What is Vertical AI?

Vertical AI refers to artificial intelligence that is specifically designed to address the needs of particular industries or sectors. Unlike horizontal AI—such as or Google Bard, which are broad, general-purpose systems that can perform a wide range of tasks across various domains—Vertical AI is a form of vertical software tailored to solve problems in specific fields like healthcare, finance, or manufacturing. The focus is on deeply understanding the unique data, processes, and challenges of an industry to provide more accurate and efficient solutions.

Vertical AI is rapidly transforming the landscape of industry-specific solutions by offering deep expertise and tailored applications that general AI systems simply can’t match. From improving efficiency in manufacturing to enhancing diagnostics in healthcare, Vertical AI is becoming essential for industries that demand specialized, data-driven solutions to optimize operations, meet customer demands, and drive growth.

1. The Emergence of Vertical AI

Evolution from Horizontal to Vertical AI

The initial wave of artificial intelligence was characterized by horizontal AI systems, designed to perform tasks across multiple industries. ChatGPT and other similar models are examples of these generalized systems that have found success due to their flexibility. However, as AI technology advanced, it became clear that industry-specific problems require more than a one-size-fits-all approach. This gave rise to Vertical AI, which tailors its capabilities to meet the unique needs of specific sectors. According to Aforza and Forbes, the shift from general AI models to industry-specialized AI is reshaping how businesses leverage AI, offering solutions that are more relevant and impactful. Additionally, the emergence of vertical AI players is notable, as they are developing specialized solutions for specific industries.

Drivers of Verticalization in AI

Several factors are pushing AI toward specialization, leading to the rise of Vertical AI. Vertical AI depends on high-quality, industry-specific data to deliver accurate and effective solutions. One key driver is the growing demand for precision in AI solutions. As businesses face increasingly complex challenges, generalized AI often falls short in providing the depth of analysis and customization needed for industry-specific tasks. Furthermore, the availability of large, domain-specific datasets allows for more accurate and effective AI systems. As noted by NFX and Redpoint, industries are also realizing that Vertical AI can deliver higher ROI by focusing on specialized use cases, which leads to more efficient resource utilization and faster decision-making.

Revenue Model for Vertical AI

Achieving a high Annual Contract Value (ACV) often requires more than a single SaaS product offering. Vertical-focused startups are finding success by expanding into multi-product models and introducing diverse revenue streams beyond their core product. Adding complementary products enables these companies to bundle offerings, upsell over time, and secure a “sticky” position across various departments within an organization.

For example, restaurant payment platform Toast expanded from payments to payroll and workforce management, enhancing its value proposition. B2B marketplaces like Provi and Pepper have introduced advertising as an additional revenue stream, while Aurora Solar has expanded into financing options for solar installers. Similarly, Procore has recently ventured into insurance, capitalizing on its data insights throughout the construction project lifecycle.

This shift is set to significantly impact future business models. As AI agents replace certain skilled roles, organizations may redirect labor budgets toward software, creating a demand for new pricing models. This opens the door for innovative approaches, such as usage-based or outcome-based pricing, tailored to deliver flexible and scalable value.

2. How Vertical AI Works

Customizing AI for Industry Needs

Vertical AI models are built differently from horizontal models. Unlike general-purpose Horizontal AI tools designed for broad use cases, Vertical AI solutions are tailored for specific industries. They are trained using data specific to a particular industry, such as healthcare or finance, allowing the AI to deeply understand the nuances of the field. This customization ensures that the AI can make more accurate predictions, provide better recommendations, and automate tasks more effectively. For example, in healthcare, Vertical AI models are trained on medical datasets to assist in diagnostics, treatment planning, and patient care, offering insights that generic AI could not provide.

Data-Driven Specialization

The key to the success of Vertical AI lies in the quality and relevance of the sector-specific datasets used to train these models. Unlike horizontal AI, which is often limited by the breadth of its training data, Vertical AI thrives on the depth and specificity of the data it receives. For instance, in finance, Vertical AI models are trained on historical market data, transaction records, and risk management practices, enabling them to make precise predictions about market trends or detect anomalies in real-time. As Forbes and Index Ventures point out, domain knowledge, combined with specialized data, allows Vertical AI to solve problems with far greater accuracy than more generalized models.

