Take 5 Report

Build the foundation before you continue to scale complexity

This HFS Take 5 report is for CIOs, CEOs, CFOs, and AI transformation leaders rebuilding the operating model to scale production AI for growth.

Executive summary

Enterprise AI is moving faster than the operating model can support. Most enterprises can deploy AI, but few can price it on outcomes, orchestrate it across the ecosystem, or make it deliver growth. The result is more complexity and less impact. The data, governance, and integration foundations required to scale production AI have not kept pace. The technology is no longer the challenge, but the operating model around AI is.

HFS Research, in partnership with Movate, surveyed senior enterprise leaders to understand how organizations are scaling production AI in 2026, and where the operating model must change to keep pace.

The survey uncovered five key takeaways:
    • Buyers want to pay for outcomes, not effort. 70% are committed to outcome-based pricing, and 75% percent are walking away from T&M.
    • Enterprises are choosing ecosystems over lock-in. Hyperscaler-led platforms lead at 70%, while 53% prefer service providers as the orchestrators.
    • AI is transforming efficiency, but growth is the gap. Only 22% expect transformational revenue growth, half the rate of every other outcome.
    • Production AI is blocked by readiness, not ambition. Data quality is a top-three barrier for 56%, while executive alignment ranks last.
    • AI buying is no longer a single-seat decision. The CIO holds 25% of AI decisions, while the rest remains disorganized.

The Bottom Line: Enterprises are scaling AI faster than they are building the foundation, the commercial discipline, and the ecosystem orchestration required to make AI deliver growth. To turn AI’s efficiency dividend into growth impact, the operating model must come first.

      • Buyers want to pay for outcomes, not effort. 70% are committed to outcome-based pricing, and 75% percent are walking away from T&M

Diverging 100% stacked bar chart showing how willing enterprises are to adopt five commercial models for AI-led services engagements, rated from will not adopt through unlikely, neutral, considering, likely to adopt, and already adopted. Outcome-based pricing (KPI-linked) leads with 70% likely to adopt or already adopting (55% likely, 15% already adopted). Platform or subscription pricing follows at 68% (45% likely, 23% already adopted), consumption-based pricing (tokens, usage) at 57% (41% likely, 16% already adopted), and hybrid (base fee plus performance incentives) at 46% (30% likely, 16% already adopted). Traditional time-and-materials or fixed price is the most rejected model, with only 17% willing to adopt (10% likely, 7% already adopted) and 75% unlikely or refusing to adopt (49% unlikely, 26% will not adopt). Source: HFS Research in partnership with Movate, 2026.

The commercial model market has tipped
Seventy percent of enterprise buyers are committed to or already adopting outcome-based pricing for AI-led services. Only 17% are willing to adopt traditional T&M, while 75% are unlikely or will not. T&M is the most rejected item across the entire survey.

Pricing has shifted from effort to outcomes
Enterprises are no longer willing to pay providers for effort. The contract is shifting toward outcomes that can be measured against business KPIs.

Build the framework with discipline
Demanding outcome-based pricing is the easy part. Executing it requires defined KPIs tied to AI specifically, attribution methods that hold up under scrutiny, and contracts that survive under stress.

    • Enterprises are choosing ecosystems over lock-in. Hyperscaler-led platforms lead at 70%, while 53% prefer service providers as the orchestrators

Diverging 100% stacked bar chart showing enterprise willingness to commit to seven approaches for deploying AI at scale over the next 12 months, rated from will not commit through unlikely, neutral, considering, likely, and already committed. Hyperscaler-led platforms (AWS, Azure, GCP) lead at 70% likely or already committed (41% likely, 29% already committed). SaaS vendor-embedded AI follows at 63% (48% likely, 15% already committed), service provider as orchestrator at 53% (37% likely, 16% already committed), best-of-breed AI tools and startups at 50% (32% likely, 18% already committed), fully custom AI solutions at 46% (34% likely, 12% already committed), and service provider proprietary AI platforms at 39% (29% likely, 10% already committed). Enterprise-owned AI platform or control plane scores lowest at 25% (20% likely, 5% already committed). Source: HFS Research in partnership with Movate, 2026.

The ecosystem model has won
Hyperscaler-led platforms (70%) and SaaS vendor-embedded AI (63%) lead the 12-month commitment race. Service provider orchestration (53%) outperforms proprietary platforms (39%). Enterprise-owned AI platforms score lowest at 25%.

Buyers reject lock-in and DIY
Enterprises are choosing ecosystems over single-stack ownership. They are committing to platforms they already trust rather than building themselves or locking in. Service providers earn their seat by orchestrating across the ecosystem.

