Measuring AI's True Value
The ROI Question
After diving deep into the world of AI Agents, I'm taking a step back to address a fundamental question: How do you measure the return on investment for your AI initiatives?
Whether you're launching a proof-of-concept or scaling an existing solution, the ROI question inevitably surfaces. How do you know your AI project is ready for primetime? More importantly, how do you convince leadership, board members, and investors that this technological expedition is worth funding?
During my years leading aerospace transformations, I learned that having a clear, concise framework for measuring ROI isn't just helpful—it's essential. Let me share my approach.
What Business Outcomes Matter?
When evaluating AI investments, I focus on two primary categories of business outcomes:
Productivity Gains: Increasing efficiency and output of existing processes and employees. This is about doing more with what you already have.
Revenue Generation: Creating new products, entering new markets, or capturing more customers. This is about expanding your business in ways that weren't possible before.
The True Cost Equation
Understanding the full cost picture is critical for accurate ROI calculations. Here's what to consider:
These are the costs that I have found to be the most impactful across multiple sectors. Infrastructure costs are important, since you will be potentially ripping through a lot of data and using those GPUs and TPUs in the sky.
The model and development costs include such things as the pay-per-call API costs that many companies charge. This will depend on the number of tokens and calls you make. There are ways to manage those costs, including the use of caching common queries to save you some money.
The risk mitigation costs rely heavily on the industry you are in, the technical complexity of the use case you are implementing and the adoption risk due to the fact that no one wants to use this grand new tool. I’ll refresh your memory on the Three Hidden Costs of AI Implementation.
Capturing the Benefits
The benefits side of the equation is equally nuanced and often where organizations struggle with measurement:
By far the easiest benefit to measure is the operational and productivity improvements. The others can become a little nebulous unless you truly work to maintain a focused effort on measuring the impact.
Real-World Impact
The evidence for AI's value is increasingly robust. Brynjolfsson and colleagues found that AI assistance increased worker productivity by 15% on average in customer support roles, measured by issues resolved per hour.
Even more striking, this week's Exponential View column reported that teams using AI as a team member are three times more likely to produce top-tier solutions than traditional specialists working without AI.
The ROI Formula
With these elements identified, calculating ROI becomes straightforward:
ROI = (Net Benefit / Total Cost) × 100
Where Net Benefit = Total Benefits - Total Costs
This seemingly simple formula requires disciplined data collection and honest assessment to yield meaningful results.
Addressing the Long-Game Skeptics
You've likely heard claims that "getting the full economic value from AI will take 10-15 years." This can be particularly discouraging for smaller companies wondering if they'll survive long enough to reap the rewards.
However, this perspective misses a crucial point: AI implementation doesn't have to be an all-or-nothing proposition. The key is identifying "thin slice" implementations that deliver immediate value while building toward your long-term vision.
Many organizations I've worked with have achieved significant ROI within months, not years, by focusing on specific, high-impact use cases rather than massive transformations.
Your Turn
What metrics are you using to track AI ROI in your organization? Have you found certain benefits easier to quantify than others? I'd love to hear your experiences in the comments below.
Coming Next: The Vendor Lock-In Trap
As you build your AI strategy and measure its ROI, there's another critical consideration looming on the horizon: vendor lock-in. Next week, I'll explore how companies can implement powerful AI solutions while maintaining their freedom and flexibility. We'll look at the hidden costs of dependency, strategies for maintaining technological autonomy, and how to build an AI ecosystem that grows with you rather than constrains you.
After all, the true ROI of your AI investment isn't just measured in today's dollars—it's measured in tomorrow's options.
Until then, keep measuring what matters!