AI are best understood as conditional acceptance, not blanket resistance. Most respondents approach AI pragmatically, with 84% expressing a cautious but practical orientation. But acceptance depends on visible trust boundaries: 62% say fair training data requires both consent and compensation, 64% accept AI surveillance only with clear notice and limits, and nearly half are unwilling to accept opaque systems at all. This leads to a clear pattern: people do not want unrestricted automation; they want governed AI.
That boundary becomes sharpest in high-stakes decisions. In hiring and promotion, 66% support AI only for screening with human review, while concerns center on lost human judgment and context. At the same time, respondents expect disruption without treating it as unmanageable: 47% see AI-driven job change as mixed but manageable, and 53% say responding to it is a shared responsibility of employers and government. The takeaway is straightforward: acceptable AI already has a blueprint—transparency, consent, bounded use, human oversight, and institutional support.
Most respondents take a pragmatic but cautious stance toward AI, showing that adoption barriers are less about hostility to the technology and more about whether its use feels legitimate and controlled.
A clear majority say AI training data is fair only when people knowingly agree and share in the value created, setting a firm boundary against unrestricted scraping.
Support for AI in employment decisions is concentrated at the screening stage, with most respondents rejecting autonomous decision-making in outcomes that materially affect careers.
More respondents place responsibility on employers and government than on workers alone, signaling that sustainable AI adoption requires coordinated transition support.
Public attitudes toward AI are no longer speculative — 84% of respondents now approach AI with a cautious but practical orientation, and acceptance depends directly on visible trust infrastructure: consent, disclosure, bounded use, and human oversight. Vendors who treat governance as an afterthought are selling into a market that has already moved on. These findings reveal six concrete go-to-market opportunities for SaaS vendors.
84% of respondents take a cautious but practical stance, and 62% say fair training data requires both consent and compensation. Buyers are not asking whether your product uses AI — they are asking how it governs AI. Vendors who lead with capability demos before addressing governance architecture are misaligned with the dominant buyer posture.
Lead your pitch and packaging with governance architecture — consent workflows, data provenance controls, and audit logs — before any AI capability demo. Make these discoverable in your security documentation and compliance pages.
66% support AI only for screening or first-pass work with mandatory human review, and 82% insist humans retain final decision authority. This is not buyer hesitancy — it is a design requirement. Products that bury human override flows in settings menus are misreading the market signal.
Make human review and override flows the most prominent features in your product UI and marketing. Frame them as power features, not concessions — buyers need to show their teams that final judgment stays human.
64% accept AI surveillance only with clear notice, legal guardrails, and strict limits, while 46% frame privacy risk primarily as hidden collection and surveillance. Any AI product that monitors users, logs behavior, or processes sensitive data sits in a category where trust signals are now table stakes.
Publish clear, human-readable data-use guardrails and user-visibility controls as first-class product marketing assets — not just as legal fine print. Proactive disclosure of what is and isn't collected is now a competitive advantage.
46% of respondents are outright opposed to deepfakes, and 72% say synthetic media is unacceptable when deceptive or harmful. Any vendor in content generation, video, martech, or sales enablement operates in a category where the perception of misuse risk is the default — without explicit guardrails, the trust cliff is close.
Add consent verification, AI-disclosure labels, and provenance tooling to any generative content feature. Treat these as a trust prerequisite — buyers will increasingly evaluate synthetic media tools on their abuse prevention architecture, not just output quality.
47% of respondents see AI-driven job change as mixed but manageable, and 53% say employers and government share responsibility for managing disruption. Enterprise buyers — especially in HR, productivity, and operations — need to show their boards and employees a managed adoption story. Vendors who ignore this narrative dynamic face longer sales cycles.
Embed workforce-transition narratives, change management guides, and internal communications templates into your onboarding and success programs — help customers tell a managed adoption story to their own stakeholders.
55% of respondents describe themselves as proactively adopting AI and upskilling, and 94% favor either pragmatic adaptation or proactive upskilling. This is a large, receptive audience that already has budget and organizational permission to invest in AI enablement tools. The competitive question is who captures it first.
Create in-product training flows, partner enablement programs, and AI literacy certifications that help customers demonstrate active upskilling — this doubles as a retention driver and an ESG and workforce-strategy proof point for enterprise procurement teams.
