56%
Guardrailed Progress Is the Dominant Position
Most respondents support moving forward with AI, but only if adoption visibly includes safeguards and acknowledges uneven readiness across society.
G2 Research
Guardrails, Not Rejection: How People Really See AI
How 121 business professionals are navigating AI adoption and governance, revealing a dominant position that favors progress with guardrails rather than rejection, where human authority remains non-negotiable.
56%
Guardrailed Progress Is The Dominant Position
59%
Work Is Seen As Task-Level Transformation
91%
Human Authority Remains Non-Negotiable
98%
Exclusion Risk Is Nearly Universal
Executive Summary
What We Found
The research points to a clear middle position: people are not rejecting AI, but they are rejecting ungoverned AI. A majority see AI as inevitable and transformative, with 55% describing it that way, and 56% favor continued progress with guardrails rather than either rapid acceleration or outright slowdown. That conditional acceptance is rooted in a belief that society is moving unevenly: AI is expected to reshape work at the task level, not wipe out work entirely, with 59% seeing it as both a complement to human work and a selective replacement for some tasks.
This leads to a deeper concern about who adapts and who benefits. While 65% expect selective displacement that can be eased through retraining, 45% believe businesses and technology controllers will capture the biggest gains, and 98% identify groups at risk of being left behind. In response, respondents draw a firm line around human authority: 91% say humans must retain final decision-making and accountability, especially as 97% raise concerns about AI reliability, truthfulness, or misuse.
59%
Work Is Seen as Task-Level Transformation
The dominant expectation is not total job replacement, but a hybrid future where AI supports people while automating selected tasks.
91%
Human Authority Remains Non-Negotiable
An overwhelming majority want humans to keep the final say and remain accountable, especially in high-stakes decisions affecting people’s lives.
98%
Exclusion Risk Is Nearly Universal
Almost everyone identified groups likely to be left behind, showing that AI is viewed not just as a technology shift, but as a distributional and equity challenge.
Why this matters · For SaaS vendors
Why SaaS Software Vendors Should Care About This Study
Business professionals have made up their minds about AI: 55% call it inevitable and transformative, and 91% insist humans must retain final decision-making authority. Yet 97% raised concerns about AI reliability, truthfulness, or misuse — making guardrails as important as capability in every purchase decision. These tensions create six concrete go-to-market opportunities for SaaS software vendors.
OPPORTUNITY 01
Position Around Augmentation, Not Replacement
94% of respondents expect AI to transform jobs rather than eliminate them, and 59% see AI as complementary to human work with selective automation of specific tasks. Buyers are not looking for tools that promise to replace their teams — they want AI that amplifies human judgment in the right places.
Vendor Implication
Reframe product messaging around augmentation and collaboration. Lead with use cases showing AI handling repetitive work while humans retain high-judgment calls — buyers who see AI as a partner will respond far better than to "automate everything" positioning.
OPPORTUNITY 02
Lead With Governance and Guardrails as a Feature
97% of respondents raised concerns about AI reliability, truthfulness, or misuse — with 68% specifically citing hallucinations and verification failures as top trust barriers. Guardrails are not a nice-to-have: they are the purchasing criterion for 56% who favor AI progress only with robust safeguards in place.
Vendor Implication
Make governance, auditability, and human oversight core product pillars — not footnotes in a security FAQ. Vendors who can demonstrate verification workflows and configurable guardrails will outcompete those leading with raw capability claims.
OPPORTUNITY 03
Sell Into the Human Control Mandate
91% of respondents said humans must retain final decision-making authority and accountability, and 66% specifically say high-stakes judgment must remain human-led. This is not skepticism — it is a clear specification for how AI tools must be designed to earn enterprise trust.
Vendor Implication
Build and market explicit human-in-the-loop controls at the workflow level. Positioning your product as one that enforces human sign-off on consequential actions — rather than merely allowing it — directly matches the preferences of 91% of your buyers.
OPPORTUNITY 04
Address the Cognitive Dependency Risk
93% of respondents raised concerns about AI dependence and the erosion of independent human thinking, and 75% say rigorous verification is the non-negotiable price of trust. Buyers who adopt your product are simultaneously worried about what it will do to their team's skills and judgment over time.
Vendor Implication
Create features and messaging around capability preservation — transparent AI reasoning, think-before-you-accept flows, and skill-building modes for new users. Vendors who help buyers avoid cognitive atrophy will command stronger retention than those optimizing for friction-free automation.
OPPORTUNITY 05
Speak to the Equitable AI Narrative
98% of respondents identified specific groups at risk of being left behind by AI, and 45% believe businesses and those controlling AI technology capture the largest gains. B2B buyers are increasingly accountable to employees, regulators, and boards on AI equity questions.
Vendor Implication
Build narratives and features that address equitable access across skill levels. Vendors who help customers deploy AI responsibly — with onboarding that works for non-technical users — will gain a compliance and governance edge as enterprise accountability requirements tighten.
OPPORTUNITY 06
Connect Product Value to Human Purpose
54% of respondents say work is no longer purpose's primary anchor, and 45% identify hobbies, creativity, and life beyond work as key sources of meaning. For SaaS vendors, this signals buyers primed to value tools that make work less draining — freeing time and cognitive load for what matters to them personally.