Go-to-Market Strategy for Vertical AI

The current excitement surrounding AI has generated both momentum and challenges for go-to-market (GTM) strategies in Vertical AI. On one hand, AI is top-of-mind for nearly every potential buyer, making it easier than ever for new companies to secure prospect calls and even pilot trials with prospective customers. However, this excitement can also create a headwind; as users are inundated with AI products, they often run multiple pilots simultaneously, leading to “pilot fatigue.” This can make it difficult to convert trials into long-term customers, as buyers become more selective and cautious in their evaluation. To stand out in this competitive landscape, Vertical AI companies must establish a clear urgency for buyers to not only try but commit to and actively use their solutions. This involves demonstrating the unique, industry-specific value of the product early on, crafting targeted use cases, and showing measurable impact quickly to drive adoption and conversion.

3. Key Differences Between Vertical and Horizontal AI

Generalized vs. Specialized AI

Horizontal AI, such as ChatGPT and Google Bard, operates across a wide range of industries, providing generalized solutions applicable to various tasks and domains. These models are designed to perform well in multiple settings but lack the depth and specificity required to address particular industry needs. For example, horizontal AI might provide useful customer service support across industries, but it lacks the deep understanding of specialized sectors like healthcare or finance.

In contrast, Vertical AI is designed with a specialized focus on specific industries, leveraging domain expertise and tailored data to address unique challenges within those sectors. By concentrating on industry-specific problems, Vertical AI delivers more relevant insights and actionable recommendations. As noted in Forbes and a16z, the deep integration of sector-specific knowledge allows Vertical AI to offer more effective solutions for problems that require expert understanding, such as predictive maintenance in manufacturing or personalized treatment plans in healthcare. This specialization is what makes Vertical AI indispensable for industries seeking more targeted and efficient AI-driven outcomes.

ROI and Efficiency

One of the primary reasons businesses are increasingly turning to Vertical AI is its ability to deliver higher ROI compared to horizontal AI. Horizontal AI, while versatile, often requires significant adaptation to fit the specialized needs of different industries, which can lead to inefficiencies. In contrast, Vertical AI is purpose-built to solve niche problems within a specific sector, resulting in faster deployment and quicker returns on investment. Vertical AI takes a significant role in revolutionizing market dynamics and enabling new business models.

By focusing on precise use cases, such as fraud detection in finance or equipment diagnostics in manufacturing, Vertical AI can streamline processes and improve decision-making. This leads to increased operational efficiency, enabling businesses to allocate resources more effectively and respond to challenges in real-time. As highlighted by IVP and BVP, the ability of Vertical AI to address critical industry challenges with precision not only boosts efficiency but also reduces costs and enhances overall productivity.

4. The Growing Need for Vertical AI

Industry-Specific Challenges and Solutions

As industries become more complex, the limitations of generalized AI become evident. Horizontal AI solutions often fail to address the specific challenges faced by sectors like healthcare, manufacturing, and finance. For instance, healthcare systems require AI models that understand the intricacies of medical data, while manufacturing processes depend on AI solutions that can optimize supply chain management or predict machine failures.

Vertical AI steps in to fill this gap by providing industry-tailored solutions. In healthcare, for example, AI models can be trained to analyze patient data and assist with diagnostics, making them highly effective in improving treatment outcomes. Similarly, in manufacturing, Vertical AI is used for predictive maintenance, helping companies reduce downtime and optimize production lines. Redpoint notes that Vertical AI's ability to address these specific needs has made it an essential tool for industries seeking to solve their unique challenges more effectively.

