Format
Point of View
Sector
Cross-industry
Service relevance
AI copilots, workflow automation
Author
Rohit Purohit, Founder and CEO
Railroads, telephones, light bulbs, electricity, automobiles, and personal computers all faced doubt and resistance before they became part of everyday life. Each was once described as dangerous, useless, or both. The telephone was dismissed by Britain's chief post office engineer in 1878. The light bulb was called "a conspicuous failure" by a leading technologist in 1879.
We are watching the same pattern with AI, only at higher speed and across more institutions at the same time. Academic work on technology adoption keeps pointing to the same factors, and the order matters: perceived risk and benefit, trust in the institutions building it, knowledge about the innovation, and cultural context.
The directional trend is positive, but the expert-public gap is the data point that should change how enterprises introduce AI. Figures below are drawn from the Stanford HAI 2026 AI Index, the Edelman 2026 Trust Barometer, and Gallup.
59%
of people globally saw more benefits than drawbacks from AI in 2025, up from 55 percent in 2024 (Stanford HAI 2026 AI Index).
52%
of people feel nervous about AI products. Optimism and nervousness run in parallel, which is unusual for a maturing technology.
56% vs 10%
of AI experts are more excited than concerned, against 10 percent of the American public.
73% vs 23%
of experts versus the public believe AI will help the job market.
56% vs 34%
of employees trust their own employer's use of AI versus their government's use of it (Edelman 2026 Trust Barometer).
22%
of Gen Z describe themselves as excited about AI in 2026, down from 36 percent in 2025; the share feeling angry rose from 22 to 31 percent (Gallup).
Edelman's work shows hands-on experience is the fastest route to trust. People who have had AI improve their understanding of a complex topic are more likely to trust it for other things. For enterprise programs, the lesson is to put usable, useful AI in front of people rather than messaging at them.
Institutions closest to the user move trust faster. Employer use is more trusted than vendor claims, which are more trusted than government regulation. If you are putting AI into your workflows, the trust you build with your own employees and customers is doing more work than any external messaging.
Resistance fades once safety, evidence, and usefulness become visible. The people raising hard questions about jobs, bias, and concentration are not blocking adoption. They are pointing at what needs to be addressed for adoption to last.
Every transformative technology has run this gauntlet. What changes the trajectory is responsible design, openness about what the technology can and cannot do, and visible evidence of benefit in the places people actually live and work. With those three, trust grows and the pattern repeats. Without them, the technology gets stuck in the gap between what experts say and what the public feels.
The job, this time, is the same as it was with electricity, the automobile, and the internet. Build something that holds up, show your work, and let the technology earn its place in daily life.
We will email the link directly.
Production copilots that earn trust through usefulness, with evidence the business can show.
AI placed inside the workflows where employees actually build trust.
The safety evidence that turns skeptics into adopters.
Makes every enterprise AI answer safe to act on, so usage builds trust instead of doubt.
Governed agents for regulated work where openness about limits matters.
Playbook
Playbook
Case Study
Book a 30-minute scoping call. We will talk through where trust gets built, where it gets stuck, and how to ship AI that earns its place in your workflows.