Article by **Chase Covington**, **Tomer Slaney** (partners, New York) and **David Pralong** (senior partner, Charlotte), published by the *McKinsey Transformation Practice* in **September 2026** (exact day unspecified, dated the 1st by convention); 8 pages, 2 exhibits whose content is not reproduced in the text. **(1)** Finding: the transformation office (TO) is slow to modernize its own tools; in a turnaround involving **3,000** employees, **more than 15** people produced a **300-page** report every four weeks. **(2)** Three pillars entrusted to agents: *performance intelligence*, *single source of truth*, *conversation facilitation*. **(3)** Reported gains: **35 to 40%** of time on core tasks, sometimes **70%**; a central team reduced from **ten to three** people. **(4)** Guardrails: cascading errors between agents, false alerts, traceability of each step. **(5)** Sequence: reporting first, action automation next, change management within the TO. The authors present the text as "a first look at the opportunity for TOs". The cases are not named, no measurement method is described, and two of the tools cited (**Wave**, **Lilli**) belong to McKinsey. The text follows on from [[sternfels-mckinsey-60000-people-20000-agents-officechai-2026-01-14]], where McKinsey already counts its agents among its workforce.
In-depth opinion piece (point of view) published on **sfeir.com** on June 24, 2026, by **Didier Girard** (Managing Director, SFEIR). **Central thesis**: in 2024 everyone was betting on **AI4Business** (AI in business processes) as the great value reservoir; by 2026 the picture has **reversed** — it is **AI4IT** (AI to produce the information system: code, SDLC, software factory) that is creating **measurable** value. The article *grounds* this thesis in the firm's tech watch: AI4Business disappointment (the MIT study "95% of pilots without ROI," contested but revealing; an **organizational** blockage / Mollick's Hayekian problem) versus quantified AI4IT evidence (Salesforce, Intercom, Raiffeisen, AWS/Bedrock, Atlassian, DORA). Mechanistic explanation: **code verifies itself** (compilation, tests, CI) whereas business processes have neither a compiler nor an immediate feedback loop. **2027 budget consequence**: a **CapEx→OpEx** shift, token price dynamics (rising peak — Fable 5 at 2× Opus — vs inference ÷280 and downward pressure from open weights/desktop), and **AI FinOps** driven by **cost per outcome**. Closes with **4 recommendations for the COMEX**.
#AI4IT#AI4Business#reversal
**Didier Girard** — Managing Director (CTO / DG) de **SFEIR** · ESN française (~1 000 personnes, France · Belgique · Luxembourg · Suisse). Auteur de l'article ; voix éditoriale du cabinet sur la transformation IA des DSI.
Case study published by the **Cornell AI Innovation Hub** (June 15, 2026): how a two-semester collaboration between the AI Hub, graduate students, and Cornell's Treasury team turned a time-consuming manual investigation into an AI tool that **recovered $100,000** in unidentified payments on a first batch. A successful **AI4Business** use case (financial process) that illustrates the **Leader-Lab-Crowd** framework of **Ethan Mollick** almost point by point: the **AI Hub** plays the role of the **Lab** (a central, ambidextrous team of technologists plus students); **Treasury** (Cheryl Barnes, Marie Graves…) is the **Crowd** carrying business knowledge and the real pain point; and the **$100,000** constitutes the **visible reward** (vivid win) that anchors adoption — exactly the incentive lever Mollick considers decisive. Key method: **"context first, then plan, then build"** via **Claude Code Plan Mode**, a chain of **fuzzy matching → Gemini Enterprise Web Search → Claude synthesis**, all within the governed **Cornell AI Gateway**. *"The $100,000 is a start."*
#Cornell AI Innovation Hub#unidentified payments#payment reconciliation
**Pete Stergion** — Desktop Engineer au Cornell AI Innovation Hub · co-tech lead du projet (avec Phil Williammee). Article institutionnel signé de l'AI Hub.