Measuring AI Adoption and Impact

Introduction

As AI continues to reshape different industries and find innovative applications, there is an increased focus on developing AI-powered systems.

However, it is important to look beyond the initial enthusiasm to ensure that those AI initiatives get adopted and generate tangible business impact.

Yes, we are talking about the return on investment (ROI). To truly understand ROI and overall impact, it’s essential to measure both the adoption and effectiveness of these AI systems.

This article will explore key metrics and strategies to assess AI adoption and its broader impact on an organization.

User Adoption

Let’s consider launching a generative AI smart assistant. One of the suggested ways to measure adoption would include the following.

How many users started using it; let’s say, within the first month, first quarter, and so on? In a way, it is important to note how many end-users are onboarded onto the platform. Following up on this, the next metric to track would include how frequently they use this assistant. While onboarding could happen only by starting to use the new smart assistant, but when you monitor the rate of increase in the number of times the users are referring to this assistant as compared to what they were using before the launch gives a signal that they are finding the model effective to help in their daily operations.

For example, ever since the launch of chatGPT, how many times have you resorted to Google Search? It might not have been the same in the early days as it is now. You’d have started experimenting with ChatGPT and explored its efficacy. Over a period of time, when you find it is helping you in your work, you might have noticed a trend towards its increased usage.

Time and Cost Savings

One of the ways to assess how sophisticated and mature your development cycle is to measure the time to launch. If earlier projects used to take 6 months from ideation to launch, how have they improved over time, maybe 2 months?

Such an efficient development cycle reflects in savings of person-hours — a quick math will provide a view of cost savings.

Let’s say this assistant could resolve internal teams’ queries — in that case, it would be good to measure how it has helped them resolve their queries faster than the previous approach. Maybe, the teams’ efficiency increased by 30% within 2 months of launch so much so that the reduced time spent on routine queries ended up freeing 15 hours per week per team member. This would be a great way to show the impact of adoption.

Return on Investment

AI implementations are very expensive and demand huge investments, especially generative AI projects. Going by the scientific nature of AI projects, we know that it is likely not all AI initiatives will be successful or generate sufficient returns that justify the cost associated with them.

Therefore, it is crucial to maintain a cost and impact sheet to keep assessing the project’s ROI during each phase of the project. Even if initial project feasibility during proof of concept signals the viability of the initiative, the project dynamics change very rapidly in AI. Keeping a check on the ROI is a must for maintaining a healthy P&L.

Training Effectiveness

Not all AI projects are built from scratch or started as a greenfield project. In most cases, there is an existing process through which those decisions are being taken or certain activities are being carried out.

So, the point here is that if an AI model is trained and validated successfully, it gets integrated into existing workflows, replacing the existing ways of working. This implies change, which by its very nature, raises the need to understand and learn the new processes.

In pursuit of seamless integration of new tools and processes, organizations conduct training sessions. As an impact metric, gauge how many training sessions were conducted since the launch and with what attendance rate.

The more the number of team members joining these sessions, the higher the likelihood of successful adoption of the new tools and processes. A strong attendance rate reflects employee engagement and readiness to embrace change.

Now that we are talking about training, just the attendance rate might not give a full picture. Your next metric should be to assess how many such attendees started demonstrating improved proficiency in using the AI system. For example, 95% of users demonstrated proficiency in using the AI assistant within 2 weeks of training.

Going into the details to check which features are more relevant, you can also assess how many features out of, let’s say 10 key features, are used more regularly. Such analysis helps you understand the widely adopted features.

Error Reduction

AI models are known to perform the computations at scale, taking into account several variables impacting the phenomenon. Their ability to identify patterns makes them highly effective, both in terms of accuracy and speed. To assess their impact, evaluate whether there’s a reduction in errors during task execution. Additionally, check if the time required to complete tasks has decreased.

For example, data entry errors decreased by 70% through AI-assisted data validation or time spent on data cleaning was reduced by 50%.

Wrapping Up

 
These metrics demonstrate the model’s ability to deliver measurable impact through the successful implementation of AI technology.

No handbook comprehensively covers the metrics-based approach to evaluate the impact of AI projects. Through this post, I have aimed to share my experience building transformative AI solutions while delivering the impact. I look forward to hearing how have you measured the value of AI projects.
 
 

Vidhi Chugh is an AI strategist and a digital transformation leader working at the intersection of product, sciences, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, an author, and an international speaker. She is on a mission to democratize machine learning and break the jargon for everyone to be a part of this transformation.