Vertical AI agents explained: The future of enterprise tech

What is vertical AI?

Vertical AI is an AI system designed, trained and implemented to address the unique challenges and processes found in a specific industry or business vertical. It offers a more targeted and specialized platform that can perform more efficiently and deliver more accurate and actionable outcomes for the specific business.

In effect, vertical AI works to tailor or streamline a general-purpose AI platform into an industry-specific AI platform. This is accomplished using AI agents that are trained using extensive data sets directly related to the specific industry vertical.

To better appreciate the difference between AI and vertical AI, consider a camera-based system designed to recognize objects. A general-purpose AI system might be trained to recognize cars, balls and animals -- but likely just a small subset of each. Now, suppose a veterinary clinic wanted a system to precisely identify every animal species arriving for treatment. Chances are that the general-purpose AI might not have the extensive library of animal expertise needed to tell a pit bull from a Yorkshire terrier (it's just a "dog"), or a rat from a mouse (it's just a "rodent").

By contrast, vertical AI could provide an extensive animal recognition library, enabling the vertical AI system to identify almost any animal patient on arrival, then associate its owner's information and payment details, load the animal's prior visit history and access a detailed knowledge base of veterinary data about that specific animal for the veterinarian's convenience. When implemented properly, such a system can save time and enhance treatment.

6 characteristics of vertical AI agents

Although vertical AI agents operate in much the same way as general-purpose AI agents, vertical AI agents are differentiated by six principal characteristics:

1. Expertise. Vertical AI agents are extensively trained using data sets that are curated for the specific industry vertical. This detailed training helps vertical AI deliver much deeper knowledge of the domain than general AI. It's worth noting that a general-purpose AI might possess some knowledge of the topic area, but a well-trained and tuned vertical AI system can deliver far more detailed knowledge that can be significantly more precise and relevant.
2. Optimization. Vertical AI agents are highly optimized entities trained using industry-specific data sets. The data might originate internally or externally (i.e., from third parties), but the extensive and relevant data can result in more accuracy and insight than general-purpose data. In addition, vertical AI agents and systems are designed to offer seamless integration with other systems, platforms and tool sets. Comprehensive integration can support -- and even enhance -- complex workflows while minimizing workflow disruptions.
3. Context and reasoning. Vertical AI agents can provide powerful and effective reasoning ability by recognizing and maintaining contextual relationships. For example, vertical AI agents employ dedicated large language models (LLMs) to process natural language, such as user queries, and understand natural language in relevant context and intent. Vertical AI agents maintain contextual relationships, remember interactions, make decisions and adapt -- effectively reasoning based on goals and plans related to their domain. Vertical AI agents can also access other systems, tapping real-time information inside and outside of the business, such as checking records in a database or searching for information on the internet.
4. Native compliance. Since vertical AI agents are designed to accommodate specific industry verticals, they are designed to consider industry-related compliance obligations in their decision-making processes and ensure those obligations are met automatically. For example, if the business is subject to PCI DSS regulations, a vertical AI agent will generally implement related compliance goals, such as data security and retention guidelines, without specific training or configuration.
5. Automation. Although automation is a vital part of any AI platform, vertical AI's agentic process automation is designed and orchestrated to complete business processes or workflows from start to finish. Human intervention is minimal by design, though some complex decisions or processes might require human approval before sensitive actions are actually completed.
6. Adaptability. Vertical AI agents can effectively adapt to changing data with little, if any, need for retraining. This kind of fine-tuning enables the AI to learn and adjust on the fly to real-time production data from varied sources such as APIs for accessing integrated systems, IoT data streams, new database records and so on. Additional learning and adaptation can also come from user input or requests processed through the LLM or user approvals of the AI's results or decisions, where "correct" or "incorrect" decisions are remembered and used to tweak subsequent decisions.

Early examples of vertical AI agents across industries

Several early examples of vertical AI agents and platforms are already emerging across major industries, including the following:

