Using AI to Support Smarter Go-to-Market Decisions

Go-to-market, or GTM, is the work of figuring out how a product reaches the right customers and turns interest into repeatable growth. It’s one of the hardest parts of building a company, and it rarely clicks overnight. Even strong teams often spend years testing channels, refining messaging, and learning what actually resonates with their target customers.

For many B2B companies, it takes closer to two years of iteration before product-market fit and a repeatable GTM motion truly take hold. Much of that time is spent doing the unglamorous but essential work like testing channels, translating product value into clear language, and figuring out whose problem our products solve.

As artificial intelligence has advanced over the past few years, more teams have started incorporating AI tools into their go-to-market efforts, hoping to speed up the process. AI indeed can speed up parts of the process, from research and positioning to draft generation and experimentation. Used thoughtfully, these tools compress learning cycles. But if used carelessly, they create more output without more clarity. The bottom line is: GTM requires back -and -forth testing, if AI is able to effectively support the process, it becomes a powerful tool to use for market growth.

We recently sat down with David Aidekman, General Manager at Docparser and Mailparser, to talk about what AI really looks like inside modern go-to-market work. Drawing on his experience using these tools day to day, and recognizing that the available tools change every week, Aidekman shares where AI earns its place, where it falls short, and how it fits into a disciplined, fundamentals-first approach to building repeatable growth.

Table of Contents:

• Common GTM challenges founders face
• How AI speeds up the GTM learning cycle
• What you shouldn’t automate in GTM
• AI in practice: How it supports positioning and iteration
• Best AI tools for early-stage GTM teams
• Final thoughts: AI as a tool, not a shortcut

Common GTM challenges founders face

Even with a strong product and capable team, go-to-market remains one of the most demanding parts of building a company. The stakes are high. If you can’t consistently reach the right customers, nothing else really matters. And while the challenges often surface later as stalled growth or rising acquisition costs, Aidekman sees many of them forming much earlier.

One of the most common issues starts with how teams define their ideal customer. Early on, it’s tempting to cast a wide net or rely on assumed personas that sound reasonable but aren’t grounded in real buying behaviour. Aidekman emphasized that clarity tends to come from narrowing the focus, choosing a specific customer, and learning exactly where they spend time, what problems they’re trying to solve, and what type of language resonates. Starting broad feels safer, but in practice it creates more distractions. Starting narrow allows for better focus.

Another friction point shows up in messaging and channel selection. Founders often understand their product deeply, but translating that into a clear, immediately understandable message is harder than it looks. If people can’t quickly grasp what a product does and why it matters, no amount of tooling or distribution will fix it. At the same time, channel testing requires patience. Aidekman points out that one of the most underestimated costs in GTM is time. Teams need to “allow enough time for data to accumulate” before drawing conclusions, otherwise they risk reacting to early noise instead of learning what actually works.

Interpreting early sales signals is where many teams struggle most. Early traction is rarely clean or linear. Some efforts show promise, others fall flat, and the feedback is often contradictory. Knowing when to double down, when to adjust, and when to walk away requires judgment that no tool can replace. This is where experience, reflection, and patience matter most.

How AI speeds up the GTM learning cycle

AI has proven most useful in go-to-market by speeding up the work that normally slows teams down. It doesn’t replace strategy or judgment, but it does compress research, synthesis, and early experimentation.

From Aidekman’s perspective, AI tools earn their place early in GTM when teams are trying to understand customers, test positioning, and figure out how to clearly communicate value. That work has always been part of the process. AI simply makes it faster and easier to iterate.

That leverage tends to show up in a few places:

• Research, by accelerating competitive analysis, customer discovery, and market sizing
• Ideation, by generating multiple ways to frame or position a product
• Drafting, by eliminating the blank page and producing first passes
• Experimentation, by lowering the cost of testing alternatives

One important caveat is that AI still works best as a supporting tool, not the final decision-maker. LLMs and agents can accelerate research, generate drafts, and surface options, but final communication and strategic decisions still require human judgement. When used thoughtfully, AI supports clearer thinking and faster learning without replacing the fundamentals that make go-to-market work.

