Roadmaps reprioritized to fund AI investment
Key findings
AI positioned as a customer-facing enhancement, not a standalone product
Cite governance, security, or trust as the top delivery constraint
Still have not settled on how to monetize AI
Why this matters now
The decision behind the data
This research examines how organizations are incorporating AI into product strategy, delivery, and commercialization: where AI sits in the value proposition, how teams are sourcing and integrating capabilities, what slows execution, and how investment decisions are shifting in response.
Most companies are no longer deciding whether to pursue AI, but how to do so in a way that strengthens product value, manages risk, and creates sustainable returns. The chapters that follow trace that decision from product strategy through delivery constraints to commercial models and roadmap investment.
Inside the interviews
Listen to the people behind the findings.
Voice 01
“When we pitch, AI features to customers, we essentially frame it as a multiplier of the core products. So it's not a separate a separate capability. It just makes the actual core product better.”AI Enablement and Revenue Operations Lead, Global Tech
Voice 02
“"Yeah. So we are actually doing both. So, we are building some models, our own proprietary models, and we also provide the capability where we are wrapping some APIs over OpenAI and Anthropic. So we do both."”Senior Director of Product Management, Enterprise Software
Voice 03
“That is a problem that we have with our customers where nine times out of 10, we'll always get the question around you know, are you training our data? How are you using our data? Is our data, you know, leaving the jurisdiction?”Chief Technology Officer
Voice 04
“We don't charge for our AI capabilities. Our AI capabilities expand the value of our and the ease of use of our value proposition.”Group Product Manager, Large Technology
Voice 05
“A lot of the budgets on the organization have now been allocated for AI related development so whether it is building networks for AI or improving customer experience of for the customer portal using AI, a lot of that budget is now moved for such efforts. And, this does impact, existing ongoing efforts to do core optimizations and and the products.”Product Manager, Telecommunications
Chapter 01
Enhancement & Sourcing
AI Is Strengthening the Core Product, Not Replacing It, and Teams Are Sourcing It Through Hybrid Build and Buy
AI investment is already paying off where it counts. 56% of organizations say it makes the core product better, and half are sourcing it through hybrid build and buy rather than fully bespoke development.
Distribution
AI’s Role in the Product Value Proposition
56%
34%
10%
- 56% AI as a customer-facing enhancement to the core product
- 34% AI as an internal enablement layer
- 10% AI as a core customer-facing capability
Source: G2 Research, 2026 AI product strategy study. Based on 544 respondents who discussed this theme; percentages may not total 100 because of rounding.
AI mostly enhances, not defines, value
56% describe AI as an embedded enhancement to the customer experience, while only 8% position it as the core access point to product value
70%
Internal AI use far outweighs customer-facing AI
70% use AI primarily for internal enablement, compared with 56% who frame it as a customer-facing enhancement and 28% who tie it to role- or use-case-specific value
3%
AI-native propositions remain rare
just 3% describe AI as a future or early-stage non-core offering, reinforcing that most companies are using AI to strengthen existing products rather than build entirely new value propositions
Strategic implication
Package AI as a premium enhancer to the existing product, not a standalone value proposition: prioritize features that improve speed, personalization, accuracy, and workflow outcomes within the core experience, and anchor messaging on measurable customer benefits rather than AI novelty.
Product Leader, Enterprise Software
“We really see this as additive to our core product. It's allowing people to do the same tasks faster, better, cheaper, more efficiently as opposed to net new things.”
Head of Strategy and Operations, On-Demand Delivery
“AI is, nice ad enabler of our core product. But our core product is not one of AI. It supports what we do, and can provide additional value to our customers Our product is not an AI product.”
Deeper in the data
Customer-Facing Core or Enhancing Value
AI is positioned as part of the customer value proposition, ranging from a primary interface to core value through to an embedded enhancement that makes the product easier, faster, or more personalized to use. This includes agentic, conversational, and workflow-improving uses that are visible to end users even when AI is not the product itself.
Based on 504 respondents coded for this dimension; categories may not total 100 because some respondents did not address it.
