A new report from Gartner reveals that the AI vendor race is not a linear sprint but a series of overlapping contests across the entire AI technology stack — from foundational model development to enterprise deployment. In The AI Vendor Race: What It Takes to Win in Volatile Conditions, Gartner argues that traditional tech strategies will no longer suffice in this fast-moving market.
“The GenAI wave is maturing faster than any previous innovation cycle,” said Anthony Bradley, Group Vice President at Gartner. “Tech providers must move beyond tools and focus on delivering measurable business outcomes.”
According to the report, by 2026, enterprises will spend more on software with GenAI capabilities than without. By 2028, annual GenAI market growth will stabilize at 38%, while AI-optimized servers will grow 91% in 2025 alone.
GenAI: From Differentiator to Baseline Requirement
Gartner highlights that GenAI, once seen as a cutting-edge advantage, is rapidly becoming a baseline requirement. Nearly all premium computing devices will be AI-enabled by 2027. However, Gartner warns that less than 20% of GenAI deployments will reach their intended business value through 2026.
This creates enormous pressure on vendors to pivot from showcasing flashy demos to embedding AI into mission-critical workflows. The focus now is on proving real ROI and sustaining competitive edge through rapid iteration, robust integration, and continuous innovation.
Rethinking Competition in the AI Stack
Gartner defines the AI vendor race as a set of layered races: model development, infrastructure provisioning, platform tools, and vertical AI applications. Each layer has its own leaders, challengers, and dynamics.
Rather than waiting for customers to derive value on their own, providers must guide them toward success. This includes reevaluating go-to-market strategies, product engineering roadmaps, and outcome-based positioning.
“The AI game has changed,” said the report. “First-mover advantage is over. Now it’s about resilience, value delivery, and scale.”
