ABI Research Global AI Market Size: The Real TAM

TL;DR: The ABI Research global artificial intelligence market size reaches US$467 billion by 2030, up from US$122 billion in 2024, at a 25% CAGR (ABI Research, 2025). That is the macro TAM enterprise AI spending flows into. But spend is not the same as value: McKinsey found 88% of organizations now use AI, while over 80% report no material profit impact yet (McKinsey, 2025). The gap is mostly about access to knowledge, which is where a connective semantic layer earns its place.

A market forecast tells you where money is going. It does not tell you whether the money works. The ABI Research numbers are large enough to anchor budgets for years, and they frame a useful question for anyone buying or building enterprise AI: once the models are paid for, what makes them actually useful inside one company?

What is the ABI Research global AI market size?

ABI Research puts the global artificial intelligence market size at US$122 billion in 2024, with the AI software market forecast to reach US$467 billion by 2030 at a 25% compound annual growth rate. The firm projects US$174.1 billion for 2025 as an intermediate step (ABI Research, 2025).

A few definitions, since the segments behave differently:

• AI software market: the full forecast above, covering models, frameworks, MLOps tooling, applications, and services.
• Traditional AI: predictive AI, AI sensing (including computer vision), and natural language processing. ABI Research expects this to keep leading total AI software revenue through 2030.
• Generative AI: the fastest-growing framework at a 34.5% CAGR, with foundation models, optimization software, and deployment tools as the biggest opportunities (ABI Research, 2025).

The headline often gets read as “generative AI is the whole story.” ABI Research is more careful. Generative AI grows fastest, but mature predictive and sensing workloads still carry the larger share of revenue. Enterprise AI is a portfolio, not a single bet.

Where is the spending concentrated?

Geography matters for anyone modeling demand. In 2025, 54% of total AI software investment comes from North American companies, driven by frontier AI builders in the United States (ABI Research, 2025).

That lead narrows. The Asia-Pacific region accounts for 33% of AI software revenue in 2025, and ABI Research expects it to reach 47% of the market by 2030 as China scales up, with China alone forecast at roughly two-thirds of Asia-Pacific revenue (US$149.5 billion). North America’s share falls to about 33% by the end of the decade (ABI Research, 2025).

ABI Research also flags a shift in how money gets spent. Large language models are moving from chatbots toward agentic systems that perform multi-step tasks, which pushes spending toward services and operational work rather than one-off licenses. The buyer’s question changes from “which model” to “how do we run this against our own data, reliably, every day.”

Why a big TAM does not guarantee returns

Here is the tension a market-size chart hides. The TAM is real, and so is the adoption. McKinsey’s 2025 survey found 88% of organizations now use AI in at least one business function, up from 78% a year earlier, and 23% are scaling an agentic AI system somewhere in the enterprise (McKinsey, 2025).

And yet most organizations are still piloting rather than scaling, and more than 80% say they see no material enterprise-level EBIT impact from generative AI (McKinsey, 2025). Spending climbs into the hundreds of billions while measured profit impact stays flat for most. That is the enterprise AI value gap: the distance between buying AI and getting paid back by it.

The gap is rarely a model-quality problem anymore. It is an access problem. A capable model that cannot see last quarter’s pricing exception, the support thread that solved an identical bug, or the contract clause that governs a renewal will produce confident, generic answers. The intelligence is bought; the context is missing. Agentic systems make this sharper, because an agent acting on stale or partial knowledge does not just answer poorly, it acts poorly.

How a semantic layer turns TAM into results

The fix is not more model spend. It is connective tissue. A knowledge graph maps the entities an organization runs on (people, documents, tickets, projects, systems) and the relationships between them, so one query can traverse across tools instead of stopping at each tool’s edge. Pair that with AI search and retrieval, and a model or an agent can ground its answers in what the company already knows.

This is the layer SemanticOS provides: an operational brain that connects fragmented tools into one semantic surface, so both people and AI agents can find and reason over institutional knowledge rather than hunting through ten systems. ABI Research itself notes that graph-based AI models stay relevant across the forecast as enterprise strategies mature (ABI Research, 2025). The retrieval layer is where macro AI spend meets a specific company’s reality.

Consider Vantage Health, a mid-size health insurer. Leadership approves a generative AI assistant for its claims and member-services teams, betting on the same growth ABI Research describes. Six months in, usage is high and satisfaction is not. The assistant drafts fluent replies but misses plan-specific exceptions, because those live in a policy wiki, a legacy claims system, and a few veteran adjusters’ inboxes. An adjuster still spends an afternoon chasing one prior-authorization precedent across three teams.

Vantage Health does not buy a bigger model. It connects the wiki, the claims system, the ticket history, and shared drives into a knowledge graph, then points the assistant at that layer. Now a single query returns the precedent, the governing clause, and who last handled it. The model did not change. Its access did. That is the difference between participating in the AI market and getting a return from it.

Key takeaways

• ABI Research sizes the global AI software market at US$122 billion in 2024, growing to US$467 billion by 2030 at a 25% CAGR (ABI Research, 2025).
• Generative AI grows fastest (34.5% CAGR), but traditional predictive and sensing AI still leads total revenue. Enterprise AI is a portfolio.
• A large TAM does not equal returns: 88% of organizations use AI, yet over 80% report no material EBIT impact yet (McKinsey, 2025).
• The value gap is an access problem. Models need grounding in an organization’s own knowledge to pay off.
• A knowledge graph plus AI search connects fragmented tools so people and agents can act on real context, converting AI spend into outcomes.