A second implication is operating-model design. If business units create agents independently, the organization may reproduce the same fragmentation that has historically slowed automation and analytics programs. CIOs and transformation leaders should clarify decision rights early: who approves use cases, who owns workflow redesign, who is accountable for model behavior, and what controls determine when an agent can act autonomously versus escalate to a human. Treating agents like digital workers is useful only if performance, risk, and accountability are measured with equal rigor.
Data and integration maturity will likely determine scale more than model choice. Agents are only as effective as the context, permissions, and system access they receive, which makes enterprise architecture, master data, API strategy, and identity controls central management concerns. Leaders should resist adding agents to broken workflows; redesigning process steps, exception handling, and handoffs may produce more value than the AI layer itself.
Practical next questions:
- Which 3–5 workflows have both measurable value and clean executive ownership?
- What shared governance, security, and audit controls apply across all agents?
- Where do data silos, legacy platforms, or weak APIs limit safe autonomy?
- How will benefits realization be tracked beyond pilot-level productivity anecdotes?
As agentic AI moves from experimentation toward enterprise deployment, the challenge is figuring out how agents can work together, connect to the systems and data they need, and operate safely across the workflows that run a business. Although agentic AI has been adopted by some 80% of Fortune 500 companies, progress toward meaningful scale remains uneven, with many organizations still working through isolated pilots.
For Arun Chandra, chief operating officer at NiCE, the first step is moving beyond experimentation for its own sake. “Everybody’s trying to figure out what can we do with this technology?” he says. But scaling requires a clearer connection to business strategy: Organizations need to define whether they are trying to increase revenue, reduce costs, or pursue another strategic or financial objective. From there, they need to rethink the workflows where agents will operate instead of just layering AI onto existing processes. “The last thing you want to do is to apply AI on an outdated or an inefficient workflow,” Chandra says.
That shift requires organizations to treat agentic AI as a cohesive system. Agents need access to the data, knowledge, and context required to make effective decisions, as well as connections to back-end systems if they are expected to take action. Fragmented information can undermine those capabilities: “The efficacy of these AI agents is purely a function of the context, the knowledge, and the data they can ingest and use,” Chandra says.
The organizational implications are equally noteworthy. Scaling agents can create a new form of fragmentation if teams build isolated systems that don’t connect with one another, while governance, privacy, security, and change management become more important as agents take on more consequential work. Chandra argues that AI agents should ultimately be held to the same standards as human workers, with organizations thinking of their workforce as a combination of humans and AI agents.
Looking ahead, that connected approach could enable agents to work proactively and even communicate with other agents to resolve customer needs. For organizations making the transition from pilots to scale, the priority is not to “boil the ocean,” Chandra says, but to instead build a connected strategy around high-value use cases, workflows, workforce changes, and measurable outcomes.
This webcast is produced in partnership with NiCE.
This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.
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