The report should open on the market’s baseline posture: people are not broadly anti-AI, but they approach it with caution. That caution is anchored in specific trust conditions—disclosure, consent, compensation, and protection against hidden surveillance or misuse—showing that acceptance is contingent on governance, not just product capability.
“AI is the same thing. So it will be something that is there and helpful, and used as a tool, but not used as a replacement for the people who are checking and making sure and maintaining that everything is working right.”
— Infrastructure and Enterprise Applications Manager, Public Transit
AI is viewed primarily as a practical tool rather than a transformational force. Roughly 84% of respondents who discussed it took a pragmatic but cautious stance, emphasizing usefulness alongside the need for oversight. Only 15% expressed clearly AI-positive enthusiasm, while just 2% focused mainly on harms.
This pattern points to conditional adoption: respondents are open to efficiency gains and broader benefits, but resist unchecked deployment, especially when jobs, governance, and accountability are at stake. The small strongly positive segment highlights productivity and societal upside, while the tiny skeptical minority centers on privacy and surveillance risks.
Pragmatism defines AI sentiment: 84% described their stance as pragmatic but cautious rather than purely enthusiastic or dismissive
Support is broad but rarely unqualified: 59% were pragmatically positive, while only 11% were strongly optimistic and 30% expressed mixed positivity
Human oversight is nonnegotiable: 100% accepted AI only with strong guardrails, making governance a universal condition of support
Lead with governed, human-in-the-loop AI offers, not autonomy-first positioning. Package core features with visible approval controls, audit trails, escalation paths, and role-based permissions, then price advanced automation as opt-in modules tied to compliance and risk management outcomes. Message AI as a productivity and decision-support tool that improves speed and consistency while preserving human accountability, and equip sales and implementation teams to prove guardrails before promising innovation.
“I think that AI needs more regulation and should be used carefully and not just quickly implemented to replace jobs.”
“So I use AI on a day to day basis. For me, it has been a huge time saver where I can enhance my work and do it faster.”
Privacy concerns around AI are led by fears of surveillance and hidden collection, cited by 46% of respondents. Another 36% focused on data misuse, storage, and profiling, showing that concern extends beyond being watched to how personal information is retained, connected, and commercially exploited.
Respondents described a risk chain that starts with invisible collection and escalates into profiling, tracking, and secondary use without consent. Only a small minority framed privacy as a tradeoff, suggesting organizations need stronger limits on data capture, retention, and downstream use, especially where commercial incentives make monitoring feel exploitative.
Surveillance fears dominate privacy risk: 51% describe localized or situational monitoring concerns, while 30% point to mass surveillance and systemic privacy erosion and 19% fear always-on or hidden monitoring
Data misuse rivals monitoring anxiety: 48% express strong concern about data storage, reuse, or profiling, nearly matching the share focused on surveillance and hidden collection overall at 46%
Control matters, but acceptance is limited: only 22% say privacy is manageable through consent, notice, or safeguards, while 27% accept privacy loss as a tradeoff for safety or convenience
Prioritize privacy-by-design in product and go-to-market: make data collection visible, minimize always-on sensing, default to local processing and short retention, and give users granular controls over monitoring, storage, and downstream reuse. Position premium trust through auditable safeguards, clear purpose limitation, and anti-profiling commitments rather than convenience alone. Segment offers by risk tolerance, reserving broader data-sharing features for explicit opt-in tiers and regulated, high-scrutiny contexts.
“I think my concern lies where there is, like, an individualized record kept of your face, your information, and your data. And that gets tied to your behavior and can be analyzed down to surveillance footage and track. Across the board.”
“I think what feels most unfair about how AI is trained is that oftentimes, a lot of our data as users is being used to train AI systems for these platforms that we might have accounts on without our knowledge and without our consent.”
Taken together, the findings point to a strategic opening: acceptable AI is not undefined. Respondents repeatedly endorse the same design principles—transparency, consent, bounded use, human oversight, and shared institutional support. The opportunity is to build AI systems and policies that align with these conditions rather than asking users to accept unrestricted automation.
