Google Cloud and Accenture just launched a joint unit to deploy trained engineers into enterprises for implementing AI tools—a move that signals how serious the race has become. Google's training 1,000 of Accenture's consultants to build custom applications on Gemini Enterprise. This follows Google's earlier $750 million investment in embedding its own engineers across firms like Capgemini and Deloitte, plus a separate deal with CVC Capital Partners to place engineers in portfolio companies. The strategy reflects a brutal reality: AI companies have spent staggering sums on infrastructure—Alphabet alone has $811 billion in purchase commitments—but the revenue from AI services isn't keeping pace. Google Cloud pulled in $24.8 billion last quarter with enterprise AI driving much of that growth, yet the ROI equation still doesn't work. The core problem is that enterprises don't know how to actually use AI effectively. They're spending money on models and tools without seeing real returns. That's where forward-deployed engineers (FDEs)—the industry's term for specialists embedded in companies—become the missing piece. These aren't just sales reps; they're supposed to combine business knowledge with AI expertise to build workflows that actually save money or generate revenue. OpenAI, Anthropic, Microsoft, and Amazon have all launched their own FDE units in the past year, betting that deployment itself could become a trillion-dollar business. Google's market position adds urgency to this push. According to Ramp data, Google captures only 6% of enterprise AI spending compared to Anthropic's 43.5% and OpenAI's 39.7%—though Google disputes this by noting the data excludes major strategic deals with companies like Oracle and Meta. Accenture faces its own pressure, juggling FDE partnerships with Microsoft, ServiceNow, and SAP alongside the Google deal. Meanwhile, specialized deployment firms like Ode (backed by Anthropic) and OpenAI's The Deployment Co. are nibbling away at traditional consultancies. The race has shifted from building better models to actually getting them installed and working inside companies.