In a previous post, we explored the investment side of return on investment (ROI) in AI within enterprises. Now, it's time to shift our focus to the pivotal aspect of measuring the return. The essence of ROI is not just about deploying AI solutions, but also understanding their impact on your organization's operations and goals. Let's dive into how you can effectively measure and optimize the return on your AI investments.
Rolling out an AI solution in your enterprise has to be viewed from the perspective of a true internal product. And any product person will tell you that your product will only be as good as the data you collect.
To measure ROI, data and analytics are your best friends. Deploying a large language model (LLM) or any AI tool isn't the endpoint; rather, it's the start. Understanding how and where it's utilized truly defines its value. It's crucial to get insights into which teams are using the tool, for what purposes, and what tangible outcomes it drives. Just as importantly, that data will tell you where your tool is struggling, allowing you to take another pass.
To effectively measure AI's impact, it's useful to categorize solutions as either vertical or horizontal. This classification provides a framework to assess ROI specificities depending on the nature of the solution implemented.
Vertical solutions are designed to enhance a specific, critical function of your business. Typically, they serve a concentrated group within your workforce. The transformational nature of these solutions makes their impact more straightforward to measure.
For instance, these solutions aim to improve metrics directly related to their function, such as increasing support ticket velocity, enhancing manufacturing output, or boosting software development productivity. The simple formula here is:
People * hours * “things”/hour * = Total number of “things”
"Things" could be any measurable unit relevant to your business activities. By tracking the increase in efficiency or output, you can clearly see the return on all your efforts.
On the other hand, horizontal solutions have a broader albeit more modest day-to-day impact. These solutions, like generative AI knowledge management solutions, touch nearly every employee and can enhance overall efficiency. One study found that workers spend ~20% of their time looking for information. Even minor gains—like a 5% time savings—can accumulate to create substantial value across the enterprise.
Because of their diffuse nature, horizontal solutions can be challenging to measure. Here, adoption is your north star. No one likes spinning their wheels finding data, writing boilerplate emails, or coming through the backlog of old email. High adoption rates indicate that the solution is integral to daily activities and suggest a correlation with increased efficiency. Ensuring widespread use and collecting feedback to improve usability are crucial strategies to maximize their impact.
To truly optimize your AI ROI, continual evaluation and iteration are essential. Use analytics to revisit and refine the ways your organization uses AI. Encourage cross-departmental communication to spread successful use cases and provide training to enhance AI literacy across teams.
Ultimately, measuring and optimizing ROI isn't a one-time task, but rather an ongoing process. By focusing on analytics, categorizing solutions, and nurturing adoption, your organization can recoup its AI investment and thrive with enhanced efficiency and innovation.
Remember, the goal is not merely to implement AI. You want to integrate it meaningfully into the fabric of your enterprise to drive substantial, measurable value.
If you're ready to get more value from your AI investments, start using these strategies today. Need help? Contact us to learn more about improving AI ROI in your business!
