In 2026, AI has moved well beyond experimentation. Enterprises now have dedicated AI budgets, more products to choose from, and much clearer expectations about what those products should deliver.
The question is no longer whether they will use AI. It's what kind of AI enterprises are actually willing to spend serious money on.
In late 2024, we argued that vertical AI agents would reshape enterprise software by going deeper into industry workflows than general-purpose tools. At the time, it was still largely a forward-looking thesis.
Fast forward two years, and the market is showing a clear picture: general-purpose AI tools such as ChatGPT, Microsoft Copilot, Claude, and Gemini still win for broad employee productivity and quick pilots. At the same time, companies are increasingly adopting AI built around specific industries, business functions, and workflows.
Below, we discuss when general-purpose AI is and isn’t enough, why vertical AI is capturing more and more enterprise spending, and when a company should build its own solution instead of buying one.
Horizontal vs Vertical: A Quick Refresher
Horizontal AI is the generalist layer: ChatGPT, Microsoft Copilot, Claude, Gemini, and similar platforms. It provides broad capabilities that can be used across industries and departments, is available to every employee, and is relatively easy to roll out. Horizontal tools are excellent at writing, summarizing, and answering general questions.
Vertical AI is the specialist layer: systems trained or heavily augmented on domain data, workflows, terminology, and compliance requirements. A legal contract review system, clinical documentation tool, insurance claims agent, or manufacturing quality system can all be considered vertical AI. The key distinction isn't necessarily the model underneath. A vertical product can use a general-purpose foundation model. What makes it vertical is the context and workflow surrounding the model.
What the 2025–2026 Data Actually Shows
Just a few years ago, almost no enterprise AI was built for a specific industry. That's no longer true, and the pace of the shift is the clearest answer to "what actually changed."
From 1% to more than 50%, in three years. Gartner predicts that more than half of the GenAI models enterprises use will be specific to an industry or business function by 2027, up from just about 1% in 2024.
The money is following the same curve. Foundation models still receive bigger enterprise spending in figures, so Vertical AI isn't the biggest category in the market yet, but is the fastest-growing one. Gartner's mid-2026 update shows that enterprise spending on domain-specific language models (DSLMs), an AI trained specifically for one industry, like healthcare or legal, is set to grow 210% in 2026. Enterprise spending on foundation generative AI models, the general-purpose kind, is set to grow 104.2% over the same period. Specialized AI spend is growing roughly twice as fast as general-purpose AI spend.
It's not just spending, it's showing up inside the software people already use. Gartner also forecasts that by the end of 2026, 40% of enterprise apps will have task-specific AI agents built directly into them, up from under 5% in 2025. That's a different angle from the spending forecast above - this one is about how many of the tools employees already use are quietly getting specialized AI features added.
And here's why it's happening: vertical AI actually pays off. According to McKinsey's State of AI research, companies that deploy vertical AI see measurable value within six months 71% of the time. Companies that deploy horizontal-only AI hit that mark just 32% of the time, which is less than half as often. Vertical deployments also generate roughly 2.3 times the average ROI of general-purpose tools. This is the number that actually explains everything. Enterprises aren't shifting budget toward vertical AI because it's trendy. They're shifting it because it's far more likely to show a return before the next budget review and a project that shows a return gets renewed, while one that doesn't gets quietly cut.
If we narrow in on agents specifically, the picture gets more honest. "Agent" implies autonomy - a system that takes multi-step action on its own, not just answers questions. That's the harder promise to keep. Many AI agents still struggle to leave the pilot stage, regardless of whether they're horizontal or vertical. Most stall on unclear success criteria, weak data access, or governance gaps, not on model quality. Some that do reach real autonomous production use tend to share one trait: a narrow, well-defined scope. Harvey (legal), Sierra (customer support), and Abridge (healthcare) are all vertical agents running at enterprise scale with substantial ARR and large customer bases.
Why Vertical Solutions Are Capturing the Higher-Value Spend
Workflow embedding beats side-car assistants
Horizontal tools (general copilots, chat interfaces, standalone AI assistants) usually sit next to the systems employees already use. A lawyer still works in their practice management system and opens ChatGPT or Copilot in another window. A doctor works in the EHR and has a separate AI tool on the side. Vertical tools are built to live inside the systems people already use every day - the EHR for clinicians, the core banking platform, the claims system, the legal practice management software, the PLM system in manufacturing, etc. Side-car tools are easier to ignore or abandon. Embedded tools become part of how the work gets done, creating stronger lock-in.
Accuracy and risk matter more in critical workflows
The requirements change when AI moves from drafting an email or summarizing a document to reviewing a legal contract, processing an insurance claim, documenting a clinical encounter, or supporting a financial workflow. In these cases, errors can have greater operational, financial, or compliance consequences. Enterprises may therefore need domain-specific evaluation, stricter controls, auditability, and reliable integration with systems of record.
Capability isn't the whole product
A more capable general-purpose model can improve many tasks, but model intelligence is only one part of an enterprise AI solution. The harder part is often connecting the model to the right data, systems, processes, and controls. A claims-processing agent, for example, may need access to policy information, customer records, claims systems, approval rules, and audit logs. It also needs to follow the company's process consistently. That's where specialization becomes valuable.
Budget ownership has shifted
This is one of the biggest differences between an AI experiment and an enterprise purchase. Horizontal tools usually compete for IT or innovation budgets. Such tools are usually bought by the IT department or a central innovation/digital transformation team. These budgets are relatively limited. Vertical tools are usually bought by the business unit that actually runs that process - the claims department, the hospital’s clinical operations, the legal department, the manufacturing team, etc. These buyers are measured on P&L results (profit and loss): cost reduction, revenue impact, throughput, error rates, headcount efficiency. The money they control is much larger because it sits inside the operating budget (or even the labor/payroll budget) of that department, not inside the central IT budget. When a vertical tool can show clear ROI, it is easier to fund from the money it was already spending on people or inefficiency, and harder to cancel.
