# AI Monetization In Transition **Author:** G2 Research **Published:** May 2026 **Last updated:** September 23, 2026 **Based on:** This research draws on 110 in-depth interviews with business professionals representing a wide mix of roles, industries, and company sizes. **Topics covered:** Pricing & Monetisation, Product Strategy AI has become strategically non-negotiable, with **95%** of respondents saying it is important to their organization, but commercialization maturity has not kept pace. Many teams still operate in build mode, and **48%** said they are intentionally investing without yet being profitable. This creates a clear monetization maturity gap: AI is expected to drive revenue, efficiency, and product value, yet the business model behind it is still being worked out in market. The biggest friction is not simply price, but uncertainty. While **97%** reported pricing as a buyer objection, the leading concern was unpredictable spend, which pushes companies toward hybrid packaging, selective cost pass-through, guided pilots, and guardrails rather than rigid models. As a result, today’s winning pattern is pragmatic flexibility: **76%** use hybrid or custom packaging, while future pricing power is expected to shift away from generic model access and toward embedded workflows, vertical specificity, and outcome-linked value. **Executive answer:** AI Is Now Strategically Non-Negotiable **On this page:** [AI Strategic Importance and Organizational Maturity](#ai-strategic-importance-and-organizational-maturity) · [Primary AI Value Case and ROI Logic](#primary-ai-value-case-and-roi-logic) · [AI Profitability Status and Investment Horizon](#ai-profitability-status-and-investment-horizon) · [AI Cost Recovery and Subsidy Posture](#ai-cost-recovery-and-subsidy-posture) · [Management of Heavy or Power Users](#management-of-heavy-or-power-users) · [Primary Buyer Pricing Objection](#primary-buyer-pricing-objection) · [AI Packaging and Commercial Structure](#ai-packaging-and-commercial-structure) · [Trial, Freemium, and Entry Strategy](#trial-freemium-and-entry-strategy) · [Pricing Predictability Controls and Guardrails](#pricing-predictability-controls-and-guardrails) · [Vertical Specificity and Sources of Pricing Power](#vertical-specificity-and-sources-of-pricing-power) · [Next Pricing and Packaging Experiment Direction](#next-pricing-and-packaging-experiment-direction) · [Future Value Capture as AI Commoditizes](#future-value-capture-as-ai-commoditizes) · [Strategic takeaways](#strategic-takeaways) · [Frequently asked questions](#frequently-asked-questions) · [How this was produced](#how-this-was-produced) --- ## AI Strategic Importance and Organizational Maturity AI is now strategically important for nearly all organizations, with 95% of respondents saying it matters to their business direction. Yet maturity remains uneven: roughly half describe AI as strategically important but still maturing, while 35% say it is already central and relatively mature. Only 15% remain in selective or early-stage adoption. Most organizations appear caught between ambition and execution. Roughly half see AI as core to strategy but are still building capabilities, while a little over one-third have embedded it more broadly across products, operations, or customer offerings. In practice, this suggests AI has won executive attention faster than budgets, governance, and scaled deployment have caught up. **Key takeaways:** - AI is now a strategic mandate: 95% say AI is strategically important, including 50% who view it as a core strategic priority and 47% who see it as strategically important but not yet core - Maturity trails strategic intent: while 95% assign AI strategic importance, only 54% say it is broadly embedded, leaving 46% still in planning, early-stage, or selective adoption - Most organizations are committed but still scaling: just 3% describe AI as peripheral or exploratory, yet 36% remain in selective or mid-stage adoption and 10% are still in planning or early-stage use **Strategic implication:** Segment AI offerings and operating plans by maturity, not interest: position enterprise solutions around scale, governance, integration, and ROI for the 54% with broad deployment, while packaging faster-start services, phased implementation, and capability-building support for the 46% still scaling. Shift messaging from “why AI” to “how to operationalize AI responsibly,” tie pricing to adoption stage and value realization, and prioritize enablement, change management, and measurable use-case expansion over broad awareness campaigns. --- ## Primary AI Value Case and ROI Logic AI’s ROI story is led by growth and strategic impact: 39% of respondents framed the primary value case around revenue, strategic outcomes, or a mix of both. Another 35% emphasized internal efficiency and cost or productivity gains, while 25% focused on product enhancement and added customer value embedded in the core offer. Commercial leaders increasingly evaluate AI across multiple