I recently authored an article for Forbes titled “Navigating The AI GTM Maze: Packaging, Pricing And Selling AI,” where I share insights drawn from my experience as a product leader in the AI industry. The article delves into the unique challenges and evolving strategies around bringing AI-driven products to market.
Highlights of Complexities of Packaging, Pricing, and Selling AI Products
Unlike traditional software, AI products come with distinct hurdles, especially in packaging and pricing. One of the biggest complexities lies in the cost structure. AI solutions often demand significant compute resources, making infrastructure costs variable and sometimes unpredictable. This means that pricing AI purely on traditional SaaS models—such as per user seat or fixed tiers—doesn’t always align well with the value delivered or the underlying costs.
I explore how many companies grapple with this “cost dilemma,” needing to find a balance between covering fluctuating expenses and offering customers transparent, understandable pricing.
Another major challenge I highlight is customer skepticism. Because AI can feel like a “black box,” potential buyers often ask: “How much better is AI than what I’m already using?” and “Can I trust it to make important decisions?” Clear packaging and explainability are essential to overcoming this hesitation. Offering free tiers or transparent reports on AI decision-making can build trust and reduce adoption friction.
To address these issues, I recommend value-based pricing models that focus on the measurable outcomes AI delivers, such as operational cost savings or improved efficiency, rather than just usage metrics. Hybrid pricing models that combine a fixed subscription fee with usage-based components also offer predictability for customers while helping companies manage infrastructure costs.
I also discuss the importance of positioning AI as an enhancement to existing workflows rather than a wholesale replacement. Framing AI tools as productivity boosters that support human expertise tends to resonate better with users and accelerate adoption.
From our real-world experience, experimenting with different go-to-market strategies is crucial. Mid-market companies tend to be more open to adopting AI automation faster than large enterprises, which often require extensive proof points and have longer sales cycles. Engaging advisors and collecting early customer feedback help refine pricing and packaging, ensuring the product delivers true business value.
An additional takeaway is the power of using AI internally to augment sales processes deploying AI-driven chatbots and knowledge assistants can streamline customer engagement and free sales teams to focus on high-impact interactions.
While the AI market is still maturing, companies that adopt agile pricing strategies, prioritize transparency, and tie AI costs to clear business outcomes will be better positioned for success.
If you want to explore these insights in greater detail, I encourage you to read the full article on Forbes.