How to measure the success of AI in your organization

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

However, not every AI initiative undertaken by businesses guarantees good results. In fact, according to research by Harvard Business School, those 80 percent industrial AI projects may fail to create tangible value.

Given the costs involved, this can easily lead to costly mistakes. Therefore, it is absolutely essential that businesses understand how to measure success or failure before starting.

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

Whether you’re just starting out or already have a few pilots going and are getting ready to scale, these can help you understand and refine your approach, giving you the best chance of success.

Align AI strategy with business strategy

First, it’s important to remember that to even have a chance to understand the impact of your AI projects, you need to know that they align with your business goals and objectives.

This may seem obvious, but I know from my own experience that it is often overlooked. Technology is never an end in itself. it is a tool for achieving goals.

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

I always recommend that businesses start by identifying as many opportunities as possible. Then narrowing that list down to a smaller, manageable number of long-term, high-value use cases, along with a few “quick win” initiatives that will allow you to quickly learn to develop solutions, experiment, and build trust.

This is the base. With a basic AI strategy in place, you can begin to assess and evaluate its impact and iteratively improve where necessary.

Basic Questions

These are the important questions every business needs to ask about its AI initiatives. We’ll go over how to answer them in the next section:

Is artificial intelligence helping us make better decisions? – We should understand whether the information we receive from artificial intelligence guides us towards achieving strategic goals, identifying opportunities and taking faster and more effective actions.

What is the buy-in level? – Our AI initiatives should encourage the cultural adoption of AI both internally within our organization and among our customers.

Do we use artificial intelligence to improve our customer experience and value? – From improving the quality of our products and services, to streamlining customer journeys, to lowering our prices and improving customer service. In short, do our AI projects make our customers’ lives better?

Does AI help us make measurable improvements and move toward our goals? – This helps us understand if AI is driving the success we want. Progress here helps us know that AI is aligned with strategic goals.

Are we delivering AI projects on time and on budget? – It is important to demonstrate effective planning and resource management around AI projects.

Is AI helping us achieve our ESG goals? – Last but certainly not least, do AI initiatives allow 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 are rapidly moving towards a world where they are not just ‘nice’!

Measurable Success – Vital KPIs for AI projects

The key to making sure we can answer these questions accurately is tracking the right metrics and indicators. Some of the key data points to monitor are:

Return on Investment (ROI) – Simply put, your AI initiatives or projects must deliver benefits that justify the cost.

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 advocate scores, and social engagement scores are all metrics that can indicate how your AI projects are impacting the customer experience.

Time-to-Value (TTV) – 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 do. Some examples of these metrics are precision, accuracy and recall, F1 rating, ROC AUC score and Matrix of confusion ratings.

Operational performance metrics – measuring the impact of AI on the amount of waste generated, man-hours per task completed, product defect rates and production costs per unit.

Building a culture of continuous evaluation and iterative improvement

We’ve identified key questions and the metrics we need to answer them, but this is just the beginning of the journey.

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

Another priority is the continuous evaluation of new technologies, tools and models as they become available almost daily. We are in the early days of the AI ​​revolution, and the tools available tomorrow will make today’s tools look like child’s play. It also needs to be measured and evaluated as to how the efficiency gains that can be achieved by moving to new and updated tools are offset against the costs and challenges of upgrading.

There should also be processes for sharing the results of your assessments. Ensuring stakeholders are informed about AI’s successes and, just as importantly, its failures is crucial to building trust and enhancing responsiveness. This openness and transparency is an important part of developing the positive AI 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 continuous, continuous improvement, which is essential for success.