There’s plenty of discussion around AI strategies, but the real question is whether you can have an AI strategy without a solid data strategy?
Setting up your data and AI strategy
Some argue that AI strategy should be built on a well-defined data strategy as data is needed to make AI work, while others see AI strategy as encompassing data needs within it. In fact, the more important sentiment is understanding that both need to be defined by your organisational goals.
Whether it’s driving efficiency, enhancing decision-making, or freeing up resources for high-value work, you must ground your data and AI strategy in your goals and challenges, incorporating practical actions that deliver value to your organisation.
When you’re defining your data and AI strategy, using a data-driven framework can really help.
At Transform, we recommend a top-down, bottom-up approach that teases out the practical and tangible actions that need to take place, keeping your goals and strategies in mind by asking what you’re trying to achieve.
Are you trying to attract new customers, deliver a better user experience, improve decision making etc?
Your answers will more easily define what the bottom-up approach needs to achieve across the foundational levels, namely data and technology. You’ll then need to work on the enablers – people, process, systems and AI — and from there, you can narrow down what the required changes are that need to happen to deliver the desired benefits.
This framework helps to identify and prioritise the right use-cases for tech, data and AI for value-driven outcomes.
It’s worth noting that, when you’re building your data and AI strategy, in addition to traditional data, people, process and technology components, you need to consider that outcomes need to be compliant and adhere to known regulatory and security requirements.
The benefits of having a Data and AI strategy
A good data and AI strategy enables the effectiveness and efficiency gains promised by AI, such as:
- Making faster, better decisions: like when we helped Historical Royal Palaces write a digital and data strategy that allowed them to be bolder when bringing people to palaces and palaces to people.
- Using AI to do repeatable, mundane tasks, freeing up resource time to do more valuable work: like the work we did with DfE, helping to automate procurement processes for schools.
Don’t forget to measure your success
The other component (often forgotten) is defining success and outlining the measurement framework for your data and AI strategy. What are you going to measure? How are you going to measure it? What limitations exist today and what new variables will you need to predict your success?
Defining what success looks like and establishing a measurement framework ensures that results aren’t just theoretical but tied to real gains. After all, you don’t want to miss the opportunity to tell your stakeholders that “this initiative saved X% time or Y£ or delivered Z% increase in engagement because our approach made us faster to serve”
Everyone is talking about data and AI, but the real benefit is in the value they deliver for your people — making customer experiences better, being faster to serve, and being more efficient when it comes to operational process.
Data readiness isn’t just about having data. It’s about making sure it serves a purpose. Without that clarity, an AI strategy is just an idea, not a driver of value.
- Data & AI