For many businesses, producing competitive intelligence is rarely the issue. Marketing and product teams already have more competitive data than they can realistically use. And without a proper framework, adding AI into the mix risks worsening that problem by simply producing more data faster.
To see success with AI for competitive intelligence, you need to focus on using it to make better decisions, not more decisions.
In this article, I'll give you 10 prompts to help you use AI effectively to gather, organize, and act on your competitive intelligence.
What is AI-powered competitive intelligence?
AI-powered competitive intelligence is the use of AI and automations to collect, organize, and analyze information about your competitors on an ongoing basis.
The main advantage of using AI for competitive intelligence is that it speeds up the gathering and sorting tasks. AI helps you make decisions based on your competitive intelligence, but it doesn’t replace human judgement.
For example, AI can read a competitor's 50-page annual report and summarize the most important strategic point within seconds. However, it cannot tell you whether those points matter to your business, nor can it act on that information on your behalf.
How AI transforms competitive intelligence
There are three main areas where AI can accelerate competitive intelligence:
Area
What AI does
How it improves your workflow
Automating data gathering
Monitors competitor activity on a schedule, and summarizes sales call transcripts and reviews as they come in
Keeps intelligence up to date without manual research and frees up time for the tasks that requires human judgment
Connecting signals
Lines up evidence across sources, such as traffic and keyword shifts, hiring activity, and competitor mentions on calls, then separates facts from inferences
Reveals patterns that isolated data points miss and backs important conclusions with more than one supporting signal
Making better decisions
Pressure-tests the decisions you're leaning toward, keeps battlecards current, answers questions from your knowledge base, and drafts weekly leadership briefs
Puts verified intelligence in front of the people who need it, from reps on live calls to leadership planning budgets, while the decision stays with them
Follow the steps below to use AI to automate and improve your competitive intelligence workflows. While the three steps below are designed as a framework to be followed sequentially, you can use any of the AI prompts independently as needed.
1. Automate data gathering and organization
Data gathering is where AI saves competitive intelligence teams the most time. It allows you to automate competitive monitoring continuously across many sources at once. This way, the data your teams use is days old, rather than months old.
AI also allows you to automate the organization of the data you gather. This means raw data can be processed and prepared for use at later stages, without a human having to spend hours organizing everything manually. This frees up more time for your team to focus on decisions that require human judgment.
Before you start, I recommend uploading a business context file to whatever AI tool you’re using (or to any knowledge base you’re linking to the AI tool, such as Notion or Confluence). In this file, add information about your positioning, ideal customer profile, pricing, target segments, strategic priorities, and known competitors.
This file will act as a reference for the AI, helping it better gather and organize data that’s truly relevant to your business. So, keep your business context file up to date to minimize the chances of the AI tool hallucinating about your own brand when preparing materials like battlecards.
Here’s an example of what this file might look like for a fictional business called Signalwell:
Here are a few examples of how AI helps with data gathering and organization that you can adapt to your workflows:
Use scheduled tasks to automatically monitor competitor changes
Use scheduled tasks in a tool like ChatGPT to automatically scan competitor pages for changes in real time. For example, you can track changes on your competitors' pricing pages without having to visit the pages yourself every week.
I recommend tracking pricing pages, change logs, homepages, and any key product or feature pages that are likely to evolve over time. Here’s a prompt you can use to do this:
(Note that I used dummy data for the initial snapshot, and these are not real changes on Productboard’s site.)
The prompt is designed to work within one chat in your preferred AI platform (ChatGPT, Claude, etc.), so you don't need to connect it to any other tools to get the full benefit here. The chat will store the previous version of the page to compare it against each time. But you could also connect this to your own knowledge base tool, or simply save it to project files within the AI tool itself.
Summarize sales calls and closed-lost notes as they come in
Use a daily scheduled task to summarize new sales call transcripts and closed-lost notes as they’re added to your knowledge base. This makes these materials far more actionable and lets you identify friction points, competitor mentions, and objections as they arise, without having to manually read every call transcript.
Some of the most useful competitive intelligence comes from your own sales conversations. Prospects often mention which competitors they're evaluating, what those competitors have promised, and why they're hesitating.
