# 70% of Product Roadmaps Are Shifting Toward AI. Monetization Is Still Unresolved.

G2 Research's 2026 AI product strategy study of 549 business professionals finds that AI investment is reshaping product roadmaps, but most organizations are still working through delivery controls, sourcing models, and commercial value capture. Seventy percent of organizations have selectively reprioritized product roadmaps toward AI, while 53% say AI value capture is still being figured out and only 7% monetize AI directly today.

## Key Findings

Seventy percent of product roadmaps are shifting toward AI, with most organizations making selective reprioritizations rather than full roadmap resets.

Fifty-six percent of respondents frame AI as a customer-facing enhancement to the core product, while only 10% position AI as the core customer-facing capability.

Seventy-two percent of teams discussing execution say governance, security, and trust concerns constrain AI delivery.

Fifty-three percent of organizations discussing monetization say value capture is still being figured out, showing that product investment is moving faster than commercial clarity.

## Chapter 1: Enhancement and Sourcing

AI is strengthening the core product rather than replacing it. Most organizations describe AI as an embedded enhancement to the customer experience, and hybrid build-and-buy sourcing is becoming the practical operating model. This chapter shows how teams balance external vendor speed with internal control, using vendors and APIs for acceleration while reserving internal development for differentiating use cases.

## Chapter 2: Governance and Trust

Governance, security, and trust concerns are the largest delivery constraint. Product teams may have AI use cases and roadmap momentum, but they still need data readiness, model risk management, security review, auditability, and customer-facing trust safeguards before AI can scale confidently.

Alexis Zheng, Chief Product and Technology Officer at G2, frames this as a shift from model capability to release readiness: "The interesting shift in this research is where the friction has moved. A few years ago the hard part of AI was getting a model to work. Today the hard part is earning the right to put it in front of a customer: knowing where their data goes, being able to explain a decision, and having someone accountable when the model is wrong. Product teams that treat trust as a feature they design, instrument, and ship, rather than a review gate at the end, are the ones who will get to scale. Everyone else will keep building AI that is ready but not releasable."

## Chapter 3: Monetization Gap

AI monetization remains unsettled. Many organizations bundle AI into the core product value, while direct AI pricing is rare. The report argues that teams should define which AI benefits drive willingness to pay, retention, expansion, or efficiency before scaling AI investment further.

Zheng also describes the commercialization gap: "Most teams have made AI part of the product before they have made it part of the business. That is not a failure; making the core product faster and smarter is exactly where AI earns trust first. But 'included' is not a strategy, it is a deferral. Every AI feature you ship changes what customers expect and what it costs you to serve them, so the pricing question is being answered whether or not you decide it. The product leaders who will win are defining now which outcomes customers would actually pay for, and building the measurement to prove it, so that when the market stops treating AI as a bonus they are ready to price it as value."

## Chapter 4: Roadmap Reallocation

Roadmaps are being selectively reprioritized to fund AI. The dominant pattern is measured change rather than wholesale replacement: organizations are shifting capacity toward AI while trying to preserve continuity for existing product commitments and customer needs.

## Recommendations

Treat governance readiness as a product enabler, not a compliance afterthought. Design AI as a modular enhancement to core product value. Use hybrid sourcing to balance speed, flexibility, and control. Move from bundled AI value toward explicit value capture logic. Reprioritize roadmaps with clear investment thresholds.

## FAQ

### Are companies treating AI as the core product or as an add-on?

Most companies are treating AI as an enhancement to the existing product rather than the core product itself. Fifty-six percent position AI as a customer-facing enhancement, while 10% describe AI as the core customer-facing capability.

### How much are organizations really changing their roadmaps for AI?

Most organizations are making selective changes rather than full resets. Seventy percent have selectively reprioritized roadmaps toward AI, while 12% report a full AI-driven roadmap reset.

### What is the biggest obstacle to delivering AI features?

Governance, security, and trust are the biggest obstacles. Seventy-two percent of teams discussing execution say these concerns constrain AI delivery.

### How are teams actually building and integrating AI today?

Teams are commonly using hybrid sourcing, combining vendor capabilities and internal development. This approach helps teams move faster while keeping some control over differentiation, integration, and portability.

### Have companies figured out how to make money from AI yet?

Many have not. Fifty-three percent say AI value capture is still being figured out, while only 7% directly monetize AI today.

## Methodology

This research is based on 549 in-depth interviews with business professionals across company sizes, industries, and roles. Interviews covered roadmap reprioritization and resource reallocation for AI, AI's role in the product value proposition, monetization and value capture models, and AI sourcing strategy, integration, and vendor dependence. G2's AI Custom Research team reviewed the interview analysis for narrative, context, and clarity.

## Analyst

**Analyst:** Anubha Garg

**Topics covered:** Pricing & Monetisation, Product Strategy