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Welcome executives and professionals. Leading enterprises connect agents to company context, delegate more substantive work, and make successful workflows easier to repeat.

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:

• OpenAI’s AI-native adoption best practices.

• The enterprise AI decision dividend.

• Why apps shouldn't price per token.

• Move faster and create more AI value.

• Transformation and technology in the news.

• Insights for Executive+ members.

• Career opportunities & events.

Read time: 4 minutes.

CASE STUDIES & BEST PRACTICE INSIGHT

OpenAI’s AI-native adoption best practices

Brief: OpenAI shared how AI-native companies turn workflows into operating capability, drawing on case studies from Basis, Clay and Exa Labs that inform practical patterns enterprise leaders can leverage.

Breakdown:

• Basis, Clay and Exa Labs have built agents into employee onboarding (image above), account management and developer growth.

• OpenAI sets out six steps to experiment and scale what works, giving employees room to test workflows, measure results and repeat wins.

• Start by picking one consequential workflow, defining the outcome and how to measure it, then drafting the agent's job description.

• Then build the human system around the agent, make experimentation visible and reusable, and carry the operating pattern forward.

Why it’s important: Together these case studies capture patterns OpenAI sees in AI-native firms: Basis turns a proven process into a reusable skill, Clay gives an agent the context to keep an evolving body of work current, and Exa adds tools, tests and reviews for bounded agent execution.

IN PARTNERSHIP WITH KOLIBRI BY KONECTA

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Brief: According to McKinsey, 39% of companies remain stuck in experimental AI pilots. Kolibri by Konecta is the industrialized Agentic AI framework that moves CX operations into production in weeks, built on 25 years of CX expertise.

The Kolibri advantage:

• 80% pre-built, 20% tailored to your own CRMs and workflows

• Proven at scale: 35% lower order costs, 65% shorter wait times

• FinOps controls and ISO governance: transparent AI ROI in 30-90 days

Explore the AI agents library for free.

BEST PRACTICE INSIGHT

The enterprise AI decision dividend

Brief: McKinsey explored how most firms know exactly what they spend on human labor and capital equipment, but few can quantify what they spend making decisions, even though decisions drive nearly all business outcomes.

Breakdown:

• For simple, single-step decisions, AI mediation delivers substantially better unit economics than human coordination (image above).

• Agentic decision costs can rise 100 to 1,000 times due to verification and refinement loops, yet still beat the manual equivalent.

• If firms can replicate these benefits at scale, a feat few have achieved, it would mean that AI has reached cost parity with labor in two years.

• Steam power took roughly 55 years to reach that threshold, the electric dynamo about 30 years, and the internet about seven years.

Why it's important: Why, then, do most organizations report little measurable impact on earnings? The answer lies in implementation. Layering copilots onto existing processes yields modest gains, while redesigning end-to-end workflows around AI lifts EBITDA by roughly 20 percent.

BEST PRACTICE INSIGHT

Why apps shouldn't price per token

Brief: Andreessen Horowitz (a16z) detailed how token pricing anchors the customer conversation to a cost curve that keeps falling. It is a weak anchor for applications whose usefulness, reliability and role in the workflow keep rising.

Breakdown:

• Token pricing began in the right place. When OpenAI launched its API in 2020, charging for computation sensibly metered raw inference.

• ChatGPT then sparked applications that combine proprietary data, tools, orchestration, and logic to complete work for the customer.

• Pricing that work in tokens imports the model provider's cost structure and ties product value to a unit whose cost keeps falling.

• a16z argues companies should price at the highest layer of value they can reliably measure, attribute, and defend (see image above).

Why it’s important: For application companies, the practical path is to translate variable work into units that customers understand, use credits to package those units when flexibility matters, and then move toward outcomes as soon as customers recognize and trust them.

AI-NATIVE PROFESSIONAL

How to prioritize client accounts with ChatGPT

Brief: In this guide, you’ll learn how to give ChatGPT client account records, usage signals, renewal context and review rules, then ask it for a ranked account brief with rationale, risks, next actions, sources and follow-up drafts.

Step-by-step:

1. Define the account segment, time window, and priority criteria. Attach the CRM export, call notes, usage data, and review rules.

2. Run the starter prompt, then ask for rationale, sources, stale context, and next actions for each account.

3. Review recommended client follow-ups, then move approved actions into your system of record manually or separately reviewed workflow.

4. Leverage the follow up prompt to test how the ranking changes under a different review rule, such as renewal date or expansion potential.

Best practice: Keep risk, upside, urgency, and missing context visible as separate fields so a ranking never hides the evidence an account owner needs.

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

BEST PRACTICE INSIGHT & CASE STUDIES

Move faster and create more AI value

Brief: McKinsey studied 20 companies that consistently created economic value from AI transformation. They are established businesses, not headline tech names, lifting EBITDA 20 percent on average after three years.

Breakdown:

• These leaders delivered bottom-line impact, turned cash accretive fast, targeted leverage points and built systems, not point solutions.

• DBS Bank spent months in Silicon Valley and with digital natives to understand what truly differentiated high-performing tech firms.

• Winners build capabilities spanning roadmap, talent, operating model, technology, data, and scaling, that mature as practices advance.

• McKinsey maps these capabilities across three stages: first wins, scaling value, and the agentic enterprise (see table in article).

Why it’s important: The companies profiled have largely mastered stage two and are only in the early innings of stage three. As these new agentic capabilities mature quickly over the next few years, the companies that master them will compound advantage over peers.

IBM published a 25-page study of 1,000 leaders finding 91% cut effort with AI in application management but only 3% run it at scale.

BCG released insights on why AI pilots rarely deliver value, arguing the companies winning with AI aren’t doing more, they’re going deeper.

Capgemini released a 17-page Everest Group report on the architecture manufacturers need to scale adaptive AI across the shop floor.

AWS detailed how Atos upskilled 400 engineers in agentic AI via a three-day AI League contest, taking them from theory to delivery.

Bloomberg interviewed Forrester CEO George Colony on whether the hype around the current AI boom is justified and what comes next.

ITCSAU encouraged boards to inventory agents and govern each as delegated authority, with a mandate, logs and a tested stop button.

Anthropic released Claude Fable 5.1, the new top-ranked model with gains on long coding and research and fewer safety rejections.

Google Cloud introduced pay-as-you-go billing and monthly spend caps for agents, plus 10% off tokens for one-year commitments, 20% for three.

Anthropic announced Enterprise Frontier Safeguards, pairing zero data retention with misuse detection, rolling out to customers from this fall.

OpenAI said it intends to cut Cursor off by Nov. 12, citing Elon Musk's contract record after SpaceX acquired the company.

Anthropic announced Claude Code weekly limit changes for Sept. 14, drawing backlash for framing a 17 percent cut as a 25% usage raise.

Dyson rolled out CameraJet, a $499 toothbrush with an AI camera that finds gaps between teeth and triggers a jet spray to clear them.

CAREER OPPORTUNITIES

OpenAI - PwC Partner Director

AstraZeneca - Head of Artificial Intelligence

Johnson & Johnson - AI Vice President

EVENTS

Responsible AI Summit - September 21-22, 2026

CDAO Defense & Security - September 22-23, 2026

HFS Research - AI Pricing Roundtable - October 6, 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.

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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.