Key Takeaways
Vertical AI is artificial intelligence built for one industry – in insurance, it is trained on policy, insurer, and claims data.
Vertical AI differs from general-purpose (horizontal) AI by understanding insurance terms and workflows without extra training.
For insurers, vertical AI returns more accurate results on tasks like quoting and submissions because it knows the domain.
Vertical AI in insurance powers document extraction, risk analysis, and account summaries that generic models handle poorly.
When a business says it uses artificial intelligence (AI), what exactly do they mean by that? AI is incredibly versatile, and how one business uses AI might be completely different from the next. One difference is whether a company is using Vertical AI. Let's look at what Vertical AI is and how it can help insurance agencies and the entire industry gain a competitive advantage and elevate the customer experience.
What is Vertical AI?
Vertical AI is industry-specific AI that tailors its abilities to meet unique needs. These AI tools tackle industry challenges and workflows.
We see Vertical AI in more real-world scenarios than we might think. In healthcare, AI-powered medical imaging platforms analyze X-rays, and for human resources, AI tools automate processes like administering Employee Benefits.
For the insurance industry, the rise of Vertical AI means greater automation and optimization. Agencies can leverage AI to streamline more time-consuming tasks and take advantage of tailored technology that fits their business models.
Benefits of vertical AI in insurance:
The enhanced expertise of this AI's capabilities means your solutions have more accurate risk assessments and reduced errors. Vertical AI can learn an agency's needs and leverage the most updated industry-specific data to identify trends, patterns, or emerging risks. The more informed an agency is, the better its decision-making will be.
Vertical AI can be tailored to enhance the customer experience. Since the datasets used are unique to each business, it can offer personalized service to customers. With a better understanding of the business, AI can help create a more customer-centric ecosystem as clients navigate the complexities of insurance.
As AI models become more and more common in the workplace, taking advantage of this technology enhances the speed and efficiency at which a business can operate. Consider the time you spend on manual tasks that could be automated, like going through emails, processing claims, and analyzing data. How many hours a week would that free up for you to work on achieving bigger-picture goals that AI agents can't do? Think of goals like growing your business, boosting profits, or deepening your brand reputation.
Vertical AI is simple to integrate into existing workflows or when you're overhauling your business model and systems. Vertical AI agents can also grow with you. As your outputs grow and change, the AI will evolve with you. Regardless of agency size, Vertical AI will fit right in.
Vertical AI versus Horizontal AI: Key differences
Leveraging AI in your workplace can provide a competitive edge, but the type of AI applications used matters.
Generative AI has both Vertical and Horizontal applications. Vertical AI solutions have more of a one-track mind and are highly accurate within their domains. You might hear Vertical AI referred to as domain-specific tools. Domain-specific emphasizes the focus of the solutions to address your unique pain points. Vertical AI has domain expertise, a deep understanding of industry rules, terminology, processes, and higher accuracy.
Vertical AI examples
Vertical AI streamlines industry-specific processes by providing high-quality solutions to challenges the given industry faces. For example, in the insurance industry, AI solutions can assess damage using image recognition, predict repair costs, and automate payout decisions to speed up the claims process. Others may provide data entry, which involves connecting systems to create quicker workflows. These are tools like HR Copilot or MathGPT, and AI-empowered capabilities embedded into insurance solutions like Applied Epic®.
Horizontal AI examples
Compared to Vertical AI, Horizontal AI is more for general purposes. Horizontal AI models have a wide range of tasks and abilities, but no industry-specific functions or features. It's highly versatile, with a wide range of use cases that many industries can take advantage of. Horizontal AI can be applied to various industries, whereas Vertical AI simply wouldn't function for anything other than what it's designed to do. You'll see these tools helping customers make returns or providing research on historical events. These are more general-purpose tools like ChatGPT, Copilot, Claude, Gemini, and other large language models.
The choice to use domain-specific or general-purpose AI depends on the user's needs and priorities. If you value aspects like precision, efficiency, and expertise, acquiring domain-specific AI is best. If you're looking for a more flexible tool that can adapt to different tasks, then general-purpose AI may be suitable.
Even the companies building horizontal AI are running into the limits of one-size-fits-all. OpenAI itself has started building industry-specific versions of ChatGPT – starting with financial services.
OpenAI's ChatGPT for Financial Services shows why Vertical AI is needed in insurance
On September 10, 2026, OpenAI introduced ChatGPT for Financial Services, a version of ChatGPT built for investment banks and equity research teams. The product was shaped by design partnerships with Morgan Stanley and Evercore, and it bundles licensed data from providers like Daloopa, PitchBook, and Crunchbase directly into the product, with additional entitlement-based access for firms that already subscribe to data from S&P Capital IQ, MSCI, Dow Jones Factiva, and Moody's.
The implication is clear: even one of the best-resourced AI companies in the world concluded that a general-purpose model wasn't enough on its own. Investment banking needed real design partners and real data covering financial statements, deal comparables, and market intelligence before a model could be trusted with research a banker would actually use. The domain had to be wrapped around the model.
That's the same argument insurance has been making about vertical AI for years – OpenAI just made it for someone else's industry. Insurance carries at least as much regulatory complexity and proprietary data as investment banking – carrier appetite, loss history, class codes, filing requirements and underwriting guidelines that vary by line of business and by carrier. None of that lives in a general-purpose model's training data, and no comparable data partnership yet exists in insurance the way OpenAI just built one for finance.
Insurance isn't waiting on a horizontal AI company to build that. On the agency side, AutoFill in Applied Epic pulls data from submission documents straight into the Epic record. And Book Builder in Applied Epic turns public business information into a structured account profile that flags coverage gaps and surfaces opportunities before the renewal conversation. On the carrier side, that same kind of digitization shows up as Applied's Cytora, which turns unstructured submission data – emails, PDFs, broker notes – into structured, decision-ready underwriting data already in production for large carriers. None of it is a layer bolted onto a general model – it's built into the products agencies and carriers already use, on data a horizontal AI product doesn't have.
