For the board
Board report
The three questions a board asks every time — where’s the value, what to invest in, how you compare — answered from behavioral evidence across Northwind Global’s connected data.
AIDE Index
64.3Advancing
Employees
41800
AI agents in production
182
1
Where's the value?
$38,875,900
est. annualized labor value from AI activity
$58,400,000
new revenue attributed to AI go-to-market & sales
409,220h
human hours redirected
4,092,200
measured AI agent actions
228
live AI opportunities tracked
Value is two-sided: cost saved (observed agent actions, ≈6 min of human time each at a blended $75/h — not surveys) and revenue created — pipeline sourced/influenced by AI agents in go-to-market and sales, joined to closed-won CRM deals.
2
What investments are needed?
- AI Team Adoption (21/100 — your weakest)Facilities & Manufacturing sit at near-zero AI literacy — fund foundational enablement there first; it is the single biggest workforce-score drag.
- AI Team Literacy (46/100 — the frozen middle)Upskill the manager band across Finance, Legal, Operations & Supply Chain — it lags both the executives above and the ICs below it.
- AI Implementation (71/100 — a strength to press)You are strong here (71/100) — this is a lever, not a gap. Scale the top-performing agent patterns from Data & AI into the thin-adoption functions to compound the return.
- AI Advocacy (69/100 — a strategic bet)Leadership advocacy is already high (69) — fund an executive AI-advocacy program and external thought leadership to widen the moat against AI-native entrants while the lead exists.
Prioritized by impact against state-of-the-art AI-native practices — a mix of gap-closing, high-leverage strengths to press, and strategic bets. Not every priority is a weakness.
3
How do we compare to competitors?
71th percentile
vs the 480-company S&P 500 AIDE cohort
ServiceNow · 68Snowflake · 66HubSpot · 64Datadog · 61Twilio · 57
AI maturity — seniority × department (for CHRO / L&D)
| Technology | Data & AI | Product | Revenue | Operations | Finance | People | Legal & Compliance | Manufacturing | Facilities | |
|---|---|---|---|---|---|---|---|---|---|---|
| Executive | 54% | 75% | 66% | 44% | 39% | 38% | 50% | 43% | 9% | 5% |
| VP | 64% | 60% | 61% | 42% | 37% | 29% | 33% | 32% | 9% | 5% |
| Director | 72% | 68% | 58% | 51% | 46% | 42% | 47% | 42% | 6% | 5% |
| Manager | 58% | 63% | 52% | 43% | 36% | 33% | 34% | 32% | 6% | 5% |
| Individual contributor | 72% | 72% | 61% | 55% | 47% | 43% | 44% | 34% | 9% | 6% |
Greener = higher observed AI engagement. Red cells are L&D priorities — target AI-literacy programs there to lift the weakest areas.
Top AI use cases by function
| Use case (agent) | Function | Owner | Actions |
|---|---|---|---|
| Access Provisioner · crewai | Information Technology | Oksana Dubois | 46,869 |
| Deal Desk Agent · EMEA · crewai | Revenue | Felix Fernandez | 46,602 |
| Close Assistant · langchain | Finance | Priya Moreno | 46,572 |
| Experiment Runner · custom | Data & AI | Hugo Petrenko | 46,084 |
| CodeReview Copilot · NA · lindy | Technology | Dana Santos | 45,930 |
| Competitive Intel Agent · n8n | Marketing | Pablo Okonkwo | 45,906 |
| Expense Auditor · EMEA · custom | Finance | Elena Doyle | 45,889 |
| Expense Auditor · NA · n8n | Finance | Pablo Okonkwo | 45,206 |
| Lead Qualifier · NA · lindy | Revenue | Raj Nair | 45,084 |
| Drift Monitor · EMEA · crewai | Data & AI | Keiko Foster | 44,567 |
| Expense Auditor · lindy | Finance | Carlos Petrova | 44,522 |
| Forecast Builder · EMEA · lyzr | Finance | Noor Muller | 43,839 |
| Revenue Forecaster · custom | Revenue | Ingrid Zhang | 43,567 |
| DeployBot · langchain | Technology | Omar Patel | 43,560 |
| Territory Planner · EMEA · crewai | Revenue | Dana Santos | 43,536 |