Vendor Implication
Frame AI efficiency gains not just as business ROI but as time returned to the individual. "Hours saved per week" is a more emotionally resonant value prop in a market where 54% of professionals are actively seeking meaning outside their jobs.
Chapter 01
A Transformative Technology Meets Uneven Readiness
Respondents largely see AI as inevitable and transformative, but they do not believe society is uniformly ready for its effects. This creates the report’s core tension: momentum is real, yet confidence in collective preparedness is uneven.
“It is going to be embedded in every aspect of your life just like how Internet is. It will be on your phone. It will be on your device.”
— Consulting Manager, Global Consulting
Listen
Key Finding
AI viewed as transformative, while apocalypse narratives largely fall flat
Overall Worldview About Ai’s Long-Term Trajectory
55%
see AI as inevitable and transformative
AI is widely seen as an unavoidable force for change, with 55% describing it as inevitable and transformative. Another 43% take a more bounded view, expecting major impact without subscribing to apocalyptic outcomes. Only 2% express outright skepticism or uncertainty about the broader AI narrative.
This split points to strong momentum behind adoption, but not blind enthusiasm. While a majority expect AI to reshape daily life and work at scale, a similarly large segment emphasizes human stewardship, tradeoffs, and the limits of extreme utopian or dystopian narratives. In practice, leaders should plan for deep integration while maintaining clear accountability and governance.
Key Takeaways
01
AI’s transformation feels inevitable, not speculative: 100% expect AI to be transformative, with 66% seeing change as gradual and 34% expecting rapid disruption
02
Apocalypse narratives fail to convince most: 97% stay away from extreme hype or doom, including 49% who are strongly pragmatic and 48% who hold mixed but still non-extreme views
03
Pragmatism edges out polarization: only 3% lean toward extreme narratives, while nearly half are firmly bounded in their outlook at 49%
Strategic Implication
Position AI offerings as essential, staged business transformation rather than revolutionary salvation or existential threat. Lead messaging with practical outcomes, deployment roadmaps, and governance assurances, since most buyers expect meaningful change but reject extremes. Price around scalable adoption tiers and expansion paths that support gradual enterprise rollout, while preserving premium options for faster movers. Equip sales and product teams to anchor conversations in ROI, risk management, and operational integration, not hype-driven disruption narratives.
Overall Worldview About AI’s Long-Term Trajectory
Inevitable and transformative
55%
Bounded pragmatism: powerful but not apocalyptic
43%
Skeptical or uncertain about AI hype
2%
Listen
“I expect an AI future where machines handle increasing complexity and scale. While humans remain responsible for meaning, values, and the consequences of decisions.”
Technology Transformation Leader, Boutique IT Consulting
when asked about Bounded pragmatism: powerful but not apocalyptic
Listen
“I think people are assuming that, yeah, we'll either save everything surpass human capabilities, you know, be another human, or, you know, destroys, everything. And I feel both extremes are wrong”
Cloud and AI Expert, Communications Provider
when asked about Bounded pragmatism: powerful but not apocalyptic
Key Finding
Most Back AI Progress, but Only With Strong Guardrails
Preferred Pace of AI Adoption and Perceived Social Readiness
56%
favor moving forward with AI, but with guardrails and recognition that readiness is uneven
Most respondents favor continued AI progress, but with guardrails: 56% support moving forward while acknowledging that social readiness is uneven. Another 36% want a slower, more cautious pace because society is not ready, while only 8% believe adoption should move faster because people and institutions can adapt.
This pattern points to a pragmatic middle ground rather than broad enthusiasm or outright resistance. The dominant concern is not AI’s potential, but whether policy, education, workforce development, and public understanding can keep pace. In practice, leaders should keep advancing AI while investing more aggressively in governance, training, and institutional readiness.
Key Takeaways
01
Most want AI progress with guardrails: 67% support continuing AI advancement with conditions, and 56% explicitly favor moving forward while recognizing that social readiness is uneven
02
Caution outweighs calls to accelerate: 38% prefer a gradual rollout and 25% want AI adoption slowed, versus just 17% who prefer faster adoption
03
Few believe society is fully keeping up: only 9% say society can broadly adapt to the current pace, while 35% are concerned about readiness even if they do not explicitly call for slower adoption
Strategic Implication
Lead with a paced-deployment strategy: launch AI in phased releases, make guardrails visible, and price around trust-building support such as onboarding, governance, and human oversight. Position faster, higher-autonomy offerings as opt-in tiers for more AI-ready segments, while default messaging emphasizes safety, control, and practical adaptation. Invest in readiness enablement—training, change management, and clear use policies—to reduce friction among the larger cautious majority and expand adoption without triggering backlash.
Preferred Pace of AI Adoption and Perceived Social Readiness
Keep moving forward, but with guardrails and uneven readiness
56%
Slower, more cautious adoption because society is not ready
36%
Faster adoption; society can adapt
8%
Listen
“Generally, in my understanding, and what I have seen happening in the society, overall, I don't think society is fully prepared for the speed at which AI is developing. Technology itself is advancing much faster than our social regulatory and institutional frameworks.”