Examples

Many companies are already leveraging Vertical AI to transform their operations. In healthcare, Vertical AI systems are being used to improve diagnostic accuracy, allowing doctors to identify diseases earlier and with greater precision. These AI models can analyze vast amounts of patient data, including medical records and imaging, to recommend the best course of treatment. For example, AI-driven diagnostic tools are helping hospitals reduce errors and enhance patient outcomes, as noted by Forbes.

In manufacturing, companies are turning to Vertical AI for predictive maintenance. By analyzing data from machinery and equipment, AI models can predict when a failure is likely to occur, allowing manufacturers to perform maintenance before costly breakdowns happen. This not only reduces downtime but also extends the life of equipment, resulting in significant cost savings. As IVP explains, these tailored AI solutions are becoming integral to optimizing efficiency in highly specialized industries. Vertical AI startups are transforming industries by offering specialized solutions to markets previously underserved by standard software, thereby making incumbents more open to adopting AI-enhanced tools.

5. Vertical AI as Industry-Specific Software

The Evolution and Constraints of Vertical SaaS

Over the past decade, the rise of industry-specific SaaS (Vertical SaaS) has produced numerous success stories, with companies like Toast, Shopify, Procore, and ServiceTitan leading the way. These examples underscore the transformative power of vertical-focused software solutions. However, there are still many sectors that remain underserved by Vertical SaaS. Foundational industries often present significant barriers to technological disruption, such as unstructured data, constrained Total Addressable Markets (TAMs), prolonged sales cycles, low annual contract values, and entrenched incumbents. Additionally, emerging sectors and those undergoing major transitions—such as the shift to electrified energy—present unique challenges.

Now, two critical developments are enabling software solutions for these challenging sectors: 1) advancements in artificial intelligence that can process unstructured data and 2) the redefinition of Vertical SaaS as Vertical Software.

AI’s Role in Unlocking New Opportunities

Previously, Vertical SaaS was limited to companies with modern tech infrastructures, specifically those using structured data systems and databases. But today’s AI advancements allow companies to harness unstructured data, making it feasible to serve markets that were once considered too complex or resistant to technological adoption.

We are also witnessing vertical-focused startups exploring beyond traditional SaaS models. Companies are now adopting embedded payments (like Toast and Shopify), advertising strategies (as seen with Pepper and Provi), and B2B marketplaces (exemplified by Faire and Novi). As AI integration grows, transformations within these models are expected to accelerate, pushing innovations in pricing structures such as usage-based or outcome-based pricing in response to reduced headcounts and seats.

A Unique Opportunity for Founders in Vertical AI

At Greylock, we see a convergence between the emergence of large language models (LLMs) and an embrace of differentiated models, creating ideal conditions for founders aiming to solve persistent industry challenges. While there are valid concerns about durability at the application layer, we believe a deep, domain-specific focus provides a viable wedge for establishing defensible competitive advantages.

LLMs may be groundbreaking, but success at scale within any vertical depends on carefully selecting an industry that aligns well with the technology, conducting thorough TAM analysis, building comprehensive workflows and data-driven products, and designing effective go-to-market strategies. Furthermore, founders need a combination of domain expertise and technological skill to execute these initiatives effectively.

Exploring Key Elements for Success

In the following sections, we will explore each of these elements in detail, examining how domain expertise, industry alignment, and strategic execution are key to thriving in the new era of Vertical AI.

6. Benefits of Vertical AI

Increased Efficiency

One of the most significant benefits of Vertical AI is its ability to improve operational efficiency. By being industry-specific, Vertical AI can automate processes, streamline workflows, and enhance decision-making in ways that horizontal AI cannot. For example, in finance, Vertical AI systems can quickly analyze financial data to detect fraud or assess risks, allowing companies to respond faster and more accurately. Similarly, in manufacturing, AI-driven solutions help optimize production schedules and improve resource allocation, leading to greater efficiency across the board.

As noted by Forbes and a16z, these efficiency gains are not just about cutting costs—they also enable companies to innovate faster, respond to market changes more effectively, and deliver higher-quality services or products.