“I think the thing is that the world and people need to be educated. If tools are being used, that should be, you know, an upfront. Not hidden in terms and conditions, but you know, right upfront saying we're using AI to read your messages or to help you do this or to know, whatever it might be, you know, around, like, just people are aware of what they're getting themselves into and and then, you know, they can decide for themselves on what's proper and what's not.”
— VP of HR
Basic transparency is the baseline expectation: 46% of respondents emphasized simple disclosure that AI is being used, while 29% went further and rejected black-box systems outright. Another 25% focused on explainability in high-stakes decisions, showing that trust starts with visibility but often rises with the consequence of the outcome.
Expectations become stricter as risk increases. In lower-stakes contexts, respondents mainly wanted upfront notice and clarity on data use; in hiring, credit, health care, or legal decisions, many expected traceable reasoning that humans could review. This creates a tiered mandate for organizations: disclose AI use by default, and provide stronger explanation where decisions materially affect people.
Traceable disclosure is the trust baseline: 66% want disclosure plus source traceability, while only 15% accept basic notice alone and 13% expect full provenance and process visibility
Explainability is expected, especially when stakes rise: 42% want explanations mainly for important or high-stakes decisions, and 44% require explanations in all cases, leaving just 10% satisfied with limited or on-demand explanation
Black-box AI still faces a hard trust ceiling: with 46% emphasizing disclosure and trust-building transparency, expectations cluster around visibility and accountability rather than opaque AI experiences
Make traceable disclosure the default product standard: surface AI use clearly, cite sources, and provide decision-level explanations at minimum for high-stakes outcomes, with always-on explainability in regulated or trust-sensitive contexts. Package transparency as a tiered offer—baseline disclosure for low-risk workflows, deeper provenance and audit trails for enterprise and compliance buyers—and shift messaging from automation speed alone to verifiable accountability, human oversight, and reduced adoption risk, since opaque AI will cap trust and conversion.
“I think it's extremely important that the AI be able to lay out steps so that a human can review them. Especially if it's making decisions that affect people livelihood or their lives.”
“It it should have, it should have transparency. And we should know how it works. We should know where it's getting its knowledge from. We should know how its outputs are being calculated.”
Consent and compensation set the clearest boundary for fair AI training data use, with 62% saying both are required. By comparison, only one in five view public data as fair game, and 17% accept use under legal or conditional fair-use rules, making unrestricted scraping a minority position.
Public availability alone does not resolve concerns for most respondents; the bigger issue is whether creators and users knowingly agreed and share in the value created. This creates a practical mandate for AI companies: transparent disclosure, explicit permission, and compensation models will resonate more strongly than relying on public-domain or ambiguous legal defenses.
Consent and compensation set the fairness line: 68% say training data use is only fair when creators give consent and receive compensation or credit, and 62% overall define fairness through these requirements
Permission outweighs mere access: while 26% say publicly available data is fair game, only 19% rely on legality or copyright alone, versus 68% who require consent plus compensation or credit
Utility arguments remain secondary to creator rights: 20% justify broad data access for innovation or usefulness, but this trails far behind the 68% who prioritize consent and compensation and the 16% who specifically require consent for personal or creator data
Build training-data strategy around licensed, opt-in supply: secure explicit creator consent, attach compensation or credit by default, and make provenance and usage terms visible in product and policy messaging. Shift investment from “publicly available” scraping toward rights-managed partnerships, creator marketplaces, and auditable consent workflows. Price enterprise offerings to reflect compliant data sourcing, and position the premium around lower legal risk, stronger brand trust, and creator-aligned AI development.
“I absolutely do think that AI is infringing on the copyright of artists. I think that there needs to be very robust, regulations regarding the use of materials, compensation for artists and materials that are used online, and very clear discretion disclosures of how it's gonna be used when and why.”
“I guess what feels unfair about it, you don't know that your work's being sampled if you uploaded to certain platforms. Which feels fair about it if you can upload your work to a platform and there is a actual statement saying, your work will be used to train the AI, and you can be compensated for it. That feels fair.”
AI is acceptable to most candidates only at the front end of talent decisions. About two-thirds support AI for screening when humans still review outcomes, while 28% reject any AI role in hiring or promotion decisions. Just 5% are open to a broader AI role, and even they expect strong safeguards.
