How To Measure AI Success In Your Organization

I believe that just about every business can benefit from AI by using it to create better products and services, drive efficiency in their operations, or improve customer experience.

However, not every AI initiative businesses embark on guarantees good results. In fact, according to research by Harvard Business School, as many as 80% of industrial AI projects can fail to generate tangible value.

Given the costs involved, this can easily lead to expensive missteps. Therefore, it’s absolutely critical that businesses understand how to measure success or failure before setting out.

Developing processes and systems for evaluating AI’s impact is essential, and in this article, I’ll highlight the questions you need to ask and the information you’ll need to gather.

Whether you’re just starting out or already have a few pilots underway and are getting ready to scale, these can help you understand and refine your approach, setting you up for the best chances at success.

Aligning AI Strategy With Business Strategy

It’s important to remember that to even have a chance of understanding the impact of your AI projects, you need to know that they’re aligned with your business targets and goals.

This may seem obvious, but I know from my own experience that it's frequently overlooked. Technology is never a goal in itself; it’s a tool for achieving goals.

This means first having a business strategy in place and then identifying opportunities to deploy AI in ways that will help you achieve it.

I always recommend that businesses begin by identifying as many opportunities as possible. Then, whittling that list down to a smaller, manageable number of long-term, high-value use cases, along with some “quick win” initiatives that will let you quickly learn to deploy solutions, experiment and build confidence.

This is the groundwork. With a basic AI strategy in place, you can begin assessing and evaluating its impact and go on to improve iteratively, where necessary.

Essential Questions

These are the important questions every business needs to ask about their AI initiatives. We’ll move on to how we answer them in the next section:

Is AI helping us to make better decisions? We should understand whether the insights we’re getting from AI are guiding us toward achieving strategic objectives, identifying opportunities, and taking quicker and more effective actions.

What is the level of buy-in? Our AI initiatives should be fostering a cultural adoption of AI both internally in our organization and among our customers.

Are we using AI to improve our customer experience and value? From improving the quality of our products and services, to streamlining customer journeys, to lowering our prices, to enhancing customer service. In short, are our AI projects making our customers’ lives better?

Is AI helping us make measurable improvements and progress toward our goals? This helps us understand whether AI is driving the success we want it to. Progress here helps us know that AI is aligned with strategic goals.

Are we implementing AI projects on time and within budget? It’s essential to demonstrate effective planning and resource management around AI projects.

Is AI helping us achieve ESG goals? Last but certainly not least, are our AI initiatives enabling us to improve energy efficiency, reduce waste, promote equality and diversity and support ethical practices? Stakeholders, including customers and investors, are increasingly prioritizing these factors, and we’re swiftly moving toward a world where they aren’t just “nice-to-haves.”

Measurable Success: Vital KPIs For AI Projects

The key to ensuring we can answer these questions accurately is monitoring the right metrics and indicators. Some of the key data points that you should be tracking are:

Return on investment: Simply put, your AI initiatives or projects need to be delivering benefits that justify the expense.

Adoption rate: What percentage of customers or employees are using your AI tools? A high score here means people trust your initiatives and find them useful.

Customer experience metrics: Customer satisfaction scores, churn rates, net promoter scores and social engagement scores are all metrics that can indicate how your AI projects affect customer experience.

Time-to-value: How long does it take for your AI projects to produce meaningful results?

Model accuracy and efficiency: Technical metrics allow you to judge whether or not your AI is performing the tasks you need it to. Some examples of these metrics are accuracy, precision and recall, F1 Score, ROC AUC score and Confusion Matrix scores.

Operational efficiency metrics: measuring the impact of AI on the amount of waste that is created, human hours per task completed, product defect rates and production cost-per-unit.

Building A Culture Of Ongoing Assessment And Iterative Improvement

We’ve identified key questions and the metrics we need in order to answer them, but this is only the start of the journey.

Becoming a successful AI-driven organization means continuously measuring progress, refining business and AI strategies in tandem and iteratively improving models and processes as both the organization and the world it operates in evolve.

Another priority is continuously evaluating new technologies, tools and models as they become available practically daily. We're in the very early days of the AI revolution, and the tools available tomorrow will make today's tools look like children’s playthings. How the efficiency gains that can be achieved from switching to new and updated tools are offset against the costs and challenges of upgrading also needs to be measured and assessed.

There should also be processes in place to share the outcomes of your assessments. Ensuring stakeholders are kept informed on AI's successes and, just as importantly, its failures is vital for building trust and fostering buy-in. This openness and transparency are an important part of developing the AI-positive culture you need for innovation to truly flourish.

By following these principles, businesses can begin to build a framework to accurately assess the impact of their AI initiatives. This will help them build a culture of ongoing, continuous improvement, which is needed for success.