[By Russ Miles]
Things move fast. When we wrote Digitalization for Financial Services, some things were clear. Taking a baby step approach and focussing on real, tangible and measurable value were obvious. Using modularity and coupling to set out your options just made sense. And using Wardley Mapping to explore your landscape was the tool of choice.
None of that has changed, but two things have seized the hearts and minds of many technology departments in the world’s Financial Services organisations that were not given the full exploration they now deserve: Platform Engineering and AI.
The hype AND potential is strong with this one: AI
Starting with AI first, it’s hard to underestimate the _potential_ that AI has for evolving and accelerating your digitalisation activities. What used to be difficult, if not impossible, for anyone to automate has become easier, as long as you can accept a little indeterminacy in the results if you happen to be relying on Large Language Models, i.e. you keep a human in the loop to check their homework.
With the advent of AI Agents, where not only are answers provided but tasks can be conducted, even more potential can be unlocked. Activities that took time, smarts and required interactions with multiple pieces of context in your organisation — think operational tasks — can now be augmented with AI agents to make them, in large part, simple and easy. However, once again, it’s important to have a human in the loop when unlocking this potential.
And potential is the key word. Right now AI is being oversold by almost everyone and their dog. Your digitalisation strategy will benefit across the board for experimenting with AI, but it will most likely take the form of augmenting your existing workforce — improving the habitat that your people work in, rather than replacing that habitat (and those people). Some tasks may disappear with time, as trust builds with your AI agents for example, but ultimate accountability and responsibility, the hallmarks of the trust that financial services organisations rely upon, will rest with your people for the foreseeable future.
Investing in AI and AI agents as an augmentation to your organisation now looks to be the smart move, and through your own Wardley Maps you can intelligently select the areas where you can experiment best with what AI can mean to your digitalisation. And that’s the other key word here: Augmentation.
Those that invest in using AI to remove will find themselves with an enormous amount of technical debt as they scrabble to replace those people when the promise of potential doesn’t quite play out. But if you choose to explore carefully how AI can augment to rethink your business processes, looking for more value from the combination of AI and your people, then that is where we suspect the gold in your Wardley Maps can be found.
Platform engineering: Intent over accident for speed at scale
Keep your product engineering teams focussed on their products, that’s the promise of an Internal Developer Platform. Maximise your development experience to enable “development and delivery flow”: how smoothly and sustainably value is delivered, end-to-end, through the system. And that system includes your teams, their communication, and the cognitive load they carry.
Building your own context-sensitive Internal Developer Platform is one way of supporting that all-important flow. The ROI is in terms of that achieving that flow at scale.
But an IDP is a product investment and as such is not a small step up. While you already have a developer platform, turning what you have into something that is intended takes time, money and so a real grasp of why you need one. Ultimately it’s a challenge of scale, with some additional features that really speak to regulated organisations.
And most successful financial services organisations run at scale.
This is why platform engineering, especially building an Internal Developer Platform as a product, has evolved rapidly into an important move for most FinServ organisations over 2024 and 2025. With some of the same goals as AI, an IDP centralises and standardises in ways that unleash the potential of your product engineering teams — your IDP augments and improves the habitat that your product engineering teams can be productive in. In 2025 and 2026 we can also expect to see more IDPs rolling out AI as another capability looking to reduce cognitive load even further on your product engineering teams and maintaining and accelerating your flow of value to production.