Technology Transformation Leader, boutique IT consulting company
when asked about Preferred Pace of AI Adoption and Perceived Social Readiness
Listen
“I don't think society is fully prepared for the current space especially in terms of the policies the training, and the ethical ethical frameworks of it all. While the benefits are significant, adaptation in education. The regulation, and workforce development, tends to move more slowly than the technology itself. I think.”
Analyst Coordinator, None
when asked about Preferred Pace of AI Adoption and Perceived Social Readiness
Chapter 02
Work Is Being Rewritten at the Task Level
People do not describe AI as simply replacing entire jobs; instead, they expect it to augment human work while automating analytical and routine tasks first. That task-level shift leads to expectations of selective job displacement, with retraining seen as a partial buffer rather than a complete solution.
“There is a possibility of lot of jobs which may get eliminated with the usage of AI, lot of repetitive task with requirement of, minimal or less intelligence. Will be easy to replace human. But where there is a decision making power must remain in hand of human. Those tasks are something which must remain with human to take decision on.”
— Chief Information and Security Officer, Financial Services
Listen
Key Finding
AI augments most work while selectively automating specific tasks
Expected Role of AI in Human Work
AI is expected to augment work more often than replace it outright, with 59% of respondents describing a hybrid future where it supports people while taking over selected tasks. Another 36% saw AI mainly as a complement to human work, while only 6% anticipated broad replacement and major job transformation.
This pattern points to a pragmatic division of labor: AI is welcomed for repetitive, operational, and productivity-oriented work, while humans retain responsibility for judgment, strategy, and high-stakes decisions. The strongest consensus sits between pure augmentation and full automation, suggesting organizations should redesign tasks and workflows, not assume wholesale job elimination.
Key Takeaways
01
AI is seen mainly as an augmenting force: 76% say AI works best with human oversight and judgment, while 59% explicitly view it as both a complement to human work and a selective replacement for specific tasks
02
Job transformation outweighs job replacement: 94% expect some jobs to be replaced but most work to be transformed, compared with just 3% who foresee broad replacement across many roles
03
Pure automation is a fringe expectation: only 2% believe AI will only automate tasks, while 23% take a strongly augmentation-first view that centers AI on supporting rather than replacing human work
Strategic Implication
Position AI offerings as human-in-the-loop productivity multipliers, and package automation as targeted modules for repetitive, low-value tasks rather than end-to-end worker replacement. Prioritize workflow redesign, training, and governance features that preserve human judgment, and sell against transformation outcomes such as speed, quality, and capacity expansion. Use value-based pricing tied to efficiency gains, while messaging emphasizes augmentation, role evolution, and selective task automation to reduce adoption resistance.
Expected Role of AI in Human Work
Hybrid complement-and-selective-replacement58%
AI as a complement to human work36%
Broad replacement and job transformation6%
Listen
“AI complements human work and replaces it in very, very different ways. In my advertising agency, AI is complementing human work very significantly, making everybody much more productive, doing work faster, doing more work.”
Strategy Director, Advertising Agency
when asked about Hybrid complement-and-selective-replacement
Listen
“I see AI as more likely to complement the human work because AI is gonna take the the part of, of work that is, less operational that is more operational. So AI takes the part that it of it that is operational, while, the humans will be able to actually concentrate their themselves on the strategic part of it.”
Automated Process Developer, Government Agency
when asked about AI as a complement to human work
Key Finding
Analytical work leads, while routine tasks follow in AI automation
Types of Work Respondents Expect AI to Automate First
Knowledge and analytical work is the leading area respondents expect AI to automate first, cited by 43%, ahead of routine administrative and repetitive tasks at 31%. Another 26% anticipate broader cross-sector disruption, extending beyond office work into customer service, factory roles, and some physical jobs.
The pattern suggests respondents are not limiting early automation to low-skill work. Many expect AI to move quickly into structured intellectual tasks such as analysis, reporting, and coding, while still absorbing repetitive admin work in parallel. In practice, this points to earlier pressure on knowledge roles than many organizations may assume.
Key Takeaways
01
Analytical work is seen as first in line: 79% expect decision, analytical, or strategic work to be automated early, far ahead of research and information tasks at 2%
02
Routine administration follows close behind: 54% point to routine admin and clerical tasks as early automation targets, compared with 35% for structured service or workflow tasks
03
Professional impact is expected to be broad, not just entry level: 19% foresee cross-sector professional roles being affected first, while only 3% expect lower-level or low-interaction roles to lead the shift
Strategic Implication
Prioritize AI products and change programs around high-value analytical workflows first—decision support, forecasting, planning, and strategy—then package routine administrative automation as a fast-follow efficiency layer. Price offerings by business impact rather than labor-hour savings, with premium messaging centered on faster, better decisions for professional teams across functions. Equip clients with governance, human-review checkpoints, and role redesign support, since disruption is expected in professional knowledge work before entry-level tasks.
Types of Work Respondents Expect AI to Automate First
Knowledge and analytical work also vulnerable early43%
Routine admin and repetitive tasks first31%
Broad cross-sector automation, including some physical roles26%
Listen
“AI is already matching or exceeding human performance, in narrow, well defined intellectual tasks. Patent recognition, optimization, large scale analysis, and information synthesis.”
