Over a conversation with our CEO, Ciaran Cosgrave, we dug into Nearform’s point of view on AI, and why the future of enterprise tech needs a very different kind of partner.
Nearform partners with the world’s leading brands in complex or regulated industries, including financial services, healthcare, life sciences and the public sector.
In this conversation, Ciaran unpacks how AI can move beyond hype to drive real results by boosting profitability and security without sacrificing an excellent user experience.
Q: What’s the core challenge Nearform solves for leading organisations?
A: We’re laser-focused on business outcomes. It’s not about building tech for the sake of it. In complex, regulated industries trust and resilience will ultimately decide if AI goes live. Our edge is delivering AI and modern software systems that are fast, secure, transparent and tied to what really counts.
Our experts cut through the noise, navigating the tough stuff like governance and security that are needed to get beyond the proof of concept stage. For example, whether it’s automating Anti-Money Laundering (AML), personalising wealth management experiences, or modernising legacy systems to unlock customer data, we move quickly and securely to deliver real value in live systems, skipping those marathon transformation programmes that go nowhere.
Q: What’s the best approach for an enterprise to unlock AI? What does that look like on the ground?
A: The truth is, the best approach isn’t starting with AI, it’s starting with the business.
For example, so many banks have been told the myth, “fix your data, and one day, you’ll unlock AI”. But that’s not reality - it’s a recipe for endless spend, with zero outcomes - as by the time platforms are ‘ready’, priorities will inevitably have moved on and the business will be fed up.
That’s why we flip the script on introducing AI to critical workflows:
- Start with one value stream, ditching the platform-first mindset. We pick a real business process, like loan approval, risk, prospecting, etc, and take one thin slice end-to-end, customer to back office. The goal is to prove value (and build organisational confidence) in a focused area first, then scale from there
- Then go deep, not wide. We’ll dive in on one or two priorities, concentrating effort, rather than spreading it across endless pilots.
- For each slice, it’s about using the data you need and nothing more. You learn by doing in production, then scale what works.
This approach means creating cross-functional pods, to bring together experts across business, data, tech, risk and compliance functions. You want to create one blended team, focused on one customer outcome - think ‘mortgage approval’ or ‘pension onboarding’, owned end-to-end. That’s how we create speed, accountability and trust at the same time.
Q: Nearform works with clients across a number of regulated industries, such as banking and financial services. Can you share an example of what impact that work delivers?
A: Our work in financial services clusters in three areas:
Risk and remediation
In banking and insurance, we help accelerate massive AML and risk programmes. What used to need armies of people (scanning docs, closing KYC gaps) now gets done by AI, flagging only the real edge cases for humans. This massively reduces cost and time for each process, while elevating accuracy and audit trails. Just as importantly, it helps organisations scale these processes with stronger controls and clearer governance.
Wealth and asset management
We also help banks better serve high-net-worth individuals and different sectors like the emerging wealthy or teen savers. Banks have rich data on spending and life goals for each of their clients, and AI can help. It can surface more relevant next-best actions and prompt advisors to engage at the right moment - whether they’re about to start a family, buy a house or are learning about financial literacy and savings. Think fintech-like experiences, which are backed by the trust of a legacy institution.
Unlocking legacy data
Many banks are sitting on gold - decades of data - that’s locked away. Modernising used to be ‘too slow’ or ‘too risky’, but we can now upgrade or wrap legacy apps to turn that hidden data into AI-ready products, fast. That can deliver true ROI in just 12 months.
Although we keep client specifics confidential, these three challenges are prevalent and well-documented throughout the financial industry.
Q: What’s the number one misstep leaders are making in AI strategy?
A: We call it ‘the field of dreams’. That is, believing that if you spend years building a perfect data foundation, value will just ‘appear’. That’s not reality - in fact, all it creates is burned budgets and lost time, and by the time platforms launch, the goalposts will have moved.
Instead, leaders need to start with real business outcomes (like loan approval or fraud) and work backwards to the data they need. If investment isn’t mapped to revenue, cost or risk, it’s being wasted.
Q: With endless options, where should CTOs or CIOs focus their AI bets?
A: Pick one high-value workstream and go ‘all in’. Choose a commercial process, whether that’s mortgage approvals or credit decisions, and build an expert squad that’s obsessed with delivering results.
True leaders create focused pods that truly understand the data, context and compliance, inside out. That kind of deep focus brings speed and trust, and when you see the results, you can scale across adjacent workflows. Essentially, it’s about being excellent somewhere, not average everywhere. In practice, that means a small, senior, cross-functional team, working on clearly defined outcomes.
Q: What are the AI blind spots most executives fail to see?
A: Leaders often think they need to spend a fortune before they’ll see real gains, so they get sold on massive projects, like rebuilding global data platforms, with no real tieback to outcomes. Even worse, they miss how quickly AI can scale from pilot to production when trust and security are baked in.
Tech isn’t the real blocker, it’s the operating and delivery model: architecture, governance, engineering discipline, talent,and reporting lines. Leaders need to focus on unlocking existing value first, reviewing their ways of working and shift away from building for the sake of building.
Q: What tough decisions should leaders stop avoiding?
A: The hardest call is reorganising around outcomes that are built into products, not functions. AI works best when teams are cross-functional and centred around a specific product that delivers real value. This means upending traditional structures to ensure business, data and compliance are able to work as one united team.
That can be an uncomfortable leap, it’s needed to ensure that the AI journey is not throttled by structure or overhead, and can fully realise its potential to deliver meaningful value.
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