Welcome executives and professionals. New forms of collaboration are emerging, creating skill partnerships between people and AI that increase demand for complementary human capabilities.

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: People, agents, and robots.

• OpenAI's practical path to scaling AI.

• OpenAI’s guide to working with agents.

• WEF & Capgemini: AI Agents in Action.

• Transformation and technology in the news.

• Career opportunities & events.

Read time: 4 minutes.

THE FUTURE OF WORK

McKinsey: People, agents, and robots

Brief: McKinsey’s 60-page report explores how AI is extending productivity and reshaping work. Realizing $2.9T in US value by 2030 requires new skills and a redefined partnership between people, agents, and physical robots.

Breakdown:

• Current technologies could automate 57% of US work hours. Some roles will shrink, others grow or shift, while new ones emerge.

• Over 70% of human skills span automatable and non-automatable tasks. Skills stay relevant, what changes is how and where they’re used.

• The Skill Change Index finds digital/info-processing skills face greatest automation exposure, while assisting/care-focused skills change least.

• Demand for AI fluency, using, directing, and managing AI tools, has risen sevenfold in two years, outpacing all other skills across US job postings.

• Managerial work is shifting from supervising people to orchestrating collaboration among people, AI agents and robots (image above).

Why it’s important: McKinsey’s research suggests that although people may be shifted out of some work activities, many of their skills will remain essential. They will also be central in guiding and collaborating with AI systems, a change that is already redefining many roles across the economy.

BEST PRACTICE INSIGHT & CASE STUDIES

OpenAI's practical path to scaling AI

Brief: OpenAI released a 25-page guide on scaling AI, drawing on its partnerships with enterprises to share what they’ve seen accelerating progress, and what stalls it. The guide distills key lessons to build AI capability.

Breakdown:

• Set the foundations for safe, scalable experimentation, including executive alignment, governance, data access, and clear goals.

• Create AI fluency by developing the skills, confidence, and culture that make AI part of everyday work rather than a separate initiative.

• Scope and prioritize by creating a clear, repeatable system for capturing, evaluating, and prioritizing opportunities across the enterprise.

• Build and scale by developing a consistent, reliable method for turning new ideas and into reliable internal and customer-facing products.

• Across the four phases, the guide provides steps to begin and examples from enterprises such as BBVA, Wayfair, Uber, Intercom, and Lowe’s.

Why it’s important: Scaling AI requires more than experimentation; it demands systems, skills, and confidence that turn early wins into long-term capability. When each phase reinforces the next, momentum builds, risk declines, and enterprise impact compounds over time.

BEST PRACTICE INSIGHT

OpenAI’s guide to working with agents

Brief: OpenAI released a 23-page guide to help leaders prepare their teams for the shift to AI agents. It lays out why now is the moment to understand how agents work, and create guardrails that ensure responsible use.

Breakdown:

• The guide explains what AI agents are, their core components, and how they differ from workflow automation and “LLM-powered steps.”

• As agents become part of daily work, the focus moves from what they can do to how people and agents work together to deliver outcomes.

• Teams learn best by doing. Trying new tasks, giving direction, reviewing the outputs, and then adjusting their prompts and instructions.

• This applied work strengthens judgment, delegation skills, and builds AI literacy as teams move from generating outputs to reviewing and acting.

• The guide also outlines how to build a culture of supervision and auditing, how to structure and organize agents, and other practical steps.

Why it’s important: Agents are quickly reshaping how work happens. This moment offers a chance to examine where teams spend time, how handoffs occur, and where friction exists. With thoughtful experimentation, leaders can uncover where agents reduce effort and enable better decisions.

BEST PRACTICE INSIGHT & CASE STUDIES

WEF & Capgemini: AI Agents in Action

Brief: WEF and Capgemini released a 34-page paper on AI agents, outlining their foundations, classification dimensions, evaluation criteria, risk assessment lifecycle, and governance practices to support responsible adoption.

Breakdown:

• The paper details the foundations of AI agents, including their architectures, communication protocols, and security considerations.

• It defines agent classifications by mapping characteristics and operational context to guide evaluation, risk assessment, and governance.

• It explores how evaluation enables performance and reliability, while risk assessment uncovers potential harms and appropriate mitigations.

• Governance guidance helps translate these insights into safeguards and accountability mechanisms that scale as agent capabilities expand.

• It also assesses future multi-agent ecosystems (e.g. A2A commerce), and risks like orchestration drift that demand standards and oversight.

Why it’s important: Agents have already begun moving into production across customer support, autonomous research, and more. As adoption advances and early use cases move from single agents to more complex interconnected systems, expectations for scalable oversight grow.

Accenture shared lessons from a series of leadership discussions with AWS clients on the realities of AI adoption in their organizations.

OpenAI outlined how evaluation frameworks ("evals') turn business objectives into consistent results and what they mean for business leaders.

Anthropic analyzed 100K Claude chats to study productivity gains, estimating that AI adoption could raise annual U.S. labor productivity growth by 1.8%.

Bain shared insights on moving AI from pilots to production, how AI bookings will rewrite the travel company playbook, and Microsoft Ignite.

PwC Strategy& provided guidance on agentic AI in HR, outlining why implementations stall, how to assess value, and what good looks like.

MIT released a study finding that AI can already replace 11.7% of the U.S. labor market, impacting up to $1.2T in wages across sectors.

BCG examined how CEOs must rethink work as AI transforms organizations and highlighted how fast-moving CMOs can win with agentic AI.

Palo Alto Networks explored agentic AI security implications, why agentic AI introduces new security challenges, and how agentic security works.

Anthropic launched Claude Opus 4.5, a new model rivaling Gemini 3 and GPT-5.1, excelling particularly in coding and agentic benchmarks.

Nvidia responded to Google's TPU advances saying its hardware is “a generation ahead” with “greater performance, versatility, and fungibility.”

OpenAI rolled out Shopping Research in ChatGPT, a new experience that personalizes product recommendations by analyzing user preferences.

The US launched the Genesis Mission, an AI initiative aiming to cut scientific discovery from years to days for “challenges of national importance.”

Microsoft released Fara-7B, an open-weight model compact enough to run on laptops, capable of navigating websites and completing tasks autonomously.

Amazon plans to invest up to $50B from 2026 to build AI and supercomputing centers for U.S. federal agencies, including defense and intelligence.

OpenAI CEO Sam Altman warned staff of incoming “rough vibes,” signaling internal unease over Google’s Gemini 3 and Nano Banana Pro.

a16z discussed how the US ceded cultural influence to China's open-weight models, whose encoded values will shape how billions interpret the world.

CAREER OPPORTUNITIES

Ericsson - AI Program Director

MGM Resorts - Enterprise AI Executive Director

Anthropic - Head of GTM Narrative

EVENTS

AWS - re:Invent - December 1-5, 2025

MIT-Stanford - Agentic AI Invisible Hand - December 10, 2025

Salesforce - The Agentic Workplace - January 29, 2026

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

If your AI product or service adds value to this audience, contact us for information on a limited number of sponsorship opportunities.

We also welcome feedback as we continue to refine the briefing.