Welcome executives and professionals. A year into the agentic AI revolution, one lesson is clear: success takes hard work. Setbacks are natural, but enterprises are learning how to get it right.

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.

Note: Previously published as Generative AI Enterprise, this briefing is now titled Enterprise AI Executive.

In today’s briefing:

• McKinsey's six agentic AI lessons.

• The AI Leadership Blueprint.

• The new era of enterprise software.

• The AI-native Office suite.

• Transformation and technology in the news.

• Career opportunities & events.

Read time: 4 minutes.

BEST PRACTICE INSIGHT

McKinsey’s six agentic AI lessons

Brief: McKinsey analyzed over 50 agentic AI builds they delivered, plus dozens more in the market. Their review distills six key lessons (L1-6) that leaders can apply to successfully capture enterprise value from agentic AI.

Breakdown:

• It’s not about the agent itself; it’s about the workflow (L1). The biggest gains come from reimagining people, processes and technology.

• Workflow focus enables teams to apply the right technology at the right point (L2), particularly important in multi-step processes (image above).

• Invest in evaluations (L3) to assess agent performance and improve outputs, and make it easy to track and verify each workflow step (L4).

• Develop agents and agent components that can be reused across workflows (L5), helping reduce effort by 30 to 50 percent on average.

• Humans remain essential, though roles evolve (L6). People need to oversee accuracy, exercise judgment, and handle edge cases.

Why it’s important: Leading firms are seeing early successes, but many struggle to capture value from agentic AI. Without a structured, learning-focused approach, mistakes are repeated, and organizations risk missing the full strategic and productivity gains these technologies can deliver.

BEST PRACTICE INSIGHT

The AI Leadership Blueprint

Brief: The AI Leadership Blueprint, a 98-page workforce transformation guide from The University of Utah, gives leaders a practical roadmap to integrate generative AI responsibly, strategically, and effectively.

Breakdown:

• Designed for leaders needing both strategic overview and actionable guidance to begin integrating generative AI responsibly.

• The Strategic Planning Pathway (Sections 1,3,6) enables consensus building on AI strategy, structure, leadership, policy, and ROI.

• The Implementation Pathway (Sections 2,4,5,6) supports deployment, from readiness checks to workforce training programs.

• The Risk and Governance Pathway (Sections 8,3,7) centers on risk management, and change management practices.

• The appendix includes nine templates, from AI policy checklists and ROI templates to risk matrices, to accelerate progress.

Why it’s important: This blueprint emphasizes frameworks for implementation, governance, ethics, and organizational readiness instead of prescribing specific technologies. With AI changing quickly, model-specific advice can become obsolete, but durable structures provide lasting value.

ARCHITECTURAL VALUE SHIFT

The new era of enterprise software

Brief: Menlo Ventures outlined a major shift in enterprise software: in the era of “systems of work,” core differentiation no longer lives in the interface. Value has shifted from visible UI states to hidden orchestration logic.

Breakdown:

• The sameness of AI apps, a prompt box or “ask anything” field, suggests commoditization, yet true differentiation now sits beyond the interface.

• Earlier software signaled value through design, like Salesforce’s fields or Slack’s channels. In AI, more value comes from what you don’t see.

• The real differentiator is scaffolding: deciding which context to retrieve, which tools to invoke, and effective sequencing around the model.

• This middleware layer is now the product itself. It’s what separates Cursor, Lovable, and Claude Code, and demo toys and production-grade tools.

• As value shifts from record/engagement layers (e.g. Salesforce in software 1.0, Slack in 2.0) to logic, we enter software 3.0: systems of work.

Why it’s important: Over the next decade, this shift will reshape how applications are designed, and where enterprise value accumulates. Winners in this new era won’t be defined by UI design, but by the intelligence, orchestration, and effectiveness embedded in their systems of work.

AI-NATIVE INSIGHT

The AI-native Office suite

Brief: Andreessen Horowitz (a16z) examined a new generation of agentic tools resembling an AI-native Office suite, benchmarking core everyday use cases to show where they excel and where they fall short.

Breakdown:

• a16z divides the AI-native tools into horizontal “all-in-one” platforms like OpenAI Operator and vertical specialists such as Gamma for slides.

• They tested five use cases: PowerPoint, Spreadsheets, Email, Research, and Note-taking, to evaluate performance against benchmarks.

• For PowerPoint, a16z tested the prompt: Design a visual-heavy, 7-slide deck about Gen Z internet behavior trends in 2025 (results above).

• In presentation generation, Anthropic’s new Claude file creation proved the fastest general-purpose agent, though visual polish lags.

• Gamma delivered the best visuals and post-generation control, while Genspark excelled at content-heavy research and analysis decks.

Why it’s important: These tests highlight patterns: horizontal general assistants and agentic browsers race to become the primary work UI, while vertical and horizontal boundaries blur. Vertical specialists expand into new categories, while horizontal platforms double down on popular workflows.

Anthropic explained how to build high-quality tools and evaluations, and how Claude can be leveraged to optimize its own tools to improve performance.

Andreessen Horowitz outlined how leading AI-era enterprise companies will likely begin as consumer apps, plus 10x products and the "cyber kill chain."

OpenAI engineering/product leaders shared how it's driving AI transformation with T-Mobile, Amgen, and Los Alamos’ air-gapped supercomputer.

BCG released a 24-page report on responsibly harnessing AI in the global AI race, plus a 7-page guide on how every employee can become an innovator.

AWS published its CTO’s guide to evolving architecture for agentic AI, covering systems and data architecture, security, monitoring, and integration.

Cisco shared a 135-slide AI Workforce Playbook with a “Build, Buy, Borrow, Bot” framework and methods to assess AI-related skill capabilities.

Wharton estimated 40% of current GDP could be impacted by AI. Occupations around the 80th percentile of earnings are the most exposed.

Moody’s published a 26-page study on AI in risk and compliance, drawing on a survey of 600 leaders who identified skills and regulation as adoption barriers.

Microsoft neared a deal to add Anthropic’s models to Office 365, while announcing the signing of a “non-binding” MoU for its OpenAI partnership.

Oracle shares jumped 40% after announcing $455B in AI infra deals, including $300B with OpenAI, briefly making Larry Ellison the world’s richest.

OpenAI expects $115B in costs over 4 years for compute, data, and talent, while launching OAI Labs to prototype new human–AI interfaces.

Anthropic added new memory and file creation features, confirmed recent quality issues, and endorsed California’s SB 53 on AI regulation.

Adobe introduced AI Agent Orchestrator with six specialized agents, such as Audience Agent and Journey Agent, to automate CX and marketing.

Eli Lilly launched TuneLab, an AI drug discovery platform built on over $1B of proprietary data to accelerate R&D and innovation in biotech.

Perplexity, the AI search company, is reportedly raising $200M in a new funding round that values the startup at $20B.

Reddit, Yahoo, and Medium introduced Real Simple Licensing, a new protocol establishing payments for AI firms training on publisher content.

CAREER OPPORTUNITIES

Amazon - AGI Autonomy GTM Lead

J.P. Morgan - AI Transformation Executive Director

BCG - Global AI Security Director

EVENTS

C-Vision - AI Agents Executive Dinner - September 16, 2025

Oracle - AI World 2025 - October 13-16, 2025

University of Oxford - Quantum AI Leadership - November 17, 2025

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.