The excitement around artificial intelligence has never been higher. Boardrooms buzz with AI strategies, innovation teams spin up pilots, and employees quietly adopt their own tools to get work done faster. However, activity does not equal progress, and many organisations are stuck in what we call the "AI Pilot Trap". This is a cycle of promising proofs of concept that never translate into enterprise value.
According to the State of AI in Business 2025 Report from MLQ.ai, a whopping 95% of custom enterprise AI tools fail to reach production. Further, only 5% of companies are actually seeing ROI from their AI investments. Meanwhile, their research indicates that 72% of AI investments are destroying value rather than creating it.
For enterprise leaders, these numbers should encourage reflection on how to mitigate and e scape the pilot trap. In this post, we will cover some ideas on how to move from fragmented experimentation to scalable AI with a focus on measurable business outcomes.
The Scale of the Problem: Shadow AI and Pilot Purgatory
The gap between AI adoption and AI value creation will become a defining challenge for 2026. While over 75% of organisations deploy AI in at least one function, only 31% of prioritised use cases actually reach full production.
What's filling the void? Shadow AI.
Microsoft research reveals that 75% of workers now use AI at work, with 78% bringing their own unsanctioned tools into the workplace. Their research shows that shadow AI spans every department, from marketing and finance to HR and operations. The State of AI in Business 2025 Report highlights this phenomenon as one of the primary barriers to enterprise AI maturity.
AI is being used to solve all sorts of unrecognised issues in the workplace, which we end up trying to fix through technology rather than the root cause of the issue. As one customer said to me recently, she uses AI so she can get to bed earlier at night since so much is expected of her.
Shadow AI is a signal of unmet need. Employees are adopting these tools because internal processes are too slow or sanctioned alternatives don't exist. For example, if an HR professional is using AI with sensitive personnel data, then this data is flowing out 'into the wild' through uncontrolled systems. These scenarios are easy to envisage if there are regulatory compliance gaps or, quite simply, that the HR professional is overloaded and simply wants to go to bed at a reasonable time. Decisions can be made on unrepeatable and unpredictable behaviour from unvetted models. Perhaps most critically, 83% of organisations report that shadow AI adoption is growing faster than IT can track. This is partly due to the finding that 69% of technology leaders lack visibility into their AI infrastructure. The risks are substantial, and organisations are finding that they need to work at the speed of AI and unauthorised adoption rather than the speed of the customer.
The GenAI Divide: Why Some Organisations Pull Ahead
The State of AI in Business 2025 Report identifies an emerging "GenAI Divide" between organisations that treat AI as a series of isolated experiments and those building genuine enterprise capability. This divide is widening rapidly.
On one side sit companies trapped in perpetual pilot mode: launching proofs of concept, celebrating early wins, then watching momentum stall as governance, integration, and scaling challenges emerge. These organisations often see AI as a technology problem to be solved by IT alone, and it becomes an issue that is blamed on users who aren't technically adept. There needs to be a wider system view of the problems that AI should be used to solve, rather than patching holes with AI tools because they are fast.
On the other side are enterprises that recognise AI as a business transformation requiring cross-functional ownership. These leaders understand that the real opportunity lies in adopting back-office automation and operational efficiency where ROI is more predictable and measurable.
The report emphasises the growing importance of agentic AI systems rather than spot solutions. There should be a focus on value-add tools that can learn, remember context, and take autonomous action within defined boundaries. Agentic systems represent the next evolution of enterprise AI that are capable of handling complex workflows across multiple systems.
However, deploying agentic AI safely requires mature governance frameworks, something most organisations still lack. This is where the pilot trap becomes most dangerous. Organisations that haven't mastered basic AI governance will struggle to harness more sophisticated capabilities as they emerge.
Five Steps to Escape the AI Pilot Trap
Moving from shadow AI and stalled pilots requires a shift in strategy. Based on the insights from the State of AI in Business 2025 Report and our work with enterprise clients, here are five actionable steps to break free.
