Here’s a pattern I keep seeing: companies deploy a general-purpose AI assistant, get mediocre results, then switch to an industry-specific agent and see 3-5x better performance on the tasks that matter. The era of one-size-fits-all AI tools is ending before it properly started.

The Horizontal AI Plateau

We spent most of 2024 and 2025 watching companies bolt general-purpose LLMs onto everything. Customer service chatbots, internal knowledge bases, document summarisation, code assistants. The pitch was always the same: this one model can do it all.

And technically, it can. A general-purpose model can write a legal brief, diagnose a network issue, and draft a marketing email. But “can” and “does well” are different things. Most horizontal AI deployments settle into what I call the “70% zone” — good enough to demo, not good enough to trust without human oversight.

The companies seeing real ROI have moved past this. They’re deploying AI agents that know one industry deeply rather than every industry superficially.

What Makes Vertical Agents Different

A vertical AI agent isn’t just a general model with a better prompt. The good ones combine three things:

Domain-specific training data. A legal AI agent trained on Australian case law, tribunal decisions, and legislation performs differently from one trained on general web text that happens to include some legal content. It knows that “consideration” means something specific in contract law. It understands jurisdictional differences between states.

Workflow integration. Horizontal tools sit in a chat window. Vertical agents embed into existing workflows. A radiology AI agent doesn’t just analyse images — it integrates with the PACS system, follows the radiologist’s preferred reporting format, and flags findings according to BI-RADS classification. The workflow context matters as much as the AI capability.

Industry-specific guardrails. A general AI assistant might confidently give medical advice it shouldn’t. A purpose-built healthcare agent knows its boundaries, understands regulatory requirements, and defaults to conservative recommendations where clinical evidence is ambiguous.

Where Vertical Agents Are Already Winning

Legal

Harvey, now valued at over $2 billion, has proven the model. Australian law firms using vertical legal AI report 40-60% reduction in initial document review time. The key isn’t that the AI reads faster — it’s that it knows what to look for in specific document types. Employment contracts have different risk patterns than commercial leases, and vertical agents understand this distinction natively.

Construction

Vertical agents in construction are handling compliance checking against the National Construction Code, generating variation claims from site diaries, and predicting project delays based on weather patterns and supply chain data. Buildxact in Australia has been integrating AI estimation tools that understand local material costs, supplier lead times, and council requirements — context that a general-purpose model would miss entirely.

Agriculture

Australian agtech companies are deploying AI agents that combine satellite imagery, weather data, soil sensors, and commodity pricing to make planting and harvesting recommendations. These agents understand the specific challenges of Australian farming — drought cycles, water allocation rules, export market dynamics — in ways that a general agricultural model trained primarily on Northern Hemisphere data simply doesn’t.

The Investment Signal

Venture capital is following this trend. In Q4 2025 and Q1 2026, vertical AI startups raised more combined funding than horizontal AI tools for the first time. The logic is straightforward: vertical agents command higher prices (customers pay for domain expertise), have lower churn (switching costs are higher when the tool is embedded in workflows), and face narrower competition.

The companies most at risk are the mid-tier horizontal SaaS players. Enterprise giants like Salesforce and Microsoft have the resources to build vertical modules. Small, focused startups can move fast in a single domain. But the horizontal SaaS tool that does “a bit of everything” is getting squeezed from both sides.

What This Means for Buyers

If you’re evaluating AI tools for your business, ask these questions:

Does this tool understand my industry’s language? Not just keywords, but the contextual meaning of domain terms. Test it with ambiguous terminology specific to your field.

Does it integrate into my existing workflow, or is it another tab? The best vertical agents feel invisible — they enhance your current tools rather than replacing them.

What are its error patterns? General models make generic errors. Good vertical agents make fewer errors, and the errors they do make are more predictable and catchable.

Who built the training data? If the answer is “we fine-tuned a general model on web data,” that’s horizontal AI with a vertical label. Real vertical agents are built with domain expert involvement from day one.

The Bigger Picture

This isn’t just a product trend — it’s an architectural shift in enterprise software. The next decade won’t be defined by which company has the best foundation model. It’ll be defined by who builds the best domain-specific agents on top of those models.

General-purpose AI will still exist, just as general-purpose search engines still exist alongside specialised databases. But the money, the productivity gains, and the competitive advantages will increasingly flow to the vertical players who understand that depth beats breadth when real work needs doing.