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.