What VCs Expect from Enterprise AI in 2026
Venture investors say discipline, not experimentation, will define the next phase of enterprise AI.
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Three years after the generative AI wave took everyone by storm, the enterprise AI landscape remains at a crossroads: exuberant strategic ambition on the one hand, and limited realized value on the other. An MIT study released earlier this year found that 95% of enterprises have not yet seen meaningful returns from their AI investments.
A TechCrunch survey of 24 enterprise-focused venture capitalists highlights why many investors believe 2026 will finally be a turning point. According to these VCs, the early phase of AI adoption—characterised by experimentation with large language models and broad pilots—will give way to more disciplined deployment, focusing on operational integration, domain-specific models, observability, orchestration, and data governance.
Value creation, they argue, will hinge less on generic LLM performance and more on embedding AI into proprietary workflows and regulated enterprise environments.
This perspective aligns with Deloitte’s 2026 predictions, which emphasise that while the gap between theorised AI potential and practical outcomes will narrow, it is unlikely to vanish entirely in 2026. Organisations are expected to accelerate AI integration into core enterprise functions, even as execution challenges persist.
Deloitte’s analysis suggests a future where agentic AI—autonomous systems capable of orchestrating multi-step workflows—will play a central role. Daily usage of AI-augmented search and context-rich interfaces is projected to surpass that of standalone tools, reshaping how information is discovered and used inside organisations.
By 2026, up to 75 % of companies may invest in autonomous agents across SaaS platforms, signalling a shift toward AI as a connective substrate for enterprise software. However, both investors and analysts acknowledge headwinds. Infrastructure constraints—including compute costs, power usage, and data-centre scaling—remain real barriers.
Deloitte forecasts that inference workloads will account for roughly two-thirds of AI compute by 2026, underscoring intensifying demand for chips and supporting hardware that enterprises must manage alongside software adoption.
Ultimately, the consensus for 2026 is one of incremental maturation rather than sudden transformation. Enterprise AI is moving from early experimentation toward production-grade value delivery.
