Enterprise AI in 2025: $37 Billion and Counting

Enterprise AI has grown from $1.7 billion in 2023 to $37 billion in 2025. That's not a typo. In just three years, AI has captured over 6% of the global SaaS market and become the fastest-scaling software category in history.

The bubble fears haven't materialized. The demand side tells a different story: broad adoption, real revenue, and productivity gains at scale.

Where the Money Goes

The $37 billion breaks down into two main buckets:

Applications ($19 billion): The user-facing products that leverage AI models

• Departmental AI: $7.3B (coding, IT, marketing, customer success)
• Horizontal AI: $8.4B (copilots, agents, productivity tools)
• Vertical AI: $3.5B (healthcare, legal, finance)

Infrastructure ($18 billion): The picks and shovels

• Foundation model APIs: $12.5B
• Model training infrastructure: $4B
• AI infrastructure (storage, retrieval, orchestration): $1.5B

The shift is notable: more than half of enterprise AI spend now goes to applications rather than infrastructure. Enterprises are prioritizing immediate productivity gains over long-term infrastructure bets.

The Buy vs. Build Flip

In 2024, enterprises were split—47% built internally, 53% purchased. A year later, it's not even close: 76% of AI use cases are now purchased rather than built internally.

The conventional wisdom that enterprises would build most AI solutions themselves didn't hold. Ready-made AI solutions are reaching production faster and demonstrating immediate value while internal builds mature.

AI Buyers Are Different

Enterprise AI buyers behave differently than traditional software buyers:

• 47% conversion rate for AI deals (vs. 25% for traditional SaaS)
• 27% of AI spend comes through product-led growth (vs. 7% for traditional software)
• Organizations surface 10+ potential use cases but focus on near-term productivity gains

That 47% conversion rate is remarkable. Once an organization commits to exploring an AI solution, deals close at nearly twice the rate of traditional software. The value is immediate and clear.

PLG Is Eating Enterprise AI

Product-led growth is transforming enterprise AI adoption. Individual users now drive AI adoption at 4x the rate of traditional software.

The examples are striking:

• Cursor reached $200 million in revenue before hiring a single enterprise sales rep
• n8n formalized contracts only after hundreds of employees were already active users
• ElevenLabs, Gamma, and others scaled the same way

When you account for "shadow AI adoption"—employees using personal credit cards for tools like ChatGPT Plus—PLG-driven tools may represent close to 40% of application AI spend.

The pattern: developers and technical teams discover tools for individual use, prove value in day-to-day work, and create bottom-up demand that converts to enterprise contracts.

Startups Are Winning the Application Layer

At the AI application layer, startups now capture nearly $2 in revenue for every $1 earned by incumbents—63% of the market, up from 36% last year.

This shouldn't be happening. Incumbents have distribution, data moats, enterprise relationships, sales teams, and massive balance sheets. Yet AI-native startups are out-executing across the fastest-growing categories.

Why startups win:

The story changes at the infrastructure layer, where incumbents hold 56% of the market. Databricks, Snowflake, MongoDB, and Datadog have seen meaningful re-acceleration as even AI-native builders choose existing platforms.

Coding: The First Killer Use Case

Coding is the breakout category. At $4 billion, it represents 55% of all departmental AI spend—the largest category across the entire application layer.

The numbers:

• 50% of developers now use AI coding tools daily (65% in top-quartile orgs)
• Code completion alone is $2.3B
• Code agents and AI app builders exploded from near-zero
• Teams report 15%+ velocity gains

AI now touches the entire software development lifecycle: prototyping (Lovable), code refactoring (OpenHands), design-to-code (Weaver), QA (Meticulous), PRs (Graphite), SRE (Resolve), and deployment (Harness).

Anthropic Is Winning

The foundation model landscape shifted decisively. Anthropic now earns an estimated 40% of enterprise LLM spend, up from 24% last year and 12% in 2023. OpenAI fell to 27% from 50% in 2023.

The three leaders—Anthropic, OpenAI, and Google—account for 88% of enterprise LLM API usage.

Anthropic's dominance comes from coding. They command 54% of the coding market (vs. 21% for OpenAI), driven by an 18-month run atop leaderboards starting with Claude Sonnet 3.5 in June 2024.

Agents: Mostly Hype (For Now)

For all the talk of agents, real production architectures remain surprisingly simple:

• Only 16% of enterprise and 27% of startup deployments qualify as true agents
• Most are still fixed-sequence or routing-based workflows around a single model call
• Prompt design remains the dominant customization technique, followed by RAG
• Fine-tuning, tool calling, and RL are still niche

Copilots dominate horizontal AI with 86% share ($7.2B). Agent platforms capture only 10% ($750M).

Strip away the hype and most "AI agents" are basic if-then logic around a model call. The architecture works for today's use cases—but it reveals how early we are.

Healthcare Leads Vertical AI

Vertical AI solutions hit $3.5 billion, nearly 3x the prior year. Healthcare alone captures 43%—approximately $1.5 billion.

The bulk concentrates in administrative and clinical-adjacent workflows:

• Ambient scribes: $600M (+2.4x YoY), minting two new unicorns (Abridge, Ambience)
• Clinicians spend one hour documenting for every five hours of care
• Scribes that reduce documentation time by 50%+ are transformational

Beyond healthcare: legal ($650M), creator tools ($360M), and government ($350M). Adoption is strongest in industries historically underserved by software—fields defined by manual, unstructured workflows that once depended on human services.

What's Next

Five predictions for 2026:

1. AI will exceed human performance in daily practical programming tasks. The best models keep getting better in verifiable domains like math and code.

2. Jevons' paradox continues. Net spend rises despite falling inference costs, driven by orders-of-magnitude increases in inference volume.

3. Explainability and governance go mainstream. As agents gain autonomy, the ability to explain and govern their decisions becomes critical. Governments will ask for audit logs.

4. Models move to the edge. Low-latency requirements, privacy, and cost push compute on-device. Mobile manufacturers ship dedicated low-power GPU compute.

5. Benchmarks saturate but fail to capture real-world efficacy. For frontier use cases like coding, users are price-insensitive and will pay more for performance.

The Takeaway

Enterprise AI is no longer speculative. It's a $37 billion market growing faster than any software category in history.

The dynamics are clear:

• Enterprises are buying, not building
• Startups are winning the application layer
• Coding is the first killer use case
• Anthropic leads the enterprise LLM market
• Agents are mostly hype—for now
• PLG is driving adoption at unprecedented rates

Three years in, the transformation is real. The value is clear. And the pace isn't slowing.