For the past several years, much of the business conversation around artificial intelligence has focused on generative AI: creating content, summarizing information, analyzing data, answering questions, and helping employees work more efficiently.
Agentic AI represents an important next step.
Rather than simply responding to individual prompts, AI agents can pursue goals, work through multiple steps, use tools and information, and take actions to complete tasks with varying degrees of human oversight.
For businesses, that changes the conversation.
The question is no longer simply:
“How can our employees use AI?”
Increasingly, organizations also need to ask:
“Where should AI agents fit into our business, and do our people have the skills to build, direct, evaluate, and govern them?”
Agentic AI Is Moving From Experimentation to the Enterprise
Deloitte’s 2026 research illustrates just how significant the shift could be.
Agentic AI Readiness Gap
Organizations expect major change, but few say they are ready.
74%
of surveyed leaders expect nearly half of their business processes to be redesigned or rebuilt around AI agents within four years.
5%
say their business processes are highly prepared for AI agents.
Source: Deloitte, 2026
That is a significant gap between where organizations expect to go and how prepared they are to get there.
The workforce implications may arrive even sooner. Deloitte found that 43% of leaders expect agentic AI to significantly disrupt their workforces within the next 12 to 18 months, while half say their organizations are not adequately investing in AI-related workforce transformation.
The challenge, then, is not simply gaining access to increasingly capable AI technology. Organizations also need the processes, leadership understanding, governance, and workforce capabilities to put that technology to work effectively.
And the opportunity extends beyond simply doing the same work faster.
Agentic AI creates the potential to rethink how work gets done.
Leaders Need to Understand Agentic AI
Agentic AI should not be viewed exclusively as an IT initiative.
Business leaders do not necessarily need to become AI developers. But they do need enough understanding to make informed decisions about where agents can create value, where human oversight is required, and where the risks may outweigh the benefits.
That means being prepared to answer questions such as:
- What business problem are we trying to solve?
- Is an AI agent appropriate for this workflow?
- What systems, tools, and data would the agent need to access?
- Which actions can be delegated and which require human approval?
- How will we evaluate the agent’s performance?
- Who is accountable when something goes wrong?
- What security, privacy, ethical, and compliance considerations need to be addressed?
These are business questions as much as technical ones.
Without that understanding, organizations risk pursuing agents because the technology is available rather than because there is a clearly defined business case for using them.
Understanding Agents Is Only Half of the Equation
Knowing where an agent could create value is different from knowing how to build one.
That creates two distinct workforce needs.
Organizations need business leaders and decision-makers who understand agentic AI strategically.
They also need technical professionals capable of translating those business requirements into functional, safe, and reliable AI agents.
Building an agent requires skills that go well beyond prompting a generative AI tool. Depending on the application, employees may need to understand reasoning processes, tool integration, retrieval-augmented generation (RAG), structured outputs, testing, monitoring, security, reliability, and deployment.
And as agents are given greater autonomy, those capabilities become even more important. Deloitte found that 61% of surveyed leaders expect most of the AI agents they use within four years to be largely autonomous, with humans primarily providing oversight.
The organization that identifies a valuable use case for an AI agent but lacks people capable of implementing it has solved only half the problem.
Access to Technology Does Not Equal Workforce Readiness
Giving employees access to an agent-building platform does not automatically make an organization agent-ready.
We saw a similar challenge with the first wave of generative AI. Access to an AI chatbot did not necessarily mean employees understood how to use it effectively, evaluate its output, protect sensitive information, or recognize when human judgment was required.
Agentic AI raises the stakes because an agent may be able to do more than produce an answer.
It may be able to take action.
That makes skills development, testing, governance, and human oversight increasingly important.
It also reinforces why organizations need different levels of agentic AI capability across their workforce.
Not every employee needs to build agents. But organizations pursuing agentic AI will increasingly need people who understand where agents make sense, alongside people who know how to build and implement them responsibly.
Building an Agent-Ready Workforce
This creates an important workforce-development opportunity.
Business leaders, managers, and other decision-makers need enough agentic AI literacy to identify appropriate opportunities, evaluate business value, understand risk, and make informed decisions about adoption.
Technical professionals need deeper capabilities to translate those opportunities into working solutions.
CertNexus has developed two complementary learning paths around those needs.
AgenticAIBIZ™ focuses on the business side of agentic AI, helping leaders and professionals understand what AI agents are, where they may create value, and how organizations can approach their adoption strategically.
Agentic AI Builder™ addresses the implementation side, providing technical professionals with a vendor-neutral path to develop and validate practical knowledge for designing, building, testing, and deploying AI agents.
Together, they reflect an important distinction for organizations preparing for agentic AI:
Leaders need to understand where and why agents should be used.
Technical professionals need the skills to build them.
As agentic AI moves further into the enterprise, organizations that develop both capabilities will be better positioned to turn emerging technology into practical business value.