How are you measuring and demonstrating the ROI of your AI investments at scale?
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It is important to start with understanding the business value you expect to achieve with your AI investment - Revenue Growth, Employee Retention, New Product/Service/Market etc. Once you have this, you can measure and demonstrate ROI against these KPIs.
In HR we are just embarking on the use of AI in our operations. So we are not yet at scale and are at a "crawl" stage. Interested and following..
Brainstorm a usecase with customer in center. Build your AI strategies around it and measure results. If a simple customer usecase can be automated ..it’s a big win. That’s how the ROI on AI investments should be measured.
To address this challenge, we have implemented a model to measure the ROI of our AI tool across six key performance indicators: time savings, effort reduction in routine tasks like document preparation and email writing, work quality, creativity enhancement, overall well-being by removing routine tasks, and user satisfaction. We compared these metrics against a reference group to establish a clear benchmark.
In practical terms, our findings have been significant. For instance, our data shows that the average user of the copilot saves approximately 23 minutes per day. This translates into substantial annual time savings, which, when offset against the cost of the AI tool, results in significant financial savings per user. In the UK, for example, this amounted to a net saving of approximately 2,000 pounds per user.
This brings up an important consideration: what happens with the time that AI saves? Does it lead to employees working more on other tasks?<br><br>
That's a crucial aspect of our approach to AI implementation. Our goal is to enhance overall well-being and work-life balance. The time saved by using AI is intended to allow employees to focus on more critical and impactful tasks, rather than extending their work hours. This shift not only increases productivity but also improves job satisfaction by eliminating time-consuming, mundane tasks.<br><br>
It depends on the several factors and use cases, but key things and underlying results and outcomes depend on the accuracy of the model, how well it was trained and how the data has been injected or integrated with a streaming platform. I shall be happy to discuss for a paid consultation call.