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Welcome executives and professionals. We are in a once-in-a-generation moment. The organizations that build the muscle to operationalize AI will define the next decade of industry leadership.

Since the previous edition, we have reviewed hundreds of the latest insights in agentic and generative AI, spanning best practices, case studies, market dynamics, and innovations.

This briefing outlines what is driving material value — and why it’s important.

In today’s briefing:

• 154 executives on enterprise AI.

• Five operating model shifts for AI.

• Microsoft's all-in-one Copilot.

• Three tests for an AI data moat.

• Transformation and technology.

• Insights for Executive+ members.

• Career opportunities & events.

Read time: 4 minutes.

Note: This quarter's executive AI transformation blueprint will be released this Friday, October 2, directly to Boardroom members' inboxes. New members will also receive the previous two blueprints. Join us inside The Boardroom.

MARKET & BEST PRACTICE INSIGHT

154 executives on enterprise AI

Brief: AWS's 108-page report explores what firms already getting value from AI do differently. It interviewed 154 executives, including CEOs, CFOs, CIOs, COOs, and Chief AI Officers, from 128 organizations across 23 industries.

Breakdown:

• The report spans ten chapters, including how to shift AI strategy from cutting costs to driving revenue and long-term growth.

• It explains why firms should stop measuring AI adoption and instead focus on the metrics leaders actually track to drive real impact.

• It sets out what's needed to redesign your workforce for AI and build the next generation of AI talent across the enterprise.

• It also addresses financial discipline, redefining leadership, data, platforms that enable AI, and how to get started.

Why it’s important: The transcripts of executive conversations comprise more than 1.1 million spoken words. The goal was to learn from those who are already turning AI into value, so each chapter moves from the problem to what to do, how to do it, and how it looks in practice.

IN PARTNERSHIP WITH THE HACKETT GROUP®

The emerging 75% AI performance gap

The first phase of enterprise AI focused largely on experimentation and deployment. Increasingly, attention is shifting to whether AI is changing operating performance.

New benchmark research from The Hackett Group® suggests a measurable gap is emerging between organizations that redesign end-to-end processes around AI and those that primarily automate existing workflows.

Across multiple business functions, the modeled performance difference reaches up to 75%, reflecting improvements in cost, productivity, speed and operating leverage.

Order-to-cash illustrates the pattern. Organizations operating at AI World Class performance levels achieve 52%-59% lower process costs, 56%-64% lower staffing requirements, 43% faster dispute resolution, and 85% fewer delinquent days than peer organizations.

Rather than resulting from a single automation initiative, these outcomes stem from changes across credit, order capture, billing, cash application and collections that reinforce one another throughout the revenue cycle.

The research examines where AI-enabled process redesign is producing measurable business outcomes and how benchmark data can be used to prioritize enterprise AI investments.

Read the research.

BEST PRACTICE INSIGHT & CASE STUDIES

Five operating model shifts for AI

Brief: Accenture argues that as AI becomes abundant, advantage shifts from owning the technology to the operating model that puts it to work. It identifies five shifts in decisions, workflows, workforce, cost, and learning.

Breakdown:

• Make decisions the unit of compound advantage. The right decision mode, how people and AI share a decision, depends on risk and complexity.

• In AI-enabled operating models, value streams cut across functions while functions keep their core responsibilities (image above).

• Manage humans and agents as one workforce, swap headcount economics for cost per outcome, and turn learning loops into advantage.

• Cases include Amazon, Mastercard, and Moderna. Amazon's robotics shows headcount is becoming a weaker proxy for productive capacity.

Why it’s important: These are structural shifts, but leaders need not wait years for results. They can start where value and friction are highest, then build a roadmap (see Accenture's insights for five steps to get started). Within 90 days, early moves will show whether the new model is taking hold.

ENTERPRISE INNOVATION

Microsoft's all-in-one Copilot

Brief: Microsoft introduced the new Copilot, bringing AI-powered work together in one experience with three capabilities, Home, Code, and Autopilot, alongside a pricing model pairing a user license with usage-based billing.

Breakdown:

• Home is the new starting point, where Chat and Cowork come together, and Office in Copilot builds in Word, Excel, and PowerPoint.

• Code helps everyone build and run their own solutions. Autopilot is a persistent, proactive agent that keeps working when you don't.

• Home and Code roll out in Microsoft's Frontier program in the coming weeks, with Autopilot entering private preview at month-end.