• Healthcare. The healthcare industry stands to benefit strongly from vertical AI. For example, AI agents can act as diagnostic tools capable of analyzing medical images, clinical notes and test results to help clinicians detect anomalies, identify medical conditions and offer recommendations for treatment. AI agents specific to healthcare are used in medical transcription, requiring in-depth familiarity with medical terms, as well as in clinical documentation. Vertical AI agents can also support administrative tasks that involve complex medical coding and billing.
• Finance. The finance industry is another sector where vertical AI agents can bring serious benefits across a broad range of tasks. For example, vertical AI agents can manage an organization's financial records, track expenditures and deductions, and handle tax or securities filings. Vertical AI agents can perform extensive analytics of historical and real-time financial data to identify and flag suspicious activities, such as potential fraud, for further investigation. Vertical agents can effectively analyze risks for activities such as mortgage or insurance underwriting. These agents also can analyze vast amounts of business data to assist in investment research and decision-making.
• Retail and supply chain. Vertical AI agents can handle complex inventory management in retail and supply chain operations, helping to ensure that needed goods and materials are where they need to be, when they need to be there. Vertical AI agents can also generate highly personalized purchase recommendations or other customer-facing environments to enhance customer engagement, satisfaction and sales.
• Customer support. Customer service tasks use vertical AI agents to provide AI-powered chatbots or telephone support agents. AI can route customer calls, assist in issue diagnostics and resolution, process transactions such as ordering new services, and offer highly personalized support.
• Manufacturing. Vertical AI agents can analyze data collected throughout the manufacturing environment to direct optimal preventive maintenance. Vertical AI agents can specialize in software development, assisting in code development, code quality evaluation and testing. Vertical AI agents can also look for optimizations in the manufacturing process -- and often couple those optimizations with inventory and supply chain AI -- to improve manufacturing efficiency.

Benefits and pitfalls of vertical AI agents

AI adopters should consider the tradeoffs involved in vertical AI agents. Knowing the tradeoffs ensures the technology will meet the organization's needs, while mitigating its possible limitations and risks. Vertical AI agents can enhance an organization's competitive position and bring an array of potential benefits, including the following:

• Expertise. Vertical AI agents are trained using vast amounts of industry-specific data. This greatly improves the AI's knowledge and results in more accurate decision-making within its vertical. However, decision-making outside of its vertical might yield no improvement over general-purpose AI agents and could even result in reduced accuracy if the vertical AI has little, if any, training on other topics.
• Productivity. Vertical AI can automate complex, time-consuming and error-prone tasks within the industry vertical. This can dramatically enhance business efficiency, and users can accomplish more work in less time with greater accuracy than when using general-purpose AI or no AI at all.
• Personalization. Vertical AI agents can offer detailed user personalization within the framework of the specific industry. For example, a chatbot can deliver 24/7 support to users, operating with access to the user's history, account status and preferences. The goal is to improve customer engagement and satisfaction.
• Compliance. Vertical AI agents can include regulatory compliance, legislative and common industry considerations in their decision-making process. This reduces errors and limits legal exposures for businesses in sensitive or highly regulated industries, such as healthcare or finance.
• Cost efficiency. Superior outcomes characterized by better accuracy and greater productivity will typically correspond to a rapid return on investment for the AI platform and result in cost-effective AI operations into the future.

Drawbacks of vertical agents

Despite the expected benefits, vertical AI agents and platforms can also pose a variety of limitations and potential pitfalls. Common drawbacks of vertical AI can include the following:

• Limited flexibility. Because vertical AI agents are designed and trained to accommodate a specific industry, any queries, decision-making and task processing outside of the intended domain might experience reduced effectiveness -- or even be rendered completely ineffective. This can pose a problem for organizations in diverse industries or where business needs are changing rapidly.
• Limited data. Vertical AI agents depend on quality data that is industry-specific. There might be situations or niche industry uses where there is simply not enough quality data available to develop a viable vertical AI agent, or the available data is cost-prohibitive to acquire. Further, the demands involved with acquiring, validating, cleaning and labeling data can be time-consuming and expensive.
• Data bias. AI decision-making is only as good as its training data. It is critical to check and validate data sets for bias and take concrete steps to mitigate bias before using the data for training. However, small or limited data sets can also result in unintended bias and skewed or discriminatory outcomes.
• Overfitting. Overfitting can pose a problem when an AI is trained "too well," creating such tightly bound decision-making that the AI cannot readily or accurately handle edge cases or unexpected variations in production data compared to its training data. In simpler terms, overfitting occurs when the AI cannot make decisions on real data if the data deviates from training cases.
• Explainability. AI can only gain trust and acceptance when its decision-making is well understood and explainable. An ethical AI system uses quality, bias-free data and makes decisions based on clear guidelines and rules. A lack of explainability can compromise trust in the AI and result in negative business outcomes.
• Security. Vertical AI agents are routinely expected to receive, store and process vast amounts of sensitive industry data. That data must be secured properly, and precautions are needed to ensure the AI does not access or process sensitive data inappropriately or exchange sensitive data in unexpected or improper ways.
• Limited integrations. Vertical AI agents must integrate with a wide array of business systems and platforms, such as databases. While integrations are a key goal for effective AI, a lack of native integrations -- especially for older or legacy systems -- can require costly and complex custom integration efforts.
• Cost. Building, training and maintaining vertical AI agents can be costly, involving software development and extensive training data requirements. Appropriate data resources might not be readily available, requiring data acquisitions from third parties, which in turn must be checked and validated for data quality and bias mitigation. The staff expertise needed to develop the software, gather and clean worthwhile data, conduct training, and manage agent testing and validation might demand additional staff.

