Mapping an AI strategy out on paper (well, on-screen) is one thing. Making it work is a whole other matter. We looked recently at seven steps to AI readiness to help you get your enterprise in shape for what’s to come. Today, we’re focusing on execution: How to align your AI projects with organizational goals, engage stakeholders, and create a data-driven culture that sustains success. Key to this are robust governance, data literacy, and careful integration. Together, these will support you to move from plan to performance with minimal pain.
The execution gap in enterprise AI strategy
Executing your AI strategy isn’t just a technical task. It requires extensive stakeholder engagement across a range of business functions (something which is also crucial to successful API governance). Skipping this element can create a troublesome gap between your best-laid AI plans and what you actually achieve when executing those plans.
This means your AI roadmap will need to allow plenty of time for consulting, training, and generally ensuring everyone understands where you’re headed and how they can get on board. This will need to span a wide range of business functions, ensuring not only the implementers and users of your AI system are engaged but also all those who will be impacted by it.
Key drivers of AI success
In addition to cross-functional stakeholder engagement, the other key drivers behind turning your AI strategy into real, enterprise-level success will be clear alignment with your business strategy and measurable outcomes and KPIs.
The need to align your AI implementation with your business strategy and goals might seem obvious on the surface. However, a strategy that aligns doesn’t always result in an execution that does. Scope creep and adjustments made to overcome unexpected hurdles during AI integration can be enough to push your system out of alignment with your original intentions – and thus with your business goals. Your AI implementation will also need to be flexible and agile enough to shift alongside your business goals when they change. After all, the market isn’t going to stand still while you implement your AI system, nor once it’s up and running. This is why a well-structured AI value chain is important in ensuring your enterprise can adopt new models, adjust to new goals and integrate carefully with your business strategy as well as your technical infrastructure.
Measurable outcomes and KPIs can help ensure you stay on track with the execution of your AI strategy. Tyk AI Studio can help with this, enabling you to monitor usage, costs, budgets and performance of your AI implementation in real-time.
Your KPIs, as ever with KPIs, will be specific to your business goals, infrastructure and requirements. They can also encompass ethical concerns, which are crucial when implementing AI systems in line with established best practice. Model override rates, bias scores and audit outcomes are all good examples of ways you can use KPIs to track and improve your AI ethics program.
Building a data-literate, AI-ready culture
Data literacy has taken on significantly more importance in recent years, not least as AI adoption has grown. The need to understand how to interpret data and make informed decisions on it has increased hand-in-hand with the growth in volume of data available.
Moving from AI strategy to enterprise-level success requires an awareness of this. Enterprises need to promote understanding and adoption of data literacy within an AI-ready culture. Doing so means teams across the business will be better able to use data to inform decision-making at all levels.
This requirement brings up the fundamental need for training non-technical stakeholders. This is crucial for leveraging AI effectively across all business functions, rather than only within those traditionally considered to be technical teams. You’ll need to consider your training and tooling strategy in light of this, ensuring full support for your non-technical stakeholders to excel.
The role of API and AI governance
Governing your AI system in a manner that ensures control, security, visibility, and scalability is crucial to its success. Likewise, you’ll need to govern your APIs in a way that ensures all this – which is why API governance is at the heart of robust AI governance.
The right approach to governing your APIs, which feed data to your LLMs, control access to them and enable AI agents to communicate and share data with one another, is a key enabler of a successful AI integration. This is where the need for an AI-ready API management platform comes in – with it, you can manage, secure, govern and scale your AI APIs, with logging and auditing for full visibility.
Turn your AI strategy into enterprise-level success
We’ve explored all the elements of turning your AI strategy into enterprise-level success in our new ebook, AI, APIs and AI readiness. You can download it for free to discover the full AI success roadmaps and essential pointers you need to ensure your AI integration aligns with your business goals and delivers maximum benefit.
