Here's how PoC's set up government and enterprise for achievable, scalable AI successes  

AI has captivated the imagination of CIOs across industries, especially in unionised and regulated sectors, where its potential to transform operations and enhance decision-making is immense.

But while the promises of AI adoption are appealing, the reality of achieving large-scale, enterprise-wide AI systems often feels very much out of reach. The pursuit of perfection, that is an AI system that seamlessly automates workflows, predicts outcomes, and delivers sweeping organisational change, can have the potential of stalling projects before they even start. Worse, it could fail after pumping a significant amount of investment into it.

And it’s not just us that say this. Gartner estimates that 80% of all AI projects in the enterprise segment fail. This is often because organisations attempt to do too much, too soon.

Complexity, unclear objectives, and the fear of failure overwhelm even the most ambitious teams. So instead of chasing perfection, organisations should focus on starting small, with targeted, proof-of-concept (PoC) projects that deliver measurable value and build a foundation for long-term success.

Iteration is king. It’s a philosophy that sits at the heart of our Pragmatic AI framework, and it emphasises practical, achievable goals that solve immediate challenges while laying the groundwork for scalable AI solutions.

The Problem with Perfection

The appeal of AI lies in its promise to solve big problems. Governments love the idea of predictive systems that streamline healthcare, optimise urban planning, or tackle fraud.

But the bigger the ambition, the greater the complexity.

Take hypothetically, for example, that the DWP decided to deploy AI to predict unemployment trends and guide policy decisions. The project would consolidate data from dozens of internal and external sources, analyse complex variables, and provide actionable insights to policymakers. Chances are that two years and tens of millions of pounds later, it becomes clear that technical, organisational, and data integration challenges make the innovation unworkable, and the project is shelved after pumping tens of millions of pounds into it. Bringing a significant amount of political fallout too.

The reality is that chasing perfection often creates more problems than it solves:

• Complexity becomes unmanageable: All-encompassing AI solutions require massive investments in infrastructure and expertise, leading to delays and spiralling costs.
• Unclear objectives lead to failure: Broad ambitions often lack actionable goals, resulting in initiatives that deliver little tangible value.
• Employee resistance grows: Workers in unionised or structured environments may see large-scale AI as a job threat, slowing adoption through increased scepticism and resistance.

When the stakes are high, the risks of aiming for perfection often outweigh the benefits.

The Power of Starting Small

Focusing on smaller, targeted AI projects isn’t just a fallback strategy, it’s the smartest way to succeed. PoC projects allow organisations to test AI’s potential in a controlled environment, delivering quick wins that build trust and confidence.

Starting small offers several key advantages:

1. Quick wins build momentum: Delivering results within weeks or months demonstrates AI’s value and secures buy-in from stakeholders.
2. Clear goals drive impact: A targeted focus ensures that projects solve specific problems, such as improving response times or reducing operational costs.
3. Scalable success: Once a PoC proves its value, it can be expanded across departments or regions, creating a roadmap for broader adoption.

The Pragmatic AI Approach

At the core of successful PoC projects is our philosophy of Pragmatic AI. It’s a framework that prioritises real-world, measurable outcomes over theoretical perfection. Pragmatic AI focuses on delivering immediate value while setting the stage for long-term scalability.

It advocates for:

• Starting where the impact is clear: Focus on high-priority opportunities where AI can deliver measurable results, such as automating manual processes or improving data analysis.
• Fitting AI into existing workflows: Avoid disruptive overhauls by integrating AI into current systems and processes.
• Keeping everything affordable and scalable: Begin with low-cost, targeted projects that can scale as needs evolve.
• Measuring outcomes, not ambitions: Use clear KPIs like Time to Value (TTV), employee adoption rates, and efficiency improvements to track success.

The real strength of the Pragmatic AI approach is that it ensures every AI initiative starts with a clear purpose and ends with tangible results—something many CIOs struggle to achieve when tackling large-scale projects from the outset.

Bridging Vision and Reality

PoC projects are more than just an experiment or testing ground; they’re the foundation for long-term AI maturity. By focusing on solving immediate problems, they lay the groundwork for scalability, foster trust among employees, and demonstrate the value of AI to stakeholders.

For CIOs, embracing a PoC-driven strategy doesn’t mean thinking small. It means building a sustainable framework for AI adoption. It’s about aligning vision with reality, delivering meaningful impact today while paving the way for tomorrow’s innovations. And we can help. The first step is to download our Pragmatic AI whitepaper which provides actionable strategies to get started.