Why Does AI Hate “Supply Chain”?

A case study on optimizing IR communications

Artificial Intelligence has become a significant, but overlooked, gatekeeper for financial communications. Most, if not all, earnings calls are now scored by AI. This scoring system is followed by key stakeholders like analysts, investors and strategic partners, who then base investment decisions on AI-generated sentiment scores. 

The Client Challenge

A recent spin-off of a global manufacturing company was struggling to communicate its differentiation and objectives to investors. The firm was rarely subject to independent press coverage, so financial communications are the primary source of news on the company. The firm saw diminishing sentiment scores on its earnings announcements in business intelligence platforms over multiple quarters. As a result, while the firm’s fundamentals were strong, its stock was trading at a discount to the underlying value. 

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The LAI Solution

We worked with the company’s Investor Relations team to establish the following key objectives:

• How can the Q2 earnings statement be adjusted to produce a better sentiment score?
• What topics and themes should the company emphasize?
• What are the DOs and DON’Ts that can optimize their future financial communications for the machine audience?

We employed our unique methodology to achieve these objectives.

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Step 1: AI Transparency and Analysis

Using our proprietary software platform, TLDR, we conducted custom research by analyzing previous earnings transcripts to identify the patterns of words and themes that were scoring negatively. This process also revealed biases in the model used by a popular business intelligence platform, notably its failure to recognize the positive context of “price increases” and its systemic bias against any mention of “Supply Chain.”

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Step 2: Strategy and Execution 

We built a custom vocabulary to optimize the firm’s investor communications for AI and the broader shareholder base, providing explicit words and phrases to use in communicating both positive and negative news. The team then structured its communications around patterns and words that are optimized for machine reading, making more effective use of investor relations as the primary channel for communicating the firm’s growth story.

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Step 3: Tracking AI Optimization

Tracking the impact of these efforts, the client saw an overall improvement in sentiment across a spectrum of the firm’s financial communications, and a 51-point quarter-to-quarter increase in sentiment scores on popular financial analysis tools. Sentiment was boosted through repetition of positive results and key phrases.

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