Internal Enablement or Emerging Non-Core Role
AI is framed primarily as an internal productivity and operational layer, or as an early-stage/future-oriented capability that is not yet central to the live customer offering. This captures behind-the-scenes efficiency, quality, integration, and strategic signaling where customer-facing value is limited, indirect, or still developing.
Based on 536 respondents coded for this dimension; categories may not total 100 because some respondents did not address it.
Distribution
AI Sourcing Strategy, Integration, and Vendor Dependence
50%Hybrid build-and-buy approach
41%Vendor-led API/integration approach
8%In-house or open-source controlled build
1%Others
Source: G2 Research, 2026 AI product strategy study. Based on 441 respondents who discussed this theme; percentages may not total 100 because of rounding.
48%
Hybrid sourcing is the clear default
48% primarily rely on external vendors and another 36% use a hybrid build-and-buy model, leaving just 15% pursuing primarily proprietary internal builds
54%
Vendor APIs are shaping integration choices
54% report managed dependence through multi-vendor mitigation, while only 17% maintain low dependence and 11% face high lock-in exposure
15%
Full-stack independence remains a minority path
with only 15% building primarily in-house and 17% reporting low dependence, most organizations are trading autonomy for faster integration and vendor-led capability access
Strategic implication
Adopt a hybrid sourcing model as the operating baseline, reserving internal development for differentiating use cases and using vendors for speed, scale, and commodity capabilities. Prioritize API-first architecture and portability clauses to reduce switching costs and lock-in risk.
AI Enablement and Revenue Operations Lead, Global Tech
“So models are accessed behind internal interfaces so we can swap providers without rewriting any product logic.”
Senior Manager, Strategy Team
“We manage the risk, by really thinking about who our end customers are, and dependent dependency on external providers. We just know that there's less control, but we're okay with less control for the ability to scale faster.”
Deeper in the data
AI Stack Sourcing Approach
How organizations assemble AI capabilities across buy, build, and hybrid approaches, including API-wrapping, off-the-shelf adoption, proprietary development, and selective internal customization. This captures the core sourcing posture behind speed, control, and differentiation.
Based on 437 respondents coded for this dimension; categories may not total 100 because some respondents did not address it.
Vendor Dependence, Lock-In, and Interoperability
How teams experience and manage dependence on external AI vendors, including concentration risk, multi-vendor mitigation, fragmentation across tools, and concerns about portability, compliance, and data control. This captures both the level of exposure and the degree of active risk management.
Based on 356 respondents coded for this dimension; categories may not total 100 because some respondents did not address it.
Chapter 02
Governance & Trust
Governance, Security, and Trust Concerns Are Blocking AI Delivery for 72% of Teams
Product strategy has an answer for AI. Delivery does not, yet. The same teams gaining traction from an enhancement-first approach hit a wall the moment they try to ship it at scale.
Part-to-whole
Execution, Governance, and Trust Constraints in AI Delivery
Governance, security, and trust constrained72%
Data/integration and execution capacity constrained17%
Lightly constrained with iterative rollout10%
Others1%
Source: G2 Research, 2026 AI product strategy study. Based on 545 respondents who discussed this theme; percentages may not total 100 because of rounding.
64%
Execution discipline is largely in place
64% report structured but manageable AI delivery governance, while only 34% face major execution bottlenecks and resource tradeoffs and 3% report little or no current delivery pressure
60%
Trust and compliance are the real blockers
60% face high trust, compliance, or data risk, compared with 32% managing moderate constraints through active controls and just 8% reporting low concern at their current maturity
Governance gaps are stalling otherwise ready teams
72% discussing this theme say AI delivery is constrained by governance, security, and trust, showing confidence in execution readiness is outpacing confidence in controls
Strategic implication
Shift AI investment from delivery acceleration to control-plane readiness: fund data governance, model risk management, security review, and auditability as gating capabilities for production scale, priced as core implementation work rather than add-ons.
Senior Director of Product Management, Enterprise Software
“"So, to manage those risk we have what we call as the trust layer. So what that ensures is that any data which is passed to vendors like OpenAI or Anthropic, needs to first go through the trust layer, which we have built ourselves."”
