From ROI to ROAI – Realizing the Benefits (and Possibilities) of AI
Embracing the AI Imperative:
The integration of AI into business operations is becoming a strategic imperative, with a recent MIT study showing that 76% of businesses have conducted at least one trial with generative AI as of the end of 2023. At this point, it’s hard to talk to someone who isn’t aware of the interest and excitement in AI, specifically Generative AI. One only needs to casually look at the current tech landscape to see that almost all technology companies are touting their work with AI across existing and new solution categories. It’s becoming pervasive. And this pervasiveness will soon spread across the broader enterprise business landscape.
Echoes of the Dot-Com Era:
Unsurprisingly, there are some similarities to how things ‘looked’ and ‘felt’ in the mid-to-late 1990s during the dot-com period. And there are some lessons that can be learned from that period, too. One key lesson is that the ‘show me the money from the technology’ expectation will soon take center stage for businesses measured on top-line and bottom-line growth. Organizations will stop being considered on-track just by adopting and deploying AI solutions – in a similar way that just having an eCommerce website wasn’t the ultimate measure of potential (or success) in the year 2000. Businesses will need to tell a more robust story about what their investments in – and advancements from – AI are delivering. And they will also be expected to articulate how they will measure progress from ongoing investments. In specific terms.
Telling New Stories with Metrics:
For many, this will mean telling ‘new stories’. Supported by existing and new metrics. They will need to start measuring and highlighting the real impact of these investments in terms that resonate with key stakeholders. While there are multiple ways to measure ROAI, one approach RGA advocates is to start by identifying and measuring a select group of key efficiency and productivity metrics that underpin desired business outcomes. Some phase-one category examples, if tuned for AI, are things like ‘revenue per employee’ and ‘cost per order dollar.’ What makes these examples of good metrics? Because they can be linked to Activity Metrics.
Linking AI to Operational Performance:
Understanding how AI is being used to improve specific activities within the organization is crucial. For instance, AI might be enhancing the efficiency of certain roles or functions, leading to the same number of people doing more of the right thing. Or to free-up some existing resources to focus on doing new things required to stay competitive. However, if you don’t know what to measure or lack the management systems to track and improve activity level detail, it will be challenging to draw a clear line between embracing AI and improving business operational performance.
Identifying the Metrics That Matter:
At Revenue Growth Associates, we emphasize the importance of identifying the “metrics that matter” to gauge the success of AI initiatives. By focusing on activity metrics, which in turn directly impact leading indicator metrics, and then flow to lagging metrics, organizations can navigate the complexities of AI integration and ensure that their investments lead to tangible improvements in revenue, profitability, and customer satisfaction.
Connect with Us:
Want to learn more about identifying, measuring, and managing the metrics that matter for your organization? We’d love to connect and discuss our AI-Enhanced Analytics approach with you. Learn more about the full suite of Advisory Capabilities we offer at: https://revenuegrowthassociates.com/advisory/
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