value levers rather than as a standalone monetized feature. Revenue and strategic upside slightly outweigh pure efficiency cases, but the split is fairly balanced, suggesting AI decisions are justified through blended business cases that combine revenue uplift, cost reduction, retention, and stronger product differentiation. **Key takeaways:** - **AI ROI splits across two dominant paths**: efficiency outcomes are led by productivity and time savings at 80%, while product enhancement and customer utility drive value for 77% - **Efficiency ROI is overwhelmingly about saved time**: 80% cite productivity and time savings, far ahead of 15% pointing to mixed efficiency and cost outcomes and just 2% focused on loss reduction and margin protection - **Growth value comes more from better products than direct monetization**: 77% tie AI ROI to product enhancement and customer utility, compared with 19% focused on adoption, retention, or market growth and only 4% on direct monetization or differentiated revenue value **Strategic implication:** Segment AI investments into two operating tracks: fund automation initiatives with hard productivity baselines, cycle-time targets, and workflow adoption KPIs, while managing product AI as a growth portfolio measured by feature usage, retention lift, and customer outcome improvement. Position pricing and messaging accordingly—sell internal AI on efficiency payback and capacity creation; package customer-facing AI as premium product value, bundling utility into core tiers first and reserving direct monetization for clearly differentiated capabilities. --- ## AI Profitability Status and Investment Horizon AI investment is running ahead of near-term returns, with 48% describing their efforts as intentional investment that is not yet profitable. About one-third, 32%, say they are already profitable or close to breakeven, while only one in five report economics that vary by product, customer, or charging model. Build-mode respondents frame AI as a strategic bet on future productivity, market share, and learning rather than immediate monetization. By contrast, profitable organizations tend to emphasize improving margins as scale increases, while mixed performers are often using time-bound subsidy models or selective pricing shifts to manage uneven economics across use cases. **Key takeaways:** - Nearly half are still building, not earning: 48% say AI is a temporary investment with a defined path to breakeven or profit, while only 25% say it is an established profit contributor today - Profitability remains uneven across AI efforts: 39% describe AI as currently unprofitable or a loss leader, compared with 33% who say it is already profitable and 27% who report near breakeven or mixed economics - AI investment is outpacing near-term returns: 75% are not yet at clear profit contribution today, combining 48% in intentional build mode with 27% treating AI as an open-ended strategic investment or subsidy **Strategic implication:** Segment AI portfolios by monetization horizon and run each track differently: scale and price proven use cases for margin now, impose milestone-based funding and strict ROI gates on build-mode initiatives, and cap open-ended bets unless they support clear strategic differentiation. Message AI offerings around measurable business outcomes rather than novelty, package pilots with explicit conversion paths to paid deployment, and reallocate capital quarterly from stalled experiments into products with demonstrated adoption, retention, and unit economics. --- ## AI Cost Recovery and Subsidy Posture Selective pass-through is the dominant AI cost recovery model, with 59% using a hybrid approach that mixes bundled pricing with usage-based fees. Another 30% mostly absorb AI costs internally, while only 10% rely on direct customer pass-through or position AI as a fully paid standalone service. Hybrid models reflect a careful balancing act: companies recover variable inference or token costs where usage is clear, but still subsidize baseline AI to drive adoption and stay competitive. Internal absorption is also meaningful, especially when AI supports efficiency rather than monetized features, while fully explicit pass-through remains a niche strategy. **Key takeaways:** - Selective pass-through is the default posture: 56% use a hybrid model, far exceeding the 20% that fully absorb AI costs internally - Bundled subsidies still shape the market: 62% mostly embed AI in existing pricing, while only 12% rely on explicit usage-, premium-, or threshold-based charging - Recovery strategies remain mixed, not all-in: 10% use stage-gated or externally subsidized recovery and 11% recover indirectly through internal budgets, reinforcing that selective monetization leads over direct pass-through **Strategic implication:** Adopt a tiered AI monetization model: bundle baseline capabilities into core offers to preserve competitiveness, then price advanced, high-cost, or high-usage features through premium tiers, thresholds, or