For example, a prospect may be choosing your rival due to an internal security requirement, a personalized discount, or an existing contract with another vendor. These are all factors you wouldn’t be able to identify by just checking your rival’s webpages.
Here’s a prompt you can use to automate this kind of data gathering and organization:
The degree to which you can automate this task depends on your specific setup. If you’re using a tool like Notion as your knowledge base, you can set up agents to run on a particular schedule. This way, the agent can check daily or weekly for newly uploaded sales call transcripts and summarize them automatically.
If you’re using a project within an AI tool like ChatGPT, you schedule tasks to do the same thing, but you’ll need to manually upload your transcripts to your project files and prompt the tool to check them.
Alternatively, you may be able to directly connect your call recording tool to do this automatically. For example, within ChatGPT, you can connect the CallRail plugin to automatically fetch your sales call transcripts:
Continuously track competitors’ search visibility
AI can pull data on your competitors’ search visibility from external tools, and then organize the information into a format you can easily understand and act on.
For example, with Semrush, you can see which keywords your competitors are gaining and how their ads are changing. Connect it to your AI assistant through the Semrush MCP connection, and the AI can pull that data and summarize what's changed since its last check.
Changes in search activity show where competitors are investing. For example, new rankings for keywords with audience modifiers, like "for small teams" or "for enterprise" can point to a move into a new segment. And new ad copy can reveal positioning a competitor is testing before it appears on their homepage.
How you connect the Semrush MCP will depend on which AI tool you’re using, but the process is typically straightforward. To connect the MCP to Claude, click the “+” icon in any chat. Then click “Add connector” > “Browse connectors.”
Search for Semrush and click “Connect.” Then follow the on-screen instructions to finalize the setup.
Here's a prompt you can use to get a comprehensive overview of competitor activity using the Semrush MCP:
2. Connect the signals
AI tools can help you connect signals across sources of competitive intelligence to identify patterns faster and automatically.
Signals arrive in different places, in different formats, and often land with different teams. For example:
- The sales team might start hearing a competitor's name more often during calls
- The marketing team might see new ad copy
- The product team might spot a major changelog update
These could all point to a new rival increasing their presence in the market. Without someone organizing all of these signals, you might miss the pattern or spot it too late to be proactive. AI can review everything you’ve gathered across all your sources and group signals into related themes. It can do this regularly, automatically, and much faster than any human could.
Here are four ways you can use AI to connect your competitive intelligence signals for more informed decision making:
Run a weekly check to flag patterns across your sources
Set up a weekly scheduled task that reviews everything logged that week and flags themes supported by at least two sources. This lets you spot patterns as they arise, with data being at most a week old.
Here’s a prompt you can use to do this:
Here’s an example output for our fictional business, Signalwell:
The AI outlined what the patterns might indicate, and how confident it was based on the number of sources used to spot the pattern. It then provided hypotheses for what else might explain the pattern. All of these insights could be useful for a team looking to make decisions based on this CI data.
Build a timeline of competitor activity to spot changes
By analyzing signals chronologically, you can get an idea of what a competitor is building toward and how far along they are.
For example, if a competitor posted mid-market sales roles in January, launched a "for growing teams" landing page in March, and added a lower pricing tier in April, they're likely moving into the mid-market, and your sales team should expect to see them in more of those deals.
Use this prompt to build a timeline of competitor moves to spot launches, strategy shifts, and new target markets:
You can leverage the AI’s broader capabilities here to specify how you want it to present the output. For example, here’s how it can look presented as a graphic timeline (that you could share with your team or stakeholders):
Compare competitor claims with buyer feedback to find positioning gaps
Gaps between a competitor's messaging and their buyers' experiences can provide your brand with positioning opportunities. For example, if a competitor promotes fast setup but reviews and sales calls keep mentioning painful onboarding, your sales team can emphasize your simple implementation in calls, and marketing can make ease of setup a bigger part of your messaging.
Here’s a prompt you can use to find positioning gaps from your competitive intelligence data:
Spot trends across competitors to see which moves warrant a bigger response
A consistent change across multiple competitors calls for a different response than an isolated change on one competitor’s site.
