Agentic Organizations
Elon Musk has a plan for a future where jobs are wiped out by artificial intelligence: a benevolent government will provide universal high income checks to deal with unemployment caused by AI.
This futuristic vision may not be realistic, but consider a workplace where humans work alongside virtual and physical AI agents to create value. Just as organizations thought they’d figured out hybrid workforces, enter an operating model known as an agentic organization.
What challenges would such a model present for workplace leaders?
As companies are rushing to adopt AI tools in the name of cost reductions and efficiency—reasons they tout to their shareholders—many leaders are missing important points.
First is that it’s expensive technology and in the short-term, the return on investment isn’t meeting expectations. When deploying AI, knowledge management and extensive training data are critical, costly and time consuming.
Second, while AI agents can support or carry out many tasks performed by customer service, compliance, HR operations, marketing and finance, it’s also adding a new layer of work that needs to be understood and supervised.
McKinsey has described the rise of the agentic organization as a new operating model where humans work alongside virtual and physical AI agents to create value. Core pillars of this shift are workforce, people and culture. AI agents will not only change tools, they will also change responsibility, judgment, accountability and the shape of human work.
The Challenges. As with any new technology agentic AI brings new challenges. This is an opportunity for HR leaders to take control.
Understanding the work. Predictions by Goldman Sachs focus on automating job tasks rather than job displacement. This requires a human capability map showing the people who understand the work deeply enough to direct and challenge it, and to be accountable for the result.
Maintaining integrity of the work. Moving too quickly to implement AI tools has risks. While AI can create new workflows, leaders must understand the judgment behind tasks before automating them, the context that made a process work, and if employees are taking responsibility for the work. Faster workflows aren’t necessarily better ones.
Orchestrating the Work. Managers will have to differentiate between the talent with deep understanding in one functional area versus those with deep expertise in several areas and the ability to connect them. They will have to be skilled in orchestrating work across people, skills, data and technology real time
The Leader’s Role. AI agents need ownership and oversight. Savvy organizations will realize that without defining the business value of AI or implementing controls for risk, AI projects will not survive. Before deciding where and when to implement AI, consider:
Where must human judgment remain close to the work?
Who understands the process beyond the task list?
Who has the institutional knowledge that should be protected before a workflow is redesigned?
Who can review AI output with enough context to challenge it?
Who has the cross-functional thinking needed to coordinate people, systems and agents?
Using AI in an organization is more than just introducing tools and training employees how to use them. It must be strategic and approached the same as any new system implementation. From the people side, at a minimum, it requires building development paths and redesigning performance systems around responsibility rather than speed. Most importantly, it means valuing people for their judgment, institutional knowledge, relationships, and understanding the work and how it gets done.