A while back I gave my first talk to a general audience about AI. The presentation went well, and it was generally very well received. Then came the Q&A.

The person who spoke first was nearly in tears. They went on at length about the evils of AI and how they would never use it. They said AI is taking jobs (true) and that all the jobs it takes can be done better by humans (not true, in many cases). The speaker used up the time scheduled for questions. They ended, less with a question and more with a challenge to rebut.

I was caught off guard, but I was neither surprised nor offended. I listened patiently, or at least I tried to convey my sincere empathy.

When given a chance to respond, I tried to validate their feelings and acknowledge that the very real uncertainty about AI's current and future effects understandably evoke anxiety and even fear. I told the full audience that their concerns were important and I encouraged them to learn and think about those concerns. I did my best in the moment, but a more thoughtful response would be useful. This is the first part of my series of posts comprising that response.

AI makes reasonable people concerned, anxious, and even afraid. Whether you have decided to support AI, fight AI, ignore AI, or haven't decided what to do, these feelings are real and will interact with your response to AI.

Much has been said about the effects of AI on society; the type, size, and speed of these effects; and the philosophical, moral, and even religious implications. Much, much more consideration and discussion is essential. I am not taking on those topics here.

My thesis in this first part of the series is that companies must take into account the varying feelings of employees, customers, and society to succeed with the company's AI strategy.

AI can generate outsized business value

A recent MIT study said in July 2025,

However, this is misleading about the value of AI in enterprise settings.

Let's compare this "failure" rate to those of other technologies that have been successful.

• 85% of machine learning POCs are never deployed.
• 90% of Tableau dashboards aren't used at all after six months.
• 90% of startups fail.
• 90% of drugs starting clinical trials never gain FDA approval.
• 95-98% of patents see no financial return.
• >95% of novels are never picked up for publication. Of those that are then self-published, >95% sell fewer than 1k copies.

A 95% "failure" rate is common with some technologies that are nonetheless worth investing in. If a use case generates large returns and the implementation uses best practices to reduce the chances of failure, the ROI can redefine or create an industry.

AI has already redefined and created industries. There are many failures, too, as the MIT study records. (FWIW, it's a clear read about some good practices.) The potential upside, though, is clear.

Feelings vary; many people are cautious

There are people without strong opinions about AI, but they are in the minority. A September 2025 Pew study shows, for example, that among U.S. adults,

• "50% say they’re more concerned than excited about the increased use of AI in daily life, up from 37% in 2021. 10% are more excited than concerned." (40 points underwater)
• "In their own lives, about six-in-ten say they’d like more control over how AI is used." (20 points underwater)
• "53% say AI will worsen people’s ability to think creatively, compared with 16% who say it will improve this." (37 points underwater)
• Young adults are more likely to use AI (unsurprising), but are more pessimistic about its effects on society.

It can be useful to think in terms of personas.

• All-in: Positive about the potential for AI, and may use it regularly outside of work.
• Open-minded: Ready to learn, curious about what it means for them.
• Reluctant: Tentative, but reachable if treated with respect rather than solely as a cost to be reduced.
• Against: Very difficult to reach.

Most people are a mix, but if your strategy addresses these four personas, you have a good chance of addressing those mixes of personas.

Failure modes: how this can go wrong

There is tension between corporate goals and the fears of some employees and customers. There are many ways these feelings can reduce or even reverse gains from AI use.

Failure modes by persona (just a few examples)

All-in

Can get frustrated with unnecessary restrictions on the use of AI. If they are used to using the latest models at home and the company offers substantially dumber tools, they will use personal AI accounts, which can lead to private data being leaked and even used as training data in public models.

Open-minded

May feel unnecesary pressure to learn AI tools on their own time in order to be able to do their current-but-being-redefined jobs. If their manager says, "We're going to reduce drudgery!"

then they might ask, "Am I going to be doing more creative work, or just being replaced? Am I even capable of doing this new work they expect of me?"

Reluctant

Redefining their jobs means they are being asked to do jobs they did not apply for, may not like, and may not be capable of. Providing training data for an AI may echo the negative psychological effects of offshoring.

Against

This persona may have significant trouble adjusting to a culture that is embracing AI. Not only is it possible that they may not use AI tools effectively, but they may also decrease the effectiveness of others and potentially, either intentionally or unintentionally, reduce the success of AI applications. Feelings about AI can be very strong if they feel it is an existential threat.

Happy path: how this can go well

There are already some industry good practices for how to cultivate, rather than impose, a work culture that supports the effective use of AI.

Transparent and frequent communication

• Proactively share why AI is being introduced, what roles it will play, and how it may impact jobs, responsibilities, and workflows.
• Address both benefits and risks, and clarify what AI can and cannot do within the organization, including specific policies on data privacy and job changes.
• Offer multiple communication channels, such as meetings, written summaries, and Q&A sessions. These can align with diverse information-processing preferences among employees.

Inclusive decision-making and employee voice

• Involve employees in AI adoption decisions through pilot projects, feedback surveys, and focus groups.
• Create safe spaces for employees to ask questions, share concerns, and express excitement or skepticism, ensuring all perspectives are valued and heard.

Strong, supportive leadership

• Leaders can model openness and curiosity about AI, sharing their own learning process to foster psychological safety for experimentation and adaptation.
• Appoint steering committees or leader champions to guide ethical adoption, answer employee questions, and maintain organizational alignment.

Personalized training and upskilling

• Provide reskilling and upskilling opportunities, supporting employees as they transition to working alongside AI or into new roles.
• Tailor training to employee experience levels. This can be advanced training for tech-savvy teams, hands-on sessions for skeptics or less experienced staff, and ongoing development for everyone.
• Emphasize how AI can help reduce repetitive work and unblock employees so they can focus on higher-value tasks and career growth.

Well-being and purpose

• Regularly check in on employee well-being and tech-related stress, and make adjustments as needed.
• Align AI adoption with the organization's purpose and values, ensuring changes do not undermine trust or culture.
• Implementing these practices ensures organizations are not only technologically innovative but also empathetic, ethical, and supportive. This can help folks with a range of attitudes toward AI feel more informed, involved, and secure.

Enabling experimentation

The recent MIT study mentioned before reported that one high-return method for generating financial impact with AI is as simple as providing access to a chatbot, such as ChatGPT.

Taking a bottom-up approach is a strong strategy because:

• It fosters creativity. (Isn't that what we're telling people their competitive advantage is relative to AI?)
• It lets people learn when the stakes are low and they're not being watched.
• It encourages curiosity, continual learning, and collaboration.
• It can help identify uses of AI that can eventually work at the team or enterprise level.
• It gets people on board with the utility (and even fun) of using AI.

Be a leader, not just a manager

Leaders who grok the difference between management and leadership know that feelings have clear effects on the bottom line. Recognizing that feelings about AI are strong and then incorporating them into AI strategy is ignored at the company's peril. Just as important, when our world is navigating a change likened to the Industrial Revolution at 21st Century speed, treating your team as humans, not resources, fosters the change you want to see in the world.