How do I see ROI and cost efficiency per AI feature?
Per-feature ROI analysis. Feature Intelligence answers the question every engineering leader and CFO needs to answer: of all the AI features we're running, which ones are worth the cost — and which ones aren't?
Feature Intelligence answers the question every engineering leader and CFO needs to answer: which AI features are worth the cost? It classifies every tagged feature as Investment, Waste, or Needs Review, and shows exactly how much is recoverable.
What Feature Intelligence shows
Feature Intelligence is a per-feature breakdown of your waste classification. For each feature you have tagged with X-Cost-Feature, it shows:
| Field | What it tells you |
|---|---|
| Monthly cost | Absolute spend — how much this feature costs to run over the selected window (7/30/90 days) |
| Waste % / recoverable $ | For Waste-classified features, the estimated recoverable cost and its percentage of the feature's total spend |
| Average output tokens | Rough proxy for prompt/response size — useful for spotting bloated context |
| Model | Which model this feature's calls actually use |
| Classification | Investment, Waste, or Needs Review |
Three summary cards sit above the classification panel: Waste Identified (total recoverable $ across all features), Investment Protected (total spend on Investment-classified features), and Needs Classification (count of unclassified features). A fourth Cache Savings card shows exact + semantic cache hit rates and total savings.
Investment vs. Waste classification
Every feature in Cognocient is classified as Investment, Waste, or left in Needs Review until you decide. Cognocient starts with an automatic classification based on call patterns and task complexity. You can override any unclassified feature with one click — an "Investment" or "Waste" button on its card.
Classified as Investment
High task complexity, appropriate model, measurable business outcome. Spend here is justified — and the board should see it framed as investment, not cost.
- contract-drafting — complex reasoning, gpt-4o, measurable outcome
- code-review — high value, Sonnet, engineers use it daily
- customer-support-lvl3 — resolves complex tickets, proven ROI
Classified as Waste
Low task complexity on a premium model, high volume with no clear outcome, or low usage rate despite high cost. Recoverable with routing or removal.
- sentiment-analysis — gpt-4o on a task gpt-4o-mini handles perfectly
- welcome-email — 800 calls/day, template content, no LLM needed
- draft-preview — 90% of sessions abandoned, no outcome
If Cognocient classifies a feature incorrectly — for example, a feature your team considers strategic but that uses a simple model — click the classification badge and set it manually. Manual overrides are preserved across Cognocient's re-analysis cycles.
How to use Feature Intelligence for decisions
Planning an optimisation sprint
- Sort by "Waste %" descending
- Identify the top 3 features with >20% waste
- For each, click through to see Cognocient's specific recommendations
- Apply the routing recommendations for instant savings
- Schedule the deeper fixes (context pruning, model swap) for the sprint
Typical result: 20–35% cost reduction with one sprint of work
Board presentation on AI ROI
- Note which features are Investment-classified and their combined spend (the "Investment Protected" summary card)
- If a feature has a linked outcome, pull its cost-per-outcome from the Outcomes page to frame spend as investment
- Pair with the AI Efficiency Score on the Executive View — the real cost-per-outcome trend, not a synthetic score
Data-driven answer to "Is our AI spend justified?"
Deciding whether to shut down a feature
- Check the feature's recoverable waste % and $ in the Waste column
- If it's linked to an outcome, compare its cost-per-outcome against the cost of a non-AI equivalent
- Check call volume in the API Call Log — features with very few calls rarely justify their fixed overhead
- Mark as Waste if the data supports it
Evidence-based feature retirement decisions
Feature Intelligence does not compute a composite 0–100 "efficiency score" per feature. The one efficiency metric Cognocient reports — the AI Efficiency Score on the Executive View — is cost-per-outcome in dollars, not a weighted model-fit/waste/cache/error-rate composite.
Related articles
Waste Detection
Automatically identify retry waste, model mismatches, and context bloat.
Context Tax Analyser
Identify features paying static prompt overhead on every call — prime caching candidates.
Token Maxing Detector
Detect frontier models used for short outputs — tasks a cheaper model could handle.