Agentic AI In 2026: Four Predictions For Business Leaders

In this segment of “Office Optional with Larry English,” Larry discusses his four Agentic AI predictions for business leaders in 2026.

AI agents loomed large throughout 2025. But agentic AI in 2026 could be the year the technology moves onto center stage, not as experimental tools, but as autonomous collaborators reshaping business functions. A few recent statistics suggest this may be the case.

A late 2025 McKinsey report found that 62% of organizations are experimenting with agentic AI, with 23% beginning to scale agents in at least one business function. The 2025 Protiviti “AI Pulse Survey” predicts that nearly 70% of organizations will integrate autonomous or semi-autonomous AI agents in 2026 into their workflows.

The important questions for leaders as we enter a new stage of agentic AI are: What does it look like to work alongside AI agents? How can leaders deploy agents effectively and responsibly? Below are four predictions for how agentic AI will take shape in 2026 and how executives can prepare.

Four Predictions for Agentic AI in 2026

Today, organizations are largely still in the experimentation stage with agentic AI. That is poised to change soon as the technology becomes the next must-have for modern organizations.

1. C-Suite roles will evolve as agentic AI becomes a strategic capability.

As agentic AI becomes deeply integrated into organizations, traditional boundaries between C-suite roles, particularly CISOs, CTOs and CIOs, will become increasingly blurry. These traditionally technical roles will become more strategic as agents begin to impact operations, workflows and decision-making.

For example, CIOs will need to figure out how to strategically implement agentic AI, while CISOs will need to expand out of siloed security concerns and begin overseeing strategic cybersecurity and organizational growth initiatives.

The real challenge will be coordinating governance and security across these roles. To create clarity across roles and responsibilities, leaders need to define all the tasks required for effectively governing and securing data for AI use. Then, come to an agreement on who is responsible for what.

Eventually, agentic AI may require a single leader to oversee governance and security of all data. This might come under a new role, such as a CAIO (Chief AI Officer), or require a shift in an existing leadership role.

2. Organizations that win will treat agentic AI like core infrastructure.

As agentic AI moves from pilots to production, the advantage will shift to leaders who master the operational realities, such as controlling consumption spend, enforcing guardrails, validating outputs, and building AI systems with the same discipline they bring to other enterprise platforms. Prompt libraries and agent democratization efforts will still be important, but less so than with previous iterations of AI technology.

In fact, AI strategies that rely too heavily on prompt engineering or the attempt to apply AI technology to every business function (even where simple automation would be a better fit) will begin to show their limits.

The successful approach with agentic AI will likely embed AI as a designed component in modern architecture. Instructional design will be a key bridge between prompt engineering and agent architecture, helping organizations achieve reliable, production-grade agentic solutions.

3. AI agents will become a new class of “intellectual worker,” requiring new human oversight skills.

AI agents will increasingly operate as intellectual workers, making decisions and generating outcomes that reflect directly on the business. This will change how organizations think about responsibility. Leaders will need to treat agents not as automated tools, but as digital representatives of the company’s values, ethics, and intent. This raises a new critical question: Who is accountable for an agent’s actions?

Organizations will need to formally assign responsibility for agent behavior, requiring new oversight skills that blend ethics, governance and judgment. The “agentic manager” will have different skills than the “people manager.” Technical knowledge will no longer be enough to maintain responsible and ethical AI. HR teams will play a larger role in recruiting and training employees to supervise agents and escalate issues when needed.

Practical steps will matter, too. These include scenario-based training, decision playbooks, supervised pilots, and clear escalation paths. Together, these will help create a workforce that understands when to rely on agents and when human judgment is required.

4. Machine identity security will be the No. 1 blind spot for agentic AI adoption.

In 2026, one of the biggest risks to scaling agentic AI will come from a blind spot most executives don’t yet see: the sheer volume of non-human “digital workers” operating inside their businesses and the lack of oversight over them.

Currently, machine identities (which include everything from automated service accounts to system credentials) outnumber human employees 82-to-1. Yet most companies continue to treat “privileged access” as a human-only issue. As agentic AI accelerates, these digital identities will become the way agents access systems, move data and make decisions. Without proper governance, these digital identities become an access point for attackers.

This risk becomes even more urgent today as digital credentials begin to expire faster and insurers require stronger controls for coverage. Nearly half of these identities already have access to sensitive systems without the governance or visibility executives expect.

In short, agents can’t be trusted partners or intellectual workers if the digital identities they rely on aren’t secure. This is why securing machine identities will become a top priority in this and coming years. The companies that get ahead of this emerging risk will be able to scale agentic AI with more confidence and speed and notably less risk.

Agentic AI in 2026 Will Separate AI Laggers From Leaders

As agentic AI in 2026 moves from hype to meaningful impact, leadership readiness will be the key difference between organizations that thrive and those that fall behind. The companies that win in 2026 will be those that build organizational frameworks to manage agents responsibly. That includes redefining executive roles, designing AI systems for production, elevating human oversight and closing machine-identity blind spots.

This article was originally published on Forbes.com.

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