Horizontal vs. Vertical AI Agents: What’s the Difference and When to Use Each

Although nearly every company is now implementing AI in some way, many organizations are finding that they aren’t getting as much out of the new tech as they had hoped for. McKinsey had an excellent report describing this issue, which they referred to as a “gen AI paradox.” 

The main reason why organizations are struggling with AI, argues McKinsey, is that companies have an imbalance between horizontal AI agents, which can be helpful but are often general and vague, and vertical AI agents, 90 percent of which never make it past pilot mode.

So what exactly are horizontal and vertical AI agents? In this blog, we’ll define what each of them is, as well as outline their strengths and weaknesses and when you should use each one for maximum strategic impact. 

What’s a horizontal AI agent?

A horizontal AI agent is a general-purpose AI system designed to operate across multiple functions, teams, or industries rather than being tailored to a single, narrow use case. These agents are built to handle a wide range of tasks such as answering questions, generating content, analyzing data, or assisting with workflows, regardless of the specific business domain. In the context of enterprise AI, horizontal agents are often positioned as flexible tools that can be deployed broadly across an organization to support productivity, automation, and decision-making.

Horizontal AI agents typically rely on large, foundational AI models that are trained on diverse datasets. This broad training allows them to adapt to many different scenarios without requiring extensive customization. As a result, they can be integrated into multiple systems or departments, from customer support and marketing to operations and analytics.

Advantages of a horizontal AI agent

Breadth across teams

A horizontal AI agent can support marketing, sales, operations, finance, and analytics using the same core capabilities. That consistency makes it easier to roll out AI across the organization without standing up separate systems for each function.

Faster deployment

Because horizontal agents are built on general-purpose AI models, they typically require less domain-specific training and configuration. Teams can begin using them quickly, especially for knowledge retrieval, content generation, summarization, and workflow assistance.

Centralized governance

A single horizontal agent framework can simplify oversight. Access controls, data policies, audit logging, and usage standards can be applied consistently across departments rather than managed separately within each function.

Disadvantages of a horizontal AI agent

Limited domain precision

General-purpose agents can struggle in highly technical or regulated contexts. For example, forecasting demand in retail or assessing financial risk may require deeper domain modeling than a horizontal system provides.

Inconsistent outputs across teams

When multiple departments use the same agent in different ways, quality and reliability can vary. Without strong guardrails, prompt design, and data controls, outputs may drift from business standards.

Risk of shallow integration

Horizontal agents often sit on top of existing systems. If underlying data models are fragmented or poorly governed, the agent’s outputs reflect those weaknesses.

What’s a vertical AI agent?

A vertical AI agent is designed to operate deeply within a specific industry, function, or use case. Instead of handling a wide range of tasks across domains, vertical AI agents focus on mastering a narrow problem space—such as supply chain optimization, financial risk analysis, or healthcare diagnostics.

Because they’re built for a single context, vertical AI agents are trained on highly specialized datasets and workflows. They use machine learning models tuned to industry-specific patterns, regulations, and edge cases, allowing them to deliver more precise outputs than general-purpose systems. This specialization makes vertical AI agents especially effective for complex tasks like AI planning and forecasting, where accuracy and domain knowledge are critical.

Advantages of a vertical AI agent

One of the biggest advantages of a vertical AI agent is depth over breadth. By focusing on a single domain, these agents can deliver more accurate insights, predictions, and recommendations than horizontal alternatives.

Vertical AI agents excel at AI planning and forecasting because they understand the nuances of the data they’re working with—seasonality, compliance constraints, historical trends, and industry-specific variables. Combined with advanced machine learning, this allows organizations to make more confident, data-driven decisions.

Another key benefit is efficiency. Vertical AI agents often integrate tightly with existing tools and workflows, enabling advanced data automation without requiring extensive customization. This reduces manual effort, speeds up processes, and minimizes the risk of errors in high-stakes environments.

Disadvantages of a vertical AI agent

The main drawback of a vertical AI agent is limited flexibility. Because it’s optimized for a specific use case, it may struggle to adapt when requirements change or when applied outside its intended domain.

Vertical AI agents can also require more upfront investment. Training models with domain-specific data, configuring machine learning pipelines, and maintaining accurate data automation workflows can be time- and resource-intensive—especially for organizations without clean or well-structured data.

Finally, reliance on specialization can create silos. While vertical AI agents are powerful within their niche, organizations may need multiple agents to cover different functions, increasing complexity compared to a single horizontal solution.

Similarities between horizontal and vertical AI agents

While horizontal and vertical AI agents differ in scope and specialization, they share several foundational characteristics that make them valuable to modern organizations.

At their core, both types of agents rely on AI to analyze large volumes of information, identify patterns, and support decision-making. Whether applied broadly across teams or deeply within a single function, each agent uses AI analytics to transform raw data into meaningful insights.

Both horizontal and vertical AI agents are also designed to produce actionable data. Rather than simply reporting what happened, they help users understand what to do next—flagging risks, highlighting opportunities, or recommending next steps based on real-time and historical inputs.

Finally, both models aim to reduce manual effort and improve consistency. By automating analysis and decision support, horizontal and vertical AI agents enable faster responses, better alignment across teams, and more scalable use of data—regardless of how broadly or narrowly the AI is applied.

Differences between horizontal and vertical AI agents

The primary difference between horizontal and vertical AI agents lies in scope. Horizontal AI agents are designed to work across multiple industries, teams, or use cases, handling a broad range of tasks with the same underlying capabilities. Vertical AI agents, by contrast, are built to solve a specific problem within a defined domain, such as finance, healthcare, or logistics.

Another key distinction is depth of domain knowledge. Horizontal AI agents rely on generalized models that can be applied in many contexts but may lack industry-specific nuance. Vertical AI agents are trained on specialized datasets and workflows, allowing them to account for regulations, terminology, and edge cases that a general-purpose agent might miss.

Implementation and customization also differ. Horizontal AI agents typically require less setup and can be deployed quickly across an organization. Vertical AI agents often demand more configuration, data preparation, and ongoing tuning—but deliver more precise results in return.

CIO described the difference between the two types of agents this way: “One powers the engine; the other provides the map.” Vertical AI agents power engines by driving specific and focused insights. On the other hand, horizontal agents are more like a map, connecting business-wide data and systems on a more general level. 

Use cases: How do you know when to use a horizontal versus a vertical AI agent?

Choosing between a horizontal and a vertical AI agent depends on the problems you’re trying to solve, the complexity of your data, and how specialized your workflows are. In many cases, the decision comes down to whether you need broad flexibility across teams or deep expertise within a specific function.

Below are common scenarios along with the type of AI agent that’s best suited for each.

You need AI support across multiple teams or departments

Best choice: Horizontal AI agent

If your goal is to enable consistent AI capabilities across marketing, sales, operations, and finance, a horizontal AI agent is the better fit. These agents are designed to work across diverse workflows and tools, often offering easier cloud integration and faster deployment.

Horizontal AI agents are ideal when teams need shared access to insights, reporting, and automation without heavy customization. They also make it easier to maintain centralized AI governance, ensuring consistent policies, data access controls, and ethical standards across the organization.

You need highly accurate predictions within a specific domain

Best choice: Vertical AI agent

In cases when precision matters—such as demand forecasting, fraud detection, or clinical decision support—a vertical AI agent is usually the stronger option. These agents are purpose-built for domain-specific challenges and excel at AI predictive analytics by using specialized data, models, and industry context.

Because they understand the nuances of a single use case, vertical AI agents can deliver more reliable forecasts and recommendations than general-purpose systems.

You’re prioritizing speed and ease of implementation

Best choice: Horizontal AI agent

If you need to move quickly, a horizontal AI agent can often be deployed with minimal configuration. With prebuilt connectors and standardized workflows, these agents integrate smoothly into existing platforms through established cloud integration patterns.

This makes them well-suited for organizations experimenting with AI for the first time or looking to scale adoption rapidly across teams.

You operate in a highly regulated or specialized environment

Best choice: Vertical AI agent

Industries with strict compliance requirements—such as healthcare, finance, or government—often benefit from vertical AI agents. Their focused design allows for tighter controls, domain-specific safeguards, and more tailored AI governance practices.

By embedding regulatory logic directly into workflows, vertical AI agents reduce risk while improving decision accuracy.

You want to unify data and insights across multiple platforms

Best choice: Horizontal AI agent

If your organization relies on multiple BI tools and data sources, a horizontal AI agent can act as a unifying layer. With strong cloud integration capabilities, these agents can connect disparate systems and deliver consistent insights across the business.

This approach supports centralized reporting, cross-team visibility, and standardized AI governance, making it easier to manage data access, model usage, and compliance at scale—without building separate AI solutions for each function.

Where AI strategy meets real business impact

While horizontal and vertical AI agents each solve different problems, both depend on trusted data, scalable analytics, and the ability to turn insights into action. That’s where Domo plays a critical role.

As a cloud-based data platform, Domo supports horizontal AI use cases by unifying data across teams, systems, and departments. At the same time, Domo enables vertical AI use cases by providing clean, contextualized data and powerful analytics that specialized AI agents rely on for accurate forecasting, automation, and decision-making. 

Whether you’re building flexible AI capabilities across the organization or deploying highly specialized AI agents within a specific function, Domo gives you the foundation to make both approaches work without sacrificing speed, accuracy, or control.

Ready to see how Domo powers AI across your business? No matter which AI strategy you choose, Domo can help you. Watch a demo today to see how.