Architect for orchestration
Build the orchestration layer first, then evaluate each vendor to see how they fit into it. Treat proprietary IP and platforms as accelerators inside orchestration, not as the foundation.

    • AI is transforming efficiency, but growth is the gap. Only 22% expect transformational revenue growth, half the rate of every other outcome

Horizontal stacked bar chart showing the expected impact of AI on five enterprise outcomes, rated from no impact through low, moderate, high, and transformational. Ranked by combined transformational or high impact, speed leads at 83%, customer experience engagement at 80%, cost at 79%, and productivity at 75%. Revenue growth lags at 55% combined transformational or high impact, with only 22% of leaders expecting a transformational impact, half the rate of every other outcome. Source: HFS Research in partnership with Movate, 2026.

Efficiency is the impact
Speed (83%), CX engagement (80%), cost (79%), and productivity (75%) all score transformational or high impact. Revenue growth lags at 55%, with only 22% expecting a transformational impact.

Revenue is the gap
Enterprises see AI as a tool for cutting cost and lifting CSAT. They have not yet seen it move the top line. The next AI investment must change that.

Underwrite AI for growth
Move AI investment from cost-reducing use cases to revenue-generating ones such as dynamic pricing, sales acceleration, and customer expansion. The next AI dollar must aim at growth.

    • Production AI is blocked by readiness, not ambition. Data quality is a top-three barrier for 56%, while executive alignment ranks last

Clustered bar chart ranking eight barriers to moving AI from pilot to production, showing the share of enterprises naming each as their number one barrier alongside the share ranking it in their top three. Data quality, availability, and readiness ranks highest, named the number one barrier by 24% and placed in the top three by 56%. Governance, compliance, security, and risk follows (14% number one; 47% top three), then integration with legacy systems (18%; 40%), talent and skills gaps (16%; 38%), change management and end-user adoption (11%; 33%), cost and unclear ROI (6%; 32%), and vendor and partner ecosystem maturity (8%; 32%). Executive alignment and prioritization ranks last, named number one by just 4% and placed in the top three by 24%. Source: HFS Research in partnership with Movate, 2026.

Data, governance, and integration top the list
Twenty-four percent of enterprises cite data quality, availability, and readiness as the top barriers, with 56% ranking them in the top three. Forty-seven percent place governance, compliance, security, and risk in the top three. Legacy integration follows next at 40%.

The wall is execution, not ambition
The top three barriers are all capability gaps in the foundation underneath their AI. Executive alignment ranking dead last means leadership commitment is not the constraint. The challenge is not whether to do AI, but whether the foundation is ready to deliver it.

Fix the foundation before scaling the ambition
Audit data quality, governance, and integration readiness as a precondition to AI scale. Fund the foundational work with executive sponsorship. Adding AI pilots on top of an unfit foundation means you are only scaling complexity.

    • AI buying is no longer a single-seat decision. The CIO holds 25% of AI decisions, while the rest remains disorganized

Bar chart showing which role primarily drives decisions for AI-led transformation across enterprise functions, as a share of all decisions. The CIO or IT and digital leadership holds the largest single share at 25%, followed by a dedicated AI or transformation office at 17%, the CEO at 15%, the CFO or finance and procurement at 14%, and a cross-functional buying committee at 14%. Business unit or regional leadership and the COO or operations each account for 6%, and the CCO or CX leadership for 4%. Source: HFS Research in partnership with Movate, 2026.

The buying decisions are scattered
The CIO holds the largest single share at 25%, but that is still only a quarter of decisions. The dedicated AI or transformation office (17%), CEO (15%), CFO (14%), and cross-functional buying committees (14%) account for another 60%.

Every seat owns a piece
AI decisions now span CIO, CEO, CFO, and the dedicated AI office. Single-stakeholder approaches miss 75% of the decision landscape. Each seat has a stake in the outcome, but they rarely make the decision together.

Unify the AI buying conversation
Treat AI buying as one C-suite conversation, not a series of disconnected approvals. Bring CIO, CEO, CFO, and the AI office to the same table from the start. The decisions that hold up under execution are owned by everyone responsible for the outcome.

