"We've been treating data like a filing cabinet when it's actually a living system"
Ahead of Banking Transformation Summit Europe in May, Pometry CEO Alhamza Alnaimi sat down to discuss why turning data into real intelligence is still so hard for banks, what traditional systems fundamentally get wrong, and what every senior leader in that room should do differently this year.
Banks have more data than ever, yet insight is still hard to unlock. Why?
”Because we’ve been treating data like a filing cabinet when it’s actually a living system. Banks have spent decades building infrastructure to store data — but storing data and understanding data are completely different things. The information that actually drives intelligent decisions isn’t sitting in a single spreadsheet. It lives in the relationships between those data points, and critically, in how those relationships are changing over time.
Trying to understand a transformation programme from a snapshot is like trying to work out the plot of a movie from a single frame. It’s impossible. So the challenge isn’t collecting more data — it’s that the tools banks rely on were built for a static world. That is the gap we’re focused on at Pometry: giving organisations a way to see their data as this interconnected, evolving thing that drives important decisions.”
“Trying to understand a transformation programme from a snapshot is like trying to work out the plot of a movie from a single frame. It’s impossible.”
Alhamza Alnaimi, CEO, Pometry
Many risks and opportunities in banking sit in relationships between data points. Why do traditional systems struggle to spot those patterns?
”Existing systems were designed to answer questions about things. What’s the balance of this account? What’s the status of that project? But they were never designed to answer questions about the connections — what depends on what, how did that dependency change, and what broke downstream when it did.
Take something as fundamental as a large-scale transformation programme. A bank will have thousands of workstreams, hundreds of systems being migrated, and every one of those is connected. A delay in one workstream creates a resourcing bottleneck in another, which pushes your regulatory deadline and changes the risk profile of the entire programme. That’s not a spreadsheet problem. That’s a context problem. And by the time the impact shows up, it’s already cascaded everywhere.
The missing piece is time. That’s exactly why temporal context graphs change everything — they give you the full picture, showing not just what’s connected, but how those connections are changing across time so you can be proactive in your decision-making.”
If you were sitting in the audience at Banking Transformation Summit, what’s the question you’d most want someone to answer?
”I would want someone to answer the really uncomfortable question: why do 60 to 70% of all major bank transformations still miss the mark? After all the investment — the consultancies, the tooling, the programme governance, the delivery optimisation. That’s billions spent. So why is the failure rate still so high?”
If every senior leader in that room only acted on one thing this year, what should it be?
”Stop asking ‘how do we adopt AI?’ and start asking ‘does our organisation have the layer that AI needs to be trustworthy?’ Everyone’s focused on the models — which LLM, which co-pilot, which automation. But the models are a commodity. What’s not a commodity is the context that institutions operate on. What makes JPMorgan JPMorgan? What makes HSBC, HSBC?
If your AI doesn’t understand the relationships inside your organisation — who owns what, what depends on what, how things have changed and why — then you’re just flying in the dark. The banks that will win aren’t the ones that deploy first. They’re the ones that give AI the richest, most accurate, most time-aware picture of the organisation.”
“Stop asking ‘how do we adopt AI’, and start asking ‘does our organisation have the layer that AI needs to be trustworthy?’. The model doesn’t matter if the context is entirely wrong.”
Alhamza Alnaimi, CEO, Pometry
In five years, how will AI and data intelligence change the way banks detect risk and make decisions?
”In five years, banks won’t just use AI. They’ll operate as intelligent, self-governing systems. And that distinction is very important. Today, AI is a tool you point at a problem — a co-pilot. But tomorrow, the entire organisation will run on a living model of itself: a constantly updating graph of its people, processes, risk obligations and decisions. Humans and AI will work within that shared model — each seeing the full context, each able to trace why a decision was made, what it’s connected to, and what has changed since.
That shifts things from detection to anticipation. Decisions made inside the enterprise will be context-rich, accurate and current — at machine speed. Something that’s genuinely impossible today.”