Support centers on efficiency tasks such as summarizing resumes, sending reminders, and narrowing options, not deciding who gets hired or promoted. The clearest fault line is authority: most respondents want humans accountable for consequential decisions, while a smaller group also worries AI could strip out human judgment or amplify bias in high-stakes employment choices.
AI is welcomed only as a filter: 42% accept AI for screening and support, and another 43% accept it only when human review is built in, showing broad support for AI in an assistive rather than autonomous role
Humans must make the final call: 78% say final authority must stay with people, while only 8% express conditional trust in AI with transparency and guardrails
Promotion decisions are where AI loses support: 13% strongly reject AI decision-making and 11% reject even support use, reinforcing that acceptance drops sharply when AI moves from screening into consequential career decisions
Position AI as a screening and workflow-acceleration layer, not a decision-maker, and build every hiring and promotion offering around explicit human sign-off. Package promotion-related features as decision support with audit trails, transparent criteria, and recruiter or manager review checkpoints, while avoiding autonomous recommendation claims in messaging. Price premium tiers around governance, explainability, and compliance controls, and market the product on speed, consistency, and reduced admin burden rather than replacing human judgment.
“Humans should always be responsible for being involved in the final decision making or in the review of the final you know, set of options that are provided, I don't think that AI should be able to make decisions on key, key decisions without some sort of human impact.”
“Should be clearly communicated for what has aspects AI is used, which is example summarizing or sending automatic reminders or replies or things like that or and, that should be a limited role, so not full decision or authority.”
Leaders largely want AI to inform employment decisions without controlling them. Roughly half, 47%, support conditional delegation with human oversight, while 46% limit AI to a support role only. Just 7% trust AI to make some decisions independently, showing that final authority remains firmly with people.
This split signals broad comfort with AI handling analysis, screening, and recommendation tasks, but not accountability for high impact outcomes. The distinction between conditional delegation and support-only use is narrow, suggesting most respondents accept AI in the workflow if humans review, validate, and own the final hiring or promotion call.
Human authority remains the red line: 82% insist humans keep final say, with 35% requiring human-only authority and 47% allowing AI input only in lower-stakes or reviewed decisions
AI is a filter, not the decider: 74% support AI handling screening or first-pass work with human review, while just 4% are comfortable with AI doing most of the process under human veto
Conditional delegation defines the dominant model: 47% favor using AI as a conditional advisor under human oversight, and only 16% want AI limited to narrow support roles alone
Position AI as a triage and recommendation layer, not an autonomous decision-maker: build workflows where AI handles screening, prioritization, and first-pass analysis, while humans retain explicit approval rights, especially for high-stakes cases. Price and package around governed oversight features—review queues, audit trails, confidence flags, and veto controls—and lead messaging with “human in command” assurance. Target adoption by selling risk reduction, speed, and consistency without surrendering executive accountability.
“AI will play a critical role in hiring or promotion decision. They will be able to assess you, evaluate you using all the data points using all the different angles and dimensions. And make a suggestion or a recommendation out of it. But the ultimate decision has to be made by a human in the loop.”
“It should be used as an aid in decision making. A tool that synthesizes facts, helps people research, potentially help, you know, connect significant datasets and and things of that nature, but, ultimately, the human being should remain in charge of making the final decision”
Shared responsibility is the dominant view on managing AI job disruption. A majority, 53%, say employers and government should share the lead, while another 11% broaden that responsibility across employers, government, and individuals. By contrast, only 7% place the burden primarily on individual workers to adapt on their own.
This points to a strong expectation that institutions, not just employees, must absorb the shock of AI-driven change. Respondents consistently pair employer duties such as ethical deployment and worker support with government roles in regulation, training, and social policy, while individuals are seen as contributors rather than the main line of defense.
