Top Reasons AI Projects Fail #2: Undefined Success Metrics
November 20, 2025 | 4 Lecture minute
Many AI teams launch projects without ever deciding what success actually means. A prototype goes live and everyone celebrates the demo. However, when procurement or leadership asks for ROI, there’s no clear answer. You’ll hear phrases like “promising signals” or “strong user interest,” but nothing that proves value.
In this part of our Top Reasons AI Projects Fail series, we’ll look at why undefined success metrics lead to wasted effort, and how to build measurement discipline into your AI workflow from the start.
Why AI Projects Fail Without a Scoreboard
AI without measurement is like a game without a scoreboard. There’s activity, but no way to tell if you’re winning. When outcomes aren’t defined early, teams default to subjective measures of success that sound positive but mean little to the business.
Devlin Liles, CCO, Improving
Undefined metrics make it impossible to align stakeholders, justify investment, or guide iteration. The project may look active, but it’s not actually moving a measurable business lever.
Why This Happens
Teams skip defining metrics not because they don’t care about impact, but because it feels secondary to the technology itself. The excitement to build often overrides the discipline to measure.
Unclear business intent. The team starts with “what we can build” instead of “what we must improve.”
No shared KPI ownership. Product, data, and business teams measure success differently or not at all.
Manual, inconsistent evaluation. Impact is measured anecdotally rather than being instrumented into the workflow.
Fear of accountability. Without a baseline results can’t be scrutinized, so some teams avoid defining one.
This leads to projects that sound exciting but fail to demonstrate tangible progress.
How to Prevent This Failure
Success must be defined before sprint one. AI projects thrive when KPIs, baselines, and measurement cadence are built into delivery just like code or models.
Lock KPIs Upfront. Identify the single most important business metric you’re paying to move. Then, define the guardrails for quality, risk, and compliance.
Baseline and Target Early. Establish the current state and the improvement goal before development begins. Every sprint should show movement toward that target.
Instrument the Workflow. Build telemetry directly into the system so ROI is automatically captured. Avoid manual reporting that introduces bias or guesswork.
Visualize the Impact. Create a simple dashboard (often just a few PowerPoint slides) that displays key metrics, owners, and measurement cadence.
Review Metrics in Every Sprint. Treat ROI visibility as part of your development rhythm. At Improving, if we don’t measure it, we don’t build it.
Define Success Before Expansion. In one client project, we implemented a sales email assistant with a clear KPI: uplift in reply rates. Secondary metrics included hallucination rate and legal compliance. With telemetry embedded in every message, we could prove impact and expand confidently.
When success is visible, teams stay aligned and sponsors stay engaged.
Key Takeaways
Undefined success metrics turn AI from a strategic investment into an expensive experiment. Defining, instrumenting, and tracking outcomes from day one ensures your AI projects prove their value and earn the right to scale.
Establish clear KPIs before development begins.
Baseline, measure, and target business impact continuously.
Automate ROI tracking through telemetry and dashboards.
Review progress during every sprint.
Expand only once measurable success is achieved.
Continue Your AI Journey with Improving
Measurement discipline separates pilots from production success. To build AI initiatives that prove their worth, check out our resources:
Ready to take the next step toward your goals? Reach out to us to get started or to speak with one of our experienced consultants.
