The adoption of GenAI represents a significant shift in behavior as companies embed it into their processes. While new tools are part of this change, the deeper transformation lies in rethinking how daily work is performed and how ways of working evolve. Critically, this change happens at the individual level: people have different motivations (WIIFM – What’s In It For Me) and distinct barriers when it comes to adopting GenAI (tools).
What are your lessons learned from the GenAI adoption journey?
Community Specialist| Project Management InstituteNewtown Square, PA, United States
Hi Maria, thanks so much for raising this question!
I'm looking forward to hearing where community members are in their GenAI adoption journey and what they have learned so far. I know I'm learning every day!
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2 replies by Dr. Arun Kumar Singh and Maria Hrabikova
Jan 23, 2026 11:36 AM
Maria Hrabikova
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Thank you, Laura.
I recently listened to Visa’s GenAI adoption journey. The company’s pain points, similar to many other organizations, were as follows:
Unclear use cases
Not enough time to learn
Data security or privacy
Limited awareness of tools
A lack of leadership buy-in
Maria
Jan 24, 2026 6:55 AM
Dr. Arun Kumar Singh
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One key lesson is that GenAI adoption is less about the tool and more about behavior change. People adopt it when it clearly reduces friction in their daily work—WIIFM must be explicit. Adoption accelerates when use cases are role-specific, learning is hands-on, and leaders model usage themselves. Where it struggles is fear—fear of irrelevance, mistakes, or loss of control—which needs to be addressed openly, not ignored.
Saving Changes...
Luis BrancoCEO| Business Insight, Consultores de Gestão, LdªCarcavelos, Lisboa, Portugal
Great question. My main lesson learned is simple: GenAI adoption rarely fails because of the technology. It fails because work is not intentionally redesigned.
A few practical insights from the field:
GenAI amplifies the existing system. If processes are unclear, bureaucratic, or poorly designed, GenAI just accelerates the mess. Without clarity on decisions, workflows, and accountability, no tool will deliver real value.
WIIFM matters, but it is not enough. People adopt when they see personal benefit, yes. But they sustain adoption when there is psychological safety, permission to experiment, and explicit acceptance that learning includes mistakes.
Change is individual, blockers are structural. Training helps, but it is rarely the main constraint. The real barriers are usually organizational: wrong incentives, fear of exposure, lack of ethical guardrails, or leaders who ask for innovation while punishing deviation.
GenAI does not replace thinking. It raises the bar for it. The teams that progress fastest use GenAI as a cognitive copilot, not as a shortcut. Better questions, better judgment, better reflection. This requires maturity, not just access.
Real adoption starts in everyday work. Not in flashy pilots or marketing stories. It starts when GenAI is embedded in real tasks, with measurable impact, and when someone is explicitly accountable for learning from its use and improving the system.
In short, GenAI is less a digital transformation and more a transformation of leadership, decision-making, and learning. Without ethical clarity and conscious accountability, adoption stays shallow. With them, impact becomes structural.
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1 reply by Maria Hrabikova
Jan 23, 2026 3:21 PM
Maria Hrabikova
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Hello Luis, Thank you for your observations - it’s a comprehensive list of points, which I fully agree with. The deployment of AI tools is fundamentally a cultural change rather than a technological one, and as such, needs a clearly defined governance model.
Hi Maria, thanks so much for raising this question!
I'm looking forward to hearing where community members are in their GenAI adoption journey and what they have learned so far. I know I'm learning every day!
Thank you, Laura.
I recently listened to Visa’s GenAI adoption journey. The company’s pain points, similar to many other organizations, were as follows:
Great question. My main lesson learned is simple: GenAI adoption rarely fails because of the technology. It fails because work is not intentionally redesigned.
A few practical insights from the field:
GenAI amplifies the existing system. If processes are unclear, bureaucratic, or poorly designed, GenAI just accelerates the mess. Without clarity on decisions, workflows, and accountability, no tool will deliver real value.
WIIFM matters, but it is not enough. People adopt when they see personal benefit, yes. But they sustain adoption when there is psychological safety, permission to experiment, and explicit acceptance that learning includes mistakes.
Change is individual, blockers are structural. Training helps, but it is rarely the main constraint. The real barriers are usually organizational: wrong incentives, fear of exposure, lack of ethical guardrails, or leaders who ask for innovation while punishing deviation.
GenAI does not replace thinking. It raises the bar for it. The teams that progress fastest use GenAI as a cognitive copilot, not as a shortcut. Better questions, better judgment, better reflection. This requires maturity, not just access.
Real adoption starts in everyday work. Not in flashy pilots or marketing stories. It starts when GenAI is embedded in real tasks, with measurable impact, and when someone is explicitly accountable for learning from its use and improving the system.
In short, GenAI is less a digital transformation and more a transformation of leadership, decision-making, and learning. Without ethical clarity and conscious accountability, adoption stays shallow. With them, impact becomes structural.
Hello Luis, Thank you for your observations - it’s a comprehensive list of points, which I fully agree with. The deployment of AI tools is fundamentally a cultural change rather than a technological one, and as such, needs a clearly defined governance model.
