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Welcome executives and professionals. AI isn't just a technical change; it's a human one. Because you can only achieve real value if agents, processes, and people work as one.

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:

• SAP & Oxford Economics: The value of AI.

• Agentic AI’s impact on the sales lifecycle.

• The commercial AI sovereignty market.

• How to control AI usage and spend.

• Transformation and technology in the news.

• Insights for Executive+ members.

• Career opportunities & events.

Read time: 4 minutes.

MARKET INSIGHT

SAP & Oxford Economics: The value of AI

Brief: SAP and Oxford Economics surveyed 2,600 leaders across 13 countries about their AI investments, ROI, and challenges, finding that 30% of tasks in the average business are supported by AI, up from 25% last year.

Breakdown:

• The share of firms leading in AI automation and generative AI is climbing, while agentic AI is moving rapidly through planning (image above).

• 83% say AI has moderate to very high potential to transform their organization, but only 3% feel they are fully prepared.

• The average firm expects to spend $28M on AI this year, up slightly from $26.7M last year. Expected to rise 45% in two years.

• Companies expect to drive an average AI ROI of US$6.3m (21%) this year, and see it reaching US$15.9m (38%) in two years.

• Data (73%), skills (57%), and governance (56%) are the top challenges to AI ROI, with data becoming an even bigger hurdle this year.

Why it’s important: The findings show organizations at an inflection point: AI spending and returns are rising, agentic AI is moving from experimentation to execution, and businesses vary in their ability to manage key challenges like data readiness, skills, and governance.

IN PARTNERSHIP WITH RESOLVE

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Brief: Resolve clears routine tickets and constant alerts, handling repetitive incidents autonomously and surfacing only what genuinely needs a person. Your team spends its time on strategic work that moves the business forward.

The Resolve Platform edge:

• Routine incidents resolved autonomously before they reach a person

• Real signal pulled from the constant stream of alerts

• Engineering hours returned to strategic, high-value work

Take a product tour → Resolve Platform Experience.

BEST PRACTICE INSIGHT & CASE STUDIES

Agentic AI’s impact on the sales lifecycle

Brief: McKinsey, drawing on a survey of nearly 4,000 buyers and sellers across 13 countries, outlined five priority impact journeys that can be rewired with agentic AI to improve the sales lifecycle and drive growth.

Breakdown:

• Chapter 1 lays out five priority impact journeys that place agentic AI at the center of how commercial teams sell and grow.

• They span finding opportunities, the right go-to-market model, the right offer, and the right price, every single time (see image).

• Chapter 2 details the operating model shifts needed to make the change stick, scale adoption, and turn gains into margin improvement.

• The insights are informed by interviews with enterprise executives and case studies from companies achieving early success.

Why it’s important: This is not a more automated version of transactional selling but an insight-led model of solution selling that frees humans to deepen relationships and improve outcomes. At its core is a shift from fragmented use cases to end-to-end impact.

MARKET INSIGHT

The commercial AI sovereignty market

Brief: Stanford published a 20-page brief evaluating commercial AI sovereignty offerings, assessing their design, marketing, and whether they meaningfully increase operational control while reducing dependencies.

Breakdown:

• The offerings Stanford surveyed show that pursuing AI sovereignty is not a binary choice but a spectrum of interdependent decisions.

• Nvidia, Microsoft, Google, AWS, and OpenAI offer greater domestic control, yet often reconfigure rather than eliminate dependence.

• Cross-stack solutions like Nvidia's AI factories promise control and integration but can also tighten vendor lock-in and reliance.

• Open-source AI represents one common and meaningful option for lowering dependency, particularly at the model layer of the stack.

Why it's important: Leaders should recognize persistent underlying dependencies, and how offerings marketed as "full-stack sovereignty" often recalibrate rather than eliminate them. Focus on building domestic capacity where critical, and leveraging external capability where efficient.

AI-NATIVE PROFESSIONAL

How to turn feedback into product ideas with Claude

Brief: In this guide, you'll learn how to synthesize feedback from call transcripts, CRM notes, messaging platforms, and other product tools to identify cross-platform patterns and generate prioritized product ideas.

Step-by-step:

1. Tell Claude what to look for, like theme frequency, cross-platform patterns, or key quotes, to focus your feedback analysis.

2. Give Cowork access to your call transcripts and connect your other feedback sources. It then pulls from them all in parallel.

3. After your initial prompt in Cowork, Claude may ask which themes matter most, then build a plan you can review in the sidebar.

4. Claude synthesizes feedback from all sources, identifies key themes with attribution, and surfaces the prioritized product ideas.

Best practice: Cowork shows which sources Claude is querying and what it is finding in real time, so you can adjust course if needed.

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

BEST PRACTICE INSIGHT

How to control AI usage and spend

Brief: OpenAI outlined practical steps for enterprise leaders to understand how AI is used across their organizations, control spend through targeted policies, and direct investment towards work that creates the most value.

Breakdown:

• Leaders need a plain view of AI usage: who uses it, which models, how much capacity, and what kind of work it supports.

• A more capable model may cost more per token, yet it can reach an acceptable result faster, with fewer attempts and less review.

• Spend controls like workspace defaults, group limits, and overrides let leaders support high-value work without raising limits broadly.

• OpenAI also addresses managing AI investments as a portfolio and matching the product, capacity, and support model to workflow demand.

Why it’s important: Token price alone does not show whether AI creates value. Leaders should look at useful work per dollar: tasks completed, time saved, and decisions improved. As teams move from chat to longer-running workflows, enterprises need clearer visibility into demand, spend, and risk.

Gartner reported 90% of CDAOs say their data architecture needs an overhaul, and outlined 8 board questions to evaluate an AI strategy.

McKinsey outlined how to rewire customer experience for the agentic era, and explored who will train tomorrow's junior talent.

Cognizant published a 28-page report finding only 12% of firms have a fully consolidated, enterprise-wide view of IT spending.

Google published a 24-page report with Oliver Wyman detailing three agentic economies set to reshape banking and finance.

BCG detailed how agentic AI will make the CMO’s role more consequential, and explored how AI is transforming energy trading.

OpenAI introduced a "Useful Intelligence per Dollar" scorecard, encouraging CFOs to judge AI value by cost per successful completed task.

Moonshot AI released Kimi K3, an open-weights model setting new highs for Chinese and open models, rivaling frontier peers.

Databricks announced funding at a $188 billion valuation, led by Coatue, to fuel Unity AI Gateway, Genie, and Lakebase.

Google rebranded NotebookLM as Gemini Notebook, pairing the name change with a new feature for deeper data analysis.

Meta faces a lawsuit from 26 employees who say AI skewed recent layoffs toward staff on medical leave, despite its strong denial.

OpenAI published new research on GPT-Red, its internal automated red-teamer used to train GPT-5.6 against prompt injection attacks.

Reuters reported Xi pitched China as leader of a new global AI order, as 29 nations launched the World AI Cooperation Organization.

CAREER OPPORTUNITIES

Citi - COO for Firm-wide AI

Deutsche Bank - AI Acceleration Director

BlackRock - Head of AI Enablement

EVENTS

OpenAI - ChatGPT Work for Sales - August 13, 2026

IDC - Google-ElevenLabs Executive Dinner - August 27, 2026

CDAO Fall - October 26-27, 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.