Shared accountability is the dominant expectation: 76% say employers and government share responsibility for managing AI job disruption, making this by far the leading view
Purely individual responsibility remains a minority stance: just 13% say individuals are primarily responsible, far below the 53% who explicitly frame the response as shared between employers and government
Adaptation is seen as collective, not solo: 30% say responsibility is shared but adaptation centers on individuals, while only 8% put primary responsibility on employers and 12% on government alone
Build AI workforce strategies as employer-led programs backed by public-sector partnerships, not employee self-service offerings alone. Package retraining, redeployment, and transition support as shared-accountability solutions, with employers funding implementation and government offsetting costs through grants, tax incentives, or workforce programs. Position messaging around joint responsibility and practical worker protection, and price offerings for enterprise and institutional buyers rather than individuals, reserving direct-to-worker products as a secondary layer.
“Companies should ethically take AI into consideration and understand that it its limits and and the cost of losing actual people. Government should look at it as far as preparing people for the future, preparing job numbers, seeing that that individual use it to prepare themselves to to stay competitive.”
“companies should be responsible for taking care of any employees that have lost jobs, period. Whether it's due to AI or other factors. Obviously, the governments have a role in that. Things like unemployment compensation, training,”
Proactive AI upskilling sets the tone, with 55% of respondents describing active efforts to adopt AI and build capability. Another 34% take a selective, bounded approach, using AI where it adds efficiency but keeping guardrails in place. Only 11% center their perspective on risks of overreliance.
This creates a workforce posture that is more pragmatic than polarized. Most respondents are not resisting AI, but many are defining clear limits around decision-making, privacy, and human judgment. In practice, the leading pattern is experimentation paired with oversight, while a smaller group worries that excessive dependence could erode expertise and cognitive development.
Proactive adaptation is the clear norm: 94% describe either pragmatic adaptation or proactive upskilling, while just 1% resist or avoid adapting to AI
Upskilling is as common as pragmatic use: 47% report proactive AI upskilling and career use, matching the 47% who see adaptation as necessary and practical
AI use stays firmly human-led: 87% favor selective use with human oversight, while only 13% center concern about harm to human thinking or agency
Build offerings around “human-in-the-loop” productivity gains: package AI as a practical co-pilot with clear oversight controls, auditability, and role-specific training pathways. Price and position in tiers that support both pragmatic adopters and proactive upskillers, pairing core workflow automation with premium enablement, certification, and career-oriented learning. Lead messaging with augmentation, safety, and accountability—not autonomy—while equipping managers to set usage boundaries, governance standards, and measurable skill-development goals.
“I've organized a full AI upskilling program with in partnership with a a major tech company. To to give employees a wonderful opportunity to really upskill in AI use so that they don't get left behind.”
“They should use it as a as a copilot or as a as a helper in getting the data together, not as something that makes those decisions.”
Once trust conditions are violated or weakened, respondents become sharply restrictive. Surveillance is tolerated only with notice and limits, while deepfakes are broadly rejected unless consent and disclosure are explicit. The downstream fear is not only personal harm but wider erosion of truth, making these use cases a visible stress test for AI legitimacy.
“For other things. Everything that is used with AI, such as if the police or the government do it should be according to the law, it should be tested first before being allowed and not after the fact.”
— User Acquisition Manager, Student Society
Nearly two-thirds, 64%, accept AI surveillance only when it comes with clear notice, legal guardrails, and strict limits on use and retention. Far fewer take a more permissive view, with 15% broadly supporting AI surveillance for safety, while 18% reject it outright in all cases.
This pattern points to conditional legitimacy rather than blanket approval. The largest group supports AI surveillance for specific security or monitoring purposes, but only when it is necessary, proportionate, and governed in advance. By contrast, broad supporters focus on crime reduction benefits, while opponents center on accountability risks and the danger of expanded enforcement.
Conditional acceptance dominates views: 66% accept AI surveillance only under specific conditions, while just 17% support broad public-safety use and 17% reject it outright
Clear limits are the price of acceptance: 64% accept AI surveillance only with notice and limits, and 52% specifically restrict it to narrow security exceptions or exclude private spaces
Transparency matters more than blanket approval: 30% require notice, consent, or visible awareness, while another 14% say strict data-use protections and oversight are necessary
Lead with a “bounded use” offer: deploy AI surveillance only in clearly defined security scenarios, exclude private or sensitive spaces by default, and make visible notice, consent options, and retention rules part of the standard product. Package governance as a core feature—not an add-on—with audit trails, access controls, and independent oversight. Position and price solutions around compliance, transparency, and risk reduction rather than broad surveillance capability.