usage triggers. Fund early rollout and experimentation through internal budgets or stage-gated subsidies, but define clear conversion points from subsidized access to paid adoption. Align sales messaging around included value at entry and measurable ROI for paid AI enhancements. --- ## Management of Heavy or Power Users Heavy or power users are usually managed proactively rather than allowed to inflate costs unchecked. Nearly all respondents, 95%, discussed this issue, and roughly half, 49%, described active controls such as thresholds, caps, monitoring, or pricing levers. Another 40% said power users do not create a material cost problem in their current model. Management approaches split between direct control and relationship-based handling. A smaller group, 11%, relies on account managers, premium service models, or customer support programs, while the largest share uses operational guardrails. In practice, this suggests most organizations either contain heavy usage through explicit limits or have product economics that prevent overconsumption from becoming significant. **Key takeaways:** - Heavy users are rarely left unmanaged: 95% describe some form of intervention, showing power usage is typically addressed rather than allowed to escalate unchecked - Pricing is the primary pressure valve: 41% manage heavy users through usage-linked pricing or tier migration, outpacing collaborative high-touch management at 18% - Control strategies split between hard limits and softer steering: 27% use strong active controls, 23% apply lighter selective controls, and 13% rely on behavioral guidance without hard caps **Strategic implication:** Design heavy-user management as a formal operating model: pair transparent usage-based pricing and automatic tier migration with graduated controls that tighten as consumption rises, reserving high-touch account intervention for strategic accounts. Segment power users by margin, growth potential, and abuse risk, then align messaging to emphasize predictable scaling, clear thresholds, and optimization guidance. Avoid unlimited positioning by default; package premium capacity, governance, and support as paid upgrades rather than absorbing escalating demand. --- ## Primary Buyer Pricing Objection Pricing objections are nearly universal, with 97% of respondents citing them as a primary buyer concern. The biggest issue is not headline price: roughly 44% pointed to unpredictable spend and cost variability, compared with 18% focused on high price or budget sensitivity, while 38% raised ROI questions or reported limited pushback so far. Buyer hesitation centers on financial predictability more than sticker shock. Nearly half described anxiety about open-ended usage models, token consumption, or spend forecasting, whereas only about one in five objected mainly to price. At the same time, a sizable 38% suggest objections can ease when value is clearly articulated, making ROI framing and cost controls critical in enterprise sales. **Key takeaways:** - Unpredictable costs drive pricing resistance: 43% cite spend unpredictability as the dominant objection, compared with just 21% who say the product is simply too expensive - Sticker price is a secondary barrier: 49% raise some level of unpredictable spend concern, while only 21% center their objection on absolute price alone - Half see limited pricing friction overall: 51% report little or no pushback on absolute price, and 47% show low or no concern about unpredictable spend **Strategic implication:** Reduce pricing friction by making total cost predictable, not just competitive. Package offers around fixed-fee tiers, usage caps, spend alerts, and upfront implementation estimates so buyers can model exposure before purchase. Equip sales teams with ROI cases that separate controllable base cost from variable spend, and position premium pricing around budget certainty, governance, and financial planning. Reserve discounting for true price-sensitive segments; for most buyers, transparency and cost control will outperform lower list price. --- ## AI Packaging and Commercial Structure Hybrid or custom AI packaging is the clear commercial default, cited by 76% of respondents. Far fewer described bundling AI into core or enterprise plans, at 15%, while only 5% positioned AI primarily as a standalone add-on or tiered product. The dominant model blends monetization flexibility with upgrade pathways. Hybrid structures typically pair baseline AI features inside enterprise tiers with paid add-ons for higher cost or more specialized capabilities. This suggests companies are using AI both as a value driver for plan upgrades and as a separate monetization lever when usage, compute intensity, or customer needs vary. Pure bundling and pure add-on approaches remain secondary options. **Key takeaways:** - Hybrid models clearly dominate AI monetization: 66% use a hybrid bundle plus add-on or usage model, while only 14% rely on standalone or paid add-on packaging and 12% use custom or