Data enablement: The foundation for AI and transformation

Data enablement: The foundation for AI and transformation

‘It is easier to act yourself into a new way of thinking, than it is to think yourself into a new way of acting.’ – Millard Fuller 

This quote captures the heart of data enablement. Sustainable transformation is not driven by strategy alone. It comes from daily behaviours, embedding data into decisions, processes, and culture until it becomes second nature. I am sharing some thoughts as a reminder of why data enablement is the true foundation for scalable, lasting change, and how shifting our mindset and behaviour can unlock value that AI and technology alone cannot deliver. 

IDC forecasts that global spending on digital transformation will reach $3.9 trillion by 2027¹. These are big signals that investment is happening, but preparedness remains patchy. 

Over the past decade, digital transformation has taken centre stage across organisations, and rightly so. The pressure to modernise services, deliver better outcomes, and respond to rapidly shifting demands has never been more urgent. In response, we have seen heavy investment in technologies and, more recently, an accelerated focus on artificial intelligence and emerging technologies. 

But there is a pattern we keep seeing. The transformation often begins and prematurely ends with technology, sidelining the very data that determines success. 

Technology is visible and tangible. You can buy it, roll it out, and show its potential. It gives a sense of momentum and progress. But underneath every successful transformation, quietly shaping its direction and determining its success, is something less visible, data. And in many cases, it is still overlooked, underutilised, or misunderstood. 

Data does not announce itself like technology. It hides in processes, shaped by human behaviour, fragmented across systems, and tied to legacy ways of working. The real value it holds, and the risks that come with it, often remain out of sight until it is too late. 

Why we need a shift in thinking. 

Data enablement goes far beyond building analytics or applying AI. It is about bringing data to the surface, understanding its whereabouts and journey, where it holds value or creates friction, and how it can be made reliable and usable. It is about seeing data as a core enabler of every decision, every service, and every outcome we care about. 

Organisations today face intense pressure to transform quickly, in some large programmes, delays have forced public services to run legacy systems alongside new ones, driving up costs and complexity. The Digital & Data profession now accounts for 5.4% of the total Civil Service workforce, just shy of the 6% target, according to the Central Digital and Data Office². This expansion reflects tangible growth in government’s investment in digital and data capabilities, expanding skills and investing in capability frameworks, across departments. But for these efforts to deliver lasting impact, we must go further. 

And this has never been more critical than it is now. 

As organisations explore automation and increasingly experiment with generative AI and machine learning, the cracks in data foundations are becoming more visible. These technologies depend entirely on the quality, clarity, and consistency of the data that feeds them. When data is not right or poorly understood, the outputs of even the most advanced AI models become questionable. At worst, they are misleading and harmful. 

According to Gartner, up to 85 percent of AI projects may fail to deliver value, often due to poor data quality or little to no relevant data³. McKinsey reports that fewer than 1 percent of organisations consider themselves AI-mature⁴. The gap between ambition and readiness remains wide. 

Many organisations have rushed into advanced analytics and AI, lured by the promise of insight, without sufficient thought to the data foundations that make those promises deliverable. At the same time, regulations like GDPR have rightly pushed organisations to take data more seriously. We have seen a stronger emphasis on privacy, security, and governance, and while this has improved some practices, it is still a long way from embedded maturity. Compliance has helped bring focus to data governance, but the shift now needs to be towards a mindset that treats compliance as just one part of a broader, balanced approach, one that enables both strong foundations and the advancement of long-term value. 

We need to go further. 

One compelling example of transformation through data enablement comes from a police force that integrated AI into child protection systems. In just eight days, the force identified 123 additional children at risk, reduced referral time by 67 percent, and compressed data review cycles from 48 hours to just three. The AI-driven dashboard, built on clean, joined-up operational data, slashed time taken for profiling by 80 percent and reduced staff workload by approximately 35 percent⁵. A national body committed to improving transparency has also launched a central data hub, enabling comparative performance tracking across forces on key metrics like knife crime and violence against women, creating a new standard for accountability through shared data⁶. In parallel, the College of Policing introduced a new national framework for data ethics and data-driven technology governance in June 2025. These Authorised Professional Practices embed ethical oversight and lawful use into operational data use across UK policing⁷. 

Growth in data maturity is not about collecting more data or chasing complexity. It is about making better use of the data we already have. It is about aligning vision and strategy through purposeful enablement. That means enabling data to move meaningfully across the organisation, embedding ethical practice, evolving operational behaviours, reducing rework and inefficiencies, and building confidence in how data is understood and used. Only then can innovation be scaled in a way that is both safe and sustainable. 

Data enablement is not a side task, it is a leadership issue. 