AI accelerates engineering but is yet to be proven as an autonomous engineering platform. Developer productivity leads at 84%, while autonomous workflows lag at 52%

Diverging 100% stacked bar chart showing how effective AI is expected to be across five areas of the engineering and development lifecycle, rated from no impact through low, moderate, high, and transformational. Ranked by combined transformational or high impact, developer productivity leads at 84% (32% transformational, 52% high). Leveraging enterprise context such as data and workflows follows at 79% (33% transformational, 46% high) and testing, QA, and release automation at 75% (37% transformational, 38% high). Autonomous or agent-led development workflows lag at 52% (13% transformational, 39% high), as does reducing tech debt and legacy complexity at 51% (17% transformational, 34% high). Source: HFS Research in partnership with Movate, 2026.

Productivity is the proven case
Developer productivity leads at 84% transformational or high impact. Enterprise context (79%) and testing/QA automation (75%) follow. Autonomous workflows (52%) and tech debt reduction (51%) lag the field.

Autonomy is the gap
AI is making existing engineers faster, but it has not yet rewritten how engineering itself works. The transformational case for autonomous workflows and tech debt remediation remains a forward bet.

Lead with productivity, bet on autonomy
Lead engineering AI investments with productivity outcomes that prove themselves quickly. Win the autonomy bet with focused workflows, strong observability, and rapid evidence loops.

Enterprises prioritize business outcomes, workforce productivity, and embedded workflows. All three sit above 84% important or highly important

Diverging 100% stacked bar chart showing the importance of six priorities when scaling AI across enterprise and IT operations, rated from not important through slightly important, considering, important, and highly important. Ranked by combined important or highly important, delivering measurable business outcomes leads at 90% (52% highly important, 38% important). Workforce productivity from AI-enabled tools follows at 85% (49% highly important, 36% important), embedding AI in existing workflows at 84% (42% highly important, 42% important), and leveraging enterprise data and context at 77% (43% highly important, 34% important). Platform-led standardization and automation sits at 67% (32% highly important, 35% important) and infrastructure, cloud, and network operations at 64% (31% highly important, 33% important). Source: HFS Research in partnership with Movate, 2026.

Outcomes are the universal priority
Ninety percent of senior leaders rate measurable business outcomes as important or highly important when scaling AI. Workforce productivity (85%), embedded workflows (84%), and enterprise data leverage (77%) follow. Infrastructure and cloud ops sit at the bottom.

Outcomes connect every priority
Every priority on the list earns its place, including infrastructure and standardization. Outcomes are the only ones that everyone agrees on. Every other priority has at least some level of dissent.

Anchor every AI conversation in outcomes
Outcomes are the test for every other priority. Workforce productivity, embedded workflows, data leverage, and infrastructure are all drivers of outcomes. Set the outcome first, then choose the priorities that deliver it.

The Bottom Line: Enterprises are scaling AI faster than they are building the foundation, the commercial discipline, and the ecosystem orchestration required to make AI deliver growth. To turn AI’s efficiency dividend into growth impact, the operating model must come first.

AI has delivered efficiency through cost, productivity, customer experience, and speed, but revenue growth remains the outstanding promise, blocked by an operating model that has not kept pace.

Enterprises are stuck because decision rights are disorganized, commercial terms still favor effort over outcomes, and the data, governance, and integration foundations lag AI investment. What is needed is a rebuilt operating model that ties AI spend to results, funds the foundation before the next pilot, and directs the next AI dollar at revenue rather than more efficiency.

Enterprises must move now to rebuild the operating model around AI. Efficiency has been delivered and revenue is the next frontier. The next round of AI must look different from the last.

    • Rebuild the AI buying model
      By aligning decisions across the full c-suite and moving pricing to outcomes.

    • Fund the foundation
      By treating data, governance, and integration as prerequisites before the next pilot.

    • Aim the next AI dollar at growth
      By moving from cost-reducing to revenue-generating use cases.

Survey demographics

Six clustered bar charts describing the survey sample of 101 director-level and above AI-led transformation decision makers headquartered in North America. By role and seniority: CIO, CTO, or CDO 23%, CMO 17%, EVP, SVP, or Global Head 15%, VP or AVP 13%, COO or CCO 13%, Director or Senior Director 10%, and CEO or Founder 10%. By annual revenue: $5B to $10B 30%, $10B to $25B 28%, $1B to $5B 25%, $25B to $50B 12%, and more than $50B 6%. By employee count: 20K to 50K 34%, 5K to 20K 33%, less than 5K 17%, 50K to 100K 10%, and more than 100K 7%. By region of headquarters: North America 100%. By AI decision involvement: part of the decision process 44%, directly responsible 41%, and influence or advise 16%. By industry: CPG 26%, and Healthcare, Hi-tech, and Retail 25% each. Source: HFS Research in partnership with Movate, 2026.

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