Gartner

Gartner forecasts worldwide AI spending will jump 49.5% to $2.7 trillion in 2026, with accelerating spend on AI agents, assistants (77.3% growth), AI security (near-doubling), and generative AI models (117% growth). AI infrastructure spending projected at 51.2% growth and AI software at 60.2%. This represents market-wide signal that enterprise AI adoption is accelerating across platforms, development tools, and security applications.

Leadership read: A product move like this reshapes Gartner's org chart as much as its roadmap. Scaling in Consulting rests on product leadership that can carry a launch to adoption and commercial hires who turn early traction into pipeline. The EMEA tell is whether senior GTM appointments follow; unsupported launches stall.

curated · 2026-09-24 · context →

Gartner released 2026 Hype Cycle for AI in IT Operations, predicting major shifts in infrastructure management with AI handling 25% of tasks autonomously by 2030 and 60% of enterprises deploying agentic AI by 2029

Leadership read: A product move like this reshapes Gartner's org chart as much as its roadmap. Scaling in Consulting rests on product leadership that can carry a launch to adoption and commercial hires who turn early traction into pipeline. The EMEA tell is whether senior GTM appointments follow; unsupported launches stall.

curated · 2026-07-20 · context →

Gartner forecast indicates massive enterprise adoption of task-specific AI agents across applications, accelerating from <5% in 2025 to 40% by end of 2026.

Leadership read: The Gartner forecast is not a market prediction; it is a planning commitment for every enterprise software buyer and vendor that reads it. A jump from under 5% to 40% agent embedment in a single calendar year is the kind of number that forces procurement cycles, vendor roadmaps, and internal architecture decisions to accelerate in lockstep. What changed operationally is that "agentic AI" moved from a futures category into a near-term delivery obligation: product teams now face explicit board-level questions about whether their platforms can support agent orchestration, and engineering organisations face the task of governing systems that plan and execute semi-autonomously rather than responding to discrete prompts. The related signals set is thin for grounding this specific read, the 12 comparable signals in the 90-day window span energy, fintech, and edtech product launches with no direct AI-agent comparables. Sarvam's trillion-parameter model and domestic inference platform is the closest data point, signalling that infrastructure investment at the model and serving layer is accelerating independently of the application-embedment story Gartner is describing. That the two trends are running in parallel matters: application-layer agent adoption and foundation-layer model build-out are creating compounding demand rather than sequential demand. Across enterprise software companies at this stage of agentic adoption, the functional pressure concentrates in three areas: product leadership that can architect human-in-the-loop governance frameworks, engineering leadership with distributed-systems and agent-orchestration depth, and risk and compliance functions capable of auditing non-deterministic execution chains in regulated environments.

curated · 2026-05-27 · context →