Head of Risk / CRO, Global Banking
“Reliability. I can't afford a mistake, and the law is unclear about who is accountable for AI hallucinations or mistakes, for example. It's not the AI tool developer. But it can be me as well. So I'm not comfortable with it.”
Deeper in the data
Delivery Capacity and Execution Discipline
Captures whether AI delivery is mainly constrained by speed, staffing, budget, coordination, and the operational discipline used to validate and ship work. This includes both hard capacity limits and structured execution safeguards such as research, staged reviews, and iterative validation.
Based on 547 respondents coded for this dimension; categories may not total 100 because some respondents did not address it.
Trust, Governance, and Data Readiness
Captures constraints related to making AI safe and dependable in production, including hallucination and reliability concerns, compliance and privacy requirements, and the readiness of data and integrations needed to support trustworthy outcomes. It also includes ecosystem lock-in, fragmentation, and the challenge of connecting AI tools and enterprise data.
Based on 546 respondents coded for this dimension; categories may not total 100 because some respondents did not address it.
Chapter 03
Monetization Gap
Monetization Still Lags Product Movement: Just 7% Charge for AI Directly
AI monetization is still unsettled for most teams. 53% say value capture is still being figured out, and only 7% monetize AI directly today.
Ranked comparison
Monetization and Value Capture Models for AI
Source: G2 Research, 2026 AI product strategy study. Based on 408 respondents who discussed this theme; percentages may not total 100 because of rounding.
68%
Bundling is the default AI monetization path
68% capture AI value inside the core offer, versus just 9% using direct AI pricing and 20% relying on indirect ecosystem or growth value
AI monetization is still being figured out
53% say value capture remains unsettled, while only 26% report having an explicit external pricing model and 15% say the pricing model is still evolving
40%
ROI discipline is shaping monetization decisions
40% describe AI investment through internal ROI or budget logic, outpacing the 26% with explicit external pricing models and reinforcing that value capture is being managed internally first
Strategic implication
Prioritize AI as a bundled value driver, not a standalone SKU: embed it in core products, package it into premium tiers, and anchor messaging on workflow and outcome gains while piloting selective pricing experiments for high-value use cases.
VP of Product
“Or do we sell it on a tokenization basis? Right now, we have not got a strategy, which is slightly worrying.”
CEO
“And how we approach monetization, we we do it in in two different ways. With fixed price, per user per month, or by by computing processing time expanded using the specific product or feature.”
Deeper in the data
Indirect and Bundled Value Capture
AI is most often treated as a value enhancer embedded in existing products, services, or ecosystems rather than as a separately priced revenue line. Payback is expected through stronger product differentiation, adoption, engagement, retention, advertising, or broader ecosystem usage.
Based on 404 respondents coded for this dimension; categories may not total 100 because some respondents did not address it.
ROI Discipline and Emerging Pricing Models
Organizations vary in how explicitly they require AI to prove financial value, from internal productivity and cost savings to formal baseline comparisons and budget gating. Where direct monetization is pursued, pricing is often still evolving across usage-based, subscription, flat-fee, or cost-recovery approaches.
Based on 336 respondents coded for this dimension; categories may not total 100 because some respondents did not address it.
Chapter 04
Roadmap Reallocation
Selective Reprioritization, Not a Full Reset, Is Reshaping Roadmaps to Fund AI
Seven in ten organizations have selectively reprioritized their product roadmaps to accelerate AI investment, the clearest sign yet that commitment is real even where the operating model underneath it is still being built.
Maturity pathway
Roadmap Reprioritization and Resource Reallocation for AI
70%
18%
12%
- 70% Selective reprioritization toward AI
- 18% Additive AI adoption with minimal disruption
- 12% Full AI-driven roadmap reset
Source: G2 Research, 2026 AI product strategy study. Based on 545 respondents who discussed this theme; percentages may not total 100 because of rounding.
