16 Apr

AI in Financial Services: Moving Beyond Strategy and Toward Execution

Danny Ivatt

In our earlier blog we outlined how organisations must take a strategy first approach to AI adoption. 

However, too often strategy is seen as a document to be delivered and read by senior leadership rather than a way of thinking and guardrails to inform decision making. 

One of the key questions I ask teams when working with organisations is ‘how does your work contribute to realising your strategy?’ Too often individuals and teams don’t know, and a vague answer of work being either tactical or strategic, meaning short or long term is given.

In the rapidly changing world of AI it’s imperative that your AI strategy is able to translate directly into execution. 

A strategy first approach means that all work that happens within an organisation is strategic, meaning it contributes to strategic goals, whether small incremental improvements or longer term initiatives.

This blog is going to talk about some specific ways of enabling that, with recognition that AI driven innovation means thinking differently about how organisations must organise themselves to execute against strategic goals. 

What outcome are we looking for?

Taking an outcome-based approach here, our priority is to ensure AI initiatives are aligned with business goals. We need to create a strong thread from the organisation's strategic objectives through to every feature being delivered to production. 

This ensures we’re executing to deliver value from AI, while incorporating the unique risks and challenges of AI and innovation. It also ensures we’re reducing waste (in terms of time and resources) to execute on AI projects, compounding our return on investment. 

What problem are we trying to solve? 

The risks of AI-led innovation are different. Organisations often lack effective approaches to manage them, so translating their AI strategy into execution using a current paradigm is ineffective. We see this in several ways:

  1. Paralysis - Organisations are stuck simply because they don’t know how to act.
  2. ‘Strategic Programmes’ Large programme delivery in areas the organisation deems critical, but lacking an overarching framework to translate strategy to execution, ensuring that all work, big or small, contributes to strategic goals. 
  3. AI innovation in isolation - Lacking a cohesive enterprise architecture and not sufficiently anchored on strategic objectives. 

We want to propose a fourth way, an approach to execute against their AI strategy that:

  • Mitigates the right risks in the right way
  • Allows all initiatives, big and small to ladder up to strategic objectives 
  • Enables innovation but avoids technical and business debt,
  • Can adapt to the uncertainty in this space.

What you need to consider to execute on your AI strategy

It’s important to remember your operating model is downstream of strategy. There is not a one size fits all model, and it should be right for how you want to execute on your strategy. Orient your operating model based on outcomes, so you’re creating opportunities for discovery not solutions. This establishes a backlog of opportunities, instead of targeting AI at low level, tactical problems, thereby limiting its potential.

To avoid siloed AI, and thereby limited value and opportunity, create multi disciplinary teams at every level, charged with continuous discovery for the opportunities AI can be targeted at. This means a multi disciplinary group who own the strategy, and perform high level opportunity discovery, down to detailed problem and solution discovery by teams tasked with executing on an individual initiative. 

The real value in AI is building capabilities, not simply pointing the technology at particular problems. Delivering early value is important for buy-in from stakeholders, but maintaining a long-term vision is paramount. To do this, develop a north star objectives framework, detailing your vision, and use this to guide what you’re prioritising.

Finally, there is a tendency to think that an approach tailored to innovation and uncertainty is how the whole organisation should transform, in reality you need different models with different horizons and different skills. A Three Horizons model with teams who focus on pioneering AI, establishing production AI services and maintaining them for the long term is a pragmatic approach that incorporates the different skills and mindsets required at each stage.

The Importance of Executing Well

Taking your venture into AI beyond strategy and into execution allows you to break the paralysis. Instead of wondering ‘what do we do about AI?’, you can start to think ‘how do we innovate and add value but take into consideration our unique risks?’. 

Beyond the step change, the potential value to be derived is enormous. Consider the objectives as laid out in your north star framework - these can now be executed against. This ensures your AI initiatives ladder up to the strategic goals of the business. This approach allows you to rapidly innovate, but within the guardrails of what’s going to create long-term value for the organisation. 

Ready to Execute? Here’s What to do Next

There are four key things you should do to lay the foundations for executing in the right way: 

  • Establish a multi disciplinary group to own AI opportunities and outcomes.
  • The group should start with discovery to uncover thematic problems, align these to business strategy goals.
  • Create a vision for your organisation’s north star vision, detailing the measurable objectives and enablers. Keep this top of mind and continue to work towards that vision.
  • Get started sooner rather than later. You need principles but not every detail to start executing. Constant iteration ensures you’re adapting to the lessons you learn along the way. Maintaining a long term vision and objectives allows sensible iteration according to new information as teams plot the best way to achieve those objectives.

Discover how Mesh-AI Improved Reporting Capabilities to Avoid Multi-million Fines for this FS Powerhouse.

Why the Clock is Ticking for the Financial Services Industry to get their AI Strategies in Order.

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