Enterprise AI moves from pilot to infrastructure as agentic platforms define the next buying cycle

Enterprise AI has crossed a threshold. What began as a wave of experimental pilots is now reshaping core business infrastructure, and the market numbers reflect that shift. According to Glean, the enterprise AI market grew from $24 billion in 2024 and is projected to reach $150–200 billion by 2030, with compound annual growth rates exceeding 30%.

That expansion is forcing procurement teams to make more precise architectural decisions. Choosing the wrong category of tool, a general-purpose chat application when a knowledge-connected platform is needed, or an agentic orchestration layer before governance controls are in place, is increasingly expensive to unwind.

The problem that built a $4.6 billion company

Glean's origin story illustrates the scale of the enterprise information problem. CEO and co-founder Arvind Jain, a former Google search engineer, first encountered the issue at his previous company, Rubrik, as it scaled past 1,000 employees. According to Turing Post, engineers who had previously written 300 lines of code per day were producing only 50, and internal surveys pointed to a single root cause.

Jain validated that the problem was not unique to Rubrik before recruiting co-founders Tony Gentilcore and Piyush Prahladka, both Google veterans, alongside T.R. Vishwanath, who had worked at Microsoft and Facebook. The team spent two and a half years in stealth before releasing the first version of their enterprise search tool, according to Turing Post.

The bet paid off. Glean raised a $260 million funding round in September 2024, reaching a $4.6 billion valuation, double what it was worth six months prior, and landed on Forbes Cloud 100 and the Madrona IA40 list that year, according to Turing Post. OpenAI reportedly flagged Glean internally as a competitive threat, a distinction few enterprise software startups can claim.

Work AI platforms vs. enterprise chat: a consequential distinction

As AI tooling proliferates, Glean draws a hard line between two categories that buyers frequently conflate. A work AI platform connects to dozens or hundreds of internal systems, builds a contextual map of an organization's people, projects, and documents, and delivers answers grounded in that proprietary knowledge. An enterprise chat application, such as ChatGPT Enterprise, wraps a powerful general-purpose language model with enterprise-grade security, then applies it to whatever information a user provides.

The architectural difference determines how answers are sourced, how permissions are enforced, and how deeply the tool can automate real workflows, according to Glean's comparative analysis. Work AI platforms enforce permission mirroring, meaning employees only receive answers drawn from data they are already authorized to access. General-purpose chat applications inherit access based on what users choose to upload or paste.

Glean recommends that organizations consider both categories as complementary rather than competing, but cautions that deploying the wrong tool for a given use case is the fastest route to a costly mismatch between team needs and deployed capability.

Agentic AI: architecture determines whether pilots reach production

Beyond search and chat, the 2026 enterprise buying conversation has moved toward agentic AI, systems that plan, act, and adapt across multi-step goals without step-by-step human instruction. Domo's enterprise buyer's guide draws a clear distinction: a chatbot that answers questions is not agentic, nor is a robotic process automation bot that follows a fixed script.

To qualify as production-ready agentic AI, Domo identifies five non-negotiable architectural components: a planner that decomposes large goals into smaller steps, the ability to call external APIs, memory that persists across sessions, feedback loops that recognize failures, and guardrails that constrain agent behavior. Governance features, identity and access management, policy-based approvals, and auditable logs, must be native to the platform, not bolted on afterward.

Domo also cautions that the platform with the broadest language model support is often not the right choice for organizations without dedicated machine learning operations staff. Teams with fewer than two ML engineers, the guide notes, should prioritize platforms with built-in planning abstractions and pre-built connectors over raw model flexibility, a finding that favors purpose-built enterprise tools over open-ended frameworks.

Governance and ROI move to the center of AI strategy

Across both the search and agentic categories, governance has emerged as the decisive differentiator in enterprise procurement. Glean's trend analysis identifies AI governance and compliance frameworks as one of the top priorities for enterprise buyers, with organizations building comprehensive structures to manage security, risk, and regulatory exposure as AI touches more mission-critical workflows.

ROI measurement is also becoming more rigorous. Glean's analysis highlights a shift toward unified oversight and impact measurement, with organizations moving away from one-off pilot metrics and toward portfolio-level tracking of AI's contribution to existing workflows. Domo echoes this with its emphasis on task success rate, the percentage of multi-step goals an agent completes without human intervention, as a key production benchmark.

Platform consolidation is emerging as a related pressure point. According to Domo, organizations see faster returns when agent building, deployment, and monitoring live in a single governed environment, reducing tool sprawl and the long integration tail that fragmented stacks create.

Product velocity signals where the category is heading

Glean's June 2026 release notes offer a concrete view of where enterprise AI product development is focused. Recent additions include real-time voice document creation, queued batch editing for documents in its Canvas workspace, and a one-click conversion tool that turns workflow agents into Auto Mode agents, a more autonomous reasoning architecture, without disrupting the original agent.

The platform also updated its Agent Insights dashboard for administrators, adding views for time saved by agents, runs by outcome, and voting feedback drill-down. New Auto Mode agents created by Glean Key customers now default to GPT-5.4, according to Glean's release notes, a signal that frontier model updates are becoming a standard feature expectation rather than a differentiator.

Taken together, the product direction points toward a market where the competitive battle is no longer about which vendor has the most capable underlying model, but which platform gives enterprise operators the visibility, control, and connective tissue to deploy AI responsibly at scale.