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AI is moving beyond copilots toward agentic systems that can plan, decide and act across business processes, such as incident response and customer support. This shift has CIOs scrambling for an agentic AI strategy.
Unlike copilots or traditional automation, agentic AI systems can execute work across multiple applications with limited human intervention. That capability raises questions about risk, governance, architecture and cost that don’t fit neatly into existing IT playbooks. For CIOs, the issue is no longer whether agentic AI will appear in the enterprise, but how intentionally they can deploy it.
“In the next three years, there will be more agents than the number of employees globally,” said Manish Jain, principal research director at Info-Tech Research Group.
A clear agentic AI strategy can help CIOs prioritize high-value use cases, manage risk and scale agents effectively across the organization.
Agentic AI vs. copilots
Agentic AI and generative AI (GenAI) copilots both rely on large language models for reasoning, but they differ in autonomy and application. GenAI copilots can suggest content or insights, but they require frequent input to guide decisions. Agentic systems, in contrast, can execute entire workflows, make decisions and take action across multiple systems with minimal human intervention.
Agentic AI is not a technology. It is a philosophy of looking at AI as agents. Manish JainPrincipal research director, Info-Tech Research Group
Agentic AI exists on a spectrum, Jain said. Some agents are fully autonomous and can run entire processes independently. Others are semi-autonomous and work alongside humans within defined boundaries. At its core, agentic AI is a mindset that treats AI as a digital worker capable of executing processes, making decisions and interacting with systems.
“Agentic AI is not a technology. It is a philosophy of looking at AI as agents,” Jain said.
5 pillars of an agentic AI strategy
To move from experimentation to scaled deployment, CIOs need a structured approach. The following pillars outline the core decisions that shape how CIOs can deploy, govern and sustain agentic AI in the enterprise.
1. Identify use cases
CIOs should first identify where AI can materially move the needle, said Andie Dovgan, chief growth officer of Creatio, an agentic AI platform vendor. Autonomous agents don’t create business value on their own. Rather, organizations must anchor them to critical workflows.
Many organizations have seen early wins in operational excellence, especially in narrow, repeatable processes that can run with strong controls and human oversight. Examples include service desk work, budget reconciliations and large-scale patch orchestration.
“We’re talking about automating repeatable, well-documented workflows where the agent can execute with strict guardrails,” said Sebastien Jean, chief technology officer in the U.S. for Phison, a Taiwan-based designer and manufacturer of NAND flash controllers and storage products.
CIOs should keep agent scopes tight and route exceptions to humans in the loop. This improves reliability, auditability and speed.
Additionally, CIOs must use caution when deploying autonomous agents in broad, horizontal workflows with high variability and complex decision-making. In these cases, current reasoning limits elevate the risk of hallucinations.
“If you apply this autonomous approach to a broader, horizontal use case, it will be very difficult to deploy because … the number of decisions you need to take is very wide and the current state of AI technology doesn’t have that level of reasoning yet,” Dovgan said.
2. Assess data readiness
Before organizations can deploy agentic AI effectively, they need to ensure the underlying data can support the workflows they want to automate. Agentic AI agents rely on structured, accessible and accurate data to make decisions across multiple systems. Without that foundation, even the most advanced agents can produce errors or unreliable outputs.
CIOs should map which data is required for each use case, identify gaps and establish processes to keep it clean and consistent. This step improves operational accuracy and reduces the risk of hallucinations as agents act autonomously.
3. Plan infrastructure
Agentic AI places different demands on enterprise infrastructure than copilots or traditional automation. As organizations scale from pilots to dozens or hundreds of agents, performance issues tend to surface across memory, storage and networking. CIOs who treat agentic AI as just another application can experience degraded performance, rising costs and brittle systems as agent counts grow.
Unlike short, stateless interactions, agents must continuously read, write and retain context across long-running workflows. That persistence creates pressure on GPU memory, data movement and storage architecture — especially when agents need to reference prior decisions to remain reliable and auditable at scale.