“I think, for example, for monitoring systems or cameras, it should be allowed to be used to check, for example, on crop control or things like that, but the images should not be stored forever.”
“There are no situations in which I feel AI powered surveillance is acceptable because AI by itself does not have a notion of accountability.”
Consumers largely reject deepfakes unless clear guardrails are in place. Nearly half, 46%, are outright opposed to deepfakes, while about one-third, 32%, would allow them only with explicit consent and disclosure. Just 23% accept them in narrow entertainment or satire contexts, showing broad discomfort with synthetic media.
Acceptance depends on transparency and permission, not enthusiasm for the technology itself. Even among those open to limited use, support centers on watermarking, disclosure, and consent, while entertainment remains the main exception. In practice, brands and platforms face a high bar: unlabeled or unauthorized deepfakes risk immediate consumer backlash.
Deepfakes face strong baseline rejection: 46% were outright opposed to deepfakes, including 38% who said they should be banned entirely
Most acceptance is tightly limited: 52% would allow deepfakes only in narrow, low-stakes situations, while just 10% support broader entertainment or creative use
Consent and disclosure define the ethical line: 72% said deepfakes are unacceptable when deceptive or harmful, and only 28% find them acceptable with clear consent and disclosure
Restrict deepfake features to opt-in, low-stakes use cases and build consent, disclosure, and provenance controls into the core product experience. Position any offering around safety, transparency, and rights protection rather than novelty, with default labels, auditable permissions, and rapid abuse reporting. Reserve premium pricing for enterprise or creator tools with strong governance and compliance features; avoid mass-market expansion, broad entertainment messaging, or ambiguous AI-generated content that could trigger immediate rejection.
“All deepfakes should be banned entirely. Because you never know who it's actually going to deceive in good faith.”
“I think it needs to be watermarked and declared that this is AI generated and not human generated. So we, the consumer, know immediately that it's an AI, production or an output.”
Truth erosion is the dominant harm people expect from deepfakes, cited by 66% of respondents who discussed risks. By comparison, reputational or identity harm accounts for 18%, while manipulation, exploitation, and scams represent 16%. The leading concern is not just individual damage, but a broader breakdown in confidence in what people see and hear.
This pattern suggests deepfakes are viewed primarily as a societal trust problem rather than only a personal reputational threat. Concerns center on false political narratives, tampered evidence, and viral deception that scales quickly across platforms. In practice, organizations face a dual challenge: protecting individuals from misuse while also preparing for a wider erosion of credibility across media, institutions, and public discourse.
Truth erosion is the dominant fear: 80% identified misinformation and trust erosion as the main harm, versus 21% who pointed to reputational or character damage
Identity abuse is a major secondary concern: 50% highlighted impersonation or exploitation of someone’s likeness, showing concern about targeted misuse even as broader truth erosion leads
Propaganda worries are overshadowed by general deception: only 4% named public manipulation or propaganda as the main harm, while 13% pointed more broadly to deception and confusion without a specific harm focus
Shift deepfake strategy from celebrity-reputation protection to trust-preservation infrastructure: prioritize detection, provenance, watermarking, and rapid verification workflows for news, civic, and platform partners, then package impersonation and likeness-abuse safeguards as a secondary layer. Message the offer around restoring confidence in what people see and hear, not just protecting individuals. Price around enterprise risk reduction, compliance, and content integrity outcomes rather than consumer-facing reputation defense.
“there's a a crisis of trust right now, I think, in culture and society, and these videos only perpetuate that by, you know, feeding false narratives potentially by, you know, convincing people of ultimately that very damaging things may actually be happening.”
“There's a lot of people that are being tricked by misinformed especially in politics. Like, we see this happen all the time. People are stitching fake stories and fake photos together and then other people who see that content jump to conclusions and then run with that story, and and it continues to spread misinformation.”
In hiring, promotion, and leadership decisions, the core concern is loss of human judgment and contextual understanding. As a result, respondents draw a consistent boundary: AI may assist with screening or advisory input, but humans should retain final authority, especially where outcomes materially affect people’s careers.
