Agentic AI has marked a pivotal moment for how work gets done. Many CIOs are already seeing benefits in IT operations and support, where AI agents can automate root-cause analysis, resolve tickets, and remediate issues proactively.
But this shift also brings uncertainty. Agentic AI challenges traditional IT models that assume humans are the ultimate decision-makers, raising questions about governance, workforce skills, decision authority, and value creation.
For leaders, the critical task is to understand how to harness these capabilities both responsibly and strategically. To navigate this transformation with confidence, CIOs and business leaders should start by asking themselves four essential questions.
AI Governance
How do we rethink governance when AI agents act independently across organizational and partner boundaries?
Traditional IT governance assumes humans sit at the center of every decision, but agentic AI disrupts that model in many instances. AI agents can make operational decisions, coordinate with other systems, and act autonomously which leaves leadership asking: how do we track who is responsible when an AI agent makes decisions and how do we maintain regulatory compliance? This is especially prevalent in a landscape where agents are used across geographies with differing regulatory frameworks.
The risks are already visible. For example, 74% of governance professionals report concerns about the accuracy of AI-generated content in corporate reporting – a clear reminder that even in structured areas, AI can introduce new risks.
Static policies no longer work for an AI-driven workplace, but instead governance must evolve to become adaptive in real time. It is the role of IT leaders to communicate how AI expands IT’s responsibilities in data, security, and performance management.
Upskilling
How will this shift change the skills our workforce really needs?
As AI becomes more integrated into daily work, employees need more than technical expertise – they must collaborate with intelligent systems, moving from simply executing tasks to orchestrating and guiding AI-driven processes. Equally critical are human-centered capabilities such as critical thinking, ethical reasoning, and strategic oversight. These skills enable employees to interpret AI outputs, anticipate unintended consequences, and ensure that automated responses align with broader business objectives and values.
The scale of this transformation is significant. Gartner predicts that 80% of existing software engineers will need to upskill in AI by 2027 to remain employable, highlighting the urgency for organizations to develop learning strategies that combine technical expertise and human capabilities.
As organizations focus more on AI oversight and compliance, entirely new roles are emerging to support those priorities. This evolution opens opportunities for everyone, not just technical experts. Technologists can move into roles focused on oversight and orchestration, while people with business or strategic experience can help guide AI adoption, ethics, and alignment with company goals.
The key leadership question is clear: How do we empower people at every level to work confidently and responsibly with AI?
Humans and The Last Word
Where do we draw the line between human and machine decision-making authority?
There are many domains where human decision making remains irreplaceable and that calls for clear boundaries to be put in place to decide what remains human and what can be delegated to AI. Defining this balance is both a governance and cultural challenge – and one that sets strong leaders apart.
Without deliberate boundaries in place, organizations risk either:
• Over-delegating – handling too much authority to AI and losing control over accountability and risk
• Under-delegating – failing to realize the productivity and innovation benefits that AI can deliver
The balance will vary by organization and industry they operate in. But the basic following rules remain:
• Human-led – decisions involving strategy, ethics, compliance, and reputation –
such as approving sensitive contracts or defining corporate priorities
• AI-supported – routine operational tasks where automation brings speed and efficiency without risk
Ultimately, it falls to CIOs and executives to define the right balance for their organizations. This starts with a clear understanding of risk tolerance and regulatory obligations, which then guides decisions about what tasks are safe to automate and what must remain under human oversight. Encouragingly, 55% of organizations have already established an AI governance board, according to Gartner – a strong signal that many companies are beginning to formalize these critical decisions.
True Value for Business Goals
How do we ensure that agentic AI creates value aligned with business goals rather than simply optimizing processes?
If AI solutions aren’t aligned with business goals, organizations risk solving the wrong problems – improving efficiency in narrow tasks while missing broader strategic outcomes. This misalignment is reflected in McKinsey’s finding that 80% of companies using AI report little or no impact on revenue or profits, showing that adoption alone doesn’t ensure results.
To bridge this gap, leadership must:
• Define success beyond efficiency – establish KPIs that reflect alignment with long-term business goals, not just short-term process gains
• Continuously monitor outcomes – AI agents require active governance to ensure their actions remain consistent with evolving business strategy
• Stay long-term oriented – CIOs should resist the temptation to measure AI solely on quick wins and instead assess whether it drives sustainable, strategic advantage
Agentic AI should serve as a business accelerator, not just an IT optimizer. The CIO’s role is to make sure the technology’s value creation is tied directly to business outcomes and this means thinking long-term, not just in short-term efficiency gains.
The overall takeaway is that agentic AI marks a defining shift in how technology and people work together. For CIOs and business leaders, success will depend on clear governance, skilled talent, and a strong link between technology and business outcomes. Getting that balance right will determine which organizations lead the way in with AI.