The “type a prompt, get a pretty landing page” phase is finished. In 2026 these tools build applications that actually work: authentication, databases, APIs, payments, deployment, usually in a single sitting. That part is genuinely impressive and I’ve stopped being cynical about it.

What I keep running into is the second half. The first version arrives fast. Then you want to change something, and the platforms start behaving very differently from each other. That’s the thing worth judging them on. Not who wins the demo. Who’s still useful on day forty.

The Economist counted about a million American jobs created by AI since 2023, and that count is solid. But look at what those jobs actually are: electricians, HVAC techs, equipment makers, data-centre technicians. People building and running the buildings, not people using the software. Only about one per cent of professional jobs are AI jobs. That matters, because a number like this tracks how much money companies are spending, not how good the technology is. If the models stopped improving tomorrow, hiring would keep going for a year because the contracts are already signed. If the models kept improving but three big spenders cut their budgets, the hiring would stop cold. So the figure is a spending indicator, not proof the technology works. And the people using it to say there’s no problem are missing that the office jobs going quiet and the construction jobs booming are two different labour markets with two different sets of people in them.

The headline numbers were large enough to move a $29 billion company 37% in two sessions. Total revenue of $1.766 billion, up 28% against a $1.66 billion consensus. Adjusted EPS of $1.87 against $1.50. Cloud revenue of $1.213 billion, up 31%, with growth accelerating rather than decaying. GAAP operating income of $211 million against a $28 million loss a year earlier. None of that is the number worth anchoring on.

Forrester has released the full conference agenda for its B2B Forum EMEA 2026, set for 28–29 September 2026 in London. The event’s theme, “Hello, GTM Singularity,” centers on a claim that’s hard to dismiss: Forrester puts the share of business buyers now using generative AI or conversational search during the buying process at roughly nine in ten. That shift is reshaping how B2B organizations approach discovery, evaluation, and purchase across marketing, sales, product, and customer success functions.

ServiceNow has expanded its platform into manufacturing with a suite of AI-native capabilities covering the full value chain from sales and configuration through quality, warranty, and factory-floor operations. The announcement, made alongside the unveiling of Industrial Connected Workforce and ServiceNow EmployeeWorks for manufacturers, positions the company directly against the fragmented stack that characterizes most industrial enterprises.

The core argument is structural. Manufacturing AI investments have accelerated over the past two years, but quality data, warranty claims, order workflows, and shop-floor processes typically run on separate systems with no shared governance layer. ServiceNow’s pitch is that AI cannot deliver end-to-end execution when the data it needs is distributed across siloed tools—and that a single platform with unified workflows changes that calculus.

We are likely in the early stages of another productivity boom.

You can feel it before you can fully measure it. Not in the official statistics yet, not neatly captured in GDP releases or quarterly productivity reports, but in the way work itself is beginning to change. Tasks that used to take hours now collapse into minutes. Layers of friction around research, drafting, analysis, coordination, and execution are starting to thin out. The shift is still uneven, still messy, but it is becoming difficult to ignore.

Enterprise IT has adopted AI-assisted tools at an uneven pace across the four functional areas. The adoption unevenness reflects a genuine difference in the maturity of AI applications across contexts — some IT functions have clear, measurable AI use cases with documented productivity gains, while others have AI vendor claims that have not translated to operational reality at the scale most enterprises require.

The honest assessment of where AI is saving time in enterprise IT is narrow but real: specific use cases within IT support, security operations, and software development assistance have demonstrated consistent productivity gains. The broader claims — AI transformation of IT operations across all functions — remain future-oriented rather than present-tense.