VICTORIA GEDDES, Executive Director.

In capital markets today, a fundamental transformation is under way across both the buy and sell side. At an industry panel FIRST Advisers attended recently in the US titled “The AI Augmented Street,” research leaders from investment banks and growth asset managers discussed how artificial intelligence is reshaping institutional workflows. The consensus was immediate: AI has moved from a novelty to an indispensable operational layer across the research teams.

Historically, an equity analyst’s workflow was dominated by manual data collection, with up to 70% of an entry-level workload spent searching, cutting and pasting disclosures. Today, generative AI is automating these administrative tasks, compressing gathering times from days into seconds. As fundamental data collection becomes commoditised, the burden on corporate Investor Relations (IR) teams and management is shifting dramatically.

Automating Maintenance to Reclaim Time for Analytics and Judgement

Across institutional investment firms, AI adoption is stripping away administrative ‘red tape’ to free up human judgment. Sell-side analysts highlight that compliance maintenance—such as updating standardised risk disclosures, formatting boilerplates, and generating expert notes—consumes significant operational energy and does little to improve returns. Automating these tasks allows associates to focus on hypothesis generation and thesis design.

“AI in the research process is like an intern on steroids. They can collect information, present it together, and find needles in a haystack—but at the end of the day, investing is still a judgment process, and portfolio managers must decide which needles matter.” Ash, Portfolio Manager, Columbia Threadneedle

This automation is expanding coverage capacity. Where an analyst previously managed one or two research ideas per week, AI tools enable them to run 10 to 15 research streams in parallel. Buy-side analysts can now digest complex disclosures from international issuers in 30 to 60 minutes of AI-assisted prep, enabling high-level engagement without weeks of preparatory research.

The ‘Red Team’ Dynamic and Shift from Fact to Analysis

For IR departments, the rapid adoption of AI introduces a powerful new dynamic. Panelists compared the emerging relationship between investors and public companies to cybersecurity operations, where investors act as a ‘Red Team’ probing a narrative for vulnerabilities, while IR teams serve as the ‘Blue Team’ defending the corporate thesis.

Because investors now use AI to generate prep packs in 30 seconds prior to a meeting, including benchmarking against peers, the nature of management Q&A has fundamentally changed.

THE SHIFT IN IR ENGAGEMENT

Traditional IR:

• • • • Fact Accumulation: ‘What was your European tax rate in 2024?’
      • Manual Data Collection: Searching filings for historical guidance numbers.

AI-Augmented IR (2026+):

• • • • Analytical Reaction: ‘Our AI model indicates your European margin will drop 20% based on new disclosures—how do you plan to offset this?’
      • Peer Benchmarking: Comparative analysis of peers generated in real time.

“The engagement between the sell side and the buy side is shifting from fact accumulation to reacting to analysis. Investors are going to come with direct questions and pre-packaged models, and IR teams must be ready to unpack that analysis.” Jim Kelly, Director of Research, Leerink Partners

As basic factual data becomes instantly accessible, investors are seeking ‘the misunderstood piece of the story’—looking beyond public filings to evaluate management credibility and culture. Direct, in-person engagement, executive body language, and non-verbal confidence cues are rising in value, as these human elements remain entirely beyond the reach of AI models.

Navigating Model Limitations

Despite significant gains in compressing the time spent on researching individual companies, panel leaders cautioned against blind reliance on generative AI outputs. A central challenge facing research directors is the distinction between probabilistic language models and deterministic financial accuracy.

Probabilistic LLMs predict the most likely sequence of words, presenting answers with confidence even when incomplete. This can be problematic in financial research when missing a single regulatory disclosure can invalidate a model.  To ensure accuracy, institutional teams are also using AI to retrieve primary sources.

In addition, compliance frameworks do not allow institutional investors to cite generative AI models as official research sources. Investor must verify outputs back to primary documents.

A Strategic Diagnostic for IR Leaders

As institutional investors harness AI to process alternative data and stress-test equity narratives in nanoseconds, corporate communications can no longer rely on passive reporting. Investors who leverage AI effectively are expanding their coverage footprint and demanding deeper operational detail from management.

To maintain control of the narrative in an AI-augmented market, executive teams and IR leaders should adopt a three-part strategy:

• • Conduct Self-Audits with AI Prep Packs: Run automated prompt routines against disclosures and peer tickers prior to conferences to anticipate Red Team questions.
  • Shift Disclosure Focus from Facts to Drivers: Provide granular detail on load-bearing operational assumptions and unit economics, moving past easily automated summaries.
  • Elevate High-Touch Executive Engagement: Double down on direct, in-person interactions and site tours to convey corporate culture and executive conviction.

Ultimately, AI will not replace fundamental investors or corporate IR professionals—but AI-enabled investors will replace those who fail to adapt. Is your investor relations strategy prepared to defend against an AI-equipped Red Team, or are you still relying on last quarter’s disclosures?