AI prompts and workflows for smarter ICP targeting and account lists

Most marketers don’t have an ICP problem. They have a bad ICP problem.

The kind where the “ideal customer profile” reads:

“B2B SaaS company with SMB, mid-market, and enterprise segments.”

That definition is useless. It doesn’t guide targeting, drive pipeline, or filter out bad fits.

In B2B, an ideal customer profile (ICP) is the domino that tips everything else that follows. Sales efficiency, pipeline quality, deal size, and retention all trace back to how sharply you’ve defined, and how consistently you’ve targeted, the right customer. Get it wrong and every marketing dollar you spend compounds waste.

Sure, AI can fix that, but not in the way most people think.

AI isn’t just for cranking out LinkedIn posts. It can be leveraged for ABM account research, enriching data, and scoring accounts so you know who is worth your time (and who’s not).

If you’ve read our foundational article on AI, ABM, and ICP strategy, this is your hands-on follow-up to apply the model. In CXL’s recent live course, Eric Linssen, B2B marketer at Keyplay, showed how top SaaS teams use AI for ICP Targeting to build and test models that increase deal size and cut pipeline costs.

Stop pretending your ICP slide means something

If your ICP deck still says “SMB and enterprise” in the same sentence, you’re not segmenting. You’re guessing.

PartnerStack had that same problem. Their ICP looked something like this:

“B2B SaaS business that sells to businesses, sells a subscription, and runs or wants to run partner programs.”

Sounds fine. But, it was so broad it covered thousands of companies that would never buy.

Linssen calls this the “Standard SaaS ICP.” It’s what happens when you confuse potential customers with ideal ones. 

“If you pull every company that fits that definition, a huge percentage will be unqualified. You’re wasting marketing spend, sales time, and customer success resources.”

— Eric Linssen

That’s the root of bad go-to-market. Talking to the wrong people, with the wrong message, for the wrong reasons.

A good ICP is not about who you could serve, but who you serve best. In other words, the customers who get real business value from your product and stick around.

That distinction changes everything.

As Linssen cited:

“Doing ICP right is solving for net revenue retention at the targeting level.” — Tyler, CMO at PartnerStack

The only ICP framework that doesn’t suck

Forget personas and surface-level firmographics. A functional ICP answers one question: Who gets the most value from your product, and why?

Here is how the best teams define it.

Start by writing a vivid description; 

“Our offering is best for [type of company] in [specific buying situation]. They focus on [desired outcomes] and are struggling with [specific problems].”

Then add what Linssen calls “better if” signals. These are secondary traits that make a good account great.

Here’s an example from Keyplay’s ICP:

• Best for: B2B SaaS companies with 100 to 2,000 employees in the U.S. and Canada;
• Selling motion: Sales led, 10+ AEs, selling into multiple verticals;
• Better if: SDR team is growing; RevOps is in-house; they use Salesforce or HubSpot; they are planning territory optimization.

Those “better if” conditions not only separate good fit accounts from dream accounts, but are also where ideal customer profile AI tools become indispensable.

Use AI for pattern recognition, not parroting

The lazy use of AI: “Write me 10 LinkedIn posts about SaaS marketing.”

The smart use: “Analyze my best and worst customers. Find the signals that predict fit.”

AI’s real power is pattern recognition at scale. It can research hundreds of companies, extract firmographic and behavioral data, and surface insights a human team would miss.

Linssen explained it clearly:

“AI is incredible at looking at an account and understanding signals that maybe your best AE would see. It can look at their website and say, ‘Yes, they sell to multiple markets,’ or, ‘They probably have an existing partner program.’”

That isn’t content automation. This is what using AI for account targeting and research looks like when applied by serious marketers.

5 prompts that turn AI into an ICP research assistant

You don’t need an expensive data platform to start. If you have an LLM, you can build an ICP enrichment process now.

Pull your customer list and disqualified leads from your CRM or client database, and then feed the following prompts that come straight from Linssen’s ICP modeling with AI workflow.

Each one helps you move from guesswork to a data-driven ICP built on signal-based precision.

Prompt 1: Identify contrast between best customers and disqualified leads

Ask AI to analyze patterns between your top customers and poor fit leads. Look for contrast, not averages. 

If both segments use Google Analytics, that tells you nothing. If disqualified leads skew to retail and winners skew to B2B SaaS, that’s a signal.

Prompt 2: Enrich accounts with signal data

Have AI collect the kind of data that indicates fit: 

• Tech stack;
• Headcount growth;
• Number of AEs;
• SDR team size;
• Markets served; or 
• Product categories. 