Enhanced Customer Experiences

Another key benefit of Vertical AI is its ability to deliver personalized experiences that meet the specific needs of customers. In industries like retail, AI-driven personalization has become a game-changer. Vertical AI can analyze customer data to offer personalized product recommendations, tailor marketing campaigns, and even adjust pricing strategies based on real-time demand. This level of customization leads to improved customer satisfaction and loyalty, as noted by Aforza.

For example, an AI model in retail might track a customer's purchasing habits and suggest products that align with their preferences, creating a more engaging and personalized shopping experience. By focusing on industry-specific data and behaviors, Vertical AI allows businesses to meet customer expectations more effectively.

Data and Knowledge Integration

Vertical AI models not only rely on large datasets but also integrate domain-specific knowledge, often incorporating insights from industry experts to enhance decision-making. In industries where specialized knowledge is critical, such as healthcare or engineering, AI systems can combine data analysis with expert insights to deliver more reliable and actionable results.

Forbes highlights how some Vertical AI systems are even capable of datafying expert knowledge, converting the experience and expertise of professionals into a form that can be used by AI models. This integration of human expertise with machine learning enables businesses to make more informed decisions, particularly in fields where domain knowledge plays a crucial role in achieving successful outcomes.

7. Key Sectors Benefiting from Vertical AI

Healthcare

In healthcare, Vertical AI is revolutionizing how doctors diagnose diseases, create treatment plans, and manage patient care. Traditional horizontal AI models may offer general healthcare solutions, but they lack the specificity required for accurate medical decision-making. Vertical AI addresses this by being trained on large datasets of medical records, diagnostic images, and real-time patient data, which allows it to make highly accurate predictions and recommendations. For example, AI systems in healthcare can analyze radiology scans with precision, identifying potential anomalies that human radiologists might miss.

Manufacturing

In the manufacturing sector, Vertical AI is driving advancements in automation, predictive maintenance, and supply chain optimization. Unlike general-purpose AI, Vertical AI is specifically designed to understand the complexities of manufacturing processes, from assembly lines to equipment management. One of the critical applications of Vertical AI in this industry is predictive maintenance, where AI systems monitor machinery in real-time to predict potential failures before they happen. This not only prevents costly downtime but also extends the lifespan of equipment by ensuring timely maintenance. Furthermore, supply chain optimization benefits from AI models that analyze large datasets, predict demand fluctuations, and suggest improvements in logistics, thereby increasing overall efficiency. According to Redpoint and IVP, many manufacturing companies are turning to Vertical AI to reduce operational costs and improve productivity, making it a crucial tool for staying competitive in an increasingly automated world.

Financial Services

The financial sector is another significant beneficiary of Vertical AI, especially in areas like fraud detection, risk management, and compliance. Financial institutions generate vast amounts of data daily, and Vertical AI models are trained specifically to handle the nuances of financial transactions, regulatory requirements, and market trends. In fraud detection, AI systems can monitor transactions in real-time, identifying suspicious activities faster and more accurately than traditional methods. Similarly, in risk management, Vertical AI can assess large portfolios, predict market changes, and offer data-driven strategies to mitigate potential risks. Additionally, compliance with ever-evolving financial regulations is simplified by AI systems that are constantly updated with the latest legal requirements. Index Ventures highlights how the adoption of Vertical AI in finance is improving security, reducing fraud losses, and enabling companies to stay ahead of regulatory changes, providing a strong return on investment.

8. The Future of Vertical AI

AI-Driven Industry Transformation

Looking ahead, Vertical AI is expected to continue transforming industries by offering more personalized and efficient solutions. As industries become more data-driven, the need for AI systems that can leverage domain-specific knowledge will only increase. Vertical AI's ability to provide tailored insights and recommendations positions it as a key player in the future of industry innovation. For example, in healthcare, AI-driven diagnostics may evolve to include personalized treatments based on genetic data, while in manufacturing, AI could further automate complex processes, reducing human intervention. According to Euclid and BVP, industries that adopt Vertical AI early will gain a significant competitive advantage by optimizing their operations and offering more value to customers.