project-based approaches - Tiered packaging is the commercial default: 39% bundle AI into higher or enterprise tiers and another 32% pair bundling with tiered or usage-based differentiation, versus just 12% that include AI in all core plans - Broad inclusion, selective monetization wins: 76% describe their AI packaging as hybrid or custom, showing most companies embed AI into existing offers while preserving upsell or usage-based revenue paths **Strategic implication:** Package AI as a hybrid commercial layer: embed baseline capabilities in core or premium tiers to drive adoption, then monetize advanced functionality through usage, seat-based, or premium add-ons. Align pricing to customer maturity by reserving higher-value automation, governance, and scale features for upper tiers or enterprise offers. Sharpen messaging around “AI included” for marketability and “pay for advanced outcomes” for expansion, while limiting standalone AI SKUs to narrow, high-intent use cases. --- ## Trial, Freemium, and Entry Strategy Controlled pilots are the dominant entry strategy, with 62% using a guided trial or proof of concept rather than broad free access. Just over a third, 35%, offer no trial or freemium at all, while only 4% use a true freemium or open free trial model. Enterprise-oriented AI sales favor tightly bounded evaluations that prove value while limiting scope, usage, or integration burden. The small share using freemium highlights how rare self-serve adoption is in this market, while the sizable no-trial group shows that some providers see even limited testing as impractical for complex, high-touch deployments. **Key takeaways:** - **Controlled pilots are the dominant entry path**: 62% used a controlled trial, pilot, or guided entry approach, far outweighing the 6% using broad freemium or standard trials - **Freemium is a niche, not the strategy**: just 6% offered broad freemium or a standard trial, versus 57% using controlled or limited trial or freemium models - **Value proof is tightly managed**: 31% relied on pilot or testing environments and 10% used guided demos or previews, while only 6% allowed selective or stage-bound trial access **Strategic implication:** Prioritize controlled pilots as the default entry motion and reserve broad freemium for narrow, low-complexity use cases. Package entry around time-bound pilots, guided onboarding, clear success criteria, and conversion checkpoints, then tie pricing and expansion to validated outcomes rather than open access. Shift messaging from “try it free” to “prove value fast,” emphasizing implementation support, measurable ROI, and staged access that qualifies buyers while protecting margin and sales efficiency. --- ## Pricing Predictability Controls and Guardrails Customers are actively seeking pricing guardrails, with 93% discussing mechanisms that make AI spend more predictable. The strongest demand centers on usage caps, tiers, and credits, mentioned by just over half of respondents, while 40% pointed to fixed, bundled, or contract-based pricing as the clearest path to cost certainty. Preferences split between flexible controls and simpler commercial structures. Roughly half want capped consumption that preserves scalability, while four in ten favor fixed modules or upfront contracts that reduce budgeting risk. Only a small minority emphasized alerts and governance controls alone, suggesting monitoring matters most when paired with hard financial boundaries. **Key takeaways:** - Contracts and budgets edge out product controls: 39% want contractual or budget-based guardrails, slightly ahead of 37% who prefer fixed or upfront pricing, making commercial structure the strongest path to predictability - Customers split across four distinct guardrail models: 37% favor fixed or upfront pricing, 29% prefer tiered allowances or bundled credits, 19% want visibility and alerts, and 17% specifically ask for hard caps or strict ceilings - Predictability is nearly universal, but enforcement varies: 93% discussed pricing guardrails overall, yet preferences range from hard limits at 17% to lighter-touch visibility and alerts at 19%, showing no single control model dominates **Strategic implication:** Build a modular predictability framework rather than forcing a single pricing model: lead with contractual guardrails and fixed-price options for budget-sensitive accounts, then layer in tiered bundles, optional hard caps, and real-time alerts for customers who prefer product-level control. Segment packaging and sales motions by governance preference, equip reps to diagnose the customer’s desired guardrail type early, and position visibility, ceilings, and bundles as configurable controls that reduce spend anxiety without constraining adoption. --- ## Vertical Specificity and Sources of Pricing Power Vertical AI and tailored solutions hold the strongest pricing power, with roughly half of respondents, 52%, saying domain specific models can command premiums. Another 26% tied higher pricing to implementation complexity and customization, while only 22% said generic AI has limited or no pricing premium.