Enterprises are delaying AI spending due to elusive ROI
Most companies that ran toward AI aren’t seeing a return yet. The expectation was straightforward: implement large AI systems and see a clear improvement in profitability or growth. What actually happened is more subtle. Organizations gained small efficiency wins, someone saves 15 minutes writing an email, an internal task is slightly faster, but these wins don’t move the needle when it comes to revenue or cost reduction. The gap between micro-level productivity and macro-level impact is where enterprise AI is currently stuck.
This is about how it’s being measured. Many teams are capturing enhancements in isolated tasks but aren’t connecting that with broader business processes or revenue flow. That’s the problem. CIOs are being asked to prove value fast, and they’re realizing that it’s difficult to calculate hard ROI when the improvements are hidden inside fragmented efforts.
That’s why firms are pushing AI budgets down the road. Forrester projects that about 25% of planned AI spending will be delayed until 2027. In sectors like healthcare and financial services, where the stakes and compliance layers are higher, that delay is even more pronounced. These organizations aren’t abandoning AI, but they are pausing to ask better questions: What’s the real cost? Where’s the measurable impact?
If your team is investing in generative AI or large-scale platforms without a clear line of sight to top-line or bottom-line gains, it’s time to step back. Refocus on use cases that scale across processes and functions. Don’t spend more, spend smarter.
Brian Hopkins, VP of Emerging Technology at Forrester, nailed it when he described how CIOs are seeing benefits they can’t fully quantify. His conversations with C-level leaders show increasing doubt about big AI deployments, not because they don’t work, but because the value isn’t being captured in metrics that justify further spend.
An AI market correction is coming, driven by mismatched expectations and delivered value
The market will correct. Not because AI is overhyped tech. It’s because the promises haven’t kept pace with what most companies can do with it today. Early claims from vendors have been ambitious, some too ambitious. Enterprises bought in based on projections of massive ROI, automation at scale, and futuristic transformation. But in many cases, deliverables have been lagging. Real outcomes are often smaller and slower than promised.
This disparity is now catching up to buying behavior. We’re seeing budget holders question the logic of massive spending on AI platforms that don’t demonstrate measurable impact. That’s a good thing. It forces rigor and discipline. It brings the conversation back to execution.
Bill Gates recently referenced the idea of an AI bubble. That doesn’t mean AI crashes tomorrow, but it does indicate that valuations and expectations may need to adjust. Some in the industry are already seeing signs of deflation. The flash is fading. And we’re moving from experimentation to scrutiny. This isn’t negative. It’s the point where smart companies start making long-term bets rather than chasing short-term buzz.
The more advanced organizations are already doing this. They haven’t pulled back on spending, but they’ve shifted their focus. They’re dissecting AI use cases, identifying where results are real, and where projections are based on hype. They’re still spending, but they’re demanding more precision in how that spend translates to business value.
Brian Kotlyar, CMO of Hightouch, said it clearly: the AI utility is “clear and indisputable,” but markets can get exuberant. Right now, AI still offers high labor augmentation potential. But leaders need to watch for inflated assumptions creeping into their P&Ls. A correction helps avoid that. It resets the foundation, it recalibrates priorities. And if managed well, it puts AI on a more sustainable path inside the enterprise.
Organizations are adopting a more selective and sophisticated approach to AI investments
Most executives are no longer approaching AI with a “spend fast, see what sticks” mindset. That phase is done. We’re seeing a shift where AI investments are being treated like any other serious business initiative. Leaders are narrowing the scope. They’re looking at where AI fits operationally, across teams, workflows, and customer-facing systems, and then deciding if it’s worth backing with budget and people.
The urgency to adopt AI remains, but the threshold for approval has changed. Early-stage curiosity is being replaced with strategic demand for performance. There’s less interest in speculative pilots and more focus on projects that integrate cleanly into existing infrastructure. If an AI tool can’t show value beyond isolated tasks, it won’t get funded. That focus is what separates serious adopters from companies chasing innovation headlines.
As AI transitions from innovation labs into production environments, decision-making is tightening. Budgets are being allocated to fewer, better-defined use cases. The buying criteria now involve technical feasibility, longer-term support, and time to value. Companies want to know what it takes to get from prototype to repeatable outcomes, and when that will show up in operating margins.
Brian Kotlyar, CMO at Hightouch, explained this well. He emphasized that while AI budgets are active, the buying process has become more mature. Organizations are applying discipline to how AI dollars are spent. Dan Zimmerman, Chief Product and Technology Officer at TreviPay, added that organizations are starting to critically evaluate earlier pilots and advance their best concepts into operational systems, where impact can be tracked and scaled.