Technology Transformation Leader, boutique IT consulting company
when asked about Types of Work Respondents Expect AI to Automate First
Listen
“So for example, AI will take over everything that is, data analysis, report writing, basic coding, research, as well as the administrative part of a company's work, and humans will be able to actually consent themselves and focus on what are the problems that are worth solving.”
Automated Process Developer, Government of Canada / Global Affairs Canada
when asked about Types of Work Respondents Expect AI to Automate First
Key Finding
Most Expect Selective AI Displacement, but Retraining Softens the Blow
Job Displacement Expectations and Labor Transition Outlook
Selective displacement is the dominant expectation: nearly two-thirds, 65%, foresee AI replacing some roles while workers adapt through retraining and reskilling. Another 28% anticipate a more balanced outcome, with job losses offset by new roles, while only 7% expect severe displacement and slow labor market adjustment.
This outlook points to cautious optimism rather than complacency. Most respondents expect disruption to concentrate in specific tasks and occupations, not eliminate work broadly, but they also tie that outcome to how quickly people and institutions can reskill. The smaller pessimistic segment highlights inequality and transition risk if adaptation lags behind automation.
Key Takeaways
01
Selective disruption is the dominant expectation: 86% expect AI displacement to concentrate in routine or sector-specific work, while only 2% foresee mass displacement
02
Retraining is seen as the main shock absorber: 66% describe the transition as manageable but uneven with adaptation, and another 6% expect new jobs and reskilling to largely offset losses
03
Most jobs are expected to change, not disappear: 12% primarily expect role transformation with work persisting, reinforcing the broader 65% view that selective displacement can be eased through retraining
Strategic Implication
Prioritize AI deployment in routine-heavy workflows while pairing every automation offer with role-redesign, retraining, and transition support. Package solutions in tiered models that combine productivity gains with workforce enablement services, and target messaging around selective disruption, resilience, and faster adaptation rather than mass replacement. Focus pricing and customer success on measurable transition outcomes—reskilled employees, redeployed capacity, and time-to-productivity—to address uneven readiness across sectors and functions.
Job Displacement Expectations and Labor Transition Outlook
Selective displacement with retraining and adaptation65%
Balanced disruption: some losses, some new jobs28%
Severe job displacement and slow adaptation7%
Listen
“As roles change and new skills become valuable, people will need to continuously adapt, reskill, and improve relevance which can actually increase the psychological importance of work.”
Technology Transformation Leader, boutique IT consulting company
when asked about Job Displacement Expectations and Labor Transition Outlook
Listen
“AI will automate a huge portion, certainly, but it will create pressure, transitions, and inequality risk if society moves too slowly for that.”
Cloud and AI Expert, a communication provider
when asked about Job Displacement Expectations and Labor Transition Outlook
Chapter 03
The Benefits Are Concentrated While the Risks Are Unevenly Distributed
Respondents believe AI’s gains will accrue disproportionately to businesses and technology gatekeepers, while vulnerable groups—especially older, lower-income, and less adaptive people—face elevated risk of exclusion. This frames AI not just as a productivity story, but as a distributional one.
“In the near to medium term, it will mainly benefit those who control it, design it, or adopt it early. Like large organizations, technology providers, government with strong digital and data skills.”
— Technology Transformation Leader, Boutique IT Consulting
Listen
Key Finding
Businesses, tech gatekeepers, and early adopters seen winning AI
Perceived Winners in the AI Economy
Businesses and technology gatekeepers are seen as AI's biggest winners, with 45% saying the greatest gains will go to organizations and actors that control the technology. Another 39% point to early adopters and highly skilled users, while only 16% expect AI's benefits to be broadly shared across society.
This points to a near-term view of AI as a concentrated advantage, favoring large organizations, platform owners, and people with the expertise to direct adoption. The overlap between control and capability matters: respondents often tie financial upside, efficiency gains, and influence over rules and deployment to those with power, while broader public benefit is seen as more distant.
Key Takeaways
01
Businesses and gatekeepers lead AI gains: 45% say businesses and those who control the technology benefit most, including 26% who point to businesses and 13% who cite technology owners specifically
02
Early and skilled adopters are pulling ahead: 53% say the biggest gains go to early or skilled users, while just 3% say late or non-adopters lose ground, showing advantage is concentrated among those who move first
03
AI benefits may spread, but unevenly: 28% believe gains will broaden over time, yet this trails the 53% who say early and skilled adopters win most, suggesting diffusion is expected but not equal
Strategic Implication
Prioritize enterprise buyers, platform partners, and high-skill early adopters with premium, capability-led offers that convert speed and expertise into measurable advantage. Build pricing around tiered access, advanced features, integration depth, and support levels, while protecting strategic control of data, models, and distribution. Position AI as an accelerator for organizations ready to move now, and add scaled enablement, training, and lighter packages to expand adoption later without eroding high-value segments.
Perceived Winners in the AI Economy
Businesses and those who control the technology benefit most45%
Early adopters and skilled users benefit most39%
Broad eventual benefit for everyone16%
Listen
“These groups are best positioned to capture efficiency gains influence how AI is deployed, and shape the rules around its use.”
Technology Transformation Leader, Boutique IT Consulting
when asked about Businesses and those who control the technology benefit most
Listen
““I think those who have mastered the art of controlling AI and programming it and rendering it for their own purposes, I think they're gonna stand a benefit the most, from it.””