Best practices for building and training vertical AI agents

Given the wide variety and unique needs of industry verticals, there is no single approach to building and training vertical AI agents that guarantees success. Still, there are several important best practices that can improve the chances of a successful vertical AI project. Here are some common agentic AI best practices to follow:

• Define the problem and the goal. Vertical AI agents are intended to serve specific purposes and accommodate a narrow range of goals within the industry. Before embarking on development, it is critical to identify the problems that the vertical AI agent is being built to solve. That requires a solid understanding of the challenges in the current business model or workflow that the AI could address. A clear goal makes it easier to quantify the agent's performance and its benefits to the business later.
• Understand the current workflow. Take the time to analyze the current workflows or processes being used, especially those that the AI is being built to benefit. See how users interact with the current workflow and study the ways data is stored, accessed and processed. Consider the ways that decisions are currently made. By mapping these relationships upfront, it's easier to synthesize these interactions through the vertical AI agent.
• Focus on data. Vertical AI agents rely on vast amounts of industry-specific data. This requires copious data collection and preparation. Data sources should include relevant, high-quality data that directly relates to the chosen industry. The data should be checked and validated for quality -- meaning accuracy, completeness and integrity -- be bias-free and be well suited for AI model training. Data might require additional preparation through labeling or tagging. All of these data tasks could require the involvement of data scientists.
• Consider the architecture. Although models and architectures can vary dramatically, there are several important architectural issues to consider. First, select a model that best addresses the kinds of problems that the AI system is supposed to solve. Different models are suited for different tasks; for example, transformer models are ideal for text generation and graph neural networks for fraud detection. Optimize the model using different parameters, new algorithms or other industry-specific considerations. Use adaptive algorithms that enable the vertical AI agent to learn and adapt to new data and user feedback. And finally, be sure to monitor training behaviors, such as accuracy, convergence and loss. Addressing these training issues can help identify possible problems and enhance the performance of the model.
• Include integrations. Consider the ways that the vertical AI agent must interact with existing business systems and platforms. This might require the simultaneous development of APIs or the adoption of a systems integration platform to facilitate smooth deployment and seamless data flows.
• Design for scalability. It's one thing to build a science project, but it's another thing to build a scalable platform for business growth. Ensure that the design of the vertical AI agent can scale up as data volumes and user demands increase over time.
• Monitor and iterate. Once a vertical AI agent is deployed, it's important to monitor its performance and accuracy over time. Monitor key metrics and look for opportunities to optimize or refine the model. Use monitoring results as a basis for updating and enhancing the agent to continue improving accuracy and performance. This can involve extensive ongoing software development work, careful version control and copious documentation.
• Focus on explainability. Ensure that the vertical AI agent exhibits explainable behaviors that are consistent with established rules and guidelines. The operation should be well understood, well documented and able to stand up to close scrutiny from data science experts. Biased or discriminatory outcomes should be evaluated and mitigated, either through model refinement or additional training.

Vertical AI agents: The future of SaaS platforms

Vertical AI agents provide a valuable step forward in enterprise operations. The combination of deep expertise, high accuracy, sophisticated automation and orchestration along with agentic AI's adaptability makes it an increasingly viable choice for a widening array of business use cases.

Today, vertical AI agents are already seen as the future of SaaS platforms. Although vertical AI agents are cutting-edge today, future development and refinement of vertical AI will likely include several factors:

• Enhanced automation and orchestration. As vertical AI entities evolve, their ability to recognize and understand business needs will simplify their adaptation of workflows -- perhaps even offering optimizations and improvements to operations that business and technology leaders might not have considered.
• Enhanced integration. Vertical AI wants to be frictionless and plug-and-play. Although integrations are already extensive, future vertical AI entities will emphasize seamless integration into existing systems and processes. This will demand comprehensive and flexible integration capabilities not found today in all vertical AI.
• Enhanced innovation. Future vertical AI systems won't just fit into the existing workflow -- they will be able to understand the existing workflow and optimize it to benefit the business while maintaining security and compliance. This can open the potential for greater business efficiency, competitiveness, and opportunities for innovation.

Stephen J. Bigelow, senior technology editor at Informa TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.