Cinder
Each quarter, drop your internal AI initiatives into Cinder. It ingests usage, cost, and revenue attribution data, then produces an evidence-based recommendation: shut down, embed into existing delivery, or keep investing - with the math your CFO will accept.
COOs, partners, and product leaders at mid-size consulting and tech firms running 3-10 internal AI projects at once
- Quarterly review pack comparing each AI initiative's full cost (compute + people) vs revenue/usage impact
- 'Embed, sunset, or sustain' recommendations grounded in usage data and revenue attribution
- Anonymized peer benchmarks showing how similar firms have handled comparable AI investments
- Manager-ready writeups that explain the math in plain English for board-level conversations
Lena's story is universal: a flashy AI platform loses $178K a quarter while an unglamorous delivery system quietly carries the business; every mid-size firm is about to have this conversation with their board.
Gartner IT Symposium/Xpo 2024 flagged AI ROI measurement as a top theme, and multiple frameworks are actively being published for measuring AI project value — strong signal boards are demanding exactly this kind of evidence-based AI portfolio review.Measuring AI becomes key pursuit as enterprises seek ROI - TechTarget ↗AI ROI Measurement: Complete Framework & Real Results 2024 - Manyforce ↗
Adjacent categories exist (SaaS rationalization via Flexera/CloudEagle.ai, enterprise application portfolio rationalization via Cognizant/AWS) but none appear purpose-built for 3-10 internal AI initiatives at mid-market consulting/tech firms, leaving room for a focused tool.Optimizing Application Portfolio Rationalization with AI - Cognizant ↗Optimize SaaS Costs with CloudEagle.ai's App Rationalization ↗
Willingness to pay is plausible — AI-powered rationalization guidance already claims up to 30% waste reduction, and a tool saving $178K/quarter easily justifies $20-50K annual pricing — but no direct pricing benchmarks exist for a mid-market AI initiative decision tool specifically.The Modern Guide to AI-Powered Software Rationalization - Licenseware ↗AI Won't Kill Consulting — But It Will Kill Firms That Fail to Adapt - LinkedIn ↗
BCG and McKinsey 2025-2026 outlooks describe an emerging 'AI-first' operating model with agentic AI reshaping platforms — the volume of internal AI projects and the pressure to rationalize them is structurally increasing, not a passing trend.Rebuilding Asset Management for an AI-First World - BCG ↗AI's edge in asset management - McKinsey ↗
Buildable but not trivial — requires multi-source data pipelines (usage telemetry, cloud cost feeds, CRM revenue attribution), a financial attribution model, and a defensible recommendation engine; complexity bumps it down from average.How to measure AI ROI in enterprise software projects - DX ↗Enterprise AI Implementation: Patterns, Frameworks, and ROI Metrics - Cybernative ↗