Technology and AI in investment research

1. What are the key potential benefits of embracing AI in investment research? Which ones are creating positive impacts already?

AI is transformative technology for investment research and is impactful across the value chain. Firstly, it speeds up the creation of narrative and quantitative content. Secondly, it helps connect themes to events, improving understanding of market movements and forecasting propensity. Finally, it changes the way investment insights are delivered and integrated into investment workflows. 

We are already seeing the impact of these changes – providers are using generative AI tools to produce content more quickly, using machine and deep learning to uncover new correlations within data, and using semantic search and hyperpersonsalisation to match intent with insight. AI  is also changing how the buy side uses research content – expanding capacity to consume more content faster.

AI is improving the quality of research insights and consumption capacity, ultimately reducing effort and improving investment outcomes.

2. What should the role of technology and platform vendors be in this transition?

Vendors must evolve from feature providers to strategic partners. They need to translate cutting-edge AI capabilities into tangible business outcomes.

This means providing open architectures that break down silos across vendor offerings to deliver seamless end-to-end workflows. Vendors must also become joint custodians of data quality that underpins AI model accuracy and output quality. They also need to ensure AI is integrated into user workflows, not delivered as adjacent solutions that impede adoption.

At Singletrack, we work hard to maintain focus on what’s important to our customers day-to-day, and while we have some very exciting new AI-backed tools for our customers to use, such as our new Semantic Search, agentic workflow and research ROI capabilities, we want to make sure that our AI development is truly a value-add for capital markets professionals.

3. How are research providers approaching the control of their IP in a world where content can be easily aggregated and summarized by clients? How should they approach it?

Aggregation and summarisation have long been core use cases for research content; AI has simply reduced the manual overhead. Providers must shift from defensive IP protection to proactive content innovation and delivery. Static PDFs and HTML5 reports are giving way to modular, interactive tools that solve last mile challenges. 

Providers must also embed validation guardrails and source-attribution metadata to ensure output integrity and client trust. Granular usage analytics will help detect unauthorised distribution, while flexible licensing models—such as outcome-based pricing—align cost with actual consumption. 

4. What will providers need to do to thrive in this evolving landscape?

The winners will be able to best synthesise the value of AI technology with a human touch to deliver a holistic service.

Research providers must reexamine their value propositions and embrace a use-case-first mindset. Instead of automating legacy outputs, they should design deliverables around specific outcomes and user needs. Building this future requires atomised content, a deep understanding of buy side intent and delivery platforms that connect insight to decision making.

5. In a market where data consumption and supply is increasing rapidly, how do the worlds of research and data integrate to create the most value?

While data and narrative content have always combined to create value (ex. charts and tables in a report) the boundaries are blurring further. As data and research integrate further, value will be created by increasing interactivity, for example downloading underlying data from charts and models. It’s about directly connecting narrative insights to data. We’ll see things like theme to scenario forecasts, improving the real-time nature of research content with live streams and delivering actionable signals and analytics.

Guided workflows and natural-language overlays will weave data and storytelling into cohesive investment theses, while built-in collaboration tools will connect market movements, research and human expertise into a unified service.

Brijesh Malkan

Published: 13/06/2025