95% of AI projects havent created measurable value. Leaders from Genesys share three key lessons for transforming AI from experimentation into real business impact.
AI is advancing faster than any other business transformation. At this year's Davos Forum, the conversation shifted from 'what AI can do' to 'what value AI is creating'.
However, the reality is that many organizations remain stuck in a cycle of experimentation, proof-of-concept, and small successes that haven't translated into sustainable business impact. The common question among leaders today is: where is the real return on investment?
MIT's research on large-scale AI generative deployments shows that approximately 95% of organizations experimenting with this technology have yet to generate measurable business value. A report from PwC also indicates that 56% of CEOs haven't seen clear financial benefits. Technology is developing rapidly, but most businesses are still struggling to translate experimentation into tangible, operational results.
This gap doesn't reflect a lack of effort, but rather shows that AI is a new wave moving too fast and requires strong organizational capacity to translate the technology into long-term operational change.
Based on the experience of leading a comprehensive AI transformation at Genesys – where over 6,000 employees were equipped with AI tools and key processes were optimized – the author concludes that success lies not in choosing a particular model or platform, but in how the organization agrees on goals, builds a solid operational foundation, and changes the way work is done.
Start with the desired outcome, not with the experiments.
A common mistake is viewing AI as a new technology to be implemented. This often leads businesses into a cycle of piecemeal experimentation, resulting in only minor improvements. Without clearly defining the business problem to be solved, the likelihood of creating real transformation is very low.
A more effective approach is to reverse the question. Instead of asking 'where can we use AI?', ask 'what problem are we trying to solve?', 'what are the goals for customers, employees, and the business?', and 'how can AI help achieve that?'. When the goals are clear, teams will have a common 'North Star' to guide them, making prioritizing and scaling initiatives easier.
In any transformation, clarity from leadership is the most powerful catalyst. When people understand why change is necessary, they will find ways to implement it themselves.
View governance, skills, and data as essential foundations.
From the outset, Genesys recognized that AI couldn't stand outside of existing operating models. If factors like governance, skills, and data were only addressed last, scaling would quickly stall.
Questions surrounding privacy, risk, fairness, or accountability are not barriers, but signals that AI is directly impacting how decisions are made and responsibilities are allocated within organizations. Without a clear structure, teams will hesitate because they don't know where the safe boundaries lie.
Establishing governance mechanisms from the outset helps create consistency. Investing in training helps employees understand and use AI responsibly. Data discipline ensures reliable output. With this foundation solid, teams can experiment faster and integrate AI more deeply into processes instead of letting initiatives exist in a fragmented way.
Governance, skills, and data do not stifle innovation. On the contrary, they are conditions for sustainable innovation.
A new culture is the real engine of transformation.
AI is not simply a technology project. It's an opportunity to reshape entire businesses, from people to processes and systems. If organizations view AI merely as an IT project, they'll miss the most crucial part: cultural change.
This requires close collaboration between business, technology, and human resources leadership. As AI changes roles, processes, and expectations, organizations need to simultaneously change how they support their employees. The goal isn't to turn everyone into a technical expert, but rather to build a 'common language' and a fundamental confidence so that everyone can work with AI proactively rather than hesitantly.
When employees understand the limits and opportunities of AI, curiosity will replace fear. As they directly experience how AI reduces friction, speeds up processing, and frees up time for higher-value tasks, cultural change will spread. AI will no longer be something 'imposed on the organization,' but rather a tool that the collective can use to shape the organization.
Conclude
AI has no end point, but the principles of successful transformation remain unchanged. When businesses focus on clear goals, build a solid operational foundation, and cultivate the right culture, technology becomes a catalyst rather than a distraction.
The real opportunity lies not in 'deploying AI,' but in using AI to create better ways of working for employees, customers, and the business itself.