Beyond the 2025 Reality Check: Why 2026 is the Year AI Really Starts Paying Its Way

Guest Editorial by Akhil Verghese, Co-Founder and CEO, Krazimo

While many businesses made significant advances in incorporating sophisticated AI solutions over the past year, others learned the painful lessons that come with poor planning, inadequate safeguards, and clumsy rollouts.

By dissecting these lessons, business leaders can avoid costly mistakes and implement AI initiatives the right way in 2026. I’ve compiled actionable guidance to enterprise leaders responsible for operations, revenue, or technology decisions on how to achieve clear ROI from AI initiatives.

AI’s Real Advances in 2025

Generative AI did not emerge fully formed and ready to handle a significant percentage of white-collar tasks in 2025. Ambitions of that kind would have been an enormous overestimation of what was possible last year.

In general, the past 12 months have focused on validation and infrastructure, rather than mass adoption. Many companies remain in experimental phases with this technology.

In practice, companies that made real progress in 2025 did several things:

1. Prepared their data for AI use (cleaned inputs, clarified ownership, defined schemas, labeled information appropriately).

2. Defined success metrics for limited AI deployments upfront.

3. Set appropriate guardrails to prevent unexpected cost overruns and other risks.

4. Planned for AI drift with scheduled maintenance, continuous monitoring, retraining, and human review.

Unfortunately, some businesses have been reckoning with the consequences of having had inadequate oversight and governance structures in place around their AI solutions. Without these protections in place, they have exposed themselves to cost overruns, disappointing returns on their investments, and other risks. According to a recent report from business research and analysis firm Gartner, these are some of the main reasons many businesses are questioning whether to continue their AI investments.

In short, the real world is messy, and cool demos should not be taken too seriously. Successful agentic AI deployments follow three simple rules:

• Fence it: Limit the agent to narrow, well-defined tasks.

• Measure it: Tie performance to explicit quantitative benchmarks.

• Escalate it: Define when humans must review or take over.

As Andrej Karpathy noted, 2025 was not the year of AI agents. Instead, 2025 to 2035 is shaping up to be the decade of agents. I personally think that might be a slight overcorrection, and the real number might be closer to five years.

We Are in the Decade of AI Agents

I estimate that 40-70% of all white-collar work will be automatable at some point in 2028. The implication for leaders is clear: the question is no longer if roles will change, but which workflows will change first. Enterprises that wait for certainty will end up reacting to competitors who moved earlier.

Please note that I say “automatable,” not “automated.” There’s a big gap between the possibility of something being automated (something being “automatable”) and actually taking the time and doing the work to automate it (something being “automated”).

That’s why a 5-year adoption curve is more realistic than shorter projections. As automation progresses, unforeseen constraints will emerge, requiring adjustments and redesign. Some workflows will be harder to automate than expected. Others will require deeper human oversight than initially assumed.

Still, 2026 is likely to mark a turning point. It will be the first year that enterprises will start seeing real ROI from significant AI agents in operation at scale. By the end of 2026, I expect approximately 15-20% of enterprises to be using AI effectively in their operations and being able to demonstrate real ROI.

As more and more companies start realizing real gains from this technology, the pressure to implement AI will grow. Otherwise, companies will find themselves losing their competitive advantage and market share. For this reason, I expect enterprise-scale adoption of AI to reach 100% before the end of 2030.

Smart business leaders will want to get in on this dynamic as early as possible. Here’s how.

How to Ensure a Successful AI Implementation

Within well-defined, narrow thresholds, today’s AI business solutions can do some remarkable things. The key is to build guardrails and wall it off in those specific areas.

Adopting agentic AI does not require deep technical knowledge of model architectures. What matters are the operational questions leaders ask before deployment. They must understand how long a task currently takes a human to complete, what that labor costs, and how success is evaluated. They need reliable performance benchmarks for existing staff and a clear estimate of what it would cost to run an AI agent at a comparable volume. Most importantly, they must define what level of performance would make the AI a net positive.

In other words, to get the most out of agentic AI in 2026, leaders must clearly define success before they begin evaluating solutions. The strongest implementations begin with a single, narrow workflow that occurs frequently enough to justify automation. From there, organizations need a clear baseline of current human performance, including both costs and outputs, so that any gains can be measured honestly rather than inferred.

Crucially, success metrics must be defined before vendors enter the conversation. Without predefined benchmarks, it becomes easy to mistake technical novelty for operational improvement.

Finally, vendor relationships should be structured around outcomes, not demonstrations. Payments tied to measurable performance ensure that AI systems are judged by the same standards as the humans they are intended to support.

Are You Ready to Deploy AI Agents in 2026?

Enterprises most likely to see ROI next year can clearly identify multiple repetitive workflows with measurable outcomes. They understand the fully loaded cost of humans performing those tasks today. They have historical performance data covering speed, accuracy, conversion rates, and error rates. Their managers are prepared to own AI performance after launch rather than treating deployment as a handoff. And they are willing to shut down agents that fail to meet benchmarks.

While 2025 might not have been the “Year of AI Agents” people hoped for, it did see substantial advances in business intelligence within this domain. The next five years will likely see widespread adoption of these incredible innovations. The winners won’t be the companies with the flashiest demos, but the ones that did the math, set the right guardrails, and held AI to the same performance standards as humans.

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Akhil Verghese is the visionary co-founder and CEO of Krazimo, which specializes in reliable, enterprise-grade generative AI. Drawing on his engineering experience at one of tech’s strongest firms, Verghese delivers AI solutions built on engineering rigor, clarity of ownership, and measurable business outcomes. Krazimo guides businesses through AI adoption, creating multi-step workflow automations, deploying multi-agent systems based on retrieval-augmented generation (RAG), and executing rapid full-stack AI-assisted development.