While ChatGPT grabbed headlines, the real business value comes from AI built for specific problems. Companies are moving beyond general-purpose AI toward systems designed for their exact needs. This shift is creating three distinct types of specialized AI – each solving different business challenges.
Which AI Type Should You Choose?
Start here → Do you have industry-specific regulations?
├── Yes → Consider Vertical AI
└── No → Do you need it working in 30 days?
├── Yes → Try Common AI first
└── No → Horizontal AI might work
Horizontal AI
Horizontal AI refers to specialized AI systems that are focused on specific business functions (e.g. marketing, sales). They can be applicable across various industries. Unlike vertical AI, Horizontal AI models are more generalized and can be adapted for multiple use cases. The key characteristics of horizontal AI include:
- Cross-industry applications: Think of horizontal AI as Swiss Army knives – useful across many industries but not perfect for any single one.
- BU-specific applications: Some horizontal solutions are focused on a specific business unit such as marketing, customer service, or HR.
- General-purpose tools: These specialized AI systems are built to perform common tasks like natural language processing (NLP), computer vision, or predictive analytics, which are useful in multiple contexts.
When Horizontal AI Fails:
- Generic solutions may not fit unique business processes
- Often requires significant customization
- May not comply with industry-specific regulations
Real-life examples for Horizontal AI
1. Chatbots and Virtual Assistants
AI-powered chatbots and virtual assistants help businesses automate customer support, answer frequently asked questions, and improve user engagement.
Zendesk Answer Bot is an example, used in customer service to provide instant responses to common inquiries and reduce the workload on human agents.
2. AI-Powered Cybersecurity
AI enhances cybersecurity by identifying threats, monitoring network traffic, and preventing attacks in real-time. Microsoft Sentinel AI delivers enterprise-level security analytics by leveraging machine learning to identify security incidents across multiple IT systems.
CrowdStrike Falcon is another cybersecurity AI application that protects endpoints from malware and unauthorized access through real-time AI-driven threat intelligence.
3. Business Intelligence and Data Analytics
Companies use AI-driven analytics tools to make data-driven decisions, optimize operations, and improve forecasting.
Tableau AI and Microsoft Power BI utilize AI to analyze large datasets, generate actionable insights, and deliver visual reports to businesses across various industries. Google Analytics applies AI to track user behavior on websites, helping digital marketers optimize their content and advertising strategies.
4. AI for Marketing and Sales
AI models are used in content recommendation, customer segmentation, and interaction analysis. Salesforce Einstein AI applies machine learning techniques to analyze business interactions and suggest relevant actions. Hootsuite AI examines engagement patterns on social media platforms. Peasy.ai and HubSpot AI process customer data to provide insights related to audience behavior.
5. AI for Automation and Process Optimization
AI-driven automation systems help automate repetitive tasks and streamline workflows. UiPath and Automation Anywhere provide AI-powered RPA solutions that automate data entry, invoice processing, and other business workflows.
6. Computer Vision Applications
AI-powered computer vision systems analyze and process images and videos for various applications. Google Vision AI detects objects, text, and faces in images, making it useful for security, retail, and healthcare applications. Amazon Rekognition is used for face recognition, identity verification, and video analysis.
Vertical AI
Vertical AI refers to AI systems specifically designed to solve problems within a particular industry or domain. These AI models are highly specialized, focusing on niche areas such as healthcare, finance, manufacturing, or legal services. The key characteristics of Vertical AI include:
- Industry-Specific Expertise: Vertical AI models are tailored to understand and address the unique challenges, regulations, and workflows of a particular industry.
- Customized Data: These models are trained on highly specific industry data, resulting in more accurate and relevant outcomes.
- Specialized Applications: Examples of Vertical AI include specialized AI systems used for medical diagnosis, financial risk assessment, supply chain optimization, and legal document analysis.
Real-life examples for Vertical AI
1. Healthcare AI
1. Healthcare AI
Tempus is one of the pioneering healthcare AI companies that has transformed precision medicine through its AI-enabled platform, processing clinical and molecular data across cancer, cardiology, depression, and infectious diseases. After going public in June 2024, Tempus has grown beyond oncology into cardiology, depression, and contagious diseases1 .
2. Finance and Banking AI
Goldman’s move means that, along with JPMorgan Chase and Morgan Stanley, the world’s top three investment banks have aggressively released generative AI tools to their workforce. JPMorgan Chase has pioneered using artificial intelligence in legal document analysis through its Contract Intelligence (COiN) platform, saving over 360,000 work hours annually by automating document analysis, translating to millions of dollars in cost savings2 .
xAI has teamed with Palantir to explore how financial services firms can employ artificial intelligence (AI). The partnership’s offerings include an “agentic workforce,” which the companies describe as “modular AI agents tailored to specific business processes3 .
