3 AI use cases to elevate your strategy

This article is based on Liza Adams’s brilliant talk at the Product Marketing Summit in Denver. As a PMA member, you can enjoy the complete recording here.

Product marketers and even CMOs rarely make it to the boardroom. In fact, only 41 members of Fortune 1000 boards are CMOs, and less than 3% of board members have managerial-level marketing experience. 

Why?

Because marketing is often dismissed as tactical – beautiful ads, catchy campaigns, and glossy brochures – while the strategic work that underpins it goes unnoticed. This misconception limits opportunities for marketers to demonstrate the true impact of their expertise on business decisions. 

But here’s the good news: AI is changing the game.

AI has the power to elevate product marketing from a tactical function to a strategic force. It enables us to align executives, refine priorities, and amplify results, making the work of product marketers more visible and valuable at the highest levels. 

Yet mastering AI isn’t a race – it’s a journey. Whether you’re just starting to explore its possibilities or already using it to shape strategy, it’s important to embrace where you are and keep learning.

In this article, I’ll show how AI can help you step into a more strategic role by focusing on three key use cases: 

1. Segmentation and targeting
2. Competitive analysis
3. Thought leadership 

These examples will demonstrate how AI can go beyond creating content to drive strategic decision-making and deliver real impact. 

Let’s dive in.

AI use case #1: Segmentation and targeting

Our first use case comes from a real scenario where I acted as a fractional CMO. The company was what I like to call a “COVID darling” – it experienced rapid growth during the pandemic; however, post-COVID, it struggled to sustain that growth. 

The executive team’s instinct was to expand their market and target more segments. My response? Don’t go broad – go deep.

Instead of spreading resources thinly across multiple segments, I encouraged the team to focus on two or three key segments. The goal was to understand these customers so thoroughly that we could become the best fit for their unique needs. Broad, shallow targeting wouldn’t deliver the value these customers required.

Here’s where the challenge got interesting. Each executive had their own idea about which segment to prioritize:

• The CEO wanted to target healthcare, citing its large market size.
• The CFO pushed for manufacturing, pointing to its high growth rate.
• The CPO advocated for retail, aligning with the product roadmap.

The truth is, they were all right – from their individual perspectives. So, the product marketing team and I developed a framework to align these viewpoints and make an informed decision.

We identified evaluation criteria for analyzing each segment, including factors like market size, growth potential, competitive intensity, number of reference customers, and partner strength. Then, we built a heatmap to visually compare how each segment performed against these criteria.

This heatmap became a game-changer. It allowed the executive team to see, at a glance, how each segment stacked up. This data-driven approach shifted the conversation from subjective opinions to objective insights, making it clear which segments offered the most strategic opportunity.

By narrowing the focus and targeting the right segments, the company could allocate resources effectively, align their teams, and maximize their market fit – rather than chasing opportunities that stretched them too thin.

The challenge of gathering data

Before I dive into how we used AI to create a market heatmap, it’s important to acknowledge the most challenging part of the process: data collection and curation. 

While the conversation with ChatGPT took about three hours, gathering and organizing the necessary data took two to three weeks. This stage was critical because feeding AI accurate, well-structured data is the foundation for meaningful insights.

Here’s a breakdown of the types of data we gathered and the sources we used:

• Market size and growth: Pulled from analyst reports, including Gartner, to estimate total addressable markets (TAMs) and growth trends.
• Competitive intensity: Sourced from customer review platforms like G2 and Capterra to understand how competitors were performing in various categories.
• Win rates: Derived from our CRM (in this case, HubSpot), including metrics on win-loss ratios.
• Product roadmap alignment: Compiled in a Google Doc to compare customer needs across segments with our current and planned product offerings.
• Partner strength: Extracted from a database tracking partner leads, conversions, and overall performance.
• Customer references: Assessed from a reference database to evaluate the strength and quantity of reference customers in each segment.

This process involved pulling data from disparate systems, formatting it consistently, and redacting sensitive information to maintain confidentiality. Only after this groundwork was done did we begin leveraging AI.

How we used ChatGPT to create our segment targeting heatmap

Once the data was ready, we uploaded it into ChatGPT in spreadsheet format and began prompting it for analysis. Here’s a simplified walkthrough of how we approached the first two rows of our heatmap – market size and growth – using AI:

1. Initial prompt: “You are an expert market researcher and analyst in the supply chain management space. Please review the attached Excel sheet, analyze it, and provide a summary of your key takeaways. I will provide further instructions after that.”
   ChatGPT’s initial response included basic insights, like identifying the verticals with the highest growth rates and highlighting steady growth areas.
2. Follow-up prompt: “Please create a table with two rows: one showing the 2025 market size and another showing the growth rate you calculated. Please order the verticals as manufacturing, healthcare, energy, food, and retail.”
   This prompt resulted in a clear, organized table, allowing us to visualize and compare the market data.
3. Heatmap creation: “Turn the table into a single heatmap reflecting forced rankings for market size and growth rate. Assign a score of 5 to the largest market size and highest growth rate, and a score of 1 to the smallest and lowest.”
   The output was a color-coded heatmap that visually represented each segment’s market size and growth potential, making it easy to prioritize opportunities.

By repeating this process for the remaining rows – competitive intensity, win rates, partner strength, and customer references – we built a comprehensive heatmap that showed the most valuable segments to target.

Presenting the analysis to the executive team 

Next, it was time to present the findings to the executive team. It’s important to note that this analysis was just a starting point – a framework to guide discussions and foster a 360-degree view of the market opportunities. 

Unlike previous conversations where each executive approached the problem from their one-dimensional perspective, this approach introduced eight dimensions of analysis, offering a more holistic view.

With the heatmap in hand, the executive team could now debate and refine the findings collaboratively. Some execs disagreed with certain rankings, so we made some on-the-fly adjustments to the data. 

We also assigned different weights to certain criteria, recognizing that not all of them were equally important. For example, market growth might carry more weight than competitive intensity, depending on the company’s priorities. 

This flexibility allowed us to fine-tune the analysis and reach a consensus. And, within a week, we validated the findings and identified the top two to three market segments to focus on.