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Product & Management Stage

How AI Agents Go to Market (and why it’s nothing like SaaS)

September 18, 2025 10:55 AM
Power Talk
🇬🇧 English
Bratysława 1

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Talk in brief

In her WaysConf 2025 presentation, Maja Voje explains why the go-to-market (GTM) strategy for AI agents differs fundamentally from traditional SaaS. Drawing from her experience launching 37 AI agents, Voje outlines four critical lessons for product builders. First, companies should adopt a sales-led approach rather than a product-led one to manage high computational costs and build user trust through controlled pilots. Second, pricing must move away from confusing credit systems toward outcome-based or hybrid models that reflect the high value captured by AI. Third, leveraging ecosystem partnerships, such as white-labeling for agencies, can exponentially scale user acquisition. Finally, Voje urges professionals to integrate AI into their own workflows while focusing human efforts on strategic trade-offs, relationship-building, and creative architecture.

Key takeaways

  1. 01

    Sales-Led GTM Builds Crucial Trust

    AI agents should initially be deployed via high-touch sales and controlled pilots rather than self-serve product-led funnels. This approach mitigates the risk of erratic agent behavior and prevents unsustainable API costs from non-paying trial users.

    Watch from 3:05
  2. 02

    Earn the Right to Automate

    Before scaling an AI agent, companies must prove its reliability through small-scale pilots and tangible case studies. Demonstrating measurable financial or operational savings is essential to overcome user skepticism and job-security fears.

    Watch from 5:53
  3. 03

    Simplify Pricing Beyond Credits

    Throwing arbitrary credit amounts at customers creates confusion and stalls conversions. Instead, GTM teams should align pricing with clear business outcomes or use hybrid models, capturing up to 50% of the added value compared to SaaS's 10-30%.

    Watch from 8:53
  4. 04

    Leverage Ecosystem Partnerships for Scale

    Partnering with intermediaries like agencies who already manage customer workflows can dramatically accelerate growth. Offering white-label options allows these partners to onboard dozens of end-users, multiplying the agent's reach.

    Watch from 13:33
  5. 05

    Focus Humans on Strategy and Relationships

    While AI can automate execution-heavy tasks like content generation and initial outreach, humans remain indispensable for strategic trade-offs and relationship building. Product teams should architect AI workflows to offload tedious tasks while retaining high-level decision-making.

    Watch from 21:16
Read edited transcript highlights

These concise notes were edited from automatic captions and checked against the talk structure. They are not a verbatim transcript.

The Pitfalls of Product-Led Growth for AI

Many technical founders naturally gravitate toward product-led growth, hoping a simple launch on platforms like Product Hunt will organically attract users. However, AI agents present unique challenges that make this approach risky. Because AI can occasionally behave unpredictably, deploying agents directly into sensitive customer environments without human oversight can damage trust. Furthermore, running advanced AI models remains highly expensive. A flood of free-trial users who do not convert can quickly lead to unsustainable computational costs, making a controlled, sales-led pilot a much safer and more financially viable starting point.

Watch from 3:34

Building Trust Through Controlled Pilots

To successfully bring an AI agent to market, builders must adopt the principle of earning the right to automate. This begins by running small, controlled pilots to ensure the technology integrates seamlessly with the client's existing software stack and delivers measurable results. Once the technology proves stable and normalizes within the workflow, companies can leverage these successful pilots to create compelling case studies. In the current market, buyers are no longer interested in theoretical use cases; they demand verified proof of ROI, such as concrete financial savings, before committing.

Watch from 5:53

Moving Away from Credit-Based Pricing

One of the most common mistakes in AI GTM strategies is presenting customers with abstract credit packages, such as offering ten thousand credits without explaining what that actually translates to in real-world output. To drive conversions, pricing must be simple and easily understood by the target buyer. While the industry is gradually moving toward outcome-based pricing—where customers only pay when a specific task is completed—the reality often requires a hybrid model combining a fixed base fee with usage-based credits. Because AI agents perform actual labor, they can capture up to fifty percent of the value they create, far exceeding the ten to thirty percent typical of traditional SaaS.

Watch from 8:53

Unlocking Growth via Agency White-Labeling

For many specialized AI tools, such as voice-based receptionists, the end-users are traditional businesses like auto shops or law firms that lack the technical expertise to set up the software themselves. To bridge this gap, GTM teams should target agencies that already manage these clients' workflows. By offering a white-label version of the platform, companies can empower agencies to brand, price, and support the technology for their own customers. This ecosystem approach can dramatically accelerate growth, as onboarding a single agency partner can instantly bring in dozens of active end-users.

Watch from 14:54

Designing Human-AI Hybrid Workflows

To understand the evolving role of humans in an AI-driven market, Voje prototyped an automated inbound content workflow for her own business. By mapping out her manual processes, she built a system utilizing specialized AI writers for different platforms alongside agents that handle competitor research, social selling, and performance coaching. While this hybrid setup significantly reduced her weekly content creation hours and boosted her pipeline, it highlighted that strategic choices, relationship building, and workflow architecture remain uniquely human domains that AI cannot replicate.

Watch from 18:34
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