Industry expertise: the overlooked opportunity for AI

After two years of rapid development, AI is starting to show where it delivers real business value. The difference is becoming less about the technology itself and more about how and where it is applied. The strongest results are coming from businesses with deep industry knowledge and established customer relationships. In particular, B2B vertical software companies are pulling ahead, using their understanding of specific sectors to embed AI into real workflows rather than standalone tools.

 

AI only delivers value when it is applied in context, and that context sits with businesses closest to the customer. 

 

 

Moving out of the sandbox 

The past phase of AI adoption focused heavily on capability. Many organisations experimented with generic tools without a clear link to operational outcomes. What is emerging now is a more targeted approach, where AI is integrated into existing products and processes in ways that reflect how businesses actually operate. 

 

This is where value is starting to concentrate. While AI adoption is now widespread, recent McKinsey research shows that many organisations are still struggling to scale it beyond isolated use cases, with fewer than one-third achieving enterprise-wide impact. 

 

That gap is where most AI initiatives stall, and where businesses with deep industry knowledge and embedded customer relationships are able to move ahead. The issue is no longer access to AI, but how it is applied. The most meaningful gains are emerging in core business functions such as customer operations, pricing and decision-making, where AI can be embedded directly into existing workflows. 

 

 

Where context creates value 

In practice, this is already translating into specific, repeatable use cases. In retail, AI is being used to adjust pricing dynamically based on competitor data, stock levels and demand signals. In financial services, it supports frontline teams by surfacing relevant insights at the point of decision-making. In logistics, it is improving route planning and operational efficiency by processing real-time data across fragmented systems. 

 

We’re seeing this play out in our own portfolio companies, which have developed an AI pricing agent (PriceShape), an employee churn prediction product (Relesys), and a suite of AI tools to identify cyber risks and assess the organisation’s ability to address these (SecureFlag). In each case, these products are built on an existing understanding of customer workflows, data environments and operational constraints, allowing AI to be applied in ways that are immediately relevant. 

 

These are not generic applications, but solutions built around known problems, informed by being close to the customer, with access to structured data and established workflows. This allows them to move quickly from experimentation to measurable outcomes. 

 

 

What separates the winners 

Three factors are consistently enabling businesses to translate AI capability into real-world outcomes.  

1. Context: A focus on B2B and vertical markets, where existing customer relationships and deep industry knowledge provide immediate access to real-world use cases. 
2. Capabilities: A modern technology stack combined with access to proprietary, structured data, enabling AI to be deployed in a way that reflects real operational environments. 
3. Leadership: Founder-led or operator-driven teams that can move quickly, prioritising execution over experimentation and integrating AI into products without lengthy transformation cycles.  

Together, these factors determine whether AI becomes a source of competitive advantage or remains an isolated initiative. 

 

 

Building on, not competing with, AI 

How these companies are building AI is just as important as where they apply it. Many are not building models from scratch. Instead, they are combining existing technologies and applying them within their own products, tailoring them to specific use cases. This approach avoids direct competition with large AI providers and focuses on building application-layer solutions on top of existing infrastructure. It also shortens the path from development to deployment and makes it easier to demonstrate tangible results tied to commercial performance. 

 

 

A more selective phase for AI 

As investment in AI continues, the gap between capability and real-world application is becoming more visible. Over the past two years, funding has been widely available , and many companies have pursued similar opportunities. As expectations tighten, the ability to demonstrate repeatable, practical use cases is becoming a key differentiator. This is where a clear divide is emerging. Businesses that can link AI directly to revenue growth, cost efficiency or customer outcomes are gaining ground. Those who cannot are finding it harder to justify continued investment. 

 

 

Growth with intention 

If there is a defining theme for the next phase of AI adoption, it is intentional growth. At a time when growth is stalling and cost pressures are rising, businesses are under pressure to translate innovation into measurable performance. 

 

AI is increasingly being deployed in areas that directly support commercial outcomes, from improving conversion rates to optimising operations and enabling faster decision-making. For the UK, this is particularly relevant. Weak productivity has been a longstanding constraint on growth, and AI presents a credible opportunity to address it where it is embedded into core operations. However, adoption remains uneven. Larger organisations are better placed to invest in integration, while smaller businesses face barriers around cost, skills and implementation. Governance is also becoming a more visible challenge, with research showing that 22% of files and 4.37% of prompts used in AI tools contain sensitive data. 

 

 

The role of access 

Ensuring broader access will be critical if AI is to deliver meaningful productivity gains. Policy will play a role here, not just in infrastructure and skills, but in widening access to tools. In markets such as the UAE, initiatives are underway to expand access to advanced large language models at a national level, helping to accelerate adoption and ensure that benefits are more widely distributed. 

 

 

A shift in competitive advantage 

Markets always reopen. When they do, businesses with strong operational foundations and credible organic growth are best positioned to capitalise. The same dynamic is now playing out in AI. The companies pulling ahead are not those waiting for perfect conditions or chasing capability for its own sake, but those building momentum through practical application. 

 

Competitive advantage is shifting. It is no longer defined by who builds the most advanced models, but by who understands where to apply them. 

 

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John Messer is Founder and Managing Partner of Copilot Capital 

  

Main image courtesy of iStockPhoto.com and BlackJack3D