General-purpose AI models have garnered significant attention for their versatility. They can create poetry, write code, and answer a range of queries across various domains.
But when it comes to delivering real business value, especially in industries with complex, high-stakes operations, vertical AI is the solution that businesses should prioritize.
The market is filled with general-purpose AI tools that may look impressive in demos but fail to meet the precision and integration needs of real-world business operations.
Vertical AI is purpose-built to solve industry-specific challenges and aligns perfectly with the workflows and data businesses rely on.
Understanding the Difference Between General and Vertical AI
General AI is often compared to a luxury SUV. It’s versatile, capable of performing many tasks, and adaptable to various needs. However, while it may work well for some applications, it’s not the right tool when precision and efficiency are required for specialized tasks.
Vertical AI, on the other hand, is built like a delivery truck. It’s optimized for specific tasks and industries. While it may not be as flexible as general AI, it’s far more efficient in the context for which it was designed. It’s a tool that excels when precision is key.
Why Vertical AI Outperforms General Models
General AI models are designed for flexibility and broad applications. This means they can perform a wide range of tasks across different domains.
However, when businesses need to solve industry-specific problems, such as handling medical data, financial transactions, or legal documents, the generalized approach often falls short.
For example, Page.AI, a vertical AI model for medical imaging, achieves 23% greater diagnostic accuracy compared to general models. This is because it’s explicitly trained to understand medical data, radiology scans, and healthcare terminology.
In industries such as healthcare, finance, and legal services, where even minor mistakes can lead to costly errors, vertical AI offers the specialized knowledge necessary for accuracy and reliability.
Speed to Value with Vertical AI
For business leaders, speed to value is essential. The faster AI solutions can be deployed and integrated into existing workflows, the sooner they can start delivering value.
Vertical AI solutions are typically pre-trained on industry-specific data, which means they require far less fine-tuning than their general-purpose counterparts.
Atomwise exemplifies this advantage, their AtomNet has demonstrated a 74% success rate in identifying novel compounds and was tested on 318 targets in collaboration with over 250 academic labs worldwide, proving its broad applicability and effectiveness as an alternative to traditional high-throughput screening (HTS).
This was possible because the AI was already tailored to the task, reducing the need for additional adjustments or customizations.
For companies looking to scale quickly, vertical AI provides a faster and more efficient path to deployment.
The models are ready to integrate directly into existing systems, such as CRM or ERP platforms, without requiring a complete overhaul of the tech stack.
System Integration: Seamless Fit with Existing Infrastructure
Vertical AI’s value isn’t limited to its ability to perform tasks efficiently; it also integrates seamlessly with existing business systems.
For businesses with complex infrastructures, such as supply chains, ERP systems, or customer relationship management (CRM) tools, the ability to integrate AI into their existing workflows without causing disruptions is crucial.
Vertical AI ensures smoother system integration, which means fewer headaches for IT teams and a faster path to value.
Built-In Compliance: Meeting Industry-Specific Regulatory Standards
For industries with strict regulatory requirements, such as healthcare and finance, compliance is a non-negotiable factor.
General AI models require significant customization to meet the regulatory standards of specific industries, which can add complexity and increase the risk of non-compliance.
Vertical AI, however, is designed with these compliance requirements in mind from the start. For example, JPMorgan Chase uses vertical AI to enhance its fraud detection systems, ensuring compliance with the Bank Secrecy Act (BSA) and Anti-Money Laundering (AML) regulations.
These models not only perform effectively but also meet the regulatory standards critical to the financial services industry. General AI models, even with adjustments, can’t achieve the same level of compliance or performance.
Vertical AI Automates, It Doesn’t Just Assist
The real power of vertical AI lies in its ability to automate complex workflows rather than merely assist with them.
From triage in healthcare to pricing optimization in retail, vertical AI is designed to execute industry-specific tasks, applying relevant data and logic directly to decision-making processes.
Upstart, a fintech company, uses vertical AI to automate 92% loan origination. This AI solution utilizes non-traditional data points to expedite lending decisions, resulting in a 43% increase in approval rates.
This is a perfect example of how vertical AI automates processes and drives tangible business outcomes.
The Future of AI: System Fit Will Define Success
The future of AI adoption depends on choosing the right tool for each specific business challenge.
Vertical AI is designed to address specific challenges within industries such as healthcare, finance, and manufacturing. These models deliver faster value, are easier to integrate, and meet regulatory requirements with fewer risks.
For businesses looking to scale efficiently, reduce costs, and stay competitive, vertical AI is the key to success. It provides the precision, compliance, and automation that general AI can’t offer, making it the right choice for enterprises serious about using AI to drive business results.
Invest in vertical AI models that already fit your business. Focus on system fit, not flexibility, and you’ll unlock the true potential of AI.