Not Totally Vertical AI, but Rather Hybrid Enterprise AI
In late 2024, the case for Vertical AI was still largely forward-looking. By 2026, the picture includes real production deployments and meaningful budgets. The most interesting enterprise AI products increasingly look less like chatbots and more like software for getting a particular job done.
AI-native companies such as Harvey in legal services, Sierra in customer support and Abridge in clinical documentation are examples of products built around specific workflows rather than general-purpose conversation.
At the same time, established vertical software vendors are adding AI to products customers already use. Construction software provider Procore, field-service platform ServiceTitan, and restaurant platform Toast are all examples of vertical software companies incorporating AI into existing operational workflows.
The pattern is important: the AI becomes part of the product and process rather than another tool employees have to remember to open. Gartner's 2026 research points to the same broader movement. Enterprises are increasingly embedding AI into existing software while also developing custom applications tailored to their needs.
But this doesn't mean enterprises are choosing vertical AI instead of horizontal AI. The market is moving toward a hybrid model. Many successful enterprises use horizontal platforms as a broad foundation for employee productivity while adding vertical solutions or specialized agents for high-stakes, regulated, or deeply embedded workflows.
Horizontal vendors are moving in the same direction, responding to the trend by shipping industry packs, domain agents, and deeper integrations with business systems.
Thus, Google Launched Gemini Enterprise for Industries, starting with financial services and legal. The stated approach explicitly combines domain expertise (skills), secure connections to industry systems/data, and agents that perform real tasks inside workflows.
Anthropic & OpenAI both released finance-specific solutions in 2026. Anthropic launched ready-to-run agent templates for the most time-consuming work in financial services, while OpenAI in partnership with PwC, one of the "Big Four" accounting firms, providing professional audit, tax, and consulting services, focused on building AI agents for CFO and treasury workflows.
Salesforce expanded Agentforce by introducing purpose-built AI agents designed to handle specific enterprise roles and workflows (sales, service, commerce, IT/HR, supply chain) right out of the box. The recent releases emphasize explicit expansion into industry verticals and existing business processes.
The boundary is becoming harder to define because a horizontal platform can become vertical through its data, tools, integrations, and workflow configuration. The underlying model may remain general-purpose while the application becomes highly specialized.
What This Means for Enterprise Buyers
For enterprise buyers, the choice is no longer simply between horizontal and vertical AI. It should start with considering your workflow, business outcome, and the risk involved. For writing, summarizing, brainstorming, research, and other general tasks, a horizontal tool may be enough. For high-stakes processes that depend on domain knowledge, proprietary data, system integrations, and measurable operational outcomes, a specialized solution may offer more value.
In practice, companies may use several approaches at once:
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Horizontal AI for broad employee productivity
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Vertical AI products for specialized industry workflows
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AI features embedded in existing enterprise software
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AI platforms for building internal applications
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Custom AI solutions developed around proprietary data and processes
Buy vs build decision
There is a simple rule here: don't build something internally just because you can. If an established product already solves the problem well, buying it is usually going to be faster and cheaper than recreating it. So, if the workflow is common across an industry, there are mature vendors that understand it, the integrations you need already exist, and speed matters, an off-the-shelf vertical product can make a lot of sense.
But the calculation changes when the workflow is unique to your business. If you have proprietary data that competitors don't have, highly specific internal processes, unusual integration requirements, or strict data-control requirements, there may not be a product that fits. And if the process directly affects your competitive advantage, building around it can be worth the investment. Sometimes the answer is simply that no existing product solves the problem well enough.
Here's the part that gets misunderstood: building an AI solution does not mean the company needs to train its own foundation model. It can build a specialized application around an existing model using RAG, proprietary data, workflow orchestration, agents, integrations, business rules, and enterprise security controls.
What This Means for AI Builders
For founders and software teams, the opportunity lies less in creating another general-purpose AI assistant and more in identifying workflows where generic AI still leaves too much work for the customer.
It could mean connecting AI to industry systems, building a specialized data layer, creating reliable workflow automation, adding domain-specific evaluation, or designing an agent that can safely execute a defined process. And it’s less about the model itself and more about everything around it: data, workflow knowledge, integrations, distribution, and trust.
A few clear leaders have already emerged in legal, healthcare, customer support, and selected industrial verticals. Many specialized niches, however, are still open. The most attractive opportunities may not be the biggest or most obvious industries. Many specialized workflows are still poorly served by generic tools, especially where employees spend significant time moving information between systems, applying complex business rules, or handling processes that require domain expertise.
The investment market offers a useful signal. Euclid Ventures analyzed more than 4,395 software and AI financings in the US and Canada in 2025 and found that vertical startups accounted for 53% of deal volume and 30% of total capital deployed. Vertical companies also represented 56% of total exit value, although several large transactions significantly influenced that figure. That doesn’t prove that every vertical AI business will succeed. It does show that specialization has become a significant part of the AI software market rather than a theoretical future category.
Final Thoughts
Enterprise AI is becoming less about choosing between a horizontal model and a vertical one.
The more important shift is from general capability to business-specific value. While horizontal platforms remain useful and continue to win broad seats for general productivity, the larger, stickier, and higher-value enterprise spending is increasingly flowing toward industry-specific solutions - tools that sit inside critical workflows, meet domain accuracy and compliance standards, and can be funded from operational or labor budgets rather than experimental IT pots. In 2024, vertical AI was still a bet on where enterprise software was heading. In 2026, that question has largely been answered. The question now is much more practical: Where can vertical AI create enough business value to justify the money and effort required to buy it, build it, or invest in it?
