Defining success: The first step in any AI use case

We’ve been speaking with many organisations that are experimenting with AI and starting to think about how to scale beyond the first few use cases. One of my favourite questions to ask is, “Did your initial AI use cases deliver the value you expected?”

To confidently answer that question, you need to have clearly defined what success looks like upfront and some mechanisms to measure it over time. Without that foundation, it’s incredibly difficult to evaluate impact or determine where to go next.

One critical aspect that often determines the perceived success or failure of an AI project is the clarity and precision with which success measures are defined before prioritising and selecting the AI use case. In this blog, we’ll explore what this looks like in practice.

1. Linking to AI and business strategy

Choosing the right AI use case should start with understanding your organisation’s broader AI or business strategy. This ensures that AI efforts aren’t treated as standalone experiments but are meaningfully connected to long-term goals.

For example, if your strategic focus is improving customer experience, then AI use cases like intelligent chatbots or personalised recommendations should take priority. Aligning use cases with strategic objectives ensures your AI investments deliver real business value.

2. Guiding use case selection

Success measures act as a roadmap for selecting the right AI use case. They provide specific targets and benchmarks that guide the decision-making process. This guidance helps in prioritising use cases that align with your organisation’s strategic objectives and have the highest potential for impact.

Let’s say your goal is to reduce customer service response time by 50% by the end of the financial year. That target immediately narrows your focus to AI solutions that optimise customer interactions. Defined success measures bring clarity to use case selection.

3. Tracking success measures

Tracking success measures is equally important as defining them. Continuous tracking ensures that your AI project remains aligned with its goals and can adapt to any changes or challenges that arise.

Regularly monitoring key performance indicators (KPIs) allows teams to make data-driven decisions, optimise performance, and demonstrate progress to stakeholders. This tracking helps in maintaining momentum and ensuring that the project delivers the expected benefits.

And, while the framework stays the same, what you measure will vary depending on your industry or use case. For instance, retailers might measure the revenue uplift from AI-powered recommendations or inventory accuracy, while financial services organisations might benchmark on risk reduction via fraud detection or compliance automation. Customising success measures to your industry or business area ensures relevance and buy-in.

5. Strengthening communication

Clear success measures enhance communication among stakeholders, including developers, project managers and business leaders. They provide a common language for discussing progress, challenges, and achievements. This shared understanding simplifies reporting and ensures that everyone is on the same page, reducing the risk of misunderstandings and miscommunications.

6. Building stakeholder confidence

AI projects with clear success criteria are easier to back. Stakeholders, whether internal sponsors, executives or external partners, are more likely to support initiatives that have measurable goals and a plan to track results. This transparency builds trust and can be the difference in securing the funding or buy-in your project needs.

7. Realising benefits

Tracking against success measures helps your organisation to clearly identify the value an AI solution is delivering, whether it’s cost savings, efficiency gains, revenue growth, improved customer satisfaction or something else.

By monitoring these metrics over time, you can validate that the project not only met its original goals but continues to create long-term business benefits.

8. Identifying roles for measuring and reporting

It’s critical to assign dedicated roles or teams responsible for tracking and reporting outcomes. Whether it’s a product owner, a data team, or a cross-functional working group, someone needs to own benefit realisation.

This includes collecting data, analysing performance, and communicating findings. Clear ownership ensures that progress doesn’t stall and that learnings are fed back into future use case planning.