Specialized AI Models: Vertical AI & Horizontal AI in 2026
While ChatGPT grabbed headlines, real business value comes from AI built for specific problems. Companies are moving beyond general-purpose AI toward systems designed for their exact needs.
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 systems focused on specific business functions (marketing, sales, customer service). They work across various industries. Unlike vertical AI models, horizontal AI models are more generalizable and can be adapted to multiple use cases.
Key Characteristics
Cross-industry application: 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 like 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
OpenAI launched two healthcare products in January 2026: ChatGPT Health (consumer-facing) and ChatGPT for Healthcare (enterprise HIPAA-compliant workspace).
ChatGPT Health allows users to securely connect medical records and wellness apps including Apple Health, MyFitnessPal, and Function, with over 230 million people globally asking health-related questions on the platform weekly.1
ChatGPT for Healthcare provides a HIPAA-compliant workspace for clinicians, researchers, and administrators, powered by GPT-5.2 models evaluated by 260+ physicians across 60 countries who reviewed 600,000+ model outputs. Leading health institutions, including AdventHealth, HCA Healthcare, Boston Children’s Hospital, Cedars-Sinai Medical Center, and Stanford Medicine Children’s Health, have adopted the platform.2
Claude Health Integration: Claude can now read and analyze health data on iOS (via Health Connect) and Android platforms, delivering activity insights, workout trends, and sleep quality analysis. HIPAA-ready Enterprise plans are available to organizations that process protected health information.3
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. 4 .
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.5 .
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 in the first major industry GenAI benchmarking study (published February 27, 2025 by Vals AI). Harvey received the highest scores in five of six evaluated tasks including document Q&A, document extraction, redlining, transcript analysis, and chronology generation—either matching or outperforming lawyer baselines in five tasks. Harvey was consistently the fastest tool, providing responses in under one minute.6
Strategic Alliance Expansion: The LexisNexis-Harvey partnership enables one-way integration, with Harvey serving as a consumption platform for LexisNexis content. Users can access Shepardized case law, primary legal content, and LexisNexis’ Protégé agentic assistant directly within Harvey’s interface.
RELX Group backed Harvey’s $300 million Series B round in February 2025. Harvey raised an additional $160 million in December 2025, reaching an $8 billion valuation with $760 million total capital raised in 2025. The companies are co-developing AI-powered legal workflows, including automated drafting for motions to dismiss and summary judgment.7
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 behavior8 .
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 needed9 .
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.10 .
Real-life examples for Common AI
Most LLMs fall under this bucket.
GPT-5 Series by OpenAI
GPT-5 series represents OpenAI’s most advanced general-purpose language model family. The system uses multi-model routing to automatically select optimal models for different tasks, balancing speed and complexity.
Performance: GPT-5.2 achieves 94.6% on AIME 2025 mathematics, 74.9% on SWE-bench Verified coding benchmark, 84.2% on MMMU multimodal understanding, and shows approximately 45% fewer factual errors than GPT-4o.11
Real-Life Application: Companies across different sectors use GPT-5 for content generation, customer support, coding assistance, and data analysis. The model’s 1 million token context window enables analysis of entire codebases and complex documents.
Model Lifecycle: As of February 13, 2026, OpenAI retired GPT-4o, GPT-4.1, and related variants from ChatGPT, consolidating users onto the GPT-5 series.12
Google Cloud AI and Gemini Models
Gemini Model Evolution (2025-2026)
Gemini 2.0 Flash became generally available in February 2025, with higher rate limits, stronger performance, and simplified pricing (a single price per input type, eliminating short/long context distinctions). Gemini 2.0 Flash-Lite, released as Google’s most cost-efficient model variant, targets large-scale text output use cases.13
The Gemini 3 series launched in December 2025, with Gemini 3 Flash achieving 78% on SWE-bench, verified for agentic coding, while costing less than a quarter of Gemini 3 Pro. Gemini 3 Pro offers a 2 million token context window with the strongest coding performance to date.14
New Features: Google launched Personal Intelligence (an optional feature allowing Gemini to access users’ Google account information), free SAT practice tests developed with The Princeton Review, Canvas (a collaborative document/code workspace), and upgraded Deep Research using Gemini 2.0 Flash Thinking models.15
Microsoft Azure AI
Use Case: Azure AI offers a range of services, including machine learning, cognitive services, and AI development tools, that can be applied across 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.
Claude 4.5 Series by Anthropic
Use Case: Claude specializes in agent coding and computer use, with enhanced safety through Constitutional AI training. The 4.5 series includes Sonnet 4.5 (best for real-world agents), Haiku 4.5 (matches Sonnet 4 performance at faster speeds), and Opus 4.5 (most powerful frontier model).
Real-Life Application: Claude Code, a command-line agentic tool, saw 5.5x revenue increase by July 2025, with developers delegating coding tasks directly from terminals. Claude Cowork is a GUI-based tool for non-technical users, reportedly built primarily by Claude Code.16
Upcoming Release: Claude Sonnet 5 (codenamed ‘Fennec’) is expected to launch in early February 2026, with leaked benchmarks suggesting coding capabilities surpassing Opus 4.5, enhanced mathematical reasoning, and pricing approximately 50% lower than Opus 4.5.17
Other specialized AI models
Above, we categorize AI models by their business applications. There are additional ways to further categorize AI models. 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:
Apple’s FastVLM, introduced at CVPR 2025, represents a breakthrough in efficient vision-language processing. Apple open-sourced FastVLM and MobileCLIP2 on the Hugging Face platform in 2025, achieving an 85-fold speed increase over comparable models. FastVLM processes high-resolution images entirely on-device via Apple’s MLX framework, reducing image encoding latency while generating 4x fewer tokens for LLM decoders. The hybrid vision encoder (FastViTHD) downsamples tensors by a factor of 32 rather than 16, enabling real-time visual-language processing on iPhones and iPads without cloud dependency.18
Apple Intelligence Updates
Foundation Models Framework: Apple released a developer framework that enables apps to integrate Apple Intelligence on-device models at no cloud API cost. Developers can implement generative capabilities with as few as 3 lines of Swift code, enabling offline AI functionality while protecting privacy. Example applications include personalized quiz generation from notes and natural language search in offline apps.19
Siri 2.0 (Late 2025): Apple released an upgraded Siri powered by Large Language Models with On-Screen Awareness, enabling cross-app agentic workflows. Users can execute complex multi-step commands, such as “Find flight details from email and add to calendar,” without manual intervention. New features include “Type to Siri” (double-tap gesture for text-based interaction), glowing edge UI indicating active listening, and seamless bilingual support. Requires A17 Pro chip or later (iPhone 15 Pro+, M1+ iPads, Apple Silicon Macs).20
Apple-Google Partnership: Apple integrated Google’s Gemini models into Siri under the Apple Intelligence platform for handling less sensitive tasks and questions, complementing Apple’s on-device AI processing.21
Multimodal AI: GPT-5 accepts any combination of text, audio, image, and video as input and generates any combination of text, audio, and image outputs.
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.
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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