Enterprise AI rollout figures are the easiest numbers in banking to misread, and the coverage of M&T Bank published on 4 September 2026 is a clean example of why. The headline said the bank was expanding enterprise AI after years of technology overhaul. The flagship figure attached to it — more than 15,000 employees with […]
Forward-deployed engineering has become the centre of the enterprise AI land grab. AWS is spending $1 billion to embed thousands of engineers with customers, Palantir’s decade-old playbook is now the industry default, and OpenAI and Anthropic hire for the role by name. This article explains what forward-deployed engineers actually do, why 95% of AI pilots failed without them, how the embedded learning loop works, what the talent shortage means, and how mid-sized businesses can buy the same loop at their own scale.
Workers in China are living through the fastest AI adoption drive on earth. Enterprise use of AI models and agents jumped from 9.6% to 47.5% in a single year, Beijing’s AI Plus plan targets 90% penetration by 2030, and programmers, scriptwriters and translators are already losing work to the tools their employers adopted. This article follows the Associated Press reporting on three workers who lived through it, sets their stories against the verified numbers — youth unemployment at 17.9%, a record 12.7 million graduates, 54% of the world’s industrial robot installations — and draws out what China’s adapt-or-be-replaced moment means for businesses everywhere else.
Is your business actually ready for AI? This guide sets out the full AI readiness assessment: what it measures, the six dimensions of AI readiness, a 1-to-5 scoring matrix with a worked example, the red flags that mean “not yet”, a costed 90-day plan to close the gaps, and an honest comparison of DIY versus consultant-led assessments for UK SMEs.
AI backlash reached a new pitch on 16 August 2026, when Anthropic chief executive Dario Amodei published a written response arguing that the anger aimed at his industry is not a communications failure at all. His diagnosis was one sentence long: “I think it is fundamentally a crisis of trust.” The remark was picked up […]
Zuckerberg AI messaging reached its loudest point on 10 August 2026, when Mark Zuckerberg published a 6,500-word essay called “The Future is for Everyone” arguing that personal superintelligence should be handed to everybody rather than concentrated inside a handful of frontier labs. The reception was not what Meta wanted. Within a week TechCrunch had run […]
Most AI business cases fail in review for the same few reasons: nobody measured the process before the build started, the cost stack stopped at go-live, and the benefit was counted in minutes nobody ever reclaimed. This guide sets out how to calculate the return on an enterprise AI project so the number survives a finance challenge. It covers the formula and its two contested inputs, the full three-year cost stack, the six benefit levers that can actually be evidenced, the baseline you must capture first, a seven-step model, the choice between payback, net present value and internal rate of return, and a fully worked invoice-processing example with three scenarios.
Microsoft 365 Copilot installs in an afternoon; the conditions that make it useful take considerably longer. This checklist works through the six dimensions a real readiness assessment measures — data estate, permissions and oversharing, compliance and retention, licensing and cost, technical prerequisites, and people — then shows how to score the findings red/amber/green, which mistakes cost the most, and how to turn the result into a 90-day rollout plan.
Almost every organisation has bought business AI and almost none has changed how the work is actually done, which is why the numbers on the board slide still describe usage rather than outcome. This guide sets out the four stages a business AI programme passes through — assistance, workflow redesign, agents, and orchestration — what has to change at each one, and why the constraint is the operating model rather than the model. It covers use case selection, the data and permission foundations that agents expose, agent identity and control planes, security threats specific to agentic deployments, the regulatory position after the EU’s Digital Omnibus delay, what business AI actually costs once consumption and oversight are included, and the metrics that survive a finance review.