For example, if one competitor cuts prices, your sales enablement team might need to update the battlecard and give reps a tailored response for that change. But if three competitors cut prices in the same quarter, that suggests buyers will start expecting lower prices across your category, which makes it a pricing decision for leadership.
Use this prompt to spot trends across multiple sources of competitive intelligence over an extended period:
I’ve included an example output below using our dummy data for our fictional business. One thing I like about this approach is that the AI flags when changes are unverified or otherwise not necessarily reliable.
For example, Claude flagged that the Pendo renewal discounts point was reported secondhand by the customer service team. It then noted that the potential Canny CRM connection was also from a secondhand source, this time from a LinkedIn post.
By flagging these potential confidence issues, there’s less risk of someone on your team treating the AI output as fact without verifying the information manually. This is crucial when it comes to making better decisions based on your AI-powered competitive intelligence, which I’ll cover in more detail in the next section.
3. Use AI to make better decisions
Making better decisions is the point of competitive intelligence, and AI helps with this in two ways:
- It gets intelligence to the people making decisions, in a format they'll use
- It challenges your thinking before you commit to anything
Different decisions are made at different speeds. For example, a sales rep might need an answer in the five minutes before a call, while leadership might ask for a summary of the month's changes before a planning meeting. One report can't serve both in a truly useful way.
Teams also tend to look for evidence that supports what they already want to do, and nobody is assigned to argue the other side.
AI helps solve both issues, as it can adapt the same intelligence into the right format for each role that needs it (e.g., a battlecard for sales, a weekly brief for leadership). And it can act as a sparring partner, testing a decision against everything it knows about your business and your competitors.
Using our Notion database for our fictional business as an example, it's easy to ask Claude to generate an up-to-date battle card for our sales team:
We can just as easily ask it to generate an executive summary report of the past month's most important findings for our leadership team.
This kind of on-the-fly reformatting and summarization simply would not be possible at this speed without AI.
Here are four more ways to use AI to make better decisions based on your competitive intelligence:
Pressure-test decisions before you commit to them
Describe the decision you're leaning toward and your reasoning, and ask the AI to challenge it using everything it knows about your business and all the competitive intelligence you’ve collected.
AI assistants often agree with whatever you tell them, so use the prompt below to encourage helpful pushback:
Below is an example output for our fictional business. Note that it draws upon several different sources in the table discussing why it’s not the right decision. While this would have taken a long time to gather and sort through manually, Claude returned this response in under a minute.
Keep battlecards updated so reps always have current information
Battlecards can go out of date within weeks, which can lead to reps making errors in sales calls or not having the right response to prospect questions. A daily scheduled task in an AI tool can update the battlecard as new intelligence arrives and log every change.
Even though the sales rep still needs to verify the data before they use it in a call, this is still much faster since they will know exactly where to look to verify the change.
Answer questions on-demand before sales calls and planning meetings
Set up a shared project or workspace where anyone can ask questions and get answers drawn from your competitive intelligence. This is useful because people on different teams have different data needs:
- Sales reps need to double check numbers five minutes before a call
- Executives want to understand broad trends at a glance
- Product teams need deep dives into competitors’ most recent feature updates
Being able to query your entire bank of competitor intelligence at any time makes it easy for anyone on your team to get the intelligence they need, in the format they require.
Send leadership a weekly brief to inform budget and strategy decisions
Set up a weekly brief that summarizes what changed in your competitive landscape, why it matters for your priorities, and what leadership may want to discuss.
For example, if two competitors move a feature like SSO to a cheaper plan within a few months and your sales team loses three deals over the same requirement, the brief puts that pattern in front of leadership before the next planning meeting, with sources and confidence levels attached.
Here’s a prompt you can use to set this up:
Take this prompt a step further by connecting your AI tool to your email platform or Slack channel so it can send the report to your leadership automatically.
Start using AI to make better decisions
AI tools can allow you to gather, organize, and analyze your competitive intelligence much more efficiently. But the quality of your outputs depends on the quality of your sources.