The Role of Generative AI in Vertical AI Solutions

Generative AI, a subset of AI that creates new content or solutions, is becoming increasingly integrated into Vertical AI applications. While generative AI models like ChatGPT are widely known for their horizontal use cases, their potential within Vertical AI is enormous. For example, generative AI could be used in healthcare to create personalized treatment plans based on a patient's unique medical history and genetics, or in finance to generate predictive models for stock market trends. As Forbes and a16z explain, the integration of generative AI into Vertical AI solutions could lead to more innovative and responsive systems, pushing the boundaries of what AI can achieve in industry-specific applications.

9. Vertical AI Agent: A New Era of Specialized AI

As Vertical AI continues to advance, a significant innovation gaining attention is the development of Vertical AI Agents. These agents are AI-powered systems specifically designed to operate autonomously within specialized industries, acting as digital experts that can perform tasks, make decisions, and interact with users in a highly efficient and tailored manner.

Unlike general AI agents like Siri or Alexa, which provide broad assistance across multiple domains, Vertical AI Agents are built for specific industries, such as healthcare, finance, or manufacturing. They are trained on industry-specific data and designed to handle specialized tasks with precision, such as diagnosing medical conditions, optimizing supply chains, or managing financial portfolios.

Key Features of Vertical AI Agents

• Industry Expertise: Vertical AI Agents are equipped with deep knowledge of the specific industry they serve. For instance, in healthcare, an AI agent might assist doctors by analyzing patient data, suggesting potential diagnoses, or recommending treatment plans based on historical and real-time medical information.

• Autonomous Decision-Making: These agents are capable of autonomously making decisions based on the data they process, allowing for faster and more accurate outcomes in time-sensitive scenarios, such as managing a manufacturing plant's operations or detecting fraud in financial transactions.

• Seamless Integration: Vertical AI Agents are designed to integrate seamlessly with existing industry-specific software and systems, providing businesses with a smooth transition to AI-driven automation without disrupting workflows.

Examples of Vertical AI Agents in Action

• Healthcare: AI agents in healthcare assist with telemedicine, allowing patients to interact with digital health advisors that can provide preliminary diagnoses, schedule follow-up appointments, and recommend treatments based on patient history.

• Manufacturing: In manufacturing, AI agents can autonomously manage predictive maintenance by monitoring machine health, scheduling repairs, and optimizing production lines, leading to fewer downtimes and more efficient operations.

• Finance: In financial services, Vertical AI Agents are used for risk management by continuously monitoring market conditions, predicting potential risks, and providing investment recommendations that help financial institutions navigate complex markets.

The Future of Vertical AI Agents

Looking ahead, Vertical AI Agents are expected to become more sophisticated, with enhanced capabilities in natural language processing and real-time decision-making. As these agents continue to evolve, they will play an increasingly vital role in industries where specialized knowledge and rapid responses are critical. For businesses, Vertical AI Agents represent an opportunity to fully leverage the potential of AI by automating routine tasks, reducing human error, and optimizing processes at an unprecedented scale.

In conclusion, the rise of Vertical AI Agents signals a new era of automation and efficiency for specialized industries. By combining industry-specific knowledge with advanced AI capabilities, these agents are set to transform how businesses operate, leading to more personalized, accurate, and efficient solutions across various sectors.

10. How to Implement Vertical AI in Your Business

Identifying Key Areas for AI Application

Before diving into Vertical AI, businesses need to identify areas where AI can deliver the most value. This involves conducting a thorough analysis of business processes, customer needs, and market trends to pinpoint specific use cases that can benefit from AI-driven solutions. For instance, companies in the retail sector might focus on personalization and inventory management, while those in manufacturing might prioritize predictive maintenance.