If you’re leading investment decisions, now is the time to audit existing AI use cases and cut those that lack a clear performance path. Double down on those unlocking productivity or producing clean data loops. Ignore hype. Assess traction.
Technical and organizational challenges are impeding the realization of clear AI ROI
There’s still a fundamental gap between proof-of-concept and full-scale AI rollout. Much of this comes down to core infrastructure and operational alignment. Many companies are not ready to take AI into production because their data isn’t structured, clean, or accessible enough. Others deal with weak internal pipelines that break when AI workflows are introduced.
Even when teams can validate a good AI use case during testing, scaling it becomes another problem. Privacy concerns, compliance standards, and lack of governance create drag. For regulated sectors, the burden to secure and audit AI behavior adds additional delay. Without long-term support structures, training, model updates, internal monitoring, the solution stalls or reverts to manual processes. That undermines ROI.
The hidden overhead is real. Deployment doesn’t just mean getting a model to work, it includes managing access, updating data sources, retraining users, integrating APIs, documenting decisions. All this slows adoption and adds costs that weren’t on the initial estimate. Few executive teams properly account for these variables upfront, which clouds ROI tracking later.
Sam Ferrise, CTO at Trinetix, pointed out that when organizations miss alignment between AI capabilities and business expectations, spending slows. His teams have seen working AI prototypes that never reach production because final security, data, or performance standards aren’t met. This delay isn’t failure, it’s a sign that processes need to be hardened before value can scale.
If you’re a decision-maker, don’t roll out AI without addressing these fundamentals. Build a path to production from day one: governance layers, well-structured data, performance monitoring. Simplify your stack where you can. The tools are getting better, but if the organization isn’t ready structurally, the benefits won’t show up.
AI spending strategies are shifting from being vision-led to outcome-led
Companies are transitioning away from enthusiasm-driven AI projects and shifting toward efforts grounded in measurable business outcomes. There’s no shortage of interest in AI, but the approach is clearly changing. Organizations are evaluating their AI roadmaps with limited tolerance for speculation. What matters now is whether a solution integrates with existing operations and produces results that can be tracked and replicated.
This shift isn’t about cutting budgets, it’s about smarter allocation. Firms are not looking to fund more pilots with no clear metric for value. They are moving AI from a conceptual exercise into a working, performance-driven system. That means the spotlight is on production-ready AI: solutions with proven functionality, scalable design, and direct impact on priority business functions. Areas like customer service, financial risk modeling, and supply chain optimization are standing out because they deliver consistent improvements and serve enterprise-critical workflows.
Leaders now expect more than automation and novelty, they want evidence of sustained gains. Internal champions need to show how AI increases velocity and drives accuracy, not only in isolated use cases but across connected teams. The winners will be projects that bring performance improvements visible to the CFO and COO, not just the IT team.
Srikrishnan Ganesan, CEO and Co-founder of Rocketlane, described it clearly: we’ve spent too long in the « vision-selling » phase. What’s coming next is a reset, market expectations are adjusting to a more grounded conversation about value. This doesn’t mean AI spending is stopping. Ganesan noted that as production-level deployments expand across companies, overall AI investment will continue to rise, but the direction of that investment is changing.
Dan Zimmerman, Chief Product and Technology Officer at TreviPay, echoed the shift. He pointed out that many early AI projects delivered limited ROI because they focused too much on experimentation. Moving forward, the emphasis must be on stable, measurable integration. His teams are seeing strong returns in areas where AI is embedded directly into core decision systems, like credit evaluation or customer service routing.
If you control budget or strategy, align AI funding with immediate business needs. Prioritize use cases where value is visible and defensible. Keep the vision, but measure success in terms that make sense across the business: cost, time, accuracy, and impact. That’s where AI proves it deserves the spend.
Main highlights
- AI return on investment remains unclear: Most companies aren’t seeing direct revenue impact from AI, with only 15% reporting earnings increases. Leaders should reevaluate deployment strategies to shift from task-level gains to scalable process improvements.
- Market correction is resetting AI expectations: The gap between vendor promises and real-world results is driving scrutiny. Executives should apply strict performance metrics to future AI investments to avoid overcommitting to underperforming platforms.
- Buyers are becoming more selective and mature: Organizations are increasingly prioritizing AI projects with tangible outcomes and sustainable integration. Leaders should fund fewer, high-impact use cases that align with operational goals and measurable results.
- Infrastructure gaps are delaying AI value: Poor data readiness, governance issues, and overlooked implementation complexity are slowing production deployment. Decision-makers must address structural barriers upfront to ensure clear and maintainable ROI.
- Spending is shifting from vision to outcome: AI investments are moving toward proven, result-driven deployments embedded in workflows like customer service and risk analysis. Executives should tie funding to business-critical operations where AI can deliver meaningful, trackable gains.
