Specialized AI Models: Vertical AI & Horizontal AI in 2026
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?
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 application: The same chatbot framework works for retail, banking, and healthcare customer service.
- Function-specific focus: Built for marketing teams, sales departments, or HR operations, not entire industries.
- Common task automation: Handles universal tasks such as email responses, data analysis, and document processing.
When Horizontal AI Fails:
- Customization hell: Adapting generic tools to specific processes eats time and budget
- Compliance gaps: Healthcare can’t use the same data handling as e-commerce
- Integration friction: Generic AI doesn’t understand your existing systems
Real-life examples for Horizontal AI
1. Chatbots and Virtual Assistants
Zendesk Answer Bot handles common inquiries across industries. The bot answers “Where’s my order?” for retailers and “What’s my account balance?” for banks using the same underlying technology.
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
Microsoft Sentinel AI monitors security across manufacturing plants, hospitals, and financial institutions. The system detects unusual login patterns, suspicious network traffic, and potential breaches across all industries.
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
Tableau AI and Microsoft Power BI analyze data for manufacturing, retail, healthcare, and finance using identical frameworks. The AI spots trends, predicts outcomes, and generates visual reports.
Google Analytics applies AI to track user behavior on websites. The system optimizes content and advertising for e-commerce sites, SaaS companies, and media publishers.
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 social media engagement patterns. 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 automate repetitive tasks, such as data entry, invoice processing, and report generation. The bots work in finance departments, HR operations, and supply chain management.
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. Retailers use it for inventory management. Healthcare uses it for medical imaging. Security teams use it for surveillance.
Amazon Rekognition handles face recognition, identity verification, and video analysis across industries.
Vertical AI
Vertical AI solves problems in specific industries. These systems understand healthcare workflows, financial regulations, or manufacturing processes. They literally speak your industry’s language.
- Industry expertise: Healthcare AI understands HIPAA. Finance AI knows SOX compliance. Manufacturing AI speaks Six Sigma.
- Specialized training data: Models learn from medical records, financial transactions, or production data, not generic internet content.
- Regulatory compliance: Built-in compliance with industry-specific regulations.
Real-life examples for Vertical AI
1. Healthcare AI
Tempus processes clinical and molecular data across cancer, cardiology, depression, and infectious diseases. The company went public in June 2024 with AI systems trained exclusively on medical data.1 .
2. Finance and Banking AI
JPMorgan Chase’s Contract Intelligence (COIN) platform reviews commercial loan agreements. The system was trained on financial documents, not general text. 2 .
xAI + Palantir Partnership: The companies offer “agentic workforce” solutions, modular AI agents tailored to financial services processes like compliance monitoring, risk assessment, and transaction analysis.3 .
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.
Harvey Assistant achieved 94.8% accuracy in document Q&A tasks, higher than general-purpose AI models. The system was trained on legal documents, court cases, and contract language4 .
Harvey integrates LexisNexis’ primary law content and generative AI technology, creating a system that understands legal precedent, citation formats, and jurisdictional differences5 .
5. AI in Transportation and Logistics
AI models improve route planning, supply chain management, and autonomous systems.
Tesla’s Autopilot processes data from cameras, radar, and ultrasonic sensors trained exclusively on driving scenarios. The system understands traffic patterns, road conditions, and driver behavior6 .
General-purpose AI couldn’t handle real-time driving decisions. Vertical AI trained on billions of driving miles makes it possible.
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.
Blue River Technology (John Deere subsidiary) developed See & Spray technology. The AI identifies crops versus weeds in real-time and applies herbicides only where needed7 .
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 widely-used models that work across domains text generation, image processing, voice recognition. These are the AI systems everyone talks about.
The generative AI market hit $25.6 billion in 2024. Common AI dominates because it deploys fast and works immediately.8 .
Real-life examples for Common AI
Most LLMs fall under this bucket.
GPT-4 by OpenAI
- Use Case: GPT-4o 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.
The demand for on-device AI processing has intensified as privacy concerns and latency requirements drive the need for local inference capabilities. Apple’s FastVLM, introduced at CVPR 2025, represents a breakthrough in efficient vision-language processing, featuring a novel hybrid vision encoder called FastViTHD designed to output fewer tokens and significantly reduce encoding time for high-resolution images.
Technical Innovation in Mobile AI
The model’s efficiency stems from its hybrid architecture approach. FastViTHD enhances the base FastViT architecture by introducing an additional stage with a downsampling layer, ensuring self-attention operates on tensors downsampled by a factor of 32 rather than 16, reducing image encoding latency while generating 4 times fewer tokens for the LLM decoder.
Unlike cloud-dependent AI systems, FastVLM embodies Apple’s commitment to on-device processing. Technologies like FastVLM could enable more sophisticated AI features to run directly on iPhones, iPads, Macs, and even the Vision Pro, without needing to send sensitive visual data to the cloud.
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
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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