The hype cycle is over; the accountability era has arrived. We unpack how gen AI has moved from pilots to proof, with daily use now common among senior leaders and measurable ROI becoming the standard.

Pulling from the Wharton GBK Collective’s year‑three findings, Google Notebook LMs agents trace the three waves of adoption and show why “accountable acceleration” is the defining theme for late 2025.

TLDR / At A Glance:

• Gen AI Usage Is Mainstream
  • 82% use Gen AI weekly (+10pp YoY)
  • 46% use it daily (+17pp YoY)
  • ChatGPT (67%), Copilot (58%), Gemini (49%) dominate
  • Interesting note: Gemini grew fastest (+9% YoY)
• 𝗥𝗢𝗜 𝗜s Now Table Stakes for Businesses
  • 72% formally track ROI metrics
  • 74% report positive returns
  • 88% expect budget increases in next 12 months
  • 60% now have Chief AI Officers - strategy has moved to the C-suite
  • 30% of Gen AI technology spending now goes to internal R&D - enterprises are building custom solutions, not just buying off-the-shelf tools

Across functions, the story is clear: practical, repeatable work is getting faster. Data analysis, meeting and document summarisation, and everyday writing see the broadest gains, while legal and operations post surprising leaps in self‑reported expertise as tools slot into structured workflows.

Google NotebookLMs agents examine the sector split too — tech, telecoms, finance, and professional services are far ahead, while retail and complex physical operations navigate slower integration and tougher data constraints.

ROI is rising, budgets are expanding, and the spend is changing shape. With 88% planning to increase investment and many allocating £5m+ to gen AI, enterprises are shifting 30% of their budgets into internal R&D to build custom capabilities on top of sanctioned platforms like ChatGPT, Copilot, and Gemini.

That move, from generic efficiency to defensible differentiation, raises the stakes on governance, data, and talent.

The toughest challenges now centre on people. Leadership is consolidating responsibility with CAIO roles, access is broadening under tighter guardrails, and AI is increasingly used to manage risk. Yet a training paradox persists: lack of training is a top barrier even as training investment softens.

Google Notebook LMs agents dig into augmentation vs skill atrophy, the scramble for advanced talent, and why many leaders expect to hire more interns for AI‑enabled entry roles.

Our closing challenge: if automation eats the routine, which human skills will you invest in to drive the next wave of return?

Full study by The Wharton School and GBK Collective here: https://knowledge.wharton.upenn.edu/special-report/2025-ai-adoption-report/

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📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK

The promise of autonomy just got practical. We explore how agentic AI moves beyond passive automation to become a goal-driven collaborator that plans, adapts, and coordinates at human and machine scale. From UPS’s ORION saving millions of miles to smart factories reducing downtime and energy use, we lay out the concrete ways agents optimise complex systems while staying aligned with human oversight.

TL;DR;

• Defining agentic AI through goals, autonomy, learning, reasoning, and collaboration
• Manufacturing shifts to predictive, dynamic operations
• Precision agriculture and resource-efficient inputs
• Smart grids balancing renewables, storage, and demand
• Labour market change and reskilling needs
• Ethics, transparency, and the EU AI Act
• A phased, governed roadmap for adoption

Across sectors, the patterns repeat: precision agriculture uses sensors and imaging to fine-tune inputs in real time; smart grids balance renewables, storage, and demand to keep power stable; research teams accelerate discovery by screening vast chemical spaces and designing better experiments; creatives partner with agents that riff on styles, structure ideas, and expand the canvas without replacing human taste. We unpack the five pillars that make this possible—goal orientation, autonomous decisions, learning and adaptation, complex reasoning, and collaboration—so you can recognise genuine capability and spot hype.

We also confront the tough trade-offs. Jobs will change, which makes reskilling and mobility essential. Governance must keep pace, with risk-based rules, transparency, and strong safety cases for high-impact deployments. Privacy and data sovereignty demand encryption, auditability, and clear accountability. And for those worried about cost or complexity, we show how cloud platforms, pre-trained models, and no-code tools lower barriers so even lean teams can run meaningful pilots and prove ROI.

If you want a pragmatic path into autonomy—one that pairs ambition with control—this conversation maps the milestones: pick outcome-centric use cases, start small, build human-in-the-loop guardrails, measure impact, and scale responsibly. Subscribe, share with a colleague who’s planning an AI pilot, and leave a review with the one question you want us to tackle next.

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𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.

☎️ https://calendly.com/kierangilmurray/results-not-excuses
✉️ kieran@gilmurray.co.uk
🌍 www.KieranGilmurray.com
📘 Kieran Gilmurray | LinkedIn
🦉 X / Twitter: https://twitter.com/KieranGilmurray
📽 YouTube: https://www.youtube.com/@KieranGilmurray

📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK

The ground just shifted under every boardroom and team room: AI isn’t a side experiment anymore, it’s a core business tool changing how work gets done. We trace the acceleration in adoption and show why hands-on leadership—not memos—separates winners from watchers. From marketing and legal to operations and customer support, we walk through concrete examples of where AI already delivers results: real‑time personalisation, faster contract review, earlier demand forecasts, and sharper executive briefings. Along the way, we unpack how small, repeated wins create measurable ROI, lift revenue and profit, and compress decision cycles without sacrificing judgment.

TL;DR:

• AI moves from IT to every function
• Executives use AI for analysis and communication
• Productivity gains reshape project cycles and models
• Proof points across quality, consulting, and support
• Risks of delay and scattered pilots
• Governance, safety, and human-in-the-loop design
• Upskilling leaders to set strategy and build trust

We also address the hard truth about timing. Most tech leaders see a near-term deadline for adoption, and the opportunity cost of waiting is rising: lost market share, slower innovation, and a tougher fight for top talent. But speed without understanding leads to scattered pilots and weak governance. We share a practical path to disciplined speed—pick high-signal workflows, define safety guardrails, and measure outcomes that matter. Expect a clear case for human-in-the-loop review, data security, and transparent audit trails that reduce risk while raising confidence.

Finally, we confront the leadership gap. Many senior teams haven’t received formal AI training and lack confidence in safe use. That’s solvable. When leaders learn the tools, they can spot real use cases, set strategy, and model the behaviour that drives adoption across the organisation. The message is simple: those who lead from the front will turn AI into lasting advantage; those who wait will be managed by it. If this helps you lead the change, follow the show, share it with a colleague, and leave a review so more leaders can find it.

Support the show

𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.

☎️ https://calendly.com/kierangilmurray/results-not-excuses
✉️ kieran@gilmurray.co.uk
🌍 www.KieranGilmurray.com
📘 Kieran Gilmurray | LinkedIn
🦉 X / Twitter: https://twitter.com/KieranGilmurray
📽 YouTube: https://www.youtube.com/@KieranGilmurray

📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK