Horizontal AI agents vs. vertical AI agents: a comparison
Explore the differences between horizontal and vertical AI agents, their enterprise applications, and how to choose the right strategy for your business needs.
Key Takeaways
Horizontal AI agents offer broad applicability across industries with their versatility in handling general tasks, but they may lack the depth needed for specialized industry applications.
Vertical AI agents excel in specific domains by leveraging industry-specific data and expertise, providing more accurate and relevant outputs for complex tasks.
The choice between horizontal and vertical AI agents should align with an organization's strategic objectives, considering factors such as use case complexity, data requirements, and integration needs.
Hybrid approaches that combine horizontal and vertical AI agents can maximize effectiveness by utilizing general capabilities for broad tasks and specialized expertise for domain-specific challenges.
Successful AI implementation requires robust data management, continuous learning, and integration with human expertise to enhance decision-making and operational efficiency.
Last year, I started Multimodal, a Generative AI company that helps organizations automate complex, knowledge-based workflows using AI Agents. Check it out here.
Over the past year, I have been developing AI agents for use in finance and insurance companies. Agentic AI is changing the way we approach workflow automation in several industries, ensuring knowledge work automation like never before. I like to think of them as digital colleagues that can perform complex tasks machines weren’t traditionally good at.
Within AI agents, there are two main types:
Horizontal AI agents
Vertical AI agents
Let’s talk about each of them a bit more, and see where they can be truly useful.
Understanding horizontal and vertical AI agents
Horizontal AI agents
Horizontal AI agents are generalist models, like ChatGPT and Google Bard. They are designed to handle a wide array of tasks across various domains. Their versatility makes them incredibly powerful, but they often lack the depth required for specialized industry applications.
Key characteristics of horizontal AI agents include:
Broad knowledge base covering multiple subjects
Ability to engage in general conversations and perform diverse tasks
Continuous learning from vast amounts of data
While horizontal agents excel at tasks like drafting emails, answering customer queries, and providing general information, they may fall short when it comes to industry-specific nuances or complex workflows.
Vertical AI agents
Vertical AI agents are tailored for specific industries or tasks, offering a deep understanding of domain-specific language, regulations, and workflows.
Key characteristics of vertical AI agents include:
In-depth knowledge of a particular industry or domain
Ability to handle complex, industry-specific tasks with high accuracy
Integration with industry-specific tools and databases
The power of vertical AI agents lies in their ability to drive significant value in specific business contexts. For instance, in law firms, vertical AI agents can analyze complex legal documents, extract critical information, and even assist in case research, significantly improving productivity.
From my experience working with various enterprises, I've observed that vertical AI agents often provide more immediate and tangible ROI. They can be seamlessly integrated into existing workflows, requiring minimal disruption to business operations while offering substantial improvements in efficiency and accuracy.
For complex industries, we'll see an increasing trend towards vertical AI applications. These specialized agents will not only enhance productivity but also enable new business models and drive incremental revenue in ways we're only beginning to imagine.
Comparative analysis: horizontal vs. vertical AI Agents
Capabilities and use cases
Horizontal AI agents
Horizontal AI agents, such as ChatGPT and Google Bard, are designed to handle a wide array of tasks across various industries. These generative AI models excel in:
Natural language processing: They can understand and generate human-like text, making them ideal for drafting emails, creating content, and engaging in general conversations.
Cross-industry applications: Their versatility allows them to be used in multiple sectors, from healthcare to finance to education.
Customer engagement: They can serve as personal assistants, answering customer queries and providing general information.
Vertical AI agents
Vertical AI agents, on the other hand, are tailored for specific industries or specialized tasks. Vertical AI agent capabilities include:
Complex data analysis: They can process and interpret industry-specific data, such as financial modeling in banking or claims analysis in insurance.
Specialized task execution: Vertical agents excel at performing specific tasks like contract analysis in law firms or patient diagnosis assistance in healthcare.
Workflow integration: They are designed to seamlessly fit into existing industry-specific workflows and tools.
Training and data requirements
Horizontal AI agents
The training process for horizontal AI agents involves:
Broad datasets: These agents are trained on vast amounts of general knowledge spanning multiple domains.
Large language models: They often utilize LLMs that can process and generate human-like text across diverse topics.
Continuous learning: Horizontal agents are designed to adapt to new information and contexts over time.
However, this broad training can sometimes result in a lack of depth in specific areas, requiring additional fine-tuning for particular use cases.
Vertical AI agents
Vertical AI agents have more specialized training requirements:
Industry-specific data: They are fine-tuned on structured and unstructured data relevant to their particular field.
Domain expertise: Training involves input from subject matter experts to ensure accuracy and relevance.
Focused datasets: Unlike horizontal agents, vertical agents prioritize depth over breadth in their training data.
This focused approach allows vertical agents to understand industry jargon, complex workflows, and specific business needs with greater accuracy.
Customization and integration
Horizontal AI agents
Implementing horizontal AI agents often involves:
Initial ease of deployment: They come with pre-built capabilities for various tasks, making them relatively easy to implement initially.
Extensive fine-tuning: To adapt to specific business requirements, these agents may require significant customization.
Broad integration possibilities: Their versatility allows for integration across multiple departments and systems.
Vertical AI agents
Vertical AI agents offer:
Pre-tailored solutions: They come equipped with industry-specific knowledge and capabilities, reducing the need for extensive customization.
Seamless workflow integration: These agents are designed to fit naturally into existing industry-specific processes and tools.
Rapid value realization: Due to their specialized nature, vertical agents often provide immediate benefits in complex domains.
A vertical AI agent in the financial sector, for example, could be quickly integrated into a bank's existing systems to assist with fraud detection or risk assessment, requiring minimal customization due to its pre-existing financial expertise.
Performance and efficiency
Horizontal AI agents
Versatility: Excel in handling a wide range of general tasks across industries.
Adaptability: Can be repurposed for different use cases with some fine-tuning.
Generalization: Perform well in scenarios that require broad knowledge but may lack depth in specific areas.
Vertical AI agents
Specialization: Outperform in industry-specific tasks and complex workflows.
Accuracy: Provide more precise and relevant outputs in their domain of expertise.
Efficiency: Often require less training data to achieve high performance in specific tasks.
Security and compliance
Horizontal AI agents
Broad security measures: Implement general security protocols suitable for various industries.
Compliance challenges: May require additional measures to meet specific industry regulations.
Vertical AI agents
Industry-specific security: Built with security features tailored to the needs of particular sectors.
Regulatory alignment: Often designed with specific industry compliance requirements in mind, such as HIPAA for healthcare or GDPR for data protection.
Challenges and opportunities
Horizontal AI agents
Challenges
1. Lack of specialization: Horizontal AI agents, while versatile, often struggle with industry-specific nuances. Their broad knowledge base can lead to generic outputs that may not fully address complex, domain-specific queries.
2. Contextual understanding: These agents may misinterpret industry jargon or fail to grasp the full context of specialized tasks, potentially leading to errors in complex workflows.
3. Data privacy concerns: The generalist nature of horizontal agents often requires them to access a wide range of data, raising potential privacy and security issues across different industries.
Opportunities
1. Wide-ranging applicability: Horizontal AI agents excel in performing general tasks across various sectors, making them cost-effective solutions for businesses with diverse needs.
2. Cross-industry insights: Their broad knowledge base allows these agents to draw connections and provide insights that might not be apparent within a single industry context.
3. Rapid deployment: The versatility of horizontal agents often means faster implementation across different departments, potentially leading to quicker ROI.
Vertical AI agents
Challenges
1. Limited versatility: Vertical AI agents are highly specialized, which can limit their applicability outside their specific domain. This narrow focus may require businesses to invest in multiple AI solutions for different departments.
2. Higher development costs: Creating and maintaining industry-specific AI agents often requires substantial investment in specialized training data and domain expertise.
3. Potential for overfitting: There's a risk that vertical agents might become too specialized, potentially struggling with tasks that slightly deviate from their core training.
Opportunities
1. Enhanced accuracy and relevance: Vertical AI agents offer deep understanding of industry-specific language, regulations, and workflows, leading to more accurate and relevant outputs.
2. Compliance and risk management: These agents can be designed with built-in compliance checks for industry-specific regulations, reducing legal and operational risks.
3. Deeper insights and decision support: By leveraging industry-specific data and models, vertical agents can provide more nuanced analysis and support for complex decision-making processes.
How enterprises can use AI agents
1. Hybrid approaches: Some organizations are finding success in combining horizontal and vertical AI agents. For example, using a horizontal agent for general customer queries and a vertical agent for specialized financial modeling tasks.
2. Continuous learning: Both types of agents benefit from ongoing training and refinement. Implementing learning agents that improve based on past interactions and new data can enhance their effectiveness over time.
3. Integration with human expertise: AI agents, whether horizontal or vertical, work best when augmenting human intelligence rather than replacing it entirely. Designing workflows that facilitate seamless collaboration between AI and human agents is crucial.
4. Data management: The effectiveness of both horizontal and vertical AI agents heavily depends on the quality and relevance of their training data. Investing in robust data management practices is essential for success.
5. Scalability considerations: While vertical AI agents excel in specific domains, businesses must consider the scalability of these solutions as they grow or expand into new markets.
I see a lot of potential for vertical AI agents in banking and insurance. Applications like mortgage processing or claims analysis have traditionally been very time-consuming, with experts spending hours collecting and analyzing unstructured and structured data.
Such knowledge-based workflows can benefit from a combination of intelligent agents that can process documents, make decisions, or file reports, essentially automating the entire workflow while still retaining human expertise for complex cases.
I also host an AI podcast and content series called “Pioneers.” This series takes you on an enthralling journey into the minds of AI visionaries, founders, and CEOs who are at the forefront of innovation through AI in their organizations.
To learn more, please visit Pioneers on Beehiiv.
Wrapping up
There are several AI tools modern enterprises can deploy, but AI agents are one of the most versatile and lead quickly to realized ROI. If you’re planning on building or deploying AI agents, here’s what you should keep in mind:
Use case complexity: Assess whether your needs require general capabilities or specialized domain expertise.
Data requirements: Evaluate the availability and quality of industry-specific data for training vertical agents.
Integration needs: Consider how the AI solution will fit into existing workflows and systems.
Compliance and security: Ensure the chosen approach aligns with industry regulations and data privacy requirements.
Scalability: Determine if the solution can grow with your business needs.
I’ll see you next week with more on building AI applications.
Until then,
Ankur.