The reign of “digital transformation” and artificial intelligence (AI) budgetary free ride is over. In its place: a brutally simple question that will define enterprise technology in 2026—not “What can this technology do?” or “What can this agentic AI do?” but “What specific business problem does it solve, and how much value does it unlock?” 

Many CIOs, industry experts, and other C-suite executives report growing pushback against technology investments that have yielded few results. Jonathan Feldman, CIO at Wake County, North Carolina, contends that this budgetary concern aligns with broader unease about seemingly bottomless budgets for widespread AI deployments.    

“There is skepticism about AI living up to current hype,” says Feldman. “Whether that’s from overextended AI usage in the company or overblown AGI forecasts and over-investment that could lead to a tech bubble or “a year of AI disillusionment,” he says. 

While there aren’t signs of such a collapse just yet, experts say there will be enterprise structural shifts in AI and broader executive digital transformation decision-making in the year ahead, driven by cost pressures, governance constraints, and maturing market realities.

In 2026, Business Outcomes Override Technology Selection

Tim Crawford, CIO strategic advisor at consultancy AVOA, is a shift from technology-first to outcomes-first decision-making that will define 2026. 

The conversation shifts from “What can this AI do?” to “What problem does it solve, and how much value does it unlock?”—and the technology that wins won’t be the most sophisticated. Still, the one that directly accelerates revenue, reduces friction in customer-facing workflows, or demonstrably improves employee productivity within a 12-month payback window. 

Crawford says this is “getting back to brass tacks. “Organizations will carefully define their business objectives, whether customer engagement, revenue growth, employee productivity, or whatever it needs to be, before selecting a technology,” he says.

Budget Cycles Compress as Payback Expectations Accelerate

Joe Batista, founder and chief creatologist at M37, says the days of multi-year digital transformation roadmaps are over. There’s growing pressure for faster outcomes, and more initiatives are being held to this standard. He notes that business leaders now prioritize projects by asking whether something is a necessity (“need to have”) or simply beneficial (“nice to have”).

In 2026, if your digital transformation project can’t demonstrate meaningful return within twelve months, it competes for oxygen with projects that can, and many won’t survive that fight, Batista says. This compression of payback expectations reflects a fundamental shift in how CFOs and boards view technology investments. 

Still, initiatives based on regulatory or compliance requirements—things mandated by law, for example—still justify longer timelines, but discretionary projects face much stricter scrutiny, Batista says. Overall, Batista explains that executives have shifted their expectations: mandatory efforts get prioritized even if the payback time is longer, whereas non-essential projects increasingly have to prove they can deliver value quickly in the face of tighter budget expectations.

Data Readiness: The Universal Constraint

Both Batista and Crawford say enterprises are heading into 2026 still not data-ready for substantial AI deployment. “Data readiness distribution is not a clean bell curve with clear leaders and laggards. Instead, it’s very gradient, with most organizations sitting in the middle with the entire curve shifted toward data, nowhere near being AI- ready,” Crawford says.

Batista adds that executives now realize a significant reason they can’t measure value from their AI investments is that their data is “fragmented, inconsistent, and poorly governed.”

The hard lesson here: AI readiness won’t come from data cleanup and hygiene alone. Enterprises must redesign upstream data collection and governance processes, which takes years, not quarters.

AI Deployments Get Surgical

As we covered above, enterprises are hesitant to deploy expensive AI tools due to high costs and uncertain returns. 

Batista says many new AI initiatives will be applied to solve very discrete business problems, such as removing friction or creating momentum where the impact of the investment is measurable. This isn’t skepticism about AI’s potential or transformative capability. “Deploying AI at every possible touch point has fallen short at scale, and business leaders are now consolidating around high-confidence use cases and deprioritizing big speculative bets,” Batista says.

Additionally, Batista and Crawford both predict an enterprise shift away from general large language models (LLMs) toward specialized language models (SLMs) built on proprietary data. “This moves the advantage from companies such as OpenAI and Anthropic to enterprises with clean, domain-specific data. This is another reason why data governance becomes “foundational,” Crawford says, rather than auxiliary.

AI Governance Models Emerge as the Gating Factor

When it comes to limiting factors in scaling successful AI deployments, Crawford says the top issue will be failures in AI governance. “AI governance will be the bottleneck that constrains an enterprise’s ability to scale AI, not AI capability itself. And enterprises rushing to deploy autonomous agents without governance infrastructure will face either painful reworks or serious operational issues.

In platforms that provide workflow automation, such as Salesforce or custom applications, autonomous agents typically inherit permissions (what they can do) from the users who trigger their execution. That makes sense at the user level. But it does create a critical blind spot. 

When many agents operate across multiple integrated systems, and there’s no unified governance in place, enterprises end up with sprawling tokens, inconsistent access controls, and fragmented policy enforcement, if any at all.

Crawford predicts third-party “authorization” platforms will likely grow in popularity, similar to how federated identity services emerged decades ago.

While the benefits from AI are real, getting everything in place to make the full use of the technology is going to take time, likely more time than people think for enterprises to digest current AI investments, get their staff trained to optimize AI use, and get their data in proper shape, along with all of the governance and security controls needed in place for it to truly take off. 

“There is value in AI, but we haven’t seen AI’s iPhone moment yet, and that may take some time,” Feldman says.