Three paradigms redefining AI product management
By Maksim Ovsyannikov, Chief Product Officer, SugarCRM
Product management has fundamentally shifted in the AI era. For two decades, enterprise roadmaps were built around platforms, workflows and automation. The job of the product leader was to decide what features to build and when to deliver them.
AI has changed the game. The new question is: How can what you deliver become better with AI? And not just because it has AI incorporated in it, but because AI itself makes the outcome better.
This shift introduces three paradigms that distinguish forward-thinking product organizations from those locked in legacy thinking.
1. It’s all about a new interaction paradigm! I call it “AI instead of UI”
Traditional product management catalogs features. AI product management redefines interaction paradigms.
Take analytics. In the past, we built dashboards and reports. Users had to locate the right report, share it across teams, and rebuild it when the business environment shifted.
Today, we deliver conversational analytics. A user, in this case a seller, simply asks: “What opportunities should I prioritize today?” and receives an answer enriched with context. The AI adapts automatically as conditions evolve – no manual reconfiguration required.
This paradigm extends beyond analytics. Every feature becomes conversational. Every workflow becomes intelligent. Every interaction becomes adaptive. Do you have an enterprise software vendor that showcases their ability to build dashboards? Tell them that’s so old school 🙂
2. When building AI, focus on your proprietary models, not just API integrations
Many vendors embed third-party AI and call it innovation. They layer ChatGPT on top of their product, generate account summaries, and present it as transformation. No wonder that numerous recent studies, among them the latest from MIT, say that most AI in enterprise is a waste of money with no real outcomes.
Real enterprise AI product management requires building proprietary models that are specific to customer data and outcomes. These models understand use-case-specific and industry-specific challenges – they are still LLMs but they understand the domain itself. A simple GPT plugin doesn’t.
Large language model augmentation becomes a skill that is required to achieve real results with enterprise AI. Enriching LLMs with context is something only you can do – after all you know your customers and the problems your company is trying to solve best.
Don’t plug and play with AI, because it will plug and play with you!
3. Make pricing a competitive advantage
AI pricing is no longer a back-office issue – it is a real product management challenge. And I am not talking about the price itself, I am talking about how it is priced.
Let me cut to the chase – token-based models create barriers to adoption: users hesitate to engage with features that might spike costs, while finance teams struggle to forecast expenses. In simpler terms, nobody understands it, and as a result this approach causes significant adoption challenges.
My advice is simple – just remove these barriers. If the new value proposition for enterprise software is heavily focused on AI, then it would seem that AI has to be the core of what you are buying in the beginning. So basically, you can’t sell me your product and then sell me tokens of AI value on top of it. That makes no sense in today’s enterprise world. Instead, embed AI as core functionality, package it transparently, and encourage frequent usage.
Predictable pricing enables economic buyer confidence, and confidence accelerates adoption.
Beyond features: Redefining the role of product management
These three paradigms point to a larger shift: AI is not simply another feature set – it is a new foundation for how enterprise software is conceived, built, and consumed. If you are building AI features, you need to take a 180-degree turn. Your product should be AI, and around it you might need to build some features that are not specifically AI. Force yourself to think this way.
Companies that thrive in this new era will be those that embrace these paradigms fully – designing for adaptive experiences, investing in proprietary intelligence, and making AI accessible through transparent business models.
The challenge is clear, but so is the opportunity. Product leaders who act decisively today are not just improving their platforms. They are setting the standard for what enterprise software will look like for the next decade.