“But I do believe that ultimately at the end, having an actual person do the hiring or promotion decisions will be better because there's subtle nuances within humans, everything from emotional balance to understanding how people work.”
— Operation and IT Manager, Tech Services
Lost human judgment is the dominant concern in AI-mediated hiring, cited in 59% of risk-related responses. Respondents most often worried that automated screening misses subtle interpersonal qualities, context, and role-specific priorities that human decision-makers weigh naturally. By comparison, 27% focused on bias reproduction and unfairness, while 14% pointed to gaming, errors, and over-automation.
These concerns suggest leaders see AI as most useful for support, not final selection. The clearest tension is that systems may promise consistency, yet flatten important distinctions unless priorities and context are explicitly encoded. Bias remains a meaningful secondary risk, and over-automation compounds the problem when strong candidates are filtered out for weak proxy signals such as keywords or optimized responses.
Human judgment loss dominates AI hiring fears: 86% express strong concern that AI strips away essential human context and judgment, and 59% overall cite this as a core hiring risk
Bias fears remain widespread and unresolved: 40% name bias as the primary risk and another 26% see it as a secondary or conditional risk, while just 7% believe AI could reduce human bias
Accuracy concerns outweigh manipulation risks: 63% say the main danger is wrong or low-quality decisions, versus just 1% focused mainly on gaming, with another 21% worried about both accuracy and keyword optimization
Position AI hiring products as decision-support, not decision-replacement: keep recruiters accountable for final calls, require human review at high-impact stages, and surface candidate context alongside scores. Prioritize investment in auditability, explanation layers, and accuracy validation over anti-gaming features, and price premium tiers around governance, bias monitoring, and compliance reporting. Messaging should emphasize “better-informed human judgment,” transparent safeguards, and reduced error risk rather than autonomous screening efficiency.
“So although you've got 10 things which or 10 skills which you have classed as essential, but in your head, you know which one is the top one. And if you've not given that information to AI, which generally doesn't happen, in companies, essential is essential, people then don't give a priority to essential criterias.”
“The problem is I see a lot of good people get thrown out because maybe their resume wasn't the best or they didn't have the right buzzwords or keywords so I think I I don't think AI is ready yet, to take on the full role.”
After establishing that disruption is expected, the narrative should show how people respond. Respondents do not frame AI-driven job change as purely catastrophic; many see it as manageable if adaptation occurs. That leads to a practical coping model: responsibility is shared across employers and government, while individuals adopt a proactive but bounded approach to upskilling and AI use.
“I personally feel AI will replace a lot of jobs Having said that, AI will also create a lot of jobs. So the nature of job, the type of work that people do, will change.”
— Global Data and AI Team, Professional Services
Workers largely expect AI to disrupt jobs, but roughly half, 47%, believe that disruption will be mixed but manageable. Another 33% take a more optimistic, adaptation-focused view, while only about one in five, 21%, frame AI primarily as a source of displacement and negative labor market effects.
This split points to cautious pragmatism rather than panic. The largest group expects jobs to change more than disappear, with AI shifting work toward higher-value tasks even as some roles are cut. Still, the negative minority highlights a real transition risk: layoffs and reskilling may unfold faster than organizations, workers, and education systems can adjust.
Adaptation outweighs alarm for many workers: 47% say AI-driven job disruption will be manageable if people and policy adapt, while 26% are mixed but net positive and 24% are strongly positive or adaptive
Disruption feels real, not hypothetical: 45% express selective or conditional concern about displacement and inequality, with another 22% leaning negative and 17% seeing AI as an immediate or accelerating threat
The dominant outlook is mixed, not extreme: the largest segment at 47% sees disruption as conditional but manageable, far exceeding the 17% who view AI as an immediate threat and signaling a nuanced middle ground rather than outright optimism or fear
Lead with a transition-ready value proposition: pair AI offerings with visible reskilling, role redesign, and governance support to convert the 47% conditional middle while reducing resistance from concerned workers. Price and package solutions as phased adoption programs—including training, change management, and policy safeguards—rather than standalone automation. Message AI as productivity augmentation with worker protections, using proof of manageable adaptation and targeted support for higher-risk roles and teams.
