After nearly 15 years working with enterprises across financial services and data strategy – from Nationwide to Deloitte to Slalom – I've observed a consistent pattern. Most organisations have been wrestling with data for at least a decade now. They know it's a value stream, and here's the thing: their data often isn't the worst part of the equation. It's usually "okay enough".
Of course there is room for improvement, however the real problem? It's not connected. To the right teams or processes.
The biggest barrier I see across enterprises is ensuring businesses can understand where data plays a part in realising strategic objectives. You have lots of different teams trying their own data initiatives, but the challenge is getting it all joined up as an enterprise to realise the right value.
This isn't a technology problem – it's an organisational one. Most enterprises have smaller teams with smaller budgets, which means only smaller items get tried. Use case prioritisation needs a proper engine, linked to the business strategy, with clear understanding of the cost to implement the solution at scale.
What organisations need to focus on is understanding how their data challenges link with other business objectives. They need to ask: How can we make it faster? What can we deliver quickly? I'm not interested in a proof-of-concept that doesn't sustain – I want to identify the high-impact pieces we can deliver that are tied to real business outcomes.
Everyone's talking about AI opportunities, particularly with new technologies like Agentic AI. But here's the reality: if you want to implement change properly, you need the backing of the business teams that will be using it day-to-day. What we consistently find is it's easy to get a POC over the line, but it doesn't cover how to embed a solution within that team.
The key is joining different teams together so they're saying the same thing. Disparate data teams must adopt more of a product engineering mindset to implement solutions into processes better.
Every organisation has unique problems, but they also have about 60-80% shared problems. That's where experience becomes invaluable – being able to quickly identify these common challenges and address them systematically before they block progress.
Let's address the elephant in the room: legacy systems. Yes, they can be costly to move away from and decommission properly. But people are dying for more modern data systems that prioritise ease of capturing data and user experience.
The solution isn't always wholesale replacement. Modern systems need to be designed with the end user in mind, focusing on intuitive interfaces that make data capture easier and ultimately improve data quality.
There's also the literacy and upskilling challenge. We need to make sure people using data and AI understand what it's doing and why. The teams owning governance need to understand how things work and be able to explain it clearly. But we should be realistic – Gen AI is just the latest technology development and broad training to all isn't always going to be the answer. Focus on governance and provide resources for users to learn as they go and adopt solutions in a scalable manner.
After years of consulting across different sectors, I've learned that clients prefer consultants who understand their business rather than those who seem to be learning on the job. This is why I'm excited about joining Mesh-AI – our focus on specific industries like financial services and energy means we're not starting from scratch with every engagement.
We understand the regulatory environment, the business challenges, and the technical constraints that our clients face. This allows us to ask the right questions from day one and ensure we're involving the right people when implementing changes.
The collaborative approach matters too. It's about working with smart people on challenging problems for exciting customers – ensuring new team members buy into customer success and embrace a culture of continuous improvement and feedback.
The path forward isn't about collecting more data or implementing flashier technology. It's about fundamentally changing how we connect data initiatives to business strategy.
This means moving beyond isolated POCs to sustainable, embedded solutions. It means aligning budgets and teams around shared strategic objectives. Most importantly, it means asking the right questions upfront to ensure we're solving the right problems.
The enterprises that crack this alignment challenge won't just see better ROI from their data investments – they'll leapfrog their competition entirely.
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