strategy

Strip away the hype and AI agents are just LLMs paired with tools and a layer enforcing your rules. From document processing to lead funnels and RFP responses, we set out five paths to value with workflows to evaluate today. Where should you start?

Break agentic AI into its parts — LLM, data, tools, reasoning and environment — and a team of agents can cluster 10,000 apps in a Jupyter notebook with no human prompting. But one ingredient is routinely overlooked. Which part actually makes agents safe to deploy?

Office chatbots like ChatGPT breed an unmanaged 'black market' of shadow IT. Wharton's Ethan Mollick argues AI agents are different. So why can agents act where chatbots only advise — and where do today's models still fall short?

Buying agentic AI from one hyperscaler looks convenient. But agents encode your business's intelligence, so handing the lot to Microsoft or Google risks core capability. Firms in banking, insurance and pharma see the danger. How do you gain without becoming a 'Business as a Service'?

You become the manager: you set the objective. Three frameworks — AutoGen, MetaGPT and ChatDev — make this real, with agents planning, writing code in Docker and fixing their errors. But LLMs hallucinate. So which tasks can you trust them with?

In one week Google launched its Vertex AI agent framework and Andrew Ng devoted The Batch to multi-agent collaboration. So why does splitting a task across 'software engineer', 'QA' and 'product manager' agents outperform a single agent — and how should leaders manage them?

A new MIT and Harvard report reaches back to Coase's 1937 theory of the firm and Gale-Shapley matching to argue the real prize isn't automating old processes. The boundaries between firms and markets start to move. So where does that leave your competitive advantage?

Across dozens of AI strategy sessions with SMEs, the same themes recur. The 'AI is hype' versus 'AI will take over' debate distracts staff from the opportunity. Everyone has the same AI, so capability is no edge. So where does an SME's advantage come from?

When cognitive labour was scarce, firms built processes to protect it. Now agents generate drafts and fixes faster than humans can inspect, and review becomes the bottleneck. If intelligence is suddenly cheap, what becomes scarce inside the firm, and what must leaders redesign?

A year ago few had heard of agentic AI. Now Nvidia's Jensen Huang calls the opportunity 'gigantic' and 96% of executives told Accenture it is significant. Venture capital sees agents unseating SaaS. So where does that leave the software your organisation already runs on?

With Simon Torrance we mapped the agentic AI ecosystem into a three-tier stack: engagement, capabilities and data. Some layers teem with innovators; others are near-empty. But the foundational layer isn't a technology at all. So which capabilities should an enterprise in-source, and which buy in?

Most businesses build 'faster horses' with AI, automating what already exists. Stanford's Erik Brynjolfsson calls this the Turing Trap: machines so good at emulating us they replace us, transferring wealth not creating it. So what are AI's superpowers — and which can't you imagine?

Agentic AI flips users from doers into managers of agent teams, yet the 'AI native' interface is still an unwritten page in the UX handbook. Tools from OpenAI, Cassidy and Zapier get the workflows right, but leave users quietly disappointed. What are designers missing?

Meta has spent billions on AI agents, yet no one has set out a coherent vision for where they are driving us. So we distilled one from the public statements of Sierra, Microsoft, DeepMind and dozens of others. What is agentic AI actually for?

METR's data shows the length of tasks AI completes reliably doubling every seven months; humans hold focus for twenty minutes. Attention, scarce for fifty years, is becoming abundant. If attention is no longer the constraint, what is, and where should you deploy agents?