Medical Operating Manager, Hospital
when asked about Early adopters and skilled users benefit most
Key Finding
Older, poorer, and resistant groups seen most at risk
Groups Seen as Most at Risk of Being Left Behind
98%
of respondents identified groups at risk of being left behind
Nearly all respondents, 98%, identified at least one group at risk of being left behind by AI. Concern spread across three segments at similar levels: 34% pointed to lower-income, low-skill, or access-constrained groups, another 34% cited people who resist or fail to adapt, and 30% highlighted older people or those with low digital literacy.
Risk is seen as a mix of capability, access, and willingness to engage. Older adults and digitally inexperienced people may struggle with adoption, while lower-income groups face exclusion from devices, connectivity, and training. At the same time, respondents drew a sharper line around personal adaptability, suggesting that those who do not learn or trust AI could lose opportunities even when access exists.
Key Takeaways
01
Resistance to change is the clearest risk marker: 42% identify non-adopters or people resistant to change as most at risk, the highest of any named group
02
Access and income divides are a major concern: 35% point to socioeconomically disadvantaged or access-limited groups, showing structural inequality is seen as a core driver of being left behind
03
Age and digital capability are tightly linked vulnerabilities: 34% cite people with low digital literacy or awareness, 25% specifically name older adults, and 16% explicitly connect age with digital skills risk
Strategic Implication
Prioritize adoption among older, lower-income, and change-resistant segments with a tiered inclusion strategy: simplify onboarding, embed hands-on training and human support, and design low-friction experiences for low-literacy users. Protect access through affordable pricing, subsidized entry tiers, and partnerships that extend connectivity and device availability. Shift messaging from innovation-led to outcome-led, emphasizing ease, relevance, trust, and immediate practical value for workers and communities most vulnerable to displacement.
Groups Seen as Most at Risk of Being Left Behind
34%
People who resist or fail to adapt to AI
People who resist or fail to adapt to AI
34%
Lower-income, low-skill, and access-constrained groups
34%
Older people and those with low digital literacy
30%
Low-skill, routine, or automatable workers at risk
2%
Listen
“I'm also concerned about, groups that are already, structurally disadvantaged. People with limited digital access, weaker education systems, or less bargaining power in the labor market.”
Technology Transformation Leader, boutique IT consulting company
when asked about Groups Seen as Most at Risk of Being Left Behind
Listen
“So without connecting connectivity or proper tools or education, people can't participate in the AI economy. So risk isn't just the job loss, but it's also the exclusion from the opportunity.”
Cloud AI Expert, Wafan
when asked about Groups Seen as Most at Risk of Being Left Behind
Chapter 04
Trust Breaks Down When AI Becomes Unreliable or Overused
Confidence in AI is constrained by two intertwined concerns: systems may produce false or harmful outputs, and heavy reliance on them may erode human thinking over time. Together, these concerns make trust contingent on both technical reliability and healthy patterns of use.
“I definitely have have had instances where I will ask something to an AI chatbot, and it will hallucinate And it's despite that it's very confident in its answers and I worry about cases where people don't realize it's hallucinating.”
— Chief of Staff / Strategy and Operations Leader, Global Tech Company
Listen
Key Finding
Trust depends on verification, guardrails, and preventing AI misuse
Trust, Truthfulness, and Misuse Risks
97%
of respondents raised trust, truthfulness, or misuse risks
Trust remains a near-universal concern, with 97% of respondents raising risks tied to truthfulness, reliability, or misuse. The biggest issue is hallucinations and verification, cited by 68%, while a smaller but still meaningful 15% focus on deepfakes, scams, bias, and other harmful applications of AI.
Verification concerns dominate the trust conversation, suggesting confidence depends less on raw capability and more on safeguards, oversight, and user awareness. Only 17% express low concern, creating a clear split between respondents who see AI as useful when checked carefully and those worried about convincing but harmful misuse.
Key Takeaways
01
Verification is the price of trust: 75% express accuracy concerns, with 62% saying AI is useful only if checked case by case and 13% viewing outputs as often unreliable and needing verification
02
Guardrails matter more than doomsday fears: 82% focus on regulation, governance, and limits on AI behavior, while just 7% center their concern on extreme misuse or harmful societal deception
03
Trust is conditional, not automatic: 97% raise trust, truthfulness, or misuse risks overall, and only 14% report low accuracy concern based on demonstrated performance
Strategic Implication
Build trust into the product and go-to-market by making verification and control core features: provide citations, confidence indicators, audit trails, human-review workflows, and clear escalation paths for high-risk use cases. Position AI as decision support rather than autonomous authority, and price premium tiers around governance, monitoring, and compliance capabilities. Lead messaging with accuracy, transparency, and guardrails, using proof of performance to earn trust segment by segment.
Trust, Truthfulness, and Misuse Risks
Concern about false information, hallucinations, and verification
68%
Low concern about trust and misuse
17%
Concern about deepfakes, scams, bias, and harmful misuse
15%
Listen
“I think it's very important that we check everything that AI is presenting to us and that that's just the rule that we don't just take what's what's given to us by, by machine learning and by AI chatbots but that we we always check.”
Infrastructure and Enterprise Applications Manager, Public Transport
when asked about Concern about false information, hallucinations, and verification
Listen
“But there there needs to be some governing there because you already have people that are creating videos videos of people that and those people you know, might be baking something or it could be Elvis Presley who's deceased saying something that he really never said.”
Deputy Director of Customer Experience, Public Transit Authority
when asked about Concern about deepfakes, scams, bias, and harmful misuse
Key Finding
AI dependence sharply splits concerns about thinking and creativity
Concern About Dependence on AI and Erosion of Human Thinking
93%
of respondents raised concerns about dependence on AI and erosion of human thinking
Concern about AI dependence is widespread, with 93% of respondents raising the risk that it could erode human thinking. Views are still split on severity: 48% express strong concern that overreliance will weaken creativity and judgment, while a slight majority, 52%, report lower concern about cognitive decline.
Those with stronger concern describe a compounding effect, where repeated outsourcing of thinking gradually reduces people’s ability to reason, navigate, and create independently. By contrast, the lower-concern group frames AI as a productivity tool that removes mundane work rather than diminishing human purpose, highlighting a clear divide in how respondents define the boundary between assistance and dependence.
Key Takeaways
01
Dependence concerns are widespread, but not unanimous: 93% raised concerns about AI dependence and erosion of human thinking, yet views split sharply with 39% expressing strong concern while 45% primarily see AI as a tool with limited dependence concern
02
Independent thinking is the core fault line: 39% fear overreliance and cognitive decline, compared with 11% who see the risk as manageable with boundaries, showing concern is more often intense than easily contained
03
Creativity risks feel situational for many: 29% show strong concern about creativity, discernment, and youth development, while the largest segment at 45% worries mainly in specific contexts like education or media and 26% report little or no concern
Strategic Implication
Segment AI offerings by autonomy tolerance: position productivity features for the 45% who view AI as a tool, while building premium safeguards for the 39% most worried about cognitive decline. Make human-in-the-loop controls, explainability, usage limits, and “thinking support, not replacement” defaults central in education, media, and youth-facing use cases where creativity concerns are highest. Price advanced governance and admin controls separately, and tailor messaging around augmentation, discernment, and skill preservation rather than speed alone.
Concern About Dependence on AI and Erosion of Human Thinking
Low concern about dependence or cognitive erosion
52%
Strong concern about overreliance weakening thinking and creativity
48%
Listen
“"The other thing is it can have sort of a spiral effect because if we're not doing the thinking, then eventually we're going to lose that the ability to do the thinking."”
Change Management Consultant, tech consulting firm
when asked about Concern About Dependence on AI and Erosion of Human Thinking
Listen
“I am worried that people will outsource all of their thinking to AI, kinda like the way people do for, like, driving around with Google Maps now and, I don't have Google Maps, can't navigate anymore when we used to be able to do that.”
Customer and User Researcher, a SaaS company
when asked about Concern About Dependence on AI and Erosion of Human Thinking
Chapter 05
Human Control Remains the Non-Negotiable Safeguard
In response to concerns about risk, respondents articulate a clear coping framework: AI may assist, but humans must retain final judgment, decision authority, and accountability—especially in high-stakes contexts. Human oversight functions as the primary safeguard people use to make AI acceptable.
“Humans should remain responsible for setting goals bound ethical boundaries, and accountability of the outcomes an AI driven future. Decisions that affects people's rights, safety, and fairness should always have human oversight.”
— Research Participant
Listen
Key Finding
People draw firm human lines around judgment and empathy
Human Boundaries and Irreplaceable Domains
66%
say high-stakes judgment and final decisions should remain human-led
Two-thirds say high-stakes judgment and final decisions should remain human-led, making accountability the clearest boundary in an AI-enabled future. Another 31% reserve specifically human domains for empathy, relationships, and emotional connection, while just 3% see few firm boundaries where people must remain in charge.
This pattern shows respondents are not broadly rejecting AI; they are drawing a line around consequence and care. Most accept automation for support tasks, but want humans to retain authority where rights, safety, fairness, or essential life outcomes are at stake. A sizable minority extends that boundary to relational roles, signaling that trust depends on both human judgment and human presence.
Key Takeaways
01
Human judgment is non-negotiable in high-stakes decisions: 75% say humans must retain final authority, while only 17% accept AI in an advisory role with close human review
02
Empathy remains a strongly human domain: 38% draw a human-only boundary around empathy, relationships, and values, and another 43% prefer humans lead with AI support
03
Most people want humans to keep the final say: 66% overall say high-stakes judgment and final decisions should remain human-led, showing clear resistance to fully automated authority
Strategic Implication
Position AI as an advisor, not an authority: design offerings that keep named humans accountable for final decisions in high-stakes workflows and make human escalation visible in every journey. Price and package around expert review, relationship management, and decision assurance rather than full automation. Lead messaging with “human-led, AI-supported” outcomes—especially where empathy, trust, and nuanced judgment matter—and avoid claims of autonomous decision-making in sensitive domains.
Human Boundaries and Irreplaceable Domains
High-stakes judgment and final decisions should remain human-led
66%
Empathy, relationships, and human connection should remain human
31%
Few firm human-only boundaries
3%
Listen
“But it shouldn't be the final authority on choices that affect people's lives. Such as health care decision, justice, employment, or access to essential services.”
