If you’re like most of the brand teams I speak with, you have a system for keeping tabs on the competition: dashboards, weekly reports, and someone scrolling through competitors’ social feeds every few days. We feel organized. We feel like we’re staying informed.
But observing competitors and understanding what their movements mean are two different tasks. I’ve looked at hundreds of competitive reports over the years, and the pattern is generally the same: They tell you what happened last week, but not what’s changing, what’s coming, or what it all means for your brand. Most social listening tools work this way too. They count mentions, note sentiments, and surface activity after the fact.
This is the retrospective version of competitive intelligence. Useful, but responsive. AI is starting to change that. Teams that use it well spend less time collecting signals and more time deciding what to do next. They use AI to track messaging changes, customer sentiment, content strategy shifts, and positioning gaps on a scale that would overwhelm most human teams.
The change isn’t really about faster reporting. It’s about moving from looking backward to looking forward.
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The real question is not “What are they doing?” »
Here’s the problem I’ve been struggling with: It’s easy to treat competitive intelligence like homework. Collect the data, organize the data, and present the data. I did it. We’ve all done it.
But the reports are filed, and little changes.
What I’ve come to believe, and what redefines the way I work with my clients, is that keeping up with competitors is the easy part. The work that really moves the business is answering three questions every time you look at a competitor:
- What does this mean for us?
- Where are we exposed?
- Where is the opening?
These three questions make up all the work. Everything else is data collection. If the work does not end with answers to these three questions, we produce a report rather than a strategy. (I say this as someone who has produced numerous book reviews.)
What’s powerful about AI, and what I spend most of my time helping clients implement, is that it can finally support data collection at a scale we couldn’t achieve before. This scalability allows our teams to focus their time on the three questions, where our judgment really matters.
What AI actually tracks
When I talk about AI-powered competitive intelligence, I’m not talking about a prettier dashboard. I’m talking about a system that can do multiple things at once that would be exhausting for any human team.
Messaging changes
Pay attention to the exact words competitors use. What problems do they claim to solve? Who are the audiences they’re starting to go after that they weren’t going after six months ago?
Public sentiment
What are real customers saying about your competitors on social media, in reviews and in forums? Don’t just look at thumbs up or down. Look at the specific themes that continue to emerge.
Content strategy
Are your competitors suddenly all about video? Investing in long-form content? Do you spot a topic they didn’t know about before? AI detects these pivots earlier than human analysis.
Positioning spaces
Where are they falling back? What conversations are they missing from? These gaps are often where our openings are.
A good analyst can track one or two of these elements across a few competitors. AI can track all of this across more competitors every day without burnout.
Most competitive intelligence tools are effective at either monitoring or summarizing, but not both. That’s why I divide this pile into two layers.
Layer 1: Monitoring
This layer monitors your competitors and tells you what has changed. You need a dedicated platform here. General purpose AI will not track pricing page edits and changelog updates on a schedule for you.
Crayon is the broadest of the dedicated platforms I have worked with. It monitors more data sources than any other product in the category, allowing it to detect subtle changes like pricing page changes and feature description updates.
It costs between $20,000 and $40,000 per year for the mid-market, and enterprise contracts can run upwards of $50,000. If you are an enterprise brand with dedicated competitive intelligence or a PMM team that tracks a broad domain, this tool is the workhorse.
Klue is more sales focused. It’s built around Battle Maps and Salesforce integration, and its Competition Agent now monitors sales calls in real time and delivers competitive context to reps without anyone having to ask. Prices range from $16,000 to $30,000 at the mid-level.
After acquiring Ignition at the end of 2025, Klue notably strengthened its product marketing capabilities. If your competitive intelligence work feeds into sales enablement, that’s where I would start.
Kompyte sits below both of these prices and is a strong appeal for mid-sized teams that want automated tracking without the company commitment.
AlphaSense and Contify are different animals. They are designed for broader market and industry insight, not deal-level CI. If your management team needs information on regulatory changes, M&A activity, or analyst commentary, AlphaSense is worth a look, although it starts at around $24,000 per user per year and goes up from there.
For teams that aren’t ready for a $20,000+ annual contract, and that’s most of us at some point, Similarweb gives you traffic and engagement data on competitors’ digital properties, and Owler, combined with Google Alerts, can put together a basic monitoring setup for next to nothing. It’s manual, but it works for one or two competitors.
Layer 2: Synthesis
This layer is where we take what the monitoring tools surface and begin to answer the three questions. This is where general-purpose AI earns its place.
Claude (from Anthropic) is where I do most of my capstone work. It has a long pop-up window, strong reasoning, and handles multi-document parsing cleanly. When I have a stack of competitor observations, customer reviews, and sample messages to test against a strategy, I bring it all to Claude.
Recently (in April 2026), Claude Cowork became generally available, providing users with a desktop workspace to run this type of recurring analysis on local files. I have used it with clients and found it very useful.
Perplexity is the other half of the way I work. It is a search engine with live web access and citations, making it useful for investigation and analyzes of the current landscape.
My workflow typically starts in Perplexity for information collection and verification, then moves to Claude for synthesis, analysis, and writing.
ChatGPT is also part of this conversation, especially for already standardized teams, and its enterprise integrations like HubSpot are currently the most mature in the category.
You don’t need all three. A synthesis tool associated with a monitoring tool constitutes a real system. Start there.
Moving from defense to offense
Here’s the change I keep coming back to. When our analytics teams spend their days reconstructing what has already happened, we are playing defense. React. Catch up. Always behind on the actual conversation.
However, when the AI takes on more oversight, the team can finally play offense. They can think about the question that really makes things happen: what should we do next?
This is different work than most insights teams do today. And it is much more valuable.
I’ve seen brand teams make this transition, and the change I notice most is not speed, but clarity. Once they stopped drowning in data collection and started working with competitive AI-generated summaries, they had time to actually think. They started asking more pointed questions. Make calls faster. Participate in management meetings with recommendations rather than recaps.
Value is not faster reporting. It’s clearer thinking.
What this looks like in practice
You don’t need to blow up your entire process to get started. Here’s how I would suggest taking it easier.
Choose a competitor. The one that keeps you up at night. You know which one.
Set up monitoring on two or three channels. If you have the budget, start a trial with Crayon or Klue. If you don’t, set up Google alerts on their management team and product news, follow them on Similarweb, and pull their G2 or Trustpilot reviews into a shared document. Either path works to begin with.
Every Friday, paste the observations of the week in Claude or Perplexité. Then ask him the three questions in this order:
- What does this mean for us?
- Where are we exposed?
- Where is the opening?
Don’t accept generic answers. Push back on AI the same way you would push back on a junior analyst. If the answer seems too mild, ask: “What specifically?” » If this sounds like a horoscope, ask: “What would I do differently on Monday because of this?” » The AI becomes sharper when you do this.
Bring the conversations to your strategy team. Not as a data dump, but as three answers with the evidence underneath. This type of meeting tends to end with decisions rather than further issues.
The shift from monitoring competitors to understanding them
Competitive intelligence has always been important. The way most of us do things (manual reporting, weekly summaries, reactive tracking) is simply not suited to the speed of the market we currently operate in.
AI does not replace our judgment. This clears the trail so we can use it.
We are all navigating this new AI landscape together. The teams that I see progressing the most are not the ones that have the most sophisticated tools. They are the ones who have turned their attention from the rearview mirror to the road and continue to ask these three questions every week without fail.
Your competitors are there right now. Some of them already use AI to understand you, so make sure you use AI to understand them too.
