Duggan Matthews | Investment Professional | Marriott Asset Management | mail me |
Our philosophy has not changed; the environment in which we execute it has. For over two decades, our investment philosophy has been grounded in a simple principle.
High-quality companies that can reliably grow dividends tend to deliver more predictable long-term outcomes. This principle remains the foundation of how we invest. However, the environment in which we apply this philosophy has changed. The volume of information has increased significantly.
The speed of change has also accelerated. In addition, the global breadth of markets has expanded. At the same time, the technology used to process this information has shifted fundamentally. Together, these forces created an opportunity. We could take a tried and tested investment philosophy to another level, especially as most firms bolt on Artificial Intelligence (AI) rather than rethink their processes.
Rebuilding research architecture from first principles
When we began exploring how AI could support our process, we made a deliberate choice. We did not retrofit the technology into old workflows. Instead, we returned to first principles and rebuilt the architecture from the ground up. We did not modernise for its own sake. Rather, we aimed to extend the consistency and reach of our philosophy. This approach stands in contrast to how most firms bolt on AI without structural redesign.
The organising principle was a simple inversion. In traditional research, analysts spend most of their time on routine analysis. As a result, they apply judgment only with the time that remains. We reversed this model. AI now handles structure, consistency and monitoring. Meanwhile, analysts focus on interpretation, context and decision-making. Therefore, the result is not the automation of judgment. Instead, it is the elevation of judgment.
The most significant gain lies in scale and depth. A focused team can now operate with the analytical reach of a much larger global structure. AI handles data processing, change monitoring and routine financial statement analysis. Consequently, our analysts cover a broader universe and spend more time on interpretation. This shift expands in-depth company coverage several-fold. It also strengthens early risk detection and improves consistency in assessments over time.
For instance, analysts previously spent three days analysing a newly released set of financial statements. This process included assessing changes in earnings quality, capital allocation and dividend sustainability. Now, the same analysis takes three hours. The system clearly highlights areas of uncertainty for further interrogation. Similarly, the system now flags and escalates a significant deterioration in cash flow quality or balance sheet strength within minutes. Previously, analysts waited days for manual review.
The structural shift across the investment industry
We recognised early that the forces reshaping our process would reshape the entire industry. As the cost of processing information falls, analytical capability becomes more accessible. Therefore, the sources of competitive advantage are shifting.
Tasks that once required large teams can now be executed by AI in a fraction of the time and cost. Consequently, what once created a competitive advantage is becoming standard infrastructure. This is particularly evident as most firms bolt on AI without rethinking their core processes.
The edge no longer lies in possessing analytical intelligence. Instead, it lies in how firms structure, govern and apply that intelligence over time. We built our approach around this shift.
From information to architecture
A firm that bolts AI onto legacy workflows gains efficiency. However, it does not achieve differentiation. This is the limitation most firms encounter when implementing AI strategies. Therefore, the real question is whether the investment process itself has been redesigned. Only then can it compound AI’s strengths over time.
Our architecture is not a collection of tools added to an existing process. Instead, we decomposed our income-focused investment process into clearly defined components. Each component has its own analytical framework. We then deployed AI within that structure. As a result, the system improves as AI improves. Therefore, the investment process does not age. It compounds.
As information processing becomes commoditised, interpretation increases in value. Consequently, boutique focus becomes a structural advantage. A smaller, stable team with a shared philosophy offers something scale cannot replicate. It brings purposeful judgement, refined over many years of collaboration. Most of our investment professionals have worked with the business for more than a decade. AI does not dilute this experience. Instead, it amplifies it.
For this reason, we invested heavily in the human side of the architecture. A formal mechanism directs analyst attention to decisions where judgment delivers the greatest impact. As a result, analysts spend time on what truly matters. Boutique focus and global reach no longer exist in tension.
The final differentiator – trust
Clients and allocators increasingly ask critical questions. How do you govern this system? Can you explain it clearly? Is it auditable? Firms that fail to answer these questions will struggle, regardless of model sophistication.
For example, our Investment Committee structure has been in place for more than twenty years. It provided the governance foundation for this architecture. The culture of collective review, documented rationale and structured challenge was already established. In contrast, most firms bolt on AI and must build oversight frameworks from scratch.
This history matters. It ensures that AI operates within a culture of accountability. It does not replace that culture. Every decision remains human. Every override is documented. Every methodology change requires Investment Committee approval. Therefore, the system remains explainable, examinable and trustworthy.
The game has changed
Scale no longer determines reach. Headcount no longer determines coverage. Advantages that once favoured large firms are now widely accessible. These include breadth, consistency and monitoring depth. Any team with the right architecture can now deploy them. As a result, size alone offers little advantage.
Firms that have not rethought their processes face a deeper issue. They are not simply late. Instead, they continue to build on foundations that are eroding beneath them. The winners in active management will not necessarily be the largest firms. Instead, they will be those who understood this shift early and acted decisively. They will be the firms that rode the wave rather than being overtaken by it.
We have run its AI-native process alongside traditional workflows for the past three months. Early results provide strong confidence. What we have built will deliver a meaningful step change in how we execute our investment philosophy for clients.
