How to upskill IT for agentic AI: 7 pathways to success

There are two prevailing schools of thought regarding the AI-agent workforce. One says organizations should prepare for agentic AI, in which the human-in-the-middle role is largely transitional and serves to buy time to improve agents’ accuracy and build trust in their decision-making. Others say AI agents will largely augment humans, but expect workflows to change drastically from task-based processes to more asynchronous, choreographed operations.

Businesses will likely have a mix of agentic and human-augmented AI agents, with many more in pilot stages. As part of this transformation, CIOs need to consider how to evolve the IT organization and upskill IT employees for this future. According to Deloitte’s 2026 Global Technology Leadership Survey, 75% of IT leaders agree their operating models and processes must change within the next 12 to 18 months to drive greater value.

“Upskilling IT for an AI-agent workforce requires more than training; it requires behavior change because as AI takes on more routine development activities, technology professionals increasingly focus on validating, governing, and directing AI-generated outputs,” says Doug Vargo, VP of consulting services and head of the national AI and alliances team at CGI. “The cognitive habits that define experienced engineers are deeply ingrained, so they need to develop new ways of working focused on reviewing outputs, framing intent, and curating the context that keeps those outputs accurate, secure, and aligned with business objectives.”

How CIOs upskill their organizations will follow several career tracks. Here are the most essential to consider.

Developing business acumen and AI literacy for IT leaders

AI is requiring more IT professionals to shift left into transformational leadership and change-agent roles. These leaders will advise business managers on when to use AI versus other technologies to automate tasks, and when to consider top-down re-engineering workflows based on AI capabilities.

“Leaders need to help their teams understand how work flows across the business, where AI fits into that process, and where humans need to stay accountable,” says Jamie Lyon, chief product and strategy officer at Lucid Software. “As AI agents take on more of the execution, critical thinking becomes even more important because people still need to provide the context, define the process, and make the decisions AI can’t.”

One of the top barriers in delivering value from AI is employee adoption. CIOs need more change agents to drive enthusiasm and help department leaders reimagine emerging job responsibilities. Upskilling IT leaders for change-agent roles often requires embedding them in business units so they can learn their processes and build relationships.

Upskilling focus: AI literacy, critical thinking, business relationship management, and change management are four primary skills. To connect problems to solutions, developing skills in architecture, design thinking, and analytics is also needed. 

Extending AI and data governance for everyone

According to Adobe’s 2026 AI and Digital Trends, 78% of technology leaders say data integration and quality is a top AI challenge, and 52% say limited data unification is holding back AI initiatives. CIOs facing data governance, integration, and management challenges risk seeing their businesses fall behind their competitors who are aggressively pursuing AI-driven opportunities.

“Upskilling for an AI agent workforce starts with understanding that the biggest challenge is the data and operational layer underneath the model itself. IT teams need to know how to connect fragmented data, engineer the context and memory that make AI agents more reliable, and support transactional, analytical, and vector workloads on a unified platform without breaking the budget,” says Adam Luciano, VP of product management at MariaDB. “They also need to understand governance, security, and observability so autonomous systems can safely execute real business processes and expand to higher-value use cases instead of simply generating recommendations.”

Data governance used to be a compliance team’s responsibility, but AI now requires many more in IT to be versed with policies, practices, and related technologies.

“As AI agents begin executing work across enterprise environments, IT teams need to build governance skills, not just AI literacy,” says Doug Gilbert, CIO and chief digital officer at Sutherland. “They should know how to assign accountability, monitor data access, enforce human-style approval workflows, and maintain complete audit trails so AI operates under the same controls as any employee and not as an exception to them.”

Upskilling focus: One upskilling focus should be on data governance, DataOps, data engineering, and data management. A second focus should address data risk management issues, such as data security and AI governance.

Expanding knowledge management to develop AI’s context layer

CIOs looking to scale from dozens of AI agents to thousands of AI-orchestrated workflows will need to develop an AI brain for their organizations, including knowledge graphs, a semantic layer, and a context layer.

“One critical place for CIOs and CISOs to focus upskilling is building the information layer that has to replace the human management layer everyone’s trying to collapse,” says Lior Gavish, co-founder and CTO at Monte Carlo. “A real part of what managers do is information work, including passing context, surfacing priorities, and keeping decisions aligned with the bigger picture. Flatten the org without replacing that function, and you get people, or agents, making locally optimized decisions on incomplete information.”

Organizations will need cross-disciplinary teams to develop and improve their context layers. Data skills to develop include extending unstructured data governance, evolving data fabrics, and building data products.

“The challenge is no longer just teaching employees how to use new tools, but ensuring teams know how to structure, manage, and govern the knowledge that powers them,” says Adam Field, chief AI officer at Tungsten Automation. “This will require new skills around contextual AI training, knowledge management, and information stewardship. Organizations that can effectively connect AI systems to trusted institutional knowledge, while maintaining appropriate security and access controls, will be better positioned to accelerate product development, improve collaboration, and increase access to critical information across their company.”