Selective reprioritization is the dominant response
70% moderately or selectively reprioritized their roadmaps toward AI, while just 12% undertook a full or major rewrite and 17% saw only additive or limited impact
17%
AI is reshaping priorities more than replacing them
only 17% made major reallocations away from other work, compared with 35% making a moderate or growing capacity shift and 47% absorbing AI with little or no displacement
Most organizations are balancing acceleration with continuity
the largest shares cluster in measured change, with 70% selectively adjusting roadmaps and 82% avoiding major resource displacement by either shifting capacity moderately or adding AI work with little disruption
Strategic implication
Prioritize AI offerings that fit selective roadmap shifts rather than enterprise-wide transformation: package modular use cases and phased deployments, and align pricing to incremental adoption through pilot, expansion, and capacity-based tiers.
VP of Product
“We had a whole another area that we were looking at adding around process optimization, and we shelve that to bring AI functionality into the road map where we thought we could add some more features and functionality to really drive the value for our customers.”
Senior Director of Product / Commercial
“Specifically in customer support, I think better tooling for reps where they would have had information about the transaction statuses and just better visibility into the system health. Was deprioritized in favor of trying an AI system that would potentially do the same but not require a user interface.”
Deeper in the data
Degree of Roadmap Disruption
Captures how much AI is changing what the organization plans to build, ranging from no meaningful roadmap change to a full strategic rewrite where AI becomes the central organizing force. This includes both selective project-level AI additions and broad, cross-cutting shifts across products, workflows, and planning cycles.
Based on 545 respondents coded for this dimension; categories may not total 100 because some respondents did not address it.
Resource Tradeoffs and Capacity Shift
Captures whether AI is being funded by reallocating engineering time, capital, or attention away from other priorities, and how large that shift is. This includes explicit deprioritization of UX, quality, expansion, infrastructure, or other initiatives, as well as cases where AI is added without displacing existing work.
Based on 540 respondents coded for this dimension; categories may not total 100 because some respondents did not address it.
Where to act
Recommendations
What should teams do about AI governance risk?
Treat Governance Readiness as a Product Enabler, Not a Compliance Afterthought
Because governance, security, and trust constrain delivery for 72% of organizations discussing execution, leaders should build approval workflows, data controls, accountability models, and customer-facing trust guardrails into the AI product lifecycle early. Faster shipping and clearer monetization are both more likely once governance confidence improves.
How should AI fit into the core product?
Design AI as a Modular Enhancement to Core Product Value
With 56% positioning AI as a customer-facing enhancement and only 10% treating it as the core customer-facing capability, teams should prioritize use cases that improve existing workflows and outcomes rather than force full product reinvention. This approach aligns better with current buyer expectations and lowers adoption risk.
How can teams reduce AI vendor lock-in?
Use Hybrid Sourcing to Balance Speed, Flexibility, and Control
Since hybrid sourcing dominates and 41% rely primarily on vendor-led API and integration approaches, organizations should architect for modularity: use vendor capabilities for speed while preserving optionality around internal differentiation. This is the most practical path when operating certainty is still evolving.
Should AI be bundled into pricing or charged for separately?
Move from Bundled AI Value to Explicit Value Capture Logic
Because 53% are still figuring out monetization and only 7% directly monetize AI today, product and commercial teams should define which AI benefits drive willingness to pay, retention, or expansion before scaling investment further. Even if AI remains bundled initially, value capture should be intentional rather than assumed.
What should teams do before reprioritizing the roadmap for AI?
Reprioritize Roadmaps with Clear Investment Thresholds
With 70% selectively reallocating roadmap capacity toward AI, organizations should formalize what gets deprioritized, what outcomes justify continued AI investment, and where AI must outperform non-AI alternatives. Selective commitment works best when tied to explicit portfolio trade-off discipline.
Common questions
Frequently asked
Are companies treating AI as the core product or as an add-on?
Mostly as an add-on to existing value. 56% framed AI as a customer-facing enhancement to the core product, 34% positioned it as an internal enablement layer, and only 10% said AI serves as a core customer-facing capability in its own right.
How much are organizations really changing their roadmaps for AI?
