The ROI of AI
AI initiatives rarely collapse in obvious ways. There’s no single moment where a model “breaks” or a system stops functioning. Instead, AI projects tend to drift. A pilot shows promise but never scales. A custom tool technically works but sits unused. Employees adopt their own solutions while leadership struggles to connect investment to outcome.
In these situations, AI becomes background noise—running, consuming resources, and generating activity without delivering clarity. The models perform. The infrastructure exists. Yet when decision-makers ask what value the organization is actually getting, the answer is often uncertain.
This quiet failure mode is what makes determining AI ROI so difficult. It’s not that AI can’t create value. It’s that value is hard to see unless it’s deliberately defined, measured, and reinforced. For many leaders, the real challenge isn’t deciding whether to invest in AI, but understanding how to prove that those investments are working—and worth expanding.
In a recent webinar, Dr. Tim Oates, Co-founder and Chief Data Scientist at Synaptiq, explored ways you can determine the return on investment of your company's AI investments.