1. Move from Build-Only to Strategic Partnerships
Based on the assumption that AI should be led by technical teams, the 'build versus buy' debate means that many organisations choose to build custom AI solutions. However, this does not always provide a competitive advantage. The research suggests that 95% of custom tools never make it to reach production, so the 'build' approach often becomes a resource drain for the business. Technology teams could have an emphasis on looking for buzzwords to populate their professional resumé rather than having the best interests of the organisation at heart.
It may seem faster to allow 'Shadow AI' to help individuals make progress, or even get to sleep more at night. However, the long view means that it may be faster overall to allow strategic partnerships that accelerate time-to-value while preserving optionality. This doesn't mean abandoning internal capability entirely, but rather being selective about where custom development genuinely differentiates your business versus where proven platforms can deliver faster results.
Consider which AI capabilities are truly proprietary to your organisation in terms of delivering small successes iteratively now, with a view on longer-term wins. For the latter, partnering with established vendors often makes more sense than reinventing the wheel.
2. Focus on Back-Office ROI First
The State of AI in Business 2025 Report reveals that organisations seeing genuine ROI from AI tend to focus on back-office operations rather than customer-facing applications. Why? Back-office processes offer clearer metrics, lower risk, and faster feedback loops.
Think invoice processing, contract analysis, internal knowledge management, and compliance monitoring. These aren't glamorous applications, but they deliver measurable efficiency gains that build organisational confidence and fund further AI investment.
Organisations can become distracted by chasing the next big AI announcement rather than asking where people waste the most time, or what problems require the most help. That's where businesses find ROI and real, impactful strategic uses of AI.
3. Establish Governance as Competitive Advantage
The instinct to ban shadow AI is understandable but counterproductive. Blanket prohibitions drive adoption underground while making enterprise-wide productivity improvements impossible to achieve.
Instead, treat governance as a competitive advantage. This means:
- Creating cross-functional AI governance teams spanning IT, security, legal, HR, and business units
- Developing clear policies that encourage responsible AI use while maintaining protection
- Providing authorised, secure alternatives that meet employee needs better than shadow tools
- Building visibility into AI usage patterns across the organisation
Nearly two in five enterprises have now introduced official AI platforms in response to bottom-up usage trends. Company-approved tools with proper licensing protect intellectual property and ensure sensitive information doesn't become training data for external vendors.
4. Empower Line Managers as AI Champions
One of the most overlooked insights from the research is the importance of line managers in successful AI deployment. These middle-layer leaders understand both strategic priorities and operational realities: making them ideal champions for AI adoption.
Empower line managers with:
- Training on AI capabilities and limitations
- Authority to identify and prioritise use cases within their teams
- Access to self-service AI tools with appropriate guardrails
- Clear metrics for measuring AI impact in their domains
This distributed approach accelerates adoption while maintaining governance, as line managers can contextualise AI policies for their specific workflows.
5. Build for Agentic Futures
While addressing today's challenges, forward-thinking organisations are preparing for agentic AI systems that can operate with greater autonomy. This preparation includes:
- Investing in data products and infrastructure that can support real-time, context-aware AI
- Developing clear frameworks for AI decision-making authority
- Creating feedback mechanisms to monitor and adjust AI behaviour
- Building organisational muscles for human-AI collaboration
The organisations that escape the pilot trap today will be best positioned to capture value from more sophisticated AI capabilities tomorrow.
The Path Forward
The AI Pilot Trap is real, but it is not all bad news. The State of AI in Business 2025 Report makes clear that a small but growing proportion of enterprises, that crucial 5%, are finding paths to genuine ROI. What distinguishes them is clarity and asking the hard questions about where AI creates value. It is important to channel adoption and harness the enthusiasm for AI, which takes patience to build sustainable capability rather than chasing quick wins.
For enterprise leaders feeling stuck in pilot purgatory, it is recommended to stop treating AI as a series of experiments. Organisations need to start treating it as an enterprise transformation by providing better alternatives. A good starting point is to focus on measurable back-office wins before pursuing moonshots. You can have an overall masterplan to build the governance foundations that will support today's AI as well as the agentic systems already emerging on the horizon.
The window for competitive advantage through AI remains open, but it's narrowing as AI keeps moving forward, fast. The question is whether your organisation will escape the trap in time, with a good way forward for sustainable changes.
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