Artificial intelligence has already reshaped how companies analyze data, automate workflows, and engage customers. But a new phase is emerging, and it goes further than anything most organizations have deployed so far.

Agentic AI refers to systems that can plan, initiate actions, and execute tasks with a degree of autonomy. Rather than waiting for instructions, these systems can monitor conditions, make decisions, and coordinate work across functions in real time. In commerce, for example, a single agentic system could monitor inventory, trigger replenishment, adjust pricing, and route approvals without a human touching the process at each step.

For investors and operators, the question is no longer whether this shift will happen. It is whether their organizations are positioned to capture it when it does. Gartner predicts that 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025.

Why agentic AI is different from the automation businesses already use

Traditional AI has largely been reactive. It classifies, predicts, and recommends. Agentic AI introduces something different: systems that can initiate and coordinate, not just respond.

That distinction matters more than it might seem. Most businesses today use AI as a layer on top of existing workflows. Agentic systems, by contrast, can manage workflows end to end. They reduce the number of handoffs required, compress execution timelines, and produce more consistent outcomes at scale.

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But most organizations are not there yet. While nearly two-thirds are experimenting with AI agents, fewer than one in four have successfully scaled them to production, according to McKinsey. The technology is advancing fast, but the gap between running a pilot and embedding agentic AI into daily operations remains wide. Closing it depends less on the AI itself than on what sits underneath it.

The step most companies skip before deploying AI

The conversation around agentic AI tends to focus on capability. What can the technology do? How fast can it act? But practitioners who have deployed AI inside complex commercial environments say the more important question is whether the organization is ready to receive it.

That readiness gap is already measurable. Many companies still run fragmented systems with overlapping responsibilities and unclear data ownership. In that kind of environment, even advanced AI will struggle to deliver results.

“Before adding new tools or AI, it helps to audit your systems and decide what system owns what data. For example, which system manages product and inventory data, which handles customer and order data, and which delivers the customer experience,” Jary Carter, co-founder and CRO at OroCommerce, told TheStreet. “Once those roles are clear, you can consolidate the stack to streamline your operations. It’s a powerful exercise to ensure technology is working for you effectively.”

That kind of operational clarity does more than make AI easier to deploy. It removes the friction that slows growth in the first place, cutting the time to launch a new portal or expand into a new market from months to weeks. The AI then has a clean foundation to work from, rather than inheriting the chaos of a tangled stack.

For investors evaluating AI readiness, this is a signal worth watching. Companies streamlining their systems and improving data governance are better positioned to capture the upside from agentic AI than those layering new tools onto a fragmented base.

Why governance will separate the winners from the rest

One of the defining features of the agentic AI era is the importance of guardrails. Systems that can act autonomously introduce new risks, from unintended decisions to compliance failures. The organizations that succeed will not necessarily be those with the most powerful AI. They will be those that deploy it with the most discipline.

“Successful AI deployments give clear guardrails and a specific task, while keeping strong oversight and the ability to audit its output,” Carter told TheStreet. “Innovation moves faster when execution is transparent. Without clear boundaries and parameters to control the flow, you’re left with a puddle, not a river.” 

That view is echoed at the enterprise level. “Governance will be integrated into every part of the product, and not just bolted on at the end,” Ravi Krishnamurthy, VP of AI platforms at ServiceNow, said. “Products that embody this principle will outpace their competitors in customer adoption and value delivered.”

Western Europeans are adapting AI faster than people in the U.S.

Mariyariya/Gettyimages

That framing cuts against the instinct to move fast and experiment broadly. But he points to a real-world signal: AI adoption in Western Europe, where government regulations impose clearer rules on deployment, has, in some respects, outpaced adoption in the US, in his view. Structure, it turns out, can accelerate rather than impede progress.

This also aligns with where regulation is heading globally. Companies that build governance into their AI programs now will be ahead of requirements, not scrambling to catch up.

How the transition to agentic AI will actually unfold

Despite the excitement around fully autonomous systems, most organizations will get there in stages. Deloitte notes that organizations must adopt a phased approach to agentification, balancing gradual implementation with bold experimentation. The path tends to follow a recognizable pattern.

The three phases of agentic AI adoption:

• Phase one: AI augments existing workflows, handling repetitive tasks and supporting decisions without changing who is accountable
• Phase two: AI begins coordinating multi-step processes, connecting data and actions across departments with less human involvement at each stage
• Phase three: AI agents execute complex strategies independently within defined constraints, with humans maintaining oversight of outcomes rather than inputs

The pace of that progression will vary by industry, risk tolerance, and how well companies have laid the groundwork. In B2B commerce, where relationships and trust drive long-term business, the shift is likely to be gradual by design. The stakes around getting a pricing decision or a supplier negotiation wrong are high enough that full autonomy will remain limited for some time.

The companies that get this right will have a real edge

Agentic AI moves AI from a supporting tool to an active participant in business execution. That is a meaningful shift, and the competitive implications are real. Companies that learn to balance capability with control will unlock efficiencies that are difficult for slower-moving competitors to replicate.

But technology alone will not be the differentiator. The organizations that win will be the ones that did the less glamorous work first: cleaning up their systems, clarifying ownership, and building the governance frameworks that allow AI to operate reliably at scale.

In that sense, the rise of agentic AI is less a technology story and more an operational one. The companies best positioned for it are the ones that have already decided to run themselves with discipline.

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