“If you don’t approach AI at the system level, you will quickly find that the performance is not what you were expecting, even though you have, for example, really great GPUs,” Jean said.
CIOs must plan agentic AI infrastructure holistically from the outset, accounting for memory, data movement and persistence rather than assuming existing platforms will absorb autonomous workloads.
4. Decide on vendors
Most CIOs will rely on vendor products when they deploy agentic AI. This approach accelerates deployment, reduces upfront risk and allows CIOs to focus on strategy rather than development.
Only organizations with substantial datasets and deep pockets — such as large banks and professional associations — should consider building proprietary models or fine-tuning small language models, Jain said. These organizations can create tailored assets that align tightly with their workflows and competitive advantages.
For organizations that lack extensive data or the resources to build their own models, the safer and faster path is to purchase products that align with their AI strategy, Jain said.
Even when buying an agentic tool, CIOs should maintain a clear inventory of all agents in their environment — purchased, built-in or employee-created — including who owns them and what workflows or data they touch. This visibility helps prevent operational surprises and ensures autonomous tools support business goals.
5. Establish governance
As organizations deploy agentic AI, CIOs face a new challenge: these tools behave less like traditional software and more like digital workers. They can act autonomously, make decisions and interact with multiple systems — but they don’t reason like humans. Without proper oversight, organizations risk mistakes and unintended access to sensitive data.
CIOs must inventory all agents in the environment, control access and define clear guardrails. This supports compliance and ensures agents are working as intended.
“Do not treat agents as just some software piece,” Jain said. “Treat them as a digital worker, because that digital worker can do a lot of things that your employees are doing.”
At the same time, CIOs must recognize the limits of agent reasoning. Agents lack intuition and common sense and will not question instructions, so human supervision is essential.
Agents aren’t worried about losing their jobs, and they don’t have common sense. Sebastien JeanCTO in the U.S., Phison
“Agents aren’t worried about losing their jobs, and they don’t have common sense,” Jean said. “When you let machines go wild, you end up with an LLM deciding to order 10,000 pencils — and it won’t realize that the pencils aren’t useful to your organization.”
Structured human oversight can fix this problem: define boundaries, implement guardrails and make humans responsible for handling exceptions. By combining proper inventory, access control and active supervision, CIOs can harness the efficiency of agentic AI while minimizing risk.
Examples of agentic AI in action
While many organizations are still early in their agentic AI journeys, CIOs have begun applying agents in specific, well-bounded workflows where automation can deliver clear operational value.
Examples include the following:
Retail operations. Agentic AI is helping a large used clothing retailer manage donated merchandise more efficiently, Jain said. AI agents automatically classify and price donated items — tasks that would otherwise require extensive human effort.
Financial services renewals. In banking, AI agents can manage renewable products such as term deposits and insurance renewals, Dovgan said. These agents track maturity dates and engage bankers to proactively connect with customers, improving operational efficiency and customer retention.
Payroll and compliance. Agentic AI is helping global payroll processors navigate complex regulatory requirements, Jain said. Agents can research tax changes across multiple jurisdictions and automatically update payroll processes. This saves time and reduces the risk of errors.
Threat detection. CIOs are exploring agentic AI in IT operations and security monitoring. Agents can detect anomalies or unusual activity in systems, surface alerts and help human teams respond faster. This enhances both operational and security resilience.
Key takeaways
As agentic AI pushes organizations beyond assistance and into process execution, CIOs must rethink their IT strategies. Organizations that see early value are not pursuing full autonomy everywhere, but are applying agents deliberately to well-defined, operational workflows with clear guardrails and human oversight. Success depends less on the sophistication of individual models and more on foundational readiness across use cases, data, infrastructure, vendor strategy and governance.
Over time, agentic AI will likely become a competitive necessity rather than a differentiator. CIOs who develop a coherent strategy now will be better positioned to scale agents safely, control costs and adapt as autonomy increases. Those who delay risk falling behind peers who use agentic systems to move faster, operate more efficiently and embed intelligence directly into core business processes.
Tim Murphy is site editor for Informa TechTarget’s IT Strategy group.
