Monisha Somji | Senior Digital Transformation & AI Executive.
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There is a story being told about artificial intelligence (AI) that goes something like this: First, AI answered our questions, then it wrote our content, and now—with agentic AI—it will execute our work. Each step is framed as one step further from human involvement and one step closer to a world where machines replace the people who once performed those tasks.
I want to tell you a different story. A recent Stanford Digital Economy Lab study of 51 companies that deployed AI found that outcomes varied not based on the sophistication of the technology, but on the quality of human leadership, process design and organizational readiness surrounding it. Former UK Prime Minister Rishi Sunak, citing the same research, put it plainly: The businesses making the most progress are not those with better technology; they are those prepared to adapt how their people operate around it.
That finding is the lens through which to understand agentic AI. This is not the technology that pushes humans furthest from the work. It is the technology that demands the most from them.
In other words, agentic AI does not take the human out of the work. Rather, it takes the mechanical out of the human and gives people back the capacity to do what only they can do.
Generative AI, anchored by ChatGPT's arrival in 2022, gave organizations a powerful way to access knowledge faster. But it was still a response machine. You prompted, it produced. Every outcome still required a human to initiate, interpret and decide.
Agentic AI removes that requirement. It can be given a goal—not a prompt, a goal—and autonomously determine the steps to achieve it, coordinate across your enterprise systems and execute without waiting to be asked at each turn. OpenAI's evolution from a conversational product to a fully agentic platform is the clearest signal of where the market is heading. The shift from text to images to photorealistic video to autonomous enterprise agents happened in under three years. The velocity is only increasing.
But greater autonomy does not mean less human involvement. It means the human involvement that remains is higher-stakes, higher-skill and more consequential than ever before.
Here is the insight that should reshape how your organization approaches this technology: You do not need a computer science degree to lead agentic AI transformation. You need to deeply understand your business.
An agent that executes a procurement workflow is only as good as the person who understood that workflow well enough to design the agent's decision logic. An agent that manages customer escalations is only as valuable as the leader who defined what good looks like at each stage. The intimate, earned, contextual knowledge of how work actually gets done inside a complex organization does not live in your engineering team. It lives in your subject matter experts such as your finance leaders, customer success managers, supply chain experts and HR business partners.
In the agentic AI era, that knowledge is the primary input into your most powerful technology deployments. Your SMEs are not the people AI will replace. They are the architects of the AI that will run alongside them. And with autonomous code generation making it increasingly possible for domain experts to build and iterate on intelligent workflows without engineering resources at every step, the barrier between deep business knowledge and intelligent automation is falling fast.
The most important question about agentic AI is not what it takes away. It is what it gives back.
Salesforce Agentforce agents can monitor deal health, draft re-engagement outreach, update CRM records and surface daily prioritized action plans for sales teams, returning hours every week to the work that actually closes revenue: building trust, understanding needs and navigating complex buying decisions. SAP-connected agents can reconcile transactions, flag anomalies and prepare close recommendations before a finance controller opens their queue, thereby returning strategic analysis capacity to people trained to think, not just record. ServiceNow agents can orchestrate employee onboarding across five departments in hours instead of two weeks, returning organizational design thinking to operations leaders instead of coordination burden.
The pattern is consistent: Agentic AI eliminates the coordination layer, the administrative overhead and the mechanical execution that consumes high-skill people's time, and gives them back the capacity to do what only humans can do.
Yes, there are legitimate security and governance concerns with autonomous AI systems. Those concerns deserve serious design attention—not as a reason to slow down, but as a requirement that shapes how agentic systems are built.
Observability tools that make every agent action traceable. Approval workflows that pause at high-stakes decision points. Escalation paths that route exceptions to the humans best positioned to handle them. These are what make agentic AI trustworthy enough to run at scale.
The most powerful AI systems ever built cannot function without human expertise at their core. In the agentic era, being human is not the limitation. It is the advantage.
Agentic AI is the story of humans becoming freed from the mechanical, empowered by intelligent systems that amplify rather than replace their expertise, and positioned to do the work that actually requires judgment, creativity and human connection.
That is the story worth telling. And the organizations that choose to lead with it—rather than react to the fear narrative—will be the ones that attract the talent, build the culture and deliver the outcomes that matter.
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