Linssen’s team uses Keyplay to enrich CRM records with firmographic, technographic, and hiring signals. But, similar insights can be pulled using AI tools connected to Clearbit, Apollo, or LinkedIn Sales Navigator to mine public data for the same purpose.

Prompt 3: Score every account 0 to 100 based on fit

Once your signals are defined, use AI to assign a fit score (0–100) to each company in your CRM. This score should weigh factors like industry, tech stack, hiring trends, team size, and growth rate, so every account is ranked by its likelihood to become a high-value customer.

Prompt 4: Flag negative signals that predict bad fits

Linssen’s team found that not all “good-fit” signals translate into real deals.

Some categories, like industries or tech stacks that match your target profile, still underperform.

Use AI to spot those patterns and mark them as negative signals: attributes that look promising on paper but correlate with low close rates.

These are the red flags that disqualify accounts before they waste sales time.

Prompt 5: Identify and interpret the key signals

Have AI summarize the “why” behind those patterns, like why companies with certain tech stacks, headcount trends, or sales motions close faster or retain longer.

Use these insights to refine your ICP model and sharpen your targeting and messaging around what truly drives revenue.

(Image Source: Keyplay)

This is what prompting AI for ICP research should look like: an analytical process that finds which companies deserve attention.

The goal is not to outsource your ICP to AI. The goal is to make your thinking sharper and faster.

How to validate your ICP model before it  tanks your funnel

Building an AI-enriched ICP is half the work. You need to test it and prove that it works.

Linssen’s teams test every model the same way:

1. Run your best customers and your disqualified leads through the scoring model;
2. If your best customers score mostly A or B and your disqualified leads score C or D, the model works;
3. If not, your signals or weights are off. Fix them and rerun.

(Image Source: Keyplay)

You’re not chasing theoretical perfection. You’re chasing predictive accuracy.

That kind of validation earns buy-in from skeptical sales teams because it proves the model correlates with real pipeline quality. 

Without testing, using AI for customer segmentation or to build a targeting model is just algorithmic guesswork dressed up as strategy.

What happens when you take ICP seriously

Every company Linssen highlighted grew faster by changing who they targeted, rather than what they said.

• Hone: Kept the marketing machine the same and changed who they targeted; ACV increased 2.3x;
• Mutiny: Doubled deal size and grew overall pipeline 35%;
• PartnerStack: Dropped their cost per dollar of pipeline created by 34% while increasing overall pipeline value by 58%;
• Airbase: Drove a 90% lift in outbound conversion by focusing outreach on the right accounts.

These are ICP clarity wins.

Linssen put it simply:

“Everything in your company is downstream of ICP. It’s a small investment, but it drives every major metric.”

Recap: The ICP to AI flywheel

If your pipeline metrics are weak, start here.

1. Define your ICP clearly

Write a vague description. Then add the “better if” qualifiers and make it specific enough that you can score it.

(Image Source: Keyplay)

2. Translate the definition into a model

Once you’ve defined who you serve best, apply AI to operationalize it. Enrich your serviceable market with the key signals you identified and let AI score each company from 0 to 100 based on predicted fit and deal potential.

(Image Source: Keyplay)

3. Test and improve continuously

A scoring model isn’t static. Run it on a cadence that matches your market; quarterly if you’re adding new segments, yearly if your ICP is stable.

Each cycle, validate it against real outcomes: do your best-fit customers still score A or B? Are disqualified leads still ranking low?

Refine signals and weights until your model mirrors reality instead of assumptions.

(Image Source: Keyplay)

4. Feed the insights into targeting

Use your fit scores to prioritize paid campaigns, outbound lists, and ABM account research.

5. Report impact by account quality, not just quantity

Don’t measure success by how many leads you generate. Instead, measure how effectively you’re moving target accounts through the funnel.

Track account penetration, progression, and conversion quality.

Replace vanity metrics like impressions or MQLs with indicators that show business impact: cost per qualified opportunity, deal size, and pipeline efficiency.

That’s the data sales and leadership will actually trust.

The next step: Learn how the pros do it

AI won’t save a bad strategy. It will multiply the impact of sound strategy. Teams like PartnerStack, Mutiny, and Thoropass use AI to make ICP precision their unfair advantage.

Want the full system? 

→Take the CXL course on ICP Modeling and Targeting with AI taught by Eric Linssen, and learn how to:

• Define ICPs that correlate with revenue;
• Enrich and score accounts using AI for account research;
• Build reporting that proves impact on pipeline.

→Or, for a sneak peak at what you can expect, check out CXL’s webinar on Using AI for ICP Targeting.