For many organizations, their artificial intelligence (AI) journey has begun with a flurry of excitement and experimentation. The promise of AI is everywhere: boardroom conversations, industry reports, and the relentless stream of headlines about the next breakthrough. Yet, for all the enthusiasm, a stubborn challenge persists. How do you move from isolated pilots to enterprise-wide adoption? Furthermore, how do you ensure it delivers measurable business value and impact?
The AI pilot purgatory problem
Companies need to balance the cost of investment with the value delivered. This equation can be significant. The sobering reality is that most AI initiatives never make it past the pilot stage. As highlighted in MIT’s State of AI in Business 2025 report, only 5% of generative AI pilots are successfully scaled. S&P Global market intelligence research found the average organisation scrapped 46% of AI projects between proof of concept and broad adoption.
These statistics should give every business leader pause for thought . The implication is clear: while it is relatively easy to experiment with AI, it is far more difficult to embed it into the core of your organization, where it can drive sustained value. Most projects simply stall, never integrating into workflows.
What are you aiming to achieve with AI?
The first step in escaping pilot purgatory is to clarify your objectives. What are you really trying to achieve with AI? The answer must go beyond technology for technology’s sake. The most successful organizations are those that use AI to solve real customer problems and drive customer-centricity.
Within our contact centres, the first thing we want to do is make sure we're solving our customers’ problems. We want to reduce any friction our customers experience and that's much more important for us than efficiency. If we could reduce headcount as a result of using a voice bot, for instance, but see a drop in conversion, that wouldn’t be a positive ROI and we wouldn't take that forward as a project.”
Senior AI Leader, speaking at Berkeley's panel discussion event, Transformational AI: beyond the pilot
This focus on customer outcomes, rather than internal metrics alone, is a hallmark of mature AI strategies.
Integrating AI into core business processes
AI should not be a bolt-on or a side project – it should be woven into the fabric of how your organization operates. This means aligning AI initiatives with business objectives, ensuring robust governance, and focusing on adoption at scale. Too often, AI pilots are run in isolation, disconnected from the processes and workflows that matter most. This can result in fragmentation and duplicated efforts, impacting the ability to scale.
The organizations that succeed are those that treat AI as a transformation lever. They anchor AI efforts in business needs, integrate it with core processes, and build trust in the tools and outcomes. This requires a willingness to invest in change management, data infrastructure, and cross-functional collaboration.
Overcoming the barriers to scale
Scaling AI is not simply a matter of investing in ‘better’ pilots. In some respects, it should be treated as any other large-scale technology implementation, requiring a deliberate approach to overcoming the barriers to adoption. These barriers often include:
- Data quality and accessibility: AI is only as good as the data it is trained on. Legacy systems, data silos, and inconsistent data standards can all undermine your efforts.
- Resistance to change: Employees may be sceptical of new technologies, or unsure how to use them effectively. Without buy-in and training, even the best AI tools will go unused.
- Governance and risk management: As AI becomes more embedded in business processes, issues of ethics, compliance, and accountability become more pressing.
- Leadership: Senior leaders must set the vision, allocate resources, and hold teams accountable for results. They must also be willing to challenge the status quo, break down silos, and foster a culture of experimentation and learning.
- Skills shortage: The correct mix of skills and knowledge is needed to both implement and embed AI at scale.
- Cross-functional collaboration: Organizations must break down silos and foster effective collaboration between functions and levels of seniority.
Beyond the AI pilot: explore the issues in depth
Our AI: beyond the pilot article series explores these key issues and more, sharing best practice principles and experience. The journey from pilot to impact is challenging but achievable with the right focus. By clarifying your objectives and investing in the right enablers of scale, you can move beyond experimentation and unlock the full potential of AI for your organization.
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