I agree strongly with Luis’ point that GenAI adoption rarely fails because of the technology. In my experience, it stalls when AI outputs are treated as explanations rather than as inputs that carry real decision obligation.
One practical pattern I’ve seen: early adoption succeeds when GenAI is embedded into moments where choices are already being made — prioritization, risk tradeoffs, scope decisions — and fails when it’s positioned as “analysis on the side.”
Teams engage more deeply when AI changes what gets decided, by whom, and when, not just how fast work gets done. Without that shift, usage stays experimental and optional.
Framed that way, adoption becomes less about training people on tools and more about being explicit about where AI is allowed to influence judgment — and where it isn’t.
Appreciate the way you framed this as a leadership and governance conversation rather than a tooling one.
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1 reply by Maria Hrabikova
Jan 24, 2026 11:26 AM
Maria Hrabikova
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Thank you, Imran.
I agree. Clarity on where AI can influence decisions, and where it should not, is critical to its responsible use.
Saving Changes...
Sergio Luis ConteHelping to create solutions for everyone| Worldwide based OrganizationsBuenos Aires, Argentina
There are two key points often missing: 1-People and organizations do not understand what GenAI (generative AI) really is. 2-The key ingredient for success is to implement Responsible AI. That is often missing. While both things? Often deliberately, through error or omission.
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1 reply by Maria Hrabikova
Jan 24, 2026 10:52 AM
Maria Hrabikova
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Thank you, Sergio. I agree with you on both points: the lack of understanding of what GenAI tools are, and the importance of responsible AI.
In addition, here is a sneak peek at the ‘Layers of Trustworthy AI (source: the CPMAI-PMI preparation course): a) Ethical AI: guidelines (a set of values, principles, and techniques) b) Responsible AI: regulation and laws c) Transparent AI: provides information about several aspects of the AI system, including how it was built, the data it uses, and how biases are measured and mitigated. d) Governed AI: is about the practices and processes that you put in place. This layer may include audits, measurements, regulations, and guidelines. e) Interpretable and Explainable AI: This layer is related to technical aspects and processes
My biggest learnings from GenAI adoption: • Start with work, not tools – map daily tasks and insert AI where it removes pain. • Make WIIFM tangible – time saved, better quality, less cognitive load. • Enable, don’t mandate – curiosity drives adoption more than policy. • Leadership behavior matters – when leaders use it, teams follow. • Trust and guardrails are critical – clarity on what’s allowed builds confidence.
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1 reply by Maria Hrabikova
Jan 24, 2026 11:34 AM
Maria Hrabikova
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Thank you, Arun. I agree with your points, in particular, that curiosity drives adoption more than policy. When people are encouraged to explore, experiment, and learn, adoption occurs more naturally.
Hi Maria, thanks so much for raising this question!
I'm looking forward to hearing where community members are in their GenAI adoption journey and what they have learned so far. I know I'm learning every day!
One key lesson is that GenAI adoption is less about the tool and more about behavior change. People adopt it when it clearly reduces friction in their daily work—WIIFM must be explicit. Adoption accelerates when use cases are role-specific, learning is hands-on, and leaders model usage themselves. Where it struggles is fear—fear of irrelevance, mistakes, or loss of control—which needs to be addressed openly, not ignored. Saving Changes...
There are two key points often missing: 1-People and organizations do not understand what GenAI (generative AI) really is. 2-The key ingredient for success is to implement Responsible AI. That is often missing. While both things? Often deliberately, through error or omission.
Thank you, Sergio. I agree with you on both points: the lack of understanding of what GenAI tools are, and the importance of responsible AI.
In addition, here is a sneak peek at the ‘Layers of Trustworthy AI (source: the CPMAI-PMI preparation course): a) Ethical AI: guidelines (a set of values, principles, and techniques) b) Responsible AI: regulation and laws c) Transparent AI: provides information about several aspects of the AI system, including how it was built, the data it uses, and how biases are measured and mitigated. d) Governed AI: is about the practices and processes that you put in place. This layer may include audits, measurements, regulations, and guidelines. e) Interpretable and Explainable AI: This layer is related to technical aspects and processes
I agree strongly with Luis’ point that GenAI adoption rarely fails because of the technology. In my experience, it stalls when AI outputs are treated as explanations rather than as inputs that carry real decision obligation.
One practical pattern I’ve seen: early adoption succeeds when GenAI is embedded into moments where choices are already being made — prioritization, risk tradeoffs, scope decisions — and fails when it’s positioned as “analysis on the side.”
Teams engage more deeply when AI changes what gets decided, by whom, and when, not just how fast work gets done. Without that shift, usage stays experimental and optional.
Framed that way, adoption becomes less about training people on tools and more about being explicit about where AI is allowed to influence judgment — and where it isn’t.
Appreciate the way you framed this as a leadership and governance conversation rather than a tooling one.
Thank you, Imran.
I agree. Clarity on where AI can influence decisions, and where it should not, is critical to its responsible use. Saving Changes...
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