A large study by Boston Consulting Group in 2026 found that 62% of CEOs believe their company’s AI investments will eventually pay off. But only 48% of the leaders actually working with AI day to day feel that confident (BCG, 2026).
This isn’t only true for AI. A few years ago, McKinsey found that only about half of companies said they felt well prepared to anticipate and respond to external shocks, and two-thirds saw their own organization as too complex and not efficient enough (McKinsey & Company, 2023). Leaders at the top often feel more confident than the situation on the ground actually supports.
Deloitte’s 2026 report makes a similar point with an even clearer number. 85% of company leaders say being able to adapt quickly is critical for their business. Only 7% say their company is actually good at it (Deloitte, 2026). Eighty-five percent think it matters. Seven percent are doing something about it.
The challenges of AI investments lie in people and ways of working
Organizations that successfully build this adaptive way of working are 2.4 times more likely to report better financial results and more meaningful work for their employees, according to Deloitte. Deloitte’s advice is simple: build the habit of adapting and learning into everyday work, instead of treating it as a separate training course that happens once a year.
If a company needs to keep adapting as part of daily work, then the real question about any AI investment is “can our people actually change how they work, fast enough to use it?”
Boston Consulting Group’s own research points the same way. In a separate study, 74% of companies said they hadn’t yet shown clear value from their AI efforts. BCG found that around 70% of the challenges companies run into during AI implementation are people- and process-related, not technological. It also reports that companies leading in AI put roughly 70% of their effort into people and processes, 20% into technology and data, and only 10% into algorithms (BCG, 2024).
That’s a striking pattern, and it’s rarely treated as the real problem. Many companies still over-focus on the technical side, and underinvest in the people and process changes needed to make AI actually stick.
From training to learning and applying it in practice
So the fix is training – workshops and online course? Decades of training research show that learning something in a course does not automatically mean people will use it differently at work. A well-known study from 1988, and a much bigger review of the evidence from 2010, both point the same way: whether training changes what people actually do at work depends heavily on the person, on how the training is designed, and on whether their workplace supports the new behavior afterward.
Learning something in a session is one thing. Actually doing it differently at work is another. A 2026 survey found that 86% of employees say they pick up new skills or knowledge by figuring things out on the job, while close to a third say the training they receive is too theoretical (TalentLMS, 2026).
With AI transformation, the hard part isn’t buying the technology. It’s changing how people actually work – who does what, how decisions get made, how a team talks to each other day to day. Many companies still handle this the old way: they often have separate training initiatives. What they lack is a regular way of adapting the work itself.
What’s usually missing is a simple habit: teams stopping every so often to talk honestly about what’s working and what isn’t, and deciding together what to try next. Not a big event. Not a formal training day. Just a habit, built into the work that’s already happening, rather than something extra bolted onto the calendar.
There’s good evidence that this kind of habit actually works. A large review of the research, covering thousands of participants across a wide range of real-world and simulated settings, found that properly conducted individual and team debriefs improved performance afterward by around 20 to 25 percent on average (Tannenbaum & Cerasoli, 2013). That’s a large improvement, and the best part is that it doesn’t need a special event. It can happen right inside the normal working day.
Organizations can develop the capabilities needed to navigate the AI transformation through structured learning. Academy of Brain helps organizations strengthen the human skills needed to make better use of AI. Our research-based microlearning combines personal reflection and meaningful team discussions, helping employees develop adaptability, collaboration and continuous learning skills as part of their everyday work. Learn more about aligning AI and human work.
Building adaptability into everyday work
Honest reflection like this depends on people feeling safe enough to talk about what actually happened, including the parts that went wrong. Google studied this for years, looking at more than 180 of its own teams. It found that who was on the team mattered less than how the team worked together, and that psychological safety – feeling safe enough to take a risk, ask a question, or admit a mistake – was the most important of the five team dynamics it identified (Google re:Work, n.d.; Edmondson, 1999). When that safety is low, honest reflection becomes harder. People are more likely to hold back questions, mistakes, and disagreement.
None of this means a company needs a big new program. It means spending a small part of the AI budget on building the habit of learning while the work changes. And the good news is, this habit doesn’t need new software or a new department. It just means using the meetings and reviews a company already has, a little differently.
A few honest questions are worth asking, if you’re the one deciding where that budget goes:
- Are you paying for training, or are you paying for actual practice?
- Is there anywhere in the company where teams already stop and talk honestly about how work is going
- And did that happen by design, or by accident?
- And is learning to adapt something built into daily work, or is it still treated as its own separate project?
Technology is going to keep getting better, faster than most companies can keep up with. What matters more, in the end, might not be which AI model a company uses. It might be how quickly its people can learn, together, while everything around them keeps changing.
Sources
Baldwin, T. T., & Ford, J. K. (1988). Transfer of training: A review and directions for future research. Personnel Psychology, 41(1), 63–105.
Blume, B. D., Ford, J. K., Baldwin, T. T., & Huang, J. L. (2010). Transfer of training: A meta-analytic review. Journal of Management, 36(4), 1065–1105.
Boston Consulting Group. (2024). AI adoption in 2024: 74% of companies struggle to achieve and scale value. https://www.bcg.com/publications/2024/ai-adoption-in-2024
Boston Consulting Group. (2026). AI Radar 2026: As AI investments surge, CEOs take the lead. https://www.bcg.com/publications/2026/as-ai-investments-surge-ceos-take-the-lead
Deloitte. (2026). 2026 Global Human Capital Trends: Winning organizations will build the human advantage. https://www.deloitte.com/us/en/about/press-room/deloitte-report-winning-organizations-will-build-the-human-advantage.html
Edmondson, A. C. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383.
Google re:Work. (n.d.). Understand team effectiveness. https://rework.withgoogle.com/intl/en/guides/understand-team-effectiveness
McKinsey & Company. (2023). The state of organizations 2023: Ten shifts transforming organizations. https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations-2023
Ojanen, V. (2026). Soft Skills as Strategic Competencies in the AI Era. Academy of Brain White Paper.
Tannenbaum, S. I., & Cerasoli, C. P. (2013). Do team and individual debriefs enhance performance? A meta-analysis. Human Factors, 55(1), 231–245.
TalentLMS. (2026). The TalentLMS 2026 L&D report: The state of workplace learning. https://www.talentlms.com/research/learning-development-report-2026
Subject matter by Ville Ojanen
(Lead Psychologist at Academy of Brain)
Ville Ojanen is a psychologist with a doctorate in cognitive neuroscience. His passion is understanding and influencing the human behavior. Ville combines an exceptionally broad and in-depth understanding of behavioral and neurosciences with practical experience in developing individuals, teams and communities.
He has worked as a clinical neuropsychologist, as an HR professional in Nokia and for fifteen years as an organizational psychologist, coach and keynote speaker. Ville is a serial entrepreneur and the co-founder of Academy of Brain Ltd. He has served in the behavioral science advisor team of the Finnish Prime minister´s office since 2020, currently as a steering group member.