\n\nIndustry specificity and delivery model shape monetization in distinct ways. Vertical offerings win when they embed proprietary context, workflows, and regulatory logic; customized implementations add another layer of willingness to pay through fine tuning and preconfigured automation. By contrast, generic models are more often treated as commoditized capabilities, with pricing anchored to licenses or market norms rather than differentiation. **Key takeaways:** - Vertical fit is the core pricing moat: 74% tied pricing power to vertical specificity, with 31% saying vertical AI directly commands a premium and 43% saying the premium comes through superior data and workflow fit - Implementation complexity outprices broad usefulness: 51% said pricing is driven by implementation or solution complexity, versus 32% who favored generic or broad usability as the stronger commercial advantage - Generic AI has reach but weaker defensibility: only 7% said generic products remain useful but less defensible on price, while 14% said there is limited or no current vertical pricing effect, reinforcing that premiums concentrate in tailored solutions **Strategic implication:** Prioritize vertical expansion where proprietary data access, workflow integration, and implementation depth are strongest, and price around measurable deployment complexity rather than broad AI utility. Package offerings by use-case and operational fit, with premium tiers tied to configuration, integration, and domain-specific performance outcomes. Shift messaging from generalized capability to faster time-to-value in specific workflows, and direct product and sales investment toward repeatable vertical playbooks that increase defensibility and willingness to pay. --- ## Next Pricing and Packaging Experiment Direction Teams are overwhelmingly leaning toward more usage-aligned and hybrid pricing as their next experiment direction, with 80% pointing there. Only 6% mentioned outcome-based or value-based models next, while 14% had no clear pricing experiment planned, showing a strong near-term bias toward models that better match consumption patterns. In practice, that points to a pragmatic shift away from pure flat fees and toward structures that combine predictable subscriptions with metered usage. A small minority is pushing further into pricing tied to business results, while others are still focused on product or market strategy rather than pricing design, suggesting most teams want lower-risk packaging changes before testing true value-based models. **Key takeaways:** - Hybrid and usage-aligned pricing dominate next tests: 80% pointed to more usage-aligned or hybrid pricing as their next experiment direction, led by hybrid models at 33% and usage-aligned pricing at 31% - Hybrid edges out tier refinement: 33% prioritized hybrid subscription-plus-usage or outcome models, compared with 31% for usage-based pricing and 24% for tiered or bundled subscription refinement - Operational readiness is the biggest gating factor: 67% said governance and operational readiness must come before pricing changes, while only 22% reported limited outcome-based pilots or near-term plans and 11% said outcome-based remains future consideration **Strategic implication:** Prioritize a hybrid pricing roadmap that pairs a core subscription with usage-based meters, while refining tiers as a secondary track rather than the primary monetization lever. Sequence experimentation around operational readiness first: establish usage instrumentation, billing logic, contract governance, and sales compensation before broader rollout. Position messaging around flexibility, scale, and value realization, and keep outcome-based pricing in targeted pilots until measurement, accountability, and customer success processes are mature enough to support wider adoption. --- ## Future Value Capture as AI Commoditizes Future value in AI is expected to concentrate in embedded workflows and outcome-led services, not the model itself. Four in five respondents pointed to workflow integration, outcomes, and product embedding as the main source of future value, while only 11% emphasized commoditization of models and 7% highlighted adjacent services or data layers. This pattern suggests differentiation will come from owning the enterprise context around AI: domain workflows, orchestration, governance, and measurable business results. As model costs fall, pricing power appears likely to move away from raw AI access and toward integrated solutions that are harder to replace, with adjacent services playing a smaller but still relevant supporting role. **Key takeaways:** - **Workflow is the dominant future moat**: 91% say the main value will come from workflow integration, outcomes, and the product layer, while only 9% still see value split between the model and workflow - **AI commoditization redirects value beyond the model**: 80% believe future