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The Problem with General AI in Enterprise

It’s a race to the bottom for OpenAi, Anthropic, Big Tech etc.

Artificial intelligence is reshaping industries, but not in the way most people expected. The early hype around general AI for enterprises—broad automation tools meant to replace entire workflows—has collided with the reality of corporate bureaucracy, siloed data, and slow adoption cycles.

Enterprise AI adoption is painfully slow. Large organizations operate with rigid budget cycles, compliance constraints, and internal resistance to change. Even if AI could improve efficiency, most companies don’t have the data infrastructure or internal expertise to implement it effectively.

This is why Vertical AI is where the real money is. 

I might coin this Vertical Saas 3.0 in the future 😀 

Many AI startups have tried to sell horizontal AI solutions—tools that automate broad functions across industries. But these approaches face significant challenges:

🚧 Siloed Data – Most companies don’t have centralized, structured datasets AI can easily learn from.
🚧 Long Sales Cycles – AI tools that require deep integration struggle to get buy-in without an internal champion.
🚧 Budget Rigidity – Corporate budgets are planned 18+ months in advance, limiting flexibility to adopt new AI solutions.
🚧 Lack of AI Expertise – Even when companies buy AI tools, they often lack the talent to implement and optimize them.

Because of these barriers, enterprise AI adoption is taking far longer than anticipated. Companies aren’t just resisting AI for fear of job loss—they simply don’t have the structure or incentives to adopt it at scale.

Why Vertical AI is the Real Opportunity

Instead of building broad AI solutions, startups that focus on Vertical AI—AI designed for specific industries like healthcare, finance, and defense—are seeing faster adoption and stronger ROI.

✔ Industry-Specific Workflows: Vertical AI is built for real problems that companies already spend money on.
✔ Better Data Access: Companies in specialized sectors are more likely to have structured, usable data AI can learn from.
✔ Faster ROI: Executives can justify investing in AI when it directly impacts revenue, compliance, or cost savings.

Where Vertical AI is Winning

• Healthcare AI – Startups using AI for radiology, drug discovery, and administrative automation are growing rapidly because they solve high-friction, high-cost problems. Example: Tempus, which applies AI to personalized cancer treatment.

• Financial AI – AI is thriving in areas like fraud detection, underwriting, and trading algorithms because these use cases have clear ROI. Example: Upstart, which uses AI for credit underwriting.

• Defense & Security AI – AI for autonomous systems, cybersecurity, and intelligence is seeing huge investment, particularly from governments and private defense contractors. Example: Anduril, which builds AI-driven defense systems.

• Manufacturing & Industrial AI – AI is transforming industrial automation, predictive maintenance, and supply chain optimization, reducing downtime and improving efficiency.
  Example: Falkonry – Uses AI-driven anomaly detection to predict equipment failures before they happen.

• Legal & Compliance AI – AI is revolutionizing contract analysis, regulatory compliance, and document automation, saving law firms and corporations significant time and resources.
  Example: Evisort – Uses AI to extract key insights from contracts and streamline legal workflows.

• Retail & E-commerce AI – AI-driven personalization, demand forecasting, and inventory optimization are helping retailers increase conversion rates and reduce waste.
  Example: Stylitics – Uses AI to recommend personalized outfits and boost online fashion sales.

• Agriculture & Food AI – AI is optimizing crop monitoring, precision farming, and food safety, helping farmers increase yield and sustainability.
  Example: Verdant Robotics – Uses AI-powered robotics for precision spraying and automated weeding.

• Energy & Climate AI – AI is improving energy grid management, carbon tracking, and renewable energy efficiency, making sustainability efforts more scalable.
  Example: Aurora Solar – Uses AI to design and optimize solar panel installations.

• Real Estate & PropTech AI – AI is streamlining property valuation, tenant screening, and predictive analytics for real estate investment.
  Example: Cherre – Uses AI to unify and analyze real estate data for better decision-making.

VCs Are Moving Their AI Bets to Vertical AI

Venture capital investors have realized that the next wave of AI unicorns won’t be generic enterprise tools—they’ll be deeply specialized.

• Enterprise AI funding is slowing down as companies struggle with adoption.

• Vertical AI startups are raising more capital because they show real traction, not just potential.

• Investors are backing industry-specific AI companies that align with existing budgets and infrastructure.

TL;DR: The Problem with General AI in Enterprise

General AI for enterprises is struggling because of slow adoption, rigid corporate structures, and siloed data. Despite the hype, broad AI automation tools face long sales cycles and budget constraints, making it difficult for companies to integrate them effectively.

🚀 Vertical AI is the real opportunity. Instead of generic AI, startups focused on industry-specific AI (healthcare, finance, defense, etc.) are seeing faster adoption and higher ROI because they solve real, high-friction problems.

💰 Winning Sectors for Vertical AI:

• Healthcare AI (radiology, drug discovery) – e.g., Tempus

• Financial AI (fraud detection, underwriting) – e.g., Upstart

• Defense & Security AI (autonomous systems, cybersecurity) – e.g., Anduril

📉 Enterprise AI funding is slowing down, while Vertical AI is attracting more VC money because these startups align with existing budgets and infrastructure, showing real traction rather than just potential.

FIND ME: 𝕏 @Trace_Cohen / in LinkedIn

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