Measuring GenAI ROI: Why 95% of Projects Fail and How to Fix It

The thesis in one line: If you never measured the baseline, you can never prove ROI.

Why most GenAI projects can't show ROI

The blocker is rarely the technology. The blocker is that the team ships a system, calls it a success, and three months later the CFO asks "what did we save?" — and the only answers available are activity metrics. Hours of usage. Demos completed. Models deployed. None of these are returns.

Worse, the expectations and the system are out of phase. Leadership expects a return on a calendar that doesn’t match how the system actually matures. The project gets defunded before it has a chance to deliver. The team writes off AI as overhyped. The cycle repeats.

What actually moves the needle

Three short observations:

1. AI should prove its value in business terms your leadership already trusts. If the metric can’t land on a P&L line, it’s a vanity metric — no matter how good the dashboard looks.
2. Set expectations around the business result, not an arbitrary calendar. Different AI systems mature on different timelines. Pretending otherwise is how projects get killed prematurely.
3. The win is measured in outcomes leadership can defend. A clean before/after on the metric that matters to the business is worth more than a hundred slides on what you built.

Who we are

Blackmount.ai works at the intersection of AI, Oil & Gas, and Supply Chain. Founded by Dr. Mehrdad Shirangi — Stanford PhD in optimization of complex energy systems, with career experience applying AI to oil & gas and supply chain operations, and Cisco’s former Head of LLMOps. We help enterprise teams ship production AI with outcomes leadership can measure.

If you have a GenAI initiative you can’t put a dollar number on, we’d like to hear about it. Reach out at enterprise@blackmount.ai.