Quite a lot, but usually through selective shifts rather than total reset. 70% selectively reprioritized their roadmaps toward AI, compared with 12% who fully reset around AI and 18% who treated AI as additive with minimal disruption.
What is the biggest obstacle to delivering AI features?
Governance, security, and trust concerns are the main blocker. Among respondents discussing execution, 72% cited those issues as the primary constraint, versus 17% pointing to data or integration gaps and 10% describing a lighter iterative rollout path.
How are teams actually building and integrating AI today?
Most are using pragmatic mixed approaches. Half combine internal model development with third-party tools in a hybrid strategy, while 41% rely primarily on vendor-led API and integration approaches, showing that external ecosystem choices are central to delivery.
Have companies figured out how to make money from AI yet?
Not fully. 53% said AI value capture is still being figured out, 40% currently bundle AI into the core product, and only 7% directly monetize AI, indicating that commercialization still lags product movement.
What does hybrid AI sourcing mean?
Hybrid AI sourcing means combining internally built AI capabilities with third-party vendor tools rather than committing fully to one approach. It is the leading pattern: 50% of organizations combine internal development with third-party tools in a hybrid build-and-buy approach, while 41% rely primarily on vendor-led APIs and 8% build primarily in-house.
What counts as roadmap reprioritization for AI?
Roadmap reprioritization for AI describes how much an organization changes its planned product work to fund AI, ranging from no meaningful change to a full strategic rewrite. Most organizations land in the middle: 70% selectively reprioritized their roadmaps toward AI, while 18% treated it as additive with minimal disruption and 12% carried out a full AI-driven roadmap reset.
Methodology
How the research was conducted.
This research draws on 549 in-depth interviews with business professionals representing a wide mix of roles, industries, and company sizes.
Interviews ran 1 to 35 minutes and covered roadmap reprioritization and resource reallocation for AI, AI’s role in the product value proposition, monetization and value capture models for AI, and AI sourcing strategy, integration, and vendor dependence. The conversational format allowed respondents to discuss their actual practices rather than select from preset options, surfacing nuance that closed-ended surveys typically miss.
Respondents included business professionals across technology, financial services, healthcare, retail, and manufacturing. All participants were selected for their direct experience with AI strategy, product development, and adoption decisions. Company sizes ranged from small businesses to large enterprises.
The analysis of 549 interview transcripts was conducted using AI for semantic understanding, with multi-iteration validation and cross-verification to ensure analysis quality. Each transcript was independently reviewed by G2's AI Custom Research team to inform narrative, context, and clarity.
549in-depth interviews
Up to 35 minutesper interview
Where this leaves the market
The research reveals a market moving decisively toward AI, but in a measured and highly practical way. The clearest transformation is not a wholesale shift to AI-native products; it is the emergence of an enhancement-first pattern in which AI is used to strengthen existing customer value, supported by selective investment and modular delivery choices. That pattern is visible in the fact that 56% position AI as a customer-facing enhancement and 70% have selectively reprioritized product roadmaps to accelerate AI investment.
The central challenge is that execution friction is suppressing commercial clarity. While organizations are willing to fund AI, delivery is slowed by governance, security, and trust barriers, cited by 72% of respondents discussing execution. In that environment, hybrid sourcing and vendor APIs become the practical workaround, but monetization remains unsettled: 53% are still figuring out value capture, 40% are bundling AI into broader product value, and direct monetization remains rare at 7%. Investment, in other words, is running ahead of operating certainty.
Looking ahead, the advantage will go to organizations that convert AI enthusiasm into disciplined operating models. That means treating governance as foundational infrastructure, continuing to pursue modular enhancement-led use cases, and building explicit monetization logic alongside product delivery rather than after it. Vendors should support this reality by enabling flexible integration and hybrid deployment models, while product leaders should use roadmap reprioritization more deliberately, linking AI investment to clear customer outcomes, trust safeguards, and value capture mechanisms.
The bottom line: the winners in AI will not be the ones who invest fastest, but the ones who turn enhancement-led adoption into governed execution and measurable value.