To illustrate our philosophy, we examine four companies in our portfolio—Relx, Adobe, Microsoft, and Deere—each harnessing AI in materially different ways, but all demonstrating a common thread: AI used with purpose.

Relx operates at the intersection of data, analytics, and decision tools across legal, risk, science, and health markets. The company is a model of AI implementation focused on enhancing professional judgement rather than replacing it. For example, its LexisNexis division uses natural language processing and machine learning to deliver better legal search and analytics tools, improving lawyer productivity and reducing research time. In healthcare, Elsevier’s clinical decision support tools use AI to personalise recommendations at the point of care. These are real-world, revenue-generating use cases that make Relx’s AI strategy a natural extension of its core mission: better information, faster.

Adobe’s Firefly and Sensei platforms are embedding AI deeply within its suite of creative and marketing tools. Rather than seeking to replace designers or marketers, Adobe’s AI functionality augments creativity—automating routine tasks like image clean-up, content resizing, or customer segmentation, allowing users to focus on higher-order work. Importantly, Adobe’s AI tools are integrated within its existing Creative Cloud and Experience Cloud products, creating a strong alignment between innovation and monetisation. This reinforces Adobe’s strategic strength: providing indispensable tools that evolve with its users' needs.

Microsoft’s Copilot suite—now embedded in Office 365 and GitHub—epitomises our preferred AI use case: productivity enhancement. AI here is not theoretical; it's built directly into the workflows of millions of users. Whether summarising meetings in Teams, generating drafts in Word, or writing code in GitHub, Microsoft’s AI integrations drive user engagement and create new monetisation paths via higher subscription tiers. Crucially, Microsoft’s access to distribution (via its enterprise footprint) and infrastructure (through Azure) provides an unmatched platform advantage when rolling out AI at scale.

Agriculture may not be the first sector that comes to mind when discussing AI, but Deere has been a quiet leader in applying machine learning to real-world challenges. Deere’s autonomous tractors and precision agriculture systems use computer vision and AI to detect crop health, optimise fertiliser usage, and improve yield outcomes. These tools reduce input costs for farmers and improve sustainability outcomes, creating a strong alignment between business success and ESG impact. Deere’s AI capability is not a side project—it is embedded in product development and customer value creation.

Across our equity portfolio, we are focused on companies that deploy AI not as a headline, but as a tool. Our preference is for investments where AI enhances the company’s existing competitive edge, is already delivering results, and contributes to long-term returns on invested capital. In an environment of fast-moving technological change, we believe this grounded, outcome-focused approach is the best way to harness the potential of AI while avoiding the pitfalls of hype.