Building Internal Expertise

Successfully implementing Vertical AI requires businesses to build internal expertise in both AI technologies and their specific industry applications. This can be achieved by either hiring AI specialists or upskilling existing employees. Alternatively, companies may choose to partner with external consultants or technology providers to accelerate the adoption process. Forbes emphasizes the importance of fostering a culture of AI literacy within an organization, ensuring that teams understand how AI works, how to interpret AI-generated insights, and how to incorporate these insights into decision-making processes.

Start with Pilot Projects

A practical approach to adopting Vertical AI is to begin with pilot projects that allow businesses to test AI solutions in real-world settings on a small scale. Pilot projects provide valuable insights into the feasibility, potential challenges, and expected impact of AI within a specific use case. This approach also allows companies to fine-tune AI models before rolling them out across the organization. As Forbes and IVP suggest, starting small helps manage risk and ensures that AI implementations are aligned with business objectives, increasing the likelihood of success.

11. Challenges and Ethical Considerations

Data Privacy and Security

As Vertical AI is designed to work with highly specialized datasets, it often handles sensitive information unique to specific industries, such as patient medical records in healthcare or financial transactions in banking. This creates a significant challenge in ensuring data privacy and maintaining security. Since Vertical AI operates on industry-specific data, any breach or misuse can have severe consequences, not only for individual privacy but also for businesses that rely on this information to maintain competitive advantages.

Managing sensitive industry data requires strict compliance with data protection regulations, such as GDPR in Europe or HIPAA in the United States for healthcare data. Furthermore, as noted by a16z, businesses adopting Vertical AI need to implement robust security measures, including encryption, access controls, and ongoing monitoring, to prevent unauthorized access and ensure that data is used responsibly. The challenge lies in balancing the need for highly detailed and relevant data while protecting it from potential cybersecurity threats.

Ethical Implications of Vertical AI

While Vertical AI offers many benefits, it also raises ethical concerns, particularly around the potential for bias due to its reliance on industry-specific datasets. Because these datasets are often limited to particular sectors or regions, they may not always be fully representative. This can lead to AI models that favor certain outcomes, reinforcing existing biases in industries such as healthcare or finance.

For example, if an AI model in healthcare is trained primarily on data from a particular demographic, it may offer less accurate recommendations for patients outside that group. As NFX highlight, ensuring transparency and fairness is critical when designing Vertical AI models. Developers must take extra care to audit their datasets for biases and continuously test their models to prevent biased outcomes. Additionally, implementing ethical AI frameworks that promote inclusivity and fairness can help mitigate these risks, ensuring that Vertical AI serves diverse populations equitably.

12. Key Takeaways of Vertical AI

Summary

Vertical AI is rapidly transforming industries by offering specialized, data-driven solutions tailored to specific sectors. From healthcare to manufacturing and finance, Vertical AI's ability to address industry-specific challenges is leading to more efficient operations, improved customer experiences, and better decision-making. Its focus on data-driven specialization allows for the creation of models that are more accurate and relevant compared to horizontal AI systems, making it a valuable tool for businesses looking to stay competitive in an increasingly automated world.

At the same time, the adoption of Vertical AI comes with challenges, particularly in ensuring data privacy, security, and ethical fairness. As industries continue to integrate AI into their operations, it will be crucial to address these concerns proactively, ensuring that AI serves not only to improve efficiency but also to uphold high standards of fairness and inclusivity.

Call to Action

Businesses across various sectors should explore how Vertical AI could enhance their operations, improve efficiency, and meet specific challenges head-on. Whether through optimizing supply chains in manufacturing, improving diagnostics in healthcare, or enhancing fraud detection in finance, the potential for Vertical AI is vast. However, companies must also remain vigilant about the ethical implications and data privacy concerns associated with these powerful tools. Proactive adaptation to AI advancements, coupled with responsible implementation, will position businesses to leverage Vertical AI effectively for long-term success.

References

• A16z: Vertical SaaS, Now with AI Inside
• NFX: The Verticalization of Everything
• IVP: The Value of Vertical AI
• Forbes: Vertical AI - The Next Revolution in Generative AI

Please Note: Content may be periodically updated. For the most current and accurate information, consult official sources or industry experts.