Transformation leaders need to start by asking different questions. Not just “What technology do we need?” but “What problem are we solving?”, “What data do we already have?”, “How do we make it usable?”, “Who needs it?”, “Where is it misaligned?” and “Where are our processes, people, and data out of sync?”. Only then should the conversation turn to how technology and AI can support that, not the other way around. This shift leads to a more holistic and grounded approach to AI and transformation. It helps uncover gaps, surface opportunities, and ensures that organisations maximise the value of their technology investment while reducing risk. 

This is what a data-first approach looks like. It starts with curiosity. It requires organisations to look beyond tools and think critically about the flows, behaviours, strategy and structures that shape how data is created and consumed. It connects people, processes, and platforms. It invites greater collaboration across business, delivery, and policy teams, and demands a clear, shared understanding of value. It turns transformation into something inclusive, not imposed. 

Done right, data enablement allows organisations to scale effectively, without starting from scratch every time a new technology emerges. It creates resilience and reduces rework. It provides a clearer picture of what is happening, what is working, and what needs to change. And it creates the conditions for more trustworthy, explainable, and effective use of AI and automation. 

But without it, the risks are significant. Poor quality, misaligned definitions, and multiple copies of the same data are not just technical headaches. They lead to confused decisions, frustrated teams, and mistrust in the systems we are trying to modernise. Emerging technologies amplify the problem rather than solve it. 

The consequences of overlooking data are becoming harder to ignore. In England and Wales, inspectors estimate that over 280,000 crimes went unrecorded last year, and anti-social behaviour recording rates dropped to just 51.9 percent, leaving major gaps in victim support and operational response⁸. Parliament has warned that nearly a third of government systems remain outdated and poorly governed, undermining AI-readiness and basic operational resilience⁹. Most recently, data specialists raised concerns that the Treasury’s next fiscal plan could be compromised by ONS data gaps and inconsistencies that remain unresolved¹⁰. These are not isolated incidents. They reflect a wider pattern of underinvestment in data behaviour, governance, and leadership. 

If transformation is to be scalable and sustainable, data must be brought to the centre, not bolted on as an afterthought. It needs to be visible, trusted, and supported at every level, from strategic intent to operational delivery. 

Technology will always evolve-but without the right data foundations, no platform or product will reach its full potential.

The organisations that move forward confidently will not be the ones with the flashiest technology, but those that understand their data, take ownership of it, and use it with purpose.

If you are exploring similar shifts or facing barriers in your data journey, what reflections can you share? Collaboration and openness across sectors will be key to building the data foundations we all depend on.

If you’d like to continue the conversation, please feel free to email or reach out to me on LinkedIn.

References 

1. IDC. Worldwide Digital Transformation Spending Forecast to Reach $3.9 Trillion by 2027. BusinessWire, 2023. 
   https://www.businesswire.com/news/home/20231101754700/en/Worldwide-Digital-Transformation-Spending-Forecast-to-Continue-Its-Double-Digit-Growth-Trajectory-According-to-IDC-Spending-Guide 

2. Central Digital and Data Office, Transforming for a Digital Future update, 2024                                                                                           Transforming for a Digital Future – latest update – Government Digital and Data                                                                                                                               
3. Gartner via Forbes. Why 85% of AI Projects May Fail. Forbes Technology Council, 2024. 
   Why 85% Of Your AI Models May Fail                                                                                                                                                                                        
4. McKinsey. Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential, 2024. 
   AI in the workplace: A report for 2025 | McKinsey                                                                                                                                                                         
5. The Times. AI there, you’re nicked – Bedfordshire Police Use AI to Accelerate Safeguarding. January 2025. 
   https://www.thetimes.co.uk/article/ai-there-youre-nicked-tech-is-reshaping-how-we-fight-crime-jb9bv7qh3                                                                                       
6. GOV.UK. Policing Transparency Data Hub Launch, 2025. 
   https://www.gov.uk/government/news/home-secretary-announces-major-policing-reforms 

7. College of Policing. Authorised Professional Practice on Data Ethics and Data-Driven Technologies, June 2025. 
   https://collegeofpolicing-newsroom.prgloo.com/news/ai-guidance-and-authorised-professional-practice-app-ondata-ethicsanddata-driven-technologieslaunched 

8. Sky News. More Than 280,000 Crimes Went Unrecorded Last Year, Police Watchdog Finds, August 2025. 
   https://news.sky.com/story/more-than-280-000-crimes-went-unrecorded-last-year-police-watchdog-finds-13419715 

9. The Guardian. Government AI Roll-Outs Threatened by Outdated IT Systems, March 2025. 
   https://www.theguardian.com/technology/2025/mar/26/government-ai-roll-outs-threatened-by-outdated-it-systems                                                                    
10. The Guardian. Fears Grow Over Impact of ONS Data Reliability on Treasury Budget, August 2025.                                                        https://www.theguardian.com/uk-news/2025/aug/22/fears-grow-over-impact-of-ons-data-reliability-on-rachel-reeves-budget