Earnings season leaves IR teams with little room for error and even less room in the schedule. Scripts need drafting, Q&A preparation needs stress testing, analyst notes arrive faster than anyone can read them and every number must be checked before the press release goes out.
With that much work compressed into a few weeks, it makes sense to explore generic AI tools like ChatGPT and Gemini to take on part of the workload. Some of the most popular use cases are drafting, summarizing and organizing earnings information.
While this can be incredibly helpful and time-saving, generic AI can’t take responsibility for financial accuracy, disclosure decisions or executive judgment. The possibility of misstating figures or missing important context, and the associated risks, is simply too high.
Here is a look at five core earnings tasks, how AI can give you a head start, where generic AI’s structural limits appear, and where industry-specific AI takes over.
AI is most effective when the task involves organizing existing information, identifying patterns or producing an initial draft from clear source material. The more specific the instructions and inputs are, the more practical the output tends to be.
Here are five earnings tasks worth testing.
1. Create a First Draft of the Earnings Call Script
Starting with a blank page can slow down the entire earnings process. An AI tool can use an approved script from the previous quarter, along with updated messaging and placeholder figures, to create a structured first draft.
It can generally preserve the order of the call, approximate the length of each section and follow established terminology. You can also ask it to identify passages that need new information rather than allowing it to fill those gaps on its own.
For example, the prompt could instruct the tool to retain the existing structure, replace outdated business commentary with clearly marked placeholders and flag every financial figure for verification.
If you are using a generic AI tool, the result will still need significant editing. It does not know how your CEO naturally speaks, which messages leadership wants to emphasize or where a subtle change in wording could affect investor interpretation. Its value comes from giving the team a draft to shape instead of asking them to build the structure from the beginning.
2. Expand the Q&A Preparation Process
Generic AI can help jumpstart Q&A prep by generating a broad list of potential questions based on your draft remarks, so you can pressure-test the script from different perspectives. While these initial lists are helpful for spotting obvious coverage gaps, generic AI operates on surface-level patterns and lacks context on your specific sell-side analysts, their financial models, or past private conversations.
This is where purpose-built AI becomes especially helpful. Instead of generating broad questions in a vacuum, purpose-built IR technology works within a connected platform that understands your company’s historical disclosures, analyst coverage styles, and past earnings transcripts. Solutions like Q by Q4 bring your approved earnings information and historical context into one secure workflow, helping you forecast high-probability analyst queries grounded in actual disclosure history and known analyst priorities
AI-generated questions should supplement the team’s knowledge of its analysts. The IR team still understands which topics matter to each analyst, how they tend to frame questions and what assumptions may already be built into their models.
3. Summarize Earnings Calls and Transcripts
Transcript summarization is one of the clearest applications for AI during earnings season. A generic tool can turn a lengthy call into a focused summary of the main financial themes, management commentary and analyst concerns.
The output becomes more valuable when the request is specific. Instead of asking for a general summary, ask the tool to separate the prepared remarks from the Q&A discussion, identify questions that management did not fully answer and note topics raised by more than one analyst.
Teams can use the same approach when reviewing peer calls. AI can create consistent summaries across several transcripts, so you can easily compare how companies discuss demand, guidance or market conditions.
What you need to keep in mind though, Broad generic summaries can easily smooth over critical nuances, strip away executive hedging, or take commentary out of context. Never share an AI-generated transcript summary with leadership without verifying the exact wording against the raw source. If you are using generic AI tools, require the prompt to output exact quote excerpts so your team can manually trace every observation back to the original transcript passage before it informs executive decision-making.
4. Review Analyst Notes and Media Coverage
In the chaotic hours following an earnings call, generic AI can process incoming research notes to categorize sentiment, list price target changes, and flag common misunderstandings.
This can help you distinguish an isolated reaction from a theme appearing across several sources. It can also surface areas where the company may need to provide additional context during follow-up conversations.
For this task, generic AI can organize the incoming flood of information, but every conclusion must remain fully traceable back to the source note so the IR team can verify the context before briefing the C-suite.
5. Check Consistency Across Earnings Materials
The press release, earnings script, presentation and Q&A document often move through separate rounds of review. This is a great use case for AI automation.
