The standout performers in 2025 are those treating AI not as a bolt on, but as a core product lever, anchored in proprietary data and built with discipline from day one.
Key Highlights
- Control of proprietary data and Application Programming Interfaces (API) – more than feature count – is now the defining moat in AI product strategy.
- Embedding monetization logic early prevents margin drag and unfavorable value expectations.
- Platform-centric bundles – linking AI insights with workflow tools – drive stickiness and cross-sell potential.
The pressure on software businesses to rethink their product strategies remains critically important, amid steady deal momentum and continued investor focus on sustainable, margin-driven performance.
Teneo’s latest research shows that while AI remains the cornerstone of optimism in product growth, it is also becoming one of the primary sources of competitiveness. For management teams and investors alike, the imperative is clear: AI products must not only be built but monetized and defended effectively from day one.
Teneo's POV
- Monetization strategy must come first: High performers define pricing and value capture during product discovery.
- Speed alone is not a strategy: Being first-to-market means little if the capability fails to deliver measurable outcomes.
- Data – not feature count – is the moat: Proprietary data and well-managed API access protect differentiation long after competitors match “surface-level’ functionality.”
- Platforms win over point solutions: Embedding AI across a unified suite reinforces the data moat, deepens stickiness, supports cross-sell and commands premium pricing.
Monetization Strategy Must Come First
The “launch now, monetize later” approach to product innovation is being quietly retired by top-performing firms. In traditional software as a service (SaaS), where value is typically well-defined, the value from AI capabilities can be more diffused and harder to pinpoint. That makes it even more important to consider monetization early, so product design and pricing evolve together, focused on where the AI actually drives measurable impact.
Our analysis shows that high performing SaaS businesses (i.e. those with significant historic growth) are 30% more likely to define monetization strategy during the discovery / product development process than low-performing businesses (i.e. those with limited historic growth).
There are still cases where prioritizing adoption makes strategic sense – especially for early-stage products entering new categories. However, even in these cases, it must be part of a broader, intentional monetization strategy defined from the start. Without that foundation, companies risk creating customer expectations that limit future pricing options or diminish perceived value, making it harder to introduce revenue models later.
For more guidance on how to monetize AI capability effectively, please visit Teneo’s article: Pricing optimization in the AI era.
Speed Alone is Not a Strategy
Many teams have rushed to add AI without applying the usual product discipline, resulting in features that generate interest but fail to deliver meaningful impact. There is growing buyer frustration with AI tools that are poorly integrated, overly generic or solve low-priority problems.
What sets high performers apart isn’t the number of AI features or how they’re priced, it’s whether they address real user needs. To succeed, companies must work closely with customers to shape AI strategies that improve the most critical user workflows and deliver measurable value from day one.
Teneo’s recent survey across 300+ software vendors found that the vast majority (83%) have already launched AI-driven features and products. Of those yet to, most stated they had AI capabilities under development.
Data – Not Feature Count – Is the Moat
Data is now the defining moat in AI. In 2024, we saw leading vertical software players outcompete larger, better-funded horizontal rivals simply through access to end-to-end user workflows (i.e. through a platform model) and proprietary data, typically generated by customer usage.
Example in action: A recent client solution gained rapid traction not through superior AI, but through unique access to structured board reporting data in regulated sectors. By deeply integrating with customer workflows and leveraging years of proprietary board pack content, they were able to deliver tangible productivity savings – over $100k annually per enterprise client, driving significant adoption. Competitors without that embedded data couldn’t replicate the value proposition.
Teneo recommends that software providers take a more deliberate approach to managing APIs and data access. While APIs can be powerful tools to drive adoption and enable integration with other platforms, they also introduce risk if not handled strategically. In particular, unrestricted or underpriced access to core data can enable third parties (or even customers themselves) to replicate key workflows outside the platform. Over time, this can erode product differentiation and weaken pricing power.
Providers should clearly distinguish between APIs that genuinely enhance ecosystem value and those that expose or dilute the product’s core value proposition.
Platforms Win Over Point Solutions
In today’s market, the real strength of AI lies not in isolated features, but in how deeply it’s embedded across a product suite. As AI becomes more agentic and context-aware, its performance improves with broader visibility across workflows – allowing it to deliver more impactful recommendations and raising the bar for point solutions that can’t access that breadth. Therefore, platform ownership becomes a strategic advantage.
In legal tech, for example, we increasingly see AI features that span document drafting, clause analysis, matter management and reporting. The more use cases a firm adopts, the greater the potential of AI performance – drawing on patterns, preferences and data from across the platform. This creates a flywheel effect: removing one module degrades the overall AI experience, making downsell more difficult and reinforces platform stickiness.
Vendors that can successfully implement an AI-enabled platform are seeing better conversion rates, stronger customer retention and more resilient monetization models – and are approximately 25% more likely to be “high performing” than those without a compelling cross-product AI offering (and low customer adoption).
Common Pitfalls
- Racing to market without a pricing plan: Launch-now, monetize-later approaches set low value expectations and erode future flexibility.
- Building AI on generic or easily replicated data: Without a unique data advantage, competitors can quickly match capabilities and undercut price.
- Siloing AI in standalone tools: Isolated modules dilute network effects, complicate the user experience and undermine the broader platform value story.
Product strategies must evolve accordingly, with monetization at the core, value proven through clear outcomes and data access as the moat – strengthened by platform models.
Conclusion
As 2025 progresses, the AI landscape is increasingly defined by who moves with the clearest plan for value creation and defensibility.
AI is not a generic growth tailwind – it is a strategic lever that will widen the gap between leaders and laggards. Product strategies must evolve accordingly, with monetization at the core, value proven through clear outcomes and data access as the moat – strengthened by platform models.
Now is the time for management teams and investors to revisit product roadmaps and ensure that AI features are built to earn, not just excite.
If you’re seeing challenges in this area across your portfolio, the Teneo team is always happy to share benchmarks and perspectives.