How do you measure and communicate the value and impact of AI in your digital projects and initiatives?

Clear goals and metrics are needed to quantify and communicate AI's usefulness in digital projects. Goals should cover primary issues, predicted benefits, and AI system performance. This links AI strategy with corporate goals and communicates stakeholder value.

When it comes to measuring AI’s impact, I go for metrics that don’t just look impressive on a slide but actually speak to real, tangible outcomes. Start with Business Goals: I anchor all AI metrics to the business goals we set at the start. Efficiency gains, revenue growth, or customer satisfaction—whatever the aim is, make sure every AI metric tells a story that ties back to it. Use Real-World Examples: Let’s say we implemented an AI-driven compliance tool in fintech. I wouldn’t just say, “Our compliance errors decreased.” I’d share that this saved us X number of hours a month and reduced compliance costs by Y%. When communicating, I skip the jargon and go for a narrative that makes sense to everyone in the room.

Do your AI goals and metrics meet with your other goals and targets? Adding completely new goals without synergy to current ones creates definitely more work and need of resources instead of making things easier and more efficient. And this is one of the most used reason for using AI solutions at all!

Systematically track key performance indicators (KPIs) linked to business goals, and employ transparent reporting to stakeholders on AI's contributions to efficiency, innovation, and customer satisfaction, and ensures ongoing evaluation to highlight AI's strategic advantages.

Initiatives like the Tech Stewardship program are great learning tools for technologists and leaders alike. The programme encourages adoption of four key values including regeneration and inclusivity to ensure we all benefit from technology. Practical tools like a pre-mortem can provide a structured framework to example potential failure points or unintended impacts. I strongly advocate embracing diversity to strength test decisions. One of the simplest is De Bono’s thinking hats which can be done at an individual or group level to examine a decision from multiple viewpoints.

To use AI in digital initiatives, examine its risks and impacts. This includes evaluating the system's personal data processing, security, fairness, and biases. Early detection and resolution can reduce legal and reputational risks, boosting user and stakeholder trust.

Users and stakeholders must be involved in project design and evaluation to deploy AI effectively in digital projects. This involves asking for input, explaining AI's pros and cons, and giving people control or opt-out. This improves user experience, customer happiness, and social responsibility.

In order to use quality data as fuel for your AI solution you need to involve your stakeholders to make sure all data safety aspects have been considered. This is also great opportunity for co-operation.

In regards to internal stakeholders, it’s key to assign a chief sponsor as the primary point of contact/team lead. The chief sponsor manages all stages of the project cycle and communicates progress, milestones and challenges to senior management. The chief sponsor also plays a pivotal role in securing resources and budget from senior management for AI deployments. They are responsible for linking all internal stakeholders and SMEs together.

Showing AI's triumphs and learnings is essential to measuring and communicating its value in digital projects. This entails monitoring, testing, improving, finding issues, and adjusting the system. Showcase these accomplishments to boost credibility, innovation, and reputation.

Similarly, consider openly sharing tools and techniques in order to accelerate growth. For example the explosion of ChatGPT has been spurred on by many users open source approach to sharing what works.