It can compare the materials and flag potential inconsistencies, such as a metric described differently in two documents or a strategic priority that appears in the script but not the presentation.
This type of comparison works best when the AI is asked to flag discrepancies without attempting to correct them. The team can then decide whether the difference is intentional and determine which approved source should control the final language. However, ensure pre-release draft materials containing MNPI are processed only within secure, enterprise-grade AI tools with strict zero-data-retention guarantees to avoid critical disclosure and compliance risks.
Before using AI in a live earnings cycle, test it on a completed past quarter:
Redact confidential data and run drafting prompts on past assets.
Conduct a blind team review to spot factual errors, hallucinated figures, or tone issues.
Enforce source citations so every claim links back to an approved document.
Measure audit time. If a draft takes 5 minutes to generate but 45 minutes to fact-check, the process adds friction, not value.
Start With Your Company’s AI and Data Policies
Do not paste unreleased earnings materials, investor information or other confidential content into a public AI tool without confirming that the tool and intended use comply with company policy.
IR teams should know what data the provider retains, whether prompts are used for model training and which information employees are permitted to enter. Legal, compliance, finance and information security may all need to be involved in setting those boundaries.
Use Approved Source Material
AI produces stronger outputs when it works from a defined set of reliable documents. Give the tool final scripts, approved disclosures, current reporting data and agreed messaging rather than relying on its general knowledge.
The prompt should state that the tool may only use the supplied materials. It should flag missing information and avoid making assumptions.
Require Source References
Ask the tool to cite the document, section or passage supporting each important statement. Source references make the output easier to review and reduce the chance that unsupported content moves forward unnoticed. This is critical when using generic AI.
This is especially important for financial figures, peer comparisons and summaries that will be shared with leadership.
Separate Drafting From Approval
AI can support the drafting stage, but it should not become part of the approval chain. The existing owners of financial accuracy, disclosure, legal review and executive communications still need to complete their usual checks.
A useful workflow makes those responsibilities visible. It should be clear who reviews each output and which source controls when two documents conflict.
A practical AI workflow still begins and ends with the IR team.
Your team provides approved source material and clear instructions. AI organizes the information, drafts the initial output or identifies patterns. Your team checks every statement against the source, adjusts the language based on company context and sends the material through the usual finance, legal and executive review.
The benefit comes from reducing the time spent assembling the first version. The responsibility for deciding what the company says, how it says it and whether the information is accurate remains with people.
Can AI Write an Earnings Call Script?
AI can create a first draft using approved scripts, current messaging and placeholder financial information. However, generic AI operates in a vacuum and cannot replicate executive voice or strategic nuance. Purpose-built IR technology solves this by pulling directly from your approved historical disclosures, peer messaging, and past quarters within a secure environment, giving IR teams a much tighter, context-aware starting point.
Can AI Predict Analyst Questions?
Generic AI can suggest likely questions based on the earnings materials and common analyst priorities. It cannot replace the IR team’s understanding of individual analysts, current expectations or recent investor conversations. Purpose-built AI tools help bridge this gap by grounding question forecasting in your specific historical disclosure data.
Is It Safe to Upload Earnings Materials to ChatGPT or Gemini?
That depends on the company’s policies, the tool’s data controls and the type of account being used. Unreleased earnings materials or Material Non-Public Information (MNPI) should never be entered into public, generic AI tools without enterprise zero-data-retention guarantees. Always consult legal, compliance, and IT security beforehand.
Can AI Summarize Peer Earnings Calls?
Yes. AI can organize peer transcripts into consistent summaries and identify recurring themes. However, summaries can strip away subtle executive hedging and important findings should be verified against the original transcript before they are used in executive briefings or external messaging. Purpose-built IR tools enhance summarization by embedding source linking, auto-linking every summary bullet directly back to the exact passage in the original transcript so teams can verify phrasing in one click.
Should AI Review Financial Numbers?
AI can flag possible inconsistencies, but it should never be treated as the final reviewer of financial information. While purpose-built AI can safely audit drafts by cross-checking numbers across press releases, scripts, and decks, human reviewers must still verify every final figure against approved financial sources.