Organizations that succeed with agentic artificial intelligence (AI) are those that design deployments with intent, embed agents into real workflows, and establish clear governance, according to DK Sharma, president and COO of Kore.ai.
“Successful deployments are designed with intent, while the ones that stall are often driven by curiosity or random acts of trying AI,” Sharma said. “Teams that succeed are very clear about the use case, the job the agent is meant to do, where its authority starts and ends, and how success will be measured. Without these boundaries, agents either become overly constrained or too unpredictable, and trust erodes quickly.”
Sharma emphasized that agentic AI must be more than a demo.
“If it can reason but can’t act across systems, it remains an interesting demo rather than something that works in production and delivers operational value,” he said.
At present, adoption of agentic systems is limited, but organizations seeing measurable benefits focus on low- to moderate-risk, knowledge-heavy workflows involving coordination, triage, and decision routing. Common examples include IT support, where an AI agent can triage issues, troubleshoot using available knowledge, escalate with clear summaries, and proactively update employees. Travel desks are another example, where AI interprets policy, identifies compliant options, seeks approvals, and closes the loop end-to-end.
“The common trait is controlled autonomy,” Sharma said. “These agents operate within defined guardrails, handle dynamic but bounded complexity, and reduce human effort by navigating systems, policies, and workflows on behalf of employees. They’re embedded inside existing tools, so adoption is seamless, and the value comes from speed, accuracy, and coordination rather than novelty.”
Despite the potential, Sharma noted that wider adoption faces barriers beyond technology.
“Enterprises are less worried about whether a model can reason and more concerned about what happens when something goes wrong. Questions around accountability, consistency, and control tend to slow things down,” he said.
Organizational challenges, such as fragmented ownership across IT, data, security, and business teams, also hinder scaling.
Companies that overcome these hurdles establish shared operating models with clearly defined decision rights, he explained.
“They agree upfront on what agents are allowed to do, who owns cross-functional accountability, when humans need to step in, and how exceptions are handled. That clarity tends to unlock momentum very quickly.”
Governance and oversight are also evolving. Mature organizations protect autonomy, permissions, and decision accountability through controlled workflows. Early deployments often operate in “recommendation-only” mode, progressing to conditional execution authority as confidence and reliability increase.
“Any governance efforts should explicitly address what decisions an agent can initiate, what it can execute independently, and what requires human validation,” Sharma said.
Best practices emerging among successful teams include starting with narrowly scoped workflows, designing graceful handoffs to humans when errors occur, and separating reasoning from execution for higher-risk actions. Autonomy expands gradually as agents prove reliable.
By 2026, Sharma expects agentic AI to become mainstream in internal operations and employee services, including IT support, travel desks, and onboarding. Customer service, financial operations, and compliance functions are likely to follow, particularly in assistive roles where agents analyze and recommend rather than decide outright.
“Businesses need predictability before they need brilliance,” Sharma said. “Leaders want to know that an agent will behave consistently, that its decisions can be understood after the fact, and that responsibility is clearly assigned when something goes wrong. Autonomy will increase gradually as systems prove themselves. When agents demonstrate reliability, transparency, and strong governance over time, organizations become far more comfortable allowing them to take on higher-stakes decisions.”
Sharma’s insights suggest that the future of agentic AI will be defined not by hype or novelty, but by practical deployment, controlled autonomy, and trust built through consistent performance. For companies looking to adopt these systems, the focus should remain on measurable value, clear accountability, and embedding agents into real-world workflows.
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