Technology Transformation Leader, Boutique IT Consulting
when asked about High-stakes judgment and final decisions should remain human-led
Listen
“"Human should always be responsible for being human. Being kind, having empathy, being emotional. They should never never never leave that piece behind. That is what make makes them different. And being sensitive."”
Individual Contributor
when asked about Empathy, relationships, and human connection should remain human
Key Finding
Humans Keep Final Say as AI Supports, Not Owns
Expectations for Human Oversight and Final Accountability
91%
said humans must have the final say and remain accountable
Human oversight is a clear expectation: 91% said people must retain final decision authority and remain accountable for outcomes. Only 8% framed the role as review-and-verify oversight, while just 2% placed little emphasis on human involvement, underscoring how strongly respondents reject fully autonomous decision-making.
Respondents draw the line most sharply where decisions affect people’s rights, wellbeing, or access to essential services. In practice, they want AI to support human judgment, not replace it, with accountability anchored in people who can interpret context, weigh ethical tradeoffs, and answer for consequences when decisions go wrong.
Key Takeaways
01
Humans keep the final say: 91% say humans must retain final decision authority and accountability, with 48% explicitly saying AI can assist but humans remain accountable
02
Oversight is expected, not optional: 34% call for ongoing monitoring or institutional guardrails, while 31% want active human verification of AI outputs
03
Review intensity rises with risk: 29% say humans need the final say in key or high-stakes cases, compared with 21% who want strong human final authority across the board and 32% who support more selective review depending on use case
Strategic Implication
Design AI offerings with explicit human-in-the-loop controls, clear approval checkpoints, and auditable accountability trails, especially for high-stakes workflows. Package governance as a core product feature: tiered oversight settings, monitoring dashboards, verification tools, and policy guardrails that scale by risk level. Position AI as decision support rather than decision owner, and align pricing and enterprise sales around compliance, review rigor, and institutional control rather than full automation alone.
Expectations for Human Oversight and Final Accountability
Humans must have the final say and remain accountable
91%
Review-and-verify human oversight
8%
Low-emphasis on human oversight
2%
Listen
“Responsibility for those outcomes need to stay with humans who can be held accountable understand context, and weigh ethical trade offs.”
Technology Transformation Leader, boutique IT consulting company
when asked about Expectations for Human Oversight and Final Accountability
Listen
“As the IBM manual manual says, computer should never be responsible for any management decision because a computer cannot be held accountable.”
Strategy Director, TBWA's Amsterdam office
when asked about Expectations for Human Oversight and Final Accountability
Chapter 06
Beyond Productivity: Reframing Meaning in an AI-Heavy Future
As AI changes the role of work, respondents point toward relationships, faith, creativity, and life beyond employment as enduring sources of meaning. This opens a forward-looking conversation about designing for human flourishing, not just labor efficiency.
“I personally don't believe that work gives people the meaning in life So even though, obviously, people need to work to survive now, it's actually actually things like community and art and culture that gives meaning to life.”
— Marketing Manager, Software Company
Listen
Key Finding
Purpose Moves Beyond Work Into Relationships, Creativity, and Faith
Sources of Meaning and Human Purpose in an AI-Heavy Future
45%
said hobbies, creativity, and life beyond work are key sources of meaning
Meaning is moving beyond work for most respondents, with 45% pointing to hobbies, creativity, and life outside work as primary sources of purpose. Another 35% center meaning in relationships, family, and faith, while only 19% still see work as central, making work-first purpose a clear minority view.
The strongest pattern is not a rejection of ambition, but a redefinition of what fulfillment looks like. Nearly four in five locate purpose in connection, creativity, or spiritual life, suggesting an AI-heavy future may elevate community, personal growth, and non-economic contribution. Still, the one-in-five who remain work-centered signals that identity and structure tied to careers will not disappear entirely.
Key Takeaways
01
Work is no longer purpose’s sole anchor: 54% say work will matter but be less central, while 14% believe purpose can exist without work entirely, compared with 32% who still see work as the core source of purpose
02
Meaning is moving into self-directed life: 52% identify hobbies, creativity, service, or enjoyment as key sources of meaning, making this the strongest non-work purpose category
03
Relationships outrank faith as purpose sources: 38% center meaning in relationships and family, versus just 4% who point primarily to faith or moral purpose
Strategic Implication
Reposition AI offerings around life enrichment, not just labor efficiency: package products and services to free time for relationships, creativity, and personal pursuits; segment messaging for three audiences—work-central users, work-light pragmatists, and purpose-beyond-work adopters; emphasize family connection, creative expression, and self-directed use cases in campaigns; add premium features for hobbies, learning, and community-building; and avoid purpose narratives anchored primarily in career identity or faith-based appeals.
Sources of Meaning and Human Purpose in an AI-Heavy Future
45%
Hobbies, creativity, and life beyond work as sources of meaning
Hobbies, creativity, and life beyond work as sources of meaning
45%
Relationships, family, and faith as main sources of meaning
35%
Work remains central to purpose
19%
Work matters but will be less central
1%
Listen
“If survival no longer required work, meaning would shift towards the creation or connection or the mastery in a certain areas, which means people will be more creative, will be more focused on those relationships and building communities in society, focus on the personal growth, service and impact”
Cloud and AI Expert, Communications Provider
when asked about Hobbies, creativity, and life beyond work as sources of meaning
Listen
“Each human is is established to worship God and enjoy him forever. Some people find worth in what they do during the day when really it's who we are in the image of God that really makes sense and how we connect with other humans.”
