Scoping AI Projects Around KPIs That Matter

A lot of AI roadmaps start with the tech: a new LLM drops, a vendor demo impresses, or someone asks about adding a chatbot. But technology-first thinking leads to solution-first scoping—and that almost always leads to:

• Mismatched stakeholder expectations
• Unclear ownership of success
• Output that doesn’t translate into action

AI systems should be scoped like any strategic initiative: around the outcomes they are meant to improve.

We use a consistent framework to help enterprise teams anchor their AI projects around measurable business value. Here’s how it works:

1. Define desired business outcomes
   What are you trying to improve? Revenue, retention, compliance, cycle time—this is your north star.
2. Identify the lagging indicators
   These are metrics tied to impact. Examples include claims processing time, manual hours, and decision latency.
3. Find the leading signals
   These are metrics you can observe quickly to validate early progress—user adoption, feedback quality, or routing accuracy.
4. Translate into delivery metrics
   What can a pod actually influence in the build cycle? Examples: average time to triage, summary accuracy, escalation rates.
5. Align on reporting cadence
   Decide early how and when metrics will be reported. Weekly visibility builds trust and reduces surprises.

Starting with outcomes doesn’t slow things down—it actually speeds them up.

Here’s what we’ve seen across industries:

• Faster stakeholder buy-in
  Everyone knows what success looks like.
• Reduced build rework
  Fewer pivots mid-sprint because goals are fixed.
• Shorter time to production
  Systems don’t get stuck in “almost ready” mode.
• Clearer ROI narratives
  KPIs tracked from day one build the story that leadership needs.

Audit Readiness Agent
Scope around time-to-document access, summarization accuracy, and gap detection success rate. 

Result: 50% faster audit cycles.

Claims Automation Workflow
Scope around reduction in manual touches, model-assisted triage precision, and workflow latency. 

Result: 70% automation of non-STP claims.

Inventory Labeling System
Scope around time-per-label, reclassification rates, and output-to-system sync latency. 

Result: 30x faster classification and 10% throughput increase.

If your roadmap is built around features instead of business results, you're not just risking wasted time—you’re risking trust.

We help enterprise teams align on what matters before the first build sprint.

→ Talk to a Turing Strategist