value will come from embedding AI into workflows and selling outcomes rather than from the core model itself - **Margin pools are shifting into services and infrastructure**: 43% say value will move primarily to services, data, hosting, hardware, and adoption layers, versus just 13% who see cost decline alone as the main value source **Strategic implication:** Reorient investment away from standalone model advantage and toward workflow-native products, implementation services, and outcome-based offerings. Package AI inside existing systems of record, build proprietary process integration and service layers, and price on business results, adoption, or managed value rather than model access alone. Shift messaging from model performance to operational impact, speed to deployment, and measurable ROI, while capturing margin through data, hosting, support, and domain-specific enablement. --- ## Strategic takeaways ### Build Commercialization As A Core AI Capability Treat monetization design as part of the product strategy, not a post-launch pricing exercise. With 95% viewing AI as strategically important but 48% still in investment mode, leaders should align product, finance, and sales on explicit cost recovery and value capture logic early. ### Reduce Buyer Anxiety By Making Spend Predictable Prioritize caps, credits, bundles, and contract structures that make usage legible before pushing for broader adoption. Since 97% reported pricing objections and the biggest issue is cost unpredictability, simpler guardrails can remove friction faster than discounting alone. ### Standardize Around Hybrid Packaging Use hybrid commercial models as the default operating pattern: bundle baseline AI to encourage adoption, then meter or charge for advanced, high-cost, or specialist usage. This fits current market behavior, where 59% use selective pass-through and 76% rely on hybrid or custom packaging. ### Use Controlled Entry Paths To Prove Value Safely Favor guided pilots and bounded proofs of value over open freemium models when costs and implementation complexity are uncertain. With 62% using controlled trials, this approach helps validate ROI while limiting uncontrolled usage and commercial risk. ### Invest Above The Model Layer For Durable Pricing Power Differentiate through vertical depth, workflow embedding, implementation expertise, and outcome alignment rather than generic AI access. Research shows 52% see premiums in domain-specific solutions and 80% expect future value to concentrate in embedded workflows and outcomes. --- ## Frequently asked questions **How mature are organizations in commercializing AI?** Strategic commitment is high, but commercial maturity is uneven. While 95% said AI is strategically important, roughly half described it as important but still maturing, and 48% said they are intentionally investing without being profitable yet. **What is the main pricing obstacle with buyers?** The dominant issue is unpredictability, not just high price. Pricing objections were mentioned by 97% of respondents, and about 44% said buyers are most concerned about variable or open-ended spend, compared with only 18% focused primarily on price level or budget sensitivity. **What packaging model seems to be working best right now?** Hybrid packaging is the clearest current pattern. 76% described their AI packaging as hybrid or custom, and 59% use a selective pass-through approach that blends bundled access with usage-based recovery for higher-cost activity. **Why are controlled pilots more common than freemium?** Teams are trying to prove value while containing cost and implementation risk. 62% use guided trials or pilots, while only 4% offer true freemium or open free trials, showing that most organizations prefer bounded adoption paths over unrestricted self-serve access. **Where will future AI pricing power come from as models commoditize?** The research points clearly beyond the model layer. About 52% said vertical or domain-specific AI commands stronger pricing power, and 80% expect future value to come from workflow integration, product embedding, and outcome-led services rather than generic model access. --- ## Methodology G2 is the world's largest and most trusted software marketplace. This research draws on 110 in-depth interviews with business professionals representing a wide mix of roles, industries, and company sizes. Interviews ran 5 to 31 minutes and covered AI strategic importance and organizational maturity, primary AI value case and ROI logic, AI cost recovery and subsidy posture, and AI profitability status and investment horizon. 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, investment, and business value realization. Company sizes ranged from small businesses to large enterprises. The analysis of 110 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. --- *Source: G2 Research — "AI Monetization In Transition" (May 2026). Based on G2 first-party research. Markdown edition structured for answer-engine optimization (AEO).*