The ChatGPT-and-insurance conversation most agencies have actually seen this year is a different one. Insurify and a handful of other consumer apps now offer auto-quote shopping inside ChatGPT, which triggered a brief broker-stock selloff in February that most analysts have since called overdone. That's a distribution question for personal-lines consumers, not an underwriting-capability question for agencies or carriers.
Leverage the Vertical AI advantage
The digital transformation in the insurance industry is changing how everyone does business. Vertical AI is becoming more widely used for efficiency and competitive advantage, from startup agencies to long-established ones. One survey found that 87% of global organizations surveyed believe AI-driven technology will give them a competitive edge over rivals. Agencies will have the advantage of eliminating time spent on manual tasks to focus on identifying substantial pain points to fix and achieve their big-picture goals.
Those who don't seize the opportunity to harness the domain expertise Vertical AI offers risk being left behind. And while Horizontal AI isn't going to slow an agency down, it doesn't match the benefits a tool with industry-specific data would provide.
Implementing Vertical AI in your business:
So, how do you get started with Vertical AI solutions? Here are five steps to help you implement Vertical AI into your business.
1. Consider your needs
Reflect on your pain points. What solution would best support the growth of your business and make a long-lasting difference? Consider solutions that help with customer service, data entry, or other problem areas. Taking the time to determine why you want to use AI will better support you as you decide what's best for your business.
2. Do your research
With your needs known, consider which specific applications would best meet your needs and workplace. One place to start is establishing whether you'd prefer software-as-a-service (SaaS) or app-based solutions. SaaS allows users to access software through the internet without downloading applications. Users subscribe to the solution and access it through their web browser of choice. Similar to Vertical AI, Vertical SaaS is also designed for a particular industry. Reflect on your business model, either existing or future, and consider what type of technology would complement it best.
3. Activate the technology
With the technology selected, it's time to make it yours. Collaborate with your Vertical AI technology partner to acquire your new solution. Your partner will assist with activating the Vertical AI, whether it's from a SaaS platform or an app-based solution. Consider this your time to fine-tune the solution as well. You can work with your partner to ensure the technology fits your style and has the datasets to provide high-quality industry-specific solutions. Consider adding fine-tuning local language models (LLMs) to support your system and take the time to ensure you and your partner have set up the technology to best fit your needs.
4. Explore your technology
Set aside time to become familiar with how your team uses AI solutions. Do you need to dedicate time to training sessions? Are there any issues with integrating the technology into your existing workflows? You might come across some issues if you've never implemented AI into your business before. If the process is rushed, errors or uncertainty could cost you and your clients. Lean on your technology provider for support and assistance with AI tech.
5. Review the best strategies for optimization
The implementation process doesn't happen overnight. Be prepared to run diagnostics or change how you use the system to optimize results. Establish strategies to measure and track performance using the platform to identify areas for improvement. Have questions? Reach out to your tech partner's support team for specific solutions. Vertical AI can make a substantial difference in how your business operates, but first, you must be confident in how to use it.
Challenges and considerations:
Adopting Vertical AI may not go as smoothly as you hoped. Although Vertical AI platforms are meant to support your business, you might run into some specific challenges. Here are some challenges you may encounter when adopting Vertical AI systems and strategies to overcome them.
Challenge 1: Responses and workflows are not to the caliber you need. Solution: Develop high-quality and domain-specific datasets. Consider LLM integrations and techniques to detect data bias. You could be using poor-quality or unstructured data, which doesn't provide a strong foundation model for the technology.
Challenge 2: The AI model isn't integrating well with your legacy system. Solution: Invest in cloud-based SaaS solutions. You will then be able to migrate data or applications to cloud platforms to leverage their flexibility for AI. Or develop a custom application programming interface (API) to facilitate better the data exchange between your legacy system and AI technology. It will act as a bridge to support communication between both systems, boosting connectivity.
Challenge 3: Implementing Vertical AI solutions can be expensive, and you worry about the return on investment (ROI). Solution: Start with pilot projects or small-scale implementation to validate the value the platform will bring to your business. Define clear AI strategies and initiatives that align with the business objects and prioritize supporting tasks with the highest potential for ROI.
Challenge 4: You have ethical concerns over implementing vertical AI. Solution: Ensure the system provides clear explanations for its decision-making to prioritize transparency and avoid the algorithm developing a bias or releasing private information. Consider establishing data privacy measures through encryption or access controls. Understand that AI doesn't replace your employees but empowers them to achieve greater work.
Using Vertical AI together
We can create an even more efficient industry by embracing Vertical AI. Agencies will no longer have to surrender their time to perform manual tasks. They'll have more time to focus on growth and revenue, and automation will improve the customer experience. Being supported by technology that's industry-specific and tailored to your unique needs will only improve your decision-making processes. Check out our insights to learn more about how Vertical AI fuels the Next Generation of Insurance.
Author
Lance Williams
Lance Williams, Chief Product Officer, leads the Product & Design organization for Applied and EZLynx, driving product strategy for the agency side of the business and spearheading Applied's AI-first product strategy. Before joining Applied, Lance served as Chief Product Officer for TurboTax and Intuit's AI-driven Expert Platform, leading the growth of TurboTax Live from its earliest stages to more than $1 billion in annual revenue. Previously, he held product leadership roles at Apple, where he led the product organization responsible for strategic products and services across Apple's global online stores, including Apple Pay and Apple Business Chat. He holds an MBA from the Helzberg School of Management at Rockhurst University.
