AI Strategy: Essential Components

We’re in the middle of an AI bubble, and companies are scrambling to catch up. Reading through investor relations transcripts from FTSE UK and S&P 500 companies, you’ll see it everywhere—AI, GenAI, machine learning. But here’s the thing: it’s not as simple as slapping some AI into your business and watching the returns roll in.

In fact, Gartner predicts that by 2027, 80% of traditional enterprises will have at least one GenAI use-case in their core processes. But if it's that inevitable, what’s the real secret to getting this AI thing right?

It’s all about value-based outcomes. To get there, you need a solid, adaptable AI strategy.

Baseline AI Strategy

Jumping into AI without a clear purpose can be a recipe for wasted investments and frustration. The companies winning in this space aren't just using AI—they’re sizing up how best to adopt AI to solve real business problems whilst rapidly experimenting to determine scaling effects.

Getting the core tenets of any AI strategy isn’t as hard as it sounds, but it does need to cover key areas in great detail for viable success:

Ambition & Use-Cases: AI can supercharge your business—if aligned with your business objectives. It’s about finding use-cases that not only improve efficiency but also drive real value. Oh, and it's important to do this in order to truly understand your AI ambition level and risk tolerenace.

Data Utilization: AI is only as good as the data feeding it. Your strategy should prioritize organizing and leveraging data assets for effective model training and deployment. Here is when you would establish your data flywheel.

Tooling & Infrastructure: AI requires the right talent, tools, and infrastructure. A well-planned strategy ensures you’re investing in the right areas without spreading resources too thin.

Risk Management: AI comes with risks — bias, ethical concerns, data privacy, performance and costs. A strong strategy anticipates and mitigates these challenges from the get-go.

Cultural Transformation: Implementing AI isn’t just about the tech; it’s about shifting the entire organization towards data-driven decision-making and faster digital adoption.

Scaling Effects: AI needs to be thought through beyond pilot projects. A clear strategy ensures you can scale AI across your business, setting up the operational framework to run future AI initiatives efficiently and think carefully about certain scaling effects across the business.

I must emphasize, any well rounded strategy must include how to evaluate and react to AI risk as well as enabling factors. We are dealing with a technology which is largely stochastic and probabilistic in nature and comes with different levels of integration complexity. Specifically, if you’re not exact about what you want to achieve with AI and LLMs, such systems can produce troubling behavior. Risk management must sit at the center of any AI strategy.