“I have impacted it firsthand. We've had a lot more layoffs. At our company recently, and they've said directly that it's because they wanna invest in AI, and some of those roles or some of the work that those teams did was just is just not done now.”
“I actually think it's enhanced jobs. I think it has allowed people, to essentially farm out things like writing emails, planning, calendar management so that they can focus on the more high level strategic aspects of their jobs.”
Across the dataset, respondents do not reject AI outright. Instead, cautious value orientation is shaped by specific trust requirements such as disclosure, consent, compensation, and clear limits. Where those conditions are present, some forms of AI use become acceptable; where they are absent, acceptance drops sharply.
Adoption strategies should focus less on persuasion and more on meeting explicit legitimacy requirements. Trust architecture—notice, consent, disclosure, and scope limits—is central to acceptance.
Perceived risks in hiring center on loss of human judgment and context, which directly aligns with respondents’ preference for AI as a screening or advisory tool rather than an autonomous decision-maker. This same logic appears more broadly in leadership decision authority, where people support conditional delegation only with human oversight.
AI products in high-stakes domains should be positioned and designed as decision-support systems, with visible human review and override, rather than as replacements for human judgment.
Respondents expect AI-driven job disruption, but many consider it manageable rather than purely harmful. That view is paired with a clear expectation that adaptation cannot rest on individuals alone: employers and government share responsibility, while workers themselves take a proactive upskilling posture within deliberate boundaries.
The path to sustainable AI adoption in the workforce is not only better tools, but coordinated transition support—reskilling, employer commitments, and public policy that distributes the burden of change.
No. The dominant posture is pragmatic rather than oppositional: 84% of respondents who discussed AI described a cautious but practical orientation, while only 2% focused mainly on harms. The bigger issue is whether AI use meets clear trust conditions.
Fairness and legitimacy hinge on notice, consent, compensation, and limits. For example, 62% said training data use is fair only when both consent and compensation are present, and 64% accepted AI surveillance only with clear notice and strict limits.
Most respondents accept AI only at the front end of talent decisions: 66% support AI for screening when humans still review outcomes. This reflects the main perceived risk in AI hiring, where 59% cited loss of human judgment and context.
Because people see them as a broader trust threat, not just a personal reputational risk. While 46% were outright opposed to deepfakes, 66% of those discussing harms pointed to misinformation and erosion of truth as the primary danger.
Not primarily. The largest group, 47%, sees AI-driven disruption as mixed but manageable, and another 33% takes an optimistic adaptation-focused view. But respondents do not think workers should handle this alone: 53% say employers and government share responsibility for managing the disruption.
Make disclosure, consent, data-use clarity, and retention limits core product and policy requirements, not add-ons. This directly addresses the conditional trust pattern seen across training data, privacy, surveillance, and transparency expectations.
In hiring, promotion, and leadership decisions, keep humans visibly accountable for final decisions while using AI for summarization, screening, and analysis. This aligns with strong preference for human authority and concern about lost context and judgment.
Require consent, disclosure, watermarking, and narrow use-case limits for deepfakes, and apply necessity and proportionality standards to surveillance deployments. These are the most visible legitimacy stress tests and can quickly erode trust when governance is weak.
Pair AI adoption with reskilling programs, employer commitments, and public policy support rather than expecting workers to adapt alone. Respondents are willing to upskill, but they expect institutions to share the burden of change.
This research draws on 92 in-depth interviews with business professionals representing a wide mix of roles, industries, and company sizes.
Interviews ran 8 to 31 minutes and covered Training Data Fairness Boundaries, Transparency and Disclosure Expectations, Human Versus AI Decision Authority, and Acceptance of AI in Hiring and Promotion. The conversational format allowed respondents to discuss their actual practices rather than select from preset options, surfacing nuance that closed-ended surveys typically miss.
Respondents included business professionals across technology, financial services, healthcare, retail, and manufacturing. All participants were selected for their direct experience with AI use in hiring and promotion decisions. Company sizes ranged from small businesses to large enterprises.
The analysis of 92 interview transcripts was conducted using AI for semantic understanding, with multi-iteration validation and cross-verification to ensure analysis quality. Each transcript was independently reviewed by G2's AI Custom Research team to inform narrative, context, and clarity.
G2 Research, June 2026
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