• The fixed-cost user license covers Chat, Office apps, and model choice. Cowork, Code, Autopilot, and frontier models are usage-billed.

Why it’s important: Microsoft reported 30 million paid Microsoft 365 Copilot seats in July, about 7% of its 450 million commercial subscriptions. It has the largest installed base in enterprise software but has yet to convert most of it. The new, unified Copilot could change that.

AI-NATIVE PROFESSIONAL

How to analyze product feedback with ChatGPT Work

Brief: In this guide, you'll learn how to use ChatGPT Work to turn conversation threads, survey exports, and issue queues into clear themes, backed by evidence, with the decisions and follow-ups for your team to act on.

Step-by-step:

1. Give ChatGPT Work your feedback sources, product area, and time period, and ask it to group repeated feedback into linked themes.

2. Have it create a Doc or Google Sheet with affected users, confidence, open questions, and the decision or follow-up needed.

3. Leverage the starter prompt for a first pass, then review the summary before turning any theme into a thread update or issue draft.

4. Refine with the follow-up prompt to split broad themes, add missing evidence, or draft updates, naming the audience and decision.

Best practice: Schedule ChatGPT Work to check feedback sources for new data, keeping review steps so nothing is posted unapproved.

For the full guide, including prompts, upgrade to Executive+ or The Boardroom.

BEST PRACTICE INSIGHT & CASE STUDIES

Three tests for an AI data moat

Brief: Oliver Wyman outlined three tests for judging whether an AI data moat is real, along with practical strategies that companies can use to strengthen that moat and protect its value against competitors.

Breakdown:

• Classifying data is easy; the real work is judging if the edge will last. A data asset must be valuable, defensible, and usable (image above).

• Make data harder, not just costlier, to recreate. Favor advantages that compound with time, scale, or access over what capital buys.

• Own more of the workflow, not just the data, then connect decisions, actions, and outcomes in ways point solutions cannot match.

• Build around what competitors can't see: usage that reveals unique customer behavior, turned into products that are hard to match.

Why it's important: As AI makes many old advantages easier to copy, companies are betting on proprietary data to hold the line. The logic: if rivals lack the data, AI can't easily replicate it, an assumption now shaping product strategy, M&A rationale, and valuations.

EY published a 21-page report on why AI pilots succeed or fail in production, with implications for CEOs, CIOs, CDOs, and CAIOs.

Citi published a 5-page interview with Group Head of AI David Griffiths on embedding AI in a bank moving nearly $6 trillion a day.

Mastercard published a 30-page white paper on how to build, budget for, and price AI solutions in the age of large language models.

Huawei published a 50-page reference for CIOs on planning, building, and operating AI data centers, with customer success stories.

KPMG outlined how FDE teams speed AI transformation, and detailed how to embed privacy across the AI lifecycle.

McKinsey outlined five principles for designing brain-powered organizations needed for AI and digital transformations.

Anthropic published internal measurements showing Claude now "leads" 26% of its AI R&D under human supervision, up from under 1%.

Google announced plans to launch its first AI chips into orbit next week under Project Suncatcher, testing space-based data centers.

OpenAI upgraded ChatGPT Voice to let users complete tasks across ChatGPT Work, email, calendars, Slack, and other connected tools.

Google debuted Gemini 3.8 Flash TTS and Flash-Lite TTS with custom voices, line-by-line direction, and dialogue in 100+ languages.

Anthropic is reportedly in talks to rent up to 1GW from Stream Data Centers, possibly using Google and Broadcom chips.

Vercel revealed open-weight models now make up 78.4% of AI Gateway tokens vs. 21.6% for closed ones, roughly reversing June's split.

CAREER OPPORTUNITIES

OpenAI - Executive Programs Lead

The Washington Post - AI Senior Director

Roche - Global Head of AI

EVENTS

Anthropic - Enterprise Transformation - September 30, 2026

EY-Snowflake - AI for Finance Leaders - October 20, 2026

Gartner - C-Suite AI Conversations - October 29, 2026

Reach enterprise AI decision-makers:

• 66% of readers are C-level executives or VP and Director-level leaders.

• 63.2% of the audience is based in the U.S., EU, UK, ANZ, and Singapore.

• Read by leaders at Microsoft, Deloitte, the Fortune 500, and more.

Guaranteed impression and custom sponsorship packages available, with post-send performance reporting.

Conceived as a practical communication for executives Lewis Walker has worked with, this briefing has become a trusted resource for thousands of senior decision-makers shaping the future of enterprise AI.

We welcome your feedback.