3. Cybersecurity AI
AI-driven security models help detect threats, prevent cyberattacks, and monitor networks. Microsoft Sentinel AI provides automated threat detection and response. CrowdStrike Falcon monitors and protects endpoints against cyber threats. FireEye Helix uses AI for vulnerability assessment and security event analysis.
4. Legal AI
AI supports legal professionals by automating research, contract analysis, and litigation prediction. ROSS Intelligence processes legal texts to assist with case law research. Lex Machina analyzes past court cases to identify trends and predict outcomes. Casetext automates contract review and legal document analysis. These AI models help legal professionals improve efficiency in research and case preparation.
Harvey Assistant emerged as the standout performer, achieving the highest scores in five of the six tasks it participated in, including an impressive 94.8% accuracy rate for document Q&A. Harvey raised another $300 million at a $5 billion valuation after securing $300 million at $3 billion just three months prior.4 .
The generative AI legal startup Harvey has entered into a strategic alliance with LexisNexis Legal & Professional by which it will integrate LexisNexis’ gen AI technology, primary law content 5 .
5. AI in Transportation and Logistics
AI models improve route planning, supply chain management, and autonomous systems. Tesla’s Autopilot is a specialized AI model designed for autonomous driving. It processes data from cameras, radar, and ultrasonic sensors to navigate roads, avoid obstacles, and assist with driving tasks.
Tesla vehicles equipped with Autopilot can perform tasks such as lane keeping, adaptive cruise control, and automated parking, making driving safer and more efficient6 .
6. AI in Agriculture
AI in agriculture helps with precision farming, pest detection, and yield prediction. Blue River Technology, a subsidiary of John Deere, uses AI for precision agriculture. Its See & Spray technology identifies crops and weeds in real-time and applies herbicides only where necessary.
Farmers using Blue River’s AI-driven equipment can significantly reduce the amount of chemicals used, lowering costs and environmental impact while maintaining crop yields7 .
7. Pharma AI
DeepMind’s AlphaFold is an AI model developed by DeepMind to predict protein structures. This model is highly specialized within the field of molecular biology and has revolutionized research in this area.
AlphaFold has been used to predict the structures of proteins that are difficult to study experimentally, accelerating drug discovery and understanding of diseases.
Common AI
Common AI refers to artificial intelligence models and systems that are widely used across different domains and everyday applications. These AI systems perform tasks such as text generation, image processing, voice recognition, and recommendation systems. Unlike vertical AI, which is industry-specific, common AI solutions are designed for broad usage across multiple fields. The generative AI market surpassed $25.6 billion in 2024, driven by rapid adoption and the increasing integration of AI capabilities across industries8 .
Real-life examples for Common AI
Most LLMs fall under this bucket.
GPT-4 by OpenAI
- Use Case: GPT-4 is a general-purpose language model that can be used across various industries for tasks like content generation, customer support, coding assistance, data analysis, and more.
- Real-Life Application: Companies across different sectors, from tech firms to media outlets, use GPT-4 for generating blog posts, automating customer service responses, drafting emails, and even creating code snippets.
Google Cloud AI and Gemini Models
Google Cloud Platform (GCP) has made significant strides in the cloud market, nearly doubling its market share from 6% in Q4 2017 to 11% in Q4 2023. Gemini 2.0 Flash is, therefore, (by far) the cheapest overall per token, with GPT-4.5’s research preview amassing a startling cost9 .
Real-Life Application: Google Cloud remains a strong contender in public cloud services, while Alphabet’s integration of AI into search and Workspace offers additional monetization avenues. Retailers use Google Cloud’s Vision AI to automate inventory management by recognizing products and tracking stock levels through images and videos10 .
Microsoft Azure AI
Use Case: Azure AI provides a range of services, including machine learning, cognitive services, and AI development tools that can be implemented across various industries.
Real-Life Application: Businesses use Azure AI for tasks such as sentiment analysis in customer feedback, predictive maintenance in manufacturing, and fraud detection in banking.
Other specialized AI models
Above, we split AI models by their application areas in business. There are other ways to categorize AI models further. These specialized AI models cater to more focused, cross-disciplinary needs or unique environments:
Other specialized AI models
These specialized AI models cater to more focused, cross-disciplinary needs or unique environments:
Edge AI: AI models optimized to run on edge devices (like IoT sensors or smartphones) rather than centralized data centers. Others are investing in edge computing, driven by the growth of IoT devices that gather and share data with little human input. Edge AI is crucial for real-time processing in applications such as autonomous vehicles, smart cities, and industrial automation.
Multimodal AI: GPT-4o accepts any combination of text, audio, image, and video as input and generates any combination of text, audio, and image outputs. Large multimodal models can process and integrate data from multiple modalities such as text, images, audio, and video, providing more comprehensive and context-rich outputs 11 .
