Edition in partnership with

Welcome executives and professionals. The leaders of the next era may not be those with the most agents, but those willing to use them to fundamentally rethink how work gets done.

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:

• McKinsey-AWS agentic AI lessons.

• Who captures value from AI.

• The operating model advantage.

• How strategy leaders adapt to AI.

• Transformation and technology in the news.

• Insights for Executive+ members.

• Career opportunities & events.

Read time: 4 minutes.

CASE STUDY & BEST PRACTICE INSIGHT

McKinsey-AWS agentic AI lessons

Brief: AWS Vice President of Global Sales Greg Pearson and McKinsey Senior Partner Liz Hilton Segel explored how AWS is applying agentic AI to rewire its business, and the lessons CEOs can learn from its progress and decisions.

Breakdown:

• It starts with a clear strategy and a defined North Star. For AWS, that meant transforming its go-to-market function end-to-end (image above).

• AWS consolidated hundreds of data sets into 20 foundational sets, allowing AI to surface patterns across previously siloed domains.

• AWS broke down how work happens, distilling 35 roles across 40,000 field members into functions where agents could reduce effort.

• Teams now use agents to move from insight to action faster. One pipeline analysis agent surfaced $77 million in new opportunities.

Why it’s important: Throughout AWS's journey, a consistent pattern emerged: Progress came from focusing on a domain that could reshape performance, building a high-quality data foundation, and embedding agents into how work gets done, turning isolated use cases into a coordinated system.

IN PARTNERSHIP WITH RESOLVE

Build any agent, in the language you speak

Brief: Resolve AgentLab gives your team a place to build the agents your business actually runs on, described in plain language and put to work across every department and system you depend on, delivering value the moment they go live.

The Resolve Platform edge:

• Agents built in natural language by the people who know the work

• Skills that operate across your departments, tools, and systems

• Value that shows up the moment an agent goes to work

Register for our AgentLab webinar.

MARKET & BEST PRACTICE INSIGHT

Who captures value from AI

Brief: BCG explored how AI is driving a historic reallocation of capital and a fundamental shift in profit pools across sectors, forcing leaders to rethink where value will settle and where defensible returns will come from next.

Breakdown:

• Five factors determine who captures AI's value and how fast it escapes a sector, from data defensibility to compute intensity.

• The framework produces strikingly different outcomes by sector (e.g. retail passes the AI dividend to consumers within a handful of years).

• Durable advantage will come from physical assets and relationships that deepen with scale and use, not capabilities AI can readily replicate.

• AI-native competitors may capture the highest-margin parts of an incumbent's business without competing for its full revenue base.

Why it’s important: Technological revolutions are often misread not because leaders ignore the technology, but because they misidentify the threat. In the AI era, the illusion is not that transformation is coming; it is that the threat will be visible in time to respond (e.g. in the form of an obvious new attacker).

BEST PRACTICE INSIGHT

The operating model advantage

Brief: Deloitte published The Operating Model Advantage, exploring how organizations move from strategy to execution and capture the value of transformation amid constant disruption, including AI.

Breakdown:

• An operating model helps close the strategy-to-execution gap by defining the ways of working, and the enabling elements, that deliver it.

• The need becomes visible when transformation programs stall, cost targets are missed, or AI investments fail to scale.

• These are symptoms of operating model debt: the model itself has drifted out of step with the strategy it was designed to deliver.

• An operating model transformation typically unfolds across three interconnected phases: mobilize, design, and implement (image above).

Why it’s important: Constant disruption defines today's operating environment. Volatility across geopolitical, regulatory, and technological shifts, including rapid AI advancement, has made change permanent rather than episodic. Asked their top challenge, CEOs cited the "pace of change."

AI-NATIVE PROFESSIONAL

How to prepare a board pack with ChatGPT

Brief: In this guide, you'll learn how to use ChatGPT to build an editable board pack update tied to clear next steps, leveraging prior packs, initiative trackers, KPI and forecast inputs, leadership notes, and owner commentary.

Step-by-step:

1. Attach a prior pack and the latest approved source files, then identify the reporting period, audience, workstreams, and owners.

2. Ask ChatGPT to find the through-line linking progress, financial and operating metrics, risks, and next milestones across the pack.

3. Run the full starter prompt and request an editable .pptx file with a clear narrative, validated proof points, and review flags.

4. Use the follow-up prompt to review the source tie-out for each changed metric and chart, then check slides for overflow and layout drift.

Best practice: Ask ChatGPT to keep a change summary and owner checklist alongside the deck so reviewers can focus on material differences.

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

MARKET & BEST PRACTICE INSIGHT

How strategy leaders adapt to AI

Brief: BCG detailed how analytical skill has long defined the strategy function. But as AI rapidly commoditizes analytical thinking, the strategist's edge increasingly lies in matching the right mode of thinking to each problem.

Breakdown:

• BCG asked 175 chief strategy officers to identify "untamed issues": areas where existing approaches and frameworks fall short.

• Four challenges emerged: rising complexity, continuous reinvention, short- versus long-term trade-offs, and harnessing AI effectively.

• The strategy function's emerging role is to foster new modes of thinking and select the right one for each problem the firm faces.

• Complexity calls for systems thinking, reinvention for counterfactual, temporal trade-offs for integrative, and AI for critical.

Why it's important: Applying the wrong kind of thinking to a problem creates a false sense of rigor. The strategy function's value increasingly lies in recognizing which kind of issue the organization faces, then activating the right mode of thinking to plan ahead.

OpenAI argued the OpenAI-Hugging Face incident was a watershed moment for cybersecurity and set out what defenders should do now.

IBM detailed why the CDO-CISO alliance is crucial to AI strategy, as data strategy and cyber defense too often operate in silos.

HFS Research, with TCS, surveyed 101 C-suite and technology leaders in the US and Canada on how enterprises improve agentic AI reliability.

BCG published a 24-page report on how flexible data center connections and operations speed energization and lower system costs.

IBM shared how to manage AI agents without wrecking the business, and the critical thinking skills needed to keep them in check.

WEF asked where senior developers will come from as AI reshapes junior coding jobs, plus a 42-page report on intelligent industrial ecosystems.

Cursor launched Origin, an early beta that hosts code repos and pull requests with agents built in, moving it onto GitHub's turf.

OpenAI introduced Computer History, an opt-in feature that logs clicks and typing to give ChatGPT and Codex a memory of recent work.

Anthropic shared more details on Claude watermarking plans, saying the marks add no cost or hidden characters and carry no traceable user info.

Stripe is reportedly acquiring AI model marketplace OpenRouter for over $7B, more than 5x the valuation it raised at just months ago.

Z AI rolled out GLM-5.3, claiming the strongest open-source coding model and top cyber benchmark scores, with weights coming in two weeks.

Harvey debuted Harvey II, letting agents inherit a legal matter's context and remember each lawyer's style, alongside its first in-house legal model.

CAREER OPPORTUNITIES

Cognizant - Client Partner, Physical AI

NTT DATA - Managing Director, AI Large Deal GTM

Anthropic - Head of Enterprise Risk

EVENTS

ISG - AI Impact Summit - September 9-10, 2026

Infosys - Aster CMO-CIO Leadership Forum - September 18, 2026

Gartner - How to Scale AI Agents - September 30, 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.