We are projecting global IT spending on AI to reach $409 billion in 2026, roughly 53% year-over-year growth, and on track to reach $700 billion by 2029. That is not a trend. That is a structural transformation of the global technology economy, playing out in real time.
And yet, for all that investment, the enterprise is not keeping up. AI is now mainstream in production use, with roughly two-thirds of organizations already using AI in live production environments as of the beginning of 2026. But most have not scaled meaningfully beyond targeted, isolated deployments. Broad, full-scale operationalization remains the exception, not the rule. IDC’s FutureScape 2026 research puts a finer point on this, projecting that nearly 50% of AI-driven digital use cases will miss their ROI targets in 2026 due to unclear business gains, weak human-machine collaboration, and poor data foundations.
This is not a technology problem. Technology is advancing faster than at any point in modern enterprise computing history. This is an adoption enablement problem. If you want to dive deeper into the reasoning behind why this is happening, check out our previously published report, The Speed of AI Value Creation in Applications: What’s Causing the Delay?. But the bottom line is that the gap is widening. AI innovation is outpacing enterprise adoption, and vendors building and selling AI software are the only ones positioned to solve it.
We have been here before. In the early cloud era, organizations ran dozens of successful pilots while struggling to migrate core workloads. In the early SaaS era, adoption stalled on integration complexity and change management, not product capability. The pattern is familiar. Technology races ahead, and the enterprise ecosystem (integrations, governance, skills, data infrastructure) takes years to catch up.
AI is repeating this cycle at a faster and more consequential pace. The vendors who recognize that and act on it will define the next era of enterprise software leadership. The vendors who do not will find that great technology alone does not close a revenue gap.
The first imperative is a reframing of what vendors are actually selling. Enterprises do not struggle to understand what AI can do in a demo. They struggle to connect AI capabilities to measurable business outcomes within their specific operating environments, data, workflows, and compliance requirements.
Vendors that lead with model benchmarks and feature roadmaps are speaking a language their buyers have stopped prioritizing. Vendors that lead with quantified outcomes (reduced invoice processing cycle times, lower error rates in demand forecasting, faster financial close) will earn the trust and the internal sponsorship needed to move from pilot to production. This is not a marketing adjustment. It is a fundamental repositioning of the value proposition with direct revenue implications. For any AI provider, growth is no longer tied to license counts. It is tied to how deeply customers embed AI into daily workflows. Outcome-led selling accelerates that depth.
One of the most important metrics vendors need to track is time-to-value: how quickly a new customer achieves a materially improved workflow through AI. Right now, that timeline is too long for too many enterprises. Integration complexity, data readiness gaps, and internal skills shortages create friction that stalls momentum and gives procurement committees reasons to pause.
Vendors can close this gap directly. Pre-built integrations for common enterprise architectures, workflow templates calibrated to specific industry use cases, and structured onboarding programs that guide customers from pilot to production are no longer nice-to-have services. They are now the product. Enterprises successfully scaling AI are doing so with vendor partners who meet them where they are, not where the vendor’s road map assumes they should be.
Poor data foundations are consistently among the top barriers to AI ROI. Our research has explicitly shown this, and it’s a core reason why roughly half of AI pilots fail to deliver ROI. Yet many vendors still mistakenly treat data readiness as a customer prerequisite rather than a shared problem.
That assumption needs to end. Vendors who win the next phase of this market will be those who help enterprises assess, clean, and structure their data as part of the implementation process, not as a precondition that customers must solve before the real engagement begins. That means investing in data readiness tooling, offering pre-implementation assessments, and building data quality explicitly into success criteria from day one. At best, handing the data problem back to the customer delays deployment. At worst, it kills the project entirely and takes the renewal with it.
Governance concerns (security, auditability, regulatory compliance, responsible AI use) are significantly slowing enterprise decision cycles. Vendors that treat governance as a layer to add later are creating their own headwinds. Enterprises that stall after a successful pilot often do so not because the AI stopped working, but because legal, compliance, or IT security raised issues that the product was not designed to address.
Building explainability, access controls, audit trails, and compliance frameworks into the core product, rather than bolting them on top, is what separates vendors well-positioned for enterprise deployments from those perpetually stuck at the proof-of-concept stage. And the window to get this right is narrowing, as governance requirements are quickly moving toward regulatory mandates.
The vendors best positioned to close the adoption gap will be those who structure their commercial relationships around customer outcomes rather than seat counts or token consumption. Oracle’s recently announced 22 Fusion Agentic Applications are a great example of this refocusing, as they shift their enterprise software solutions from being passive “systems of record” to autonomous “systems of outcomes”. SAP and ServiceNow both recently announced similar positioning. SAP is moving beyond traditional SaaS toward an outcome-oriented model where agentic AI, anchored by Joule, sits between the user and enterprise execution. Users state their business intent, and SAP’s autonomous systems handle the rest, more closely aligning execution with outcome. ServiceNow also made a deliberate pivot toward outcome-driven execution, repositioning itself from a platform of record into what it now calls an “AI control tower.” The shift extends to its partner ecosystem as well, where updated programs now reward vendors based on actual customer outcomes and successful deployments rather than traditional membership tiers.
Vendors need to continually focus more on outcome-based pricing, adoption milestone incentives, and dedicated customer success resources to ensure they’re clearly demonstrating to clients that their financial interests are aligned with customers’ results, and not just with the initial transaction. That alignment builds the trust required for long-term expansion that benefits both parties.
The widening gap between AI innovation and enterprise adoption is real, but it’s closeable. Technology is not the constraint. The path to closing it runs directly through how software vendors show up for their customers, not just at the point of sale, but across the full journey from pilot to production to scale. Vendors who embrace that responsibility will win twice: earning enterprise trust and capturing the market share that comes with it.
Eric Newmark is Group Vice President & General Manager of IDC’s SaaS, Enterprise Software, CX, and Workplace Solutions Division, which includes several teams of analysts covering SaaS, 18 enterprise application markets, software monetization, business platforms, marketplaces, and services firms focused…
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