Cross Functional Manager
when asked about Relationships, family, and faith as main sources of meaning
Strategic Patterns
Cross-Cutting Themes
PATTERN 01
The Conditional Acceptance Pattern
Respondents are not broadly rejecting AI; they see it as inevitable and support continued progress. But that acceptance is conditional on acknowledging uneven societal readiness, addressing trust and misuse risks, and preserving human final authority.
Implication
AI adoption strategies should not frame the choice as acceleration versus resistance. The stronger path is governed adoption: move forward, but visibly embed safeguards, oversight, and accountability to earn legitimacy.
PATTERN 02
The Task Automation to Social Stratification Chain
Because respondents expect AI to automate analytical and routine work first, they foresee selective displacement rather than total job collapse. That labor transition is then linked to an unequal distribution of gains, with businesses and technology controllers benefiting most while less adaptive and lower-resource groups risk being left behind.
Implication
The key challenge is not only whether AI automates work, but who has the capacity to adapt. Interventions should focus on transition support, reskilling access, and reducing concentration of advantage.
PATTERN 03
The Human Reserve of Judgment
Even as respondents accept AI as a powerful tool, they consistently carve out protected human domains: final decisions, accountability, high-stakes judgment, and authentic human connection. Concerns about hallucinations, misuse, and cognitive erosion reinforce the idea that some forms of authority and meaning must remain human-led.
Implication
The most credible AI systems will be those designed around augmentation rather than substitution in sensitive domains, explicitly preserving human judgment, responsibility, and relational value.
Quick Answers
Common Questions
Key Insight
Are People Generally for or Against AI Adoption?
People are broadly for continued progress, but not without conditions. 56% favor moving forward with AI with guardrails, 36% want a slower pace because society is not ready, and only 8% want adoption to move faster.
Question 02
Do Respondents Think AI Will Replace Jobs Outright?
Not primarily. 59% expect AI to both complement human work and replace selected tasks, while 36% see it mainly as a complement. Only 6% anticipate broad replacement and major job transformation.
Question 03
What Kinds of Work Do People Think AI Will Automate First?
Respondents most often pointed to knowledge and analytical work, cited by 43%, followed by routine administrative and repetitive tasks at 31%. Another 26% expect broader disruption across sectors, including service and some physical roles.
Question 04
Who Is Expected to Benefit Most From Ai—and Who Is Most at Risk?
The biggest expected winners are businesses and those controlling the technology, according to 45% of respondents, with another 39% pointing to early adopters and highly skilled users. At the same time, 98% identified groups at risk of being left behind, especially lower-income people, less adaptive individuals, and older adults with lower digital literacy.
Question 05
What Makes AI Trustworthy Enough for People to Accept?
Trust depends on both reliability and oversight. 97% raised concerns about truthfulness, misuse, or harmful outputs, with 68% specifically citing hallucinations and verification. In response, 91% said humans must retain final decision authority and accountability.
Strategic Recommendations
What This Means for You
01
Critical
Lead AI Adoption With Visible Human Accountability
Design AI systems so final decisions, exception handling, and responsibility clearly stay with people, especially in high-stakes domains. This directly responds to the 91% who expect human final authority and the 66% who reserve judgment-heavy decisions for humans.
02
Critical
Frame AI as Governed Augmentation, Not Autonomous Replacement
Position AI initiatives around task support, productivity, and decision assistance rather than full substitution. This aligns with the 59% who expect a hybrid human-AI model and helps build legitimacy among audiences who support progress only with guardrails.
03
High
Invest Early in Transition Support for Vulnerable Groups
Prioritize reskilling, digital access, and targeted support for lower-income, older, and less adaptive populations before displacement intensifies. This is critical given that 65% expect selective job disruption and 98% see clear risk of groups being left behind.
04
High
Build Trust Through Verification and Misuse Safeguards
Treat accuracy checks, source transparency, human review, and anti-misuse protections as core product and policy features, not add-ons. Trust will remain fragile unless organizations address hallucinations and harmful use cases head-on, as reflected in the 97% who raised these concerns.
05
Moderate
Plan for Human Flourishing Beyond Productivity Alone
Complement workforce strategy with a broader social narrative about meaning, creativity, relationships, and life outside work. This reflects the finding that purpose is already shifting beyond employment for most respondents, creating an opportunity to frame AI progress in more human terms.
Research Methodology
This research draws on 121 in-depth interviews with business professionals representing a wide mix of roles, industries, and company sizes.
Interviews ran 6 to 30 minutes and covered preferred pace of AI adoption and perceived social readiness, expected role of AI in human work, types of work respondents expect AI to automate first, and human boundaries and irreplaceable domains. 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, manufacturing, and retail. All participants were selected for their direct experience with AI adoption and its impact on work. Company sizes ranged from small businesses to large enterprises.
The analysis of 121 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
G2 is the world's largest and most trusted software marketplace, helping 90 million people every year make smarter software decisions based on authentic peer reviews.
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