Generative AI: The generative AI market surpassed $25.6 billion in 2024, driven by rapid adoption and the increasing integration of AI capabilities across industries. Generative AI models can create new content such as text, images, music, and even video by learning from existing data. These models include technologies like Generative Adversarial Networks (GANs) and variational autoencoders.
Explainable AI (XAI): AI systems designed to provide transparent and understandable decision-making processes. Explainability is important for accuracy, transparency and more when working with an LLM. Users (and stakeholders) need to understand what the LLM’s response means and why (and how) it came to that decision. This is increasingly important in sectors where AI decisions must be audited or justified, such as healthcare, finance, and law12 .
FAQ
How can you find vertical AI / horizontal AI solutions?
Vertical AI can be found in the form of vertical SaaS (vSaaS). Users can search industry analysts like AIMultiple to find new solutions. For example, AIMultiple’s AP AI list contains horizontal AI vendors in the accounts payable (AP) domain.
What is the future of specialized AI technologies?
Both established SaaS companies and AI startups are launching new specialized AI solutions. As these solutions mature, AIMultiple expects vendors to consolidate their offerings to help enterprises:
Minimize application switching for employees
vendor sprawl
However, given that specialized AI is an emerging field, we don’t expect this consolidation in the next few years.
How can you find vertical AI / horizontal AI solutions
For Vertical AI solutions, you can start by researching AI vendors or platforms that specialize in your industry. These solutions are often tailored to address the unique challenges and regulatory requirements of a particular sector, such as healthcare, finance, or manufacturing. Industry conferences, trade publications, and professional networks can also provide insights into leading Vertical AI providers. On the other hand, Horizontal AI solutions are more generalized and can be applied across multiple industries. These solutions are typically offered by major tech companies or AI platforms, such as cloud service providers (e.g., AWS, Google Cloud, Microsoft Azure), which provide AI tools for a wide range of applications, including natural language processing, predictive analytics, and computer vision. Exploring these platforms, reviewing case studies, and consulting with specialized AI consultants can help you identify the right Horizontal AI tools that can be adapted to your specific business needs. Additionally, attending AI-focused conferences or webinars can expose you to both Vertical and Horizontal AI solutions and how they are being used effectively across different domains.
Why is specialized AI relevant now?
From an inference (i.e. running a machine learning model to produce predictions) perspective, we have hit the limits of increasing the scale of dense transformer models. A state-of-the-art 8xGPU cluster can not serve a multi-trillion parameter dense transformer model at a fast enough speed to keep a human reader engaged.
This is why OpenAI relied on a Mixture of Experts architecture in GPT-4.13 . Such architectures enable building smaller expert machine learning models that act together to solve a diverse set of problems.
Since our capabilities in building larger deep learning models may grow slowly, we need to increase the specialization of these models to increase performance. There has been numerous examples of specialized data improving model performance.14
Notes & external links
All models mentioned in this article are narrow AI models and not generalized artificial intelligence models. Generalized AI is not likely to happen in this decade.
External Links
- 1. AI in Healthcare Diagnostics: Revolutionizing Medical Accuracy in 2024.
- 2. Harvey and CoCounsel receive top scores in first major industry GenAI benchmarking study - Legal IT Insider. Legal IT Insider
- 3. Harvey and CoCounsel receive top scores in first major industry GenAI benchmarking study - Legal IT Insider. Legal IT Insider
- 4. LexisNexis and Harvey Announce Strategic Alliance to Integrate Trusted, High-Quality AI Technology and Legal Content and Develop Advanced Workflows | LexisNexis PressRoom. LexisNexis
- 5. LexisNexis and Harvey Announce Strategic Alliance to Integrate Trusted, High-Quality AI Technology and Legal Content and Develop Advanced Workflows | LexisNexis PressRoom. LexisNexis
- 6. Autopilot | Tesla.
- 7. Welcome - Welcome | Blue River Technology .
- 8. Who is winning the cloud AI race? Microsoft vs. AWS vs. Google. IoT Analytics GmbH
- 9. Who is winning the cloud AI race? Microsoft vs. AWS vs. Google. IoT Analytics GmbH
- 10. State of the Cloud: Gen AI & Azure, Google, AWS. Channel Futures
- 11. State of the Cloud: Gen AI & Azure, Google, AWS. Channel Futures
- 12. GPT-4.5 vs. Claude 3.7 Sonnet vs. Gemini 2.0 Flash: A No-Nonsense Guide - DEV Community. DEV Community
- 13. GPT-4 Architecture, Infrastructure, Training Dataset, Costs, Vision, MoE – SemiAnalysis. SemiAnalysis
- 14. Introducing BloombergGPT, Bloomberg’s 50-billion parameter large language model, purpose-built from scratch for finance | Press | Bloomberg LP.
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