Upskilling focus: To develop the context layer needed by AI agents, CIOs should promote collaboration and communication skills alongside key data management, integration, and governance skills. In addition, agile data teams will need strong business acumen to partner with department leaders and subject matter experts.

Establishing an AI quality center of excellence

DevOps teams accelerating their deployment cycles while underinvesting in continuous testing were left with one of two bad options. Some tried to get business users to perform extensive user acceptance testing. Others deployed applications with minimal testing, hoping their observability and monitoring would catch errors before users escalated issues.

Underinvesting in testing and automating evaluations of AI agents can lead to significant issues, including increased costs, compliance violations, and operational impacts.

Sanjay Gidwani, CEO and founder at Kosmos, says the skill that will matter more than building AI agents is in confirming their accuracy. “Agents increasingly act on correlations drawn across disconnected systems, and a correlation that a human never confirmed is a decision waiting to go sideways at high speed. Upskill your teams to serve as the confirmation layer for what agents do before anything is acted on,” Gidwani says.

CIOs should think about AI agent quality from three perspectives:

• When are AI agents in experimental and pilot stages delivering high enough quality to be released into production?
• Once in production, how are quality metrics used to build trust in which decisions AI agents can automate, versus those that require people’s involvement?
• How are AI agents’ quality benchmarked in production to detect when their models are drifting and the agents’ performance degrading?

Upskilling focus: CIOs should upskill teams in data quality, test automation, and analytics. Organizations scaling the number of AI agents in production should consider developing an AI quality center of excellence.

Revisiting the skills needed by product and program managers

Before developing that center of excellence, consider how AI is changing the nature of team collaboration. Three examples:

• CIOs are developing product-based IT organizations to deliver business value from AI investments.
• Teams are revisiting their agile practices in the AI era to ensure that developer productivity results in faster feature delivery and less technical debt.
• DevOps has new challenges with AI agent observability and agent deployment risks.

These three spinning process wheels inside IT, with evolving AI capabilities, are one reason why many CIOs are rethinking the IT organization for the AI era. According to Atlassian’s The State of Teams 2026, AI-augmented teams need more coordination, not less: 77% say they expect more horizontal teams with fewer layers, and 73% have blended roles with hybrid responsibilities.

Mal Vivek, CEO and founder at Zeb, says the most valuable capability CIOs can build for an agent workforce is judgment. “Teach teams to decompose work into clear objectives, constraints, and feedback. These skills won’t come from a one-off course or certification; it takes redesigning roles so that human judgment compounds,” Vivek says.

Upskilling focus: CIOs will need more business-facing roles to lead discussions on where to invest in AI. Skills to develop include Six Sigma process skills, product management disciplines, and agile planning practices.

Upskilling junior developers beyond coding skills

If 41% of all global code is AI-generated, do CIOs still need engineers?

According to Karat’s AI Workforce Transformation Report, 73% say strong engineers are now worth at least three times their total compensation. That’s likely because the top engineers were never just coders; they were stewards of the software development lifecycle, drivers of sound architectures, and advocates for addressing technical debt.

“Agent verification should be a top priority for CIOs and CISOs, training professionals to look beyond raw AI outputs and to get ahead of the review burden that can come with increased AI use,” says Samar Abbas, CEO at Temporal. “As agents move to writing more code, tech talent needs to embrace becoming primary evaluators, interrogating an agent’s design decisions, defending the generated architecture under questioning, and confidently proving its correctness.”

Upskilling focus: CIOs should consider apprenticeship programs to accelerate junior developers into senior-level roles and entry-level architecture responsibilities. To start, junior developers will need training in systems thinking and in resolving issues flagged by code review tools. Beyond these basics, guide developers to build technical domain expertise in two to three focus areas such as testing, data, identity management, application performance, API development, integration, and security.

Maturing AgenticOps in IT operations

While many organizations are still in pilot stages with AI agents, others are deploying thousands into production and using AI orchestration platforms to build complex workflows.

“As apps evolve from traditional software into autonomous AI agents, IT’s role shifts from maintaining systems to managing a digital workforce,” says Nikhil Mungel, head of AI R&D at Cribl. “IT teams will need to learn how to onboard and supervise AI agents, ensure they comply with company policies, and monitor for unusual or harmful behavior. The organizations that succeed will be those that invest in teaching IT teams to govern and manage AI systems in production.”

Upskilling focus: AgenticOps skills to focus on include identity management, root cause analysis, and monitoring AI agents. CIOs deploying hundreds of AI agents should plan to extend site reliability engineering to include tracking AI agent reliability and diagnosing their performance issues.

Developing a world-class IT department is not just about delivering business value. Top CIOs recognize that they need to plan their IT organizations to support future needs and update their skills and learning development programs. AI capabilities are evolving quickly, and CIOs need to guide employees on the new skills needed to enable the AI agent workforce.