What you shouldn’t automate in GTM

AI is helpful in go-to-market, but only up to a point. The areas where teams struggle most are often the ones AI cannot solve on its own.

Strategy still requires human conviction
Surfacing options, summarizing tradeoffs, and generating recommendations are well within AI’s capabilities, but deciding what matters most isn’t. Choosing which customer segment to focus on, which problems to solve first, or what to de-prioritize requires context, real-world tradeoffs, and conviction. 

Customer insight can’t be outsourced to a tool.
AI is effective at summarizing call notes, clustering feedback, or spotting patterns across conversations. But the most valuable go-to market insights come from direct conversations, hearing hesitation in a customer’s voice, and asking uncomfortable follow-up questions.

Timing and decision-making remain human work.
AI can assist with inputs, but the final call and the accountability belongs with the team. Without careful thinking before and after using these tools, teams risk mistaking volume of output for progress and activity for clarity.

AI in practice: How it supports positioning and iteration

AI has been most useful in positioning and early brand development. When evaluating how a product fits within a competitive landscape, Aidekman has used AI to synthesize large amounts of information quickly and generate multiple positioning directions to consider. 

In one case, he built a dedicated AI agent to analyze competitor messaging and customer patterns. What might have taken days of manual review was reduced to a fraction of the time, producing several structured positioning alternatives aligned with the available data. Those outputs weren’t final answers, but they created a strong starting point for discussion and refinement.

Still, most teams underestimate how much setup AI actually requires. Aidekman notes that many expect strong outputs from limited prompts and minimal context. In reality, the quality of the output is directly tied to the quality of the input. As he explains, “current AI tools perform best when they are boosted and bounded by detailed context, precise prompts, and good judgment about the quality of the outputs.” Without that structure, early results are often underwhelming and require significant refinement to become useful.

AI has also helped teams overcome the blank page problem. Instead of starting from scratch on website copy, email sequences, or landing pages, teams can generate multiple draft variations across tones and angles. That makes A/B testing easier and shortens feedback loops, especially for lean teams that do not have dedicated design or copy resources.

As Aidekman sees it, the value is partly about automation and more about iteration. AI makes it cheaper to explore ideas. The real leverage comes from what teams do next: selecting, refining, and testing the strongest direction with customers.

Best AI tools for early-stage GTM teams

For early-stage teams, AI works best when it’s simple. Rather than chasing every new tool, the focus should be on using a small number well, to help teams learn faster without getting distracted. 

If you’re just getting started, these are a few tools that Aidekman points to most often:

• Google Gemini: Highly accessible for teams already on Google Workspace. Gemini is useful for recurring research prompts, drafting, and synthesizing information across Docs, Sheets, and emails. Its low setup cost makes it a practical starting point for teams who want to experiment with AI without changing their workflow. 
  
  Google Gems enable repeatable AI prompts and context that can be recursively refined for improved outputs.

• Claude: Well-suited for deeper reasoning and agent-style workflows. Claude performs best when given detailed instructions and context, making it useful for tasks like synthesizing competitive insights, stress-testing positioning, or running structured GTM analyses. With Claude Code or Claude Cowork, agents can use your local files as context to support detailed business-specific insights.
  
  As with any LLM, though, the quality of the output depends heavily on the quality of the prompt and the clarity of the objective.

• Agent skills: When combined with clear prompts and boundaries, AI agents can support repeatable GTM tasks such as research, draft generation, comparison analysis, and output evaluation against internal standards. 
  
  The key is defining the process clearly upfront and reviewing outputs carefully, so agents support thinking rather than replacing it.

Final thoughts: AI as a tool, not a shortcut

AI doesn’t replace go-to-market strategy, but it can compress the learning cycle. The teams that make progress are usually the ones with clear priorities and disciplined execution, not the ones experimenting with the most tools.

At SureSwift, AI is treated as support, not strategy. It can accelerate research, generate drafts, and expand the range of ideas worth testing. But defining the customer, choosing what to focus on, and deciding what to ignore still require judgment.

If you’re interested in how SureSwift leaders approach growth and technology in practice, explore the rest of our Leadership series for more operator-led insights.