Leading in the agentic era: How to put people first as you adopt AI agents

Over the past few years, our Google Cloud Consulting team has partnered with many companies to introduce AI into their daily operations. In that time, we’ve seen the focus shift from generative AI that answers questions to agentic systems that fully carry out the work. These agents can create multi-step plans and manage a project from start to finish. While budget, compute, and tooling can help launch an agent program, enterprise adoption takes intentional culture work. People need to feel empowered enough to experiment with agents, and leaders need to show teams what agents mean for their roles. Based on our work across these organizations, we’ve put together five practical lessons for guiding enterprises through an agentic shift.  1. Make workflows agent-executed, human-directed Most enterprise workflows run like a relay race: one person completes a task, checks it, and hands it off to the next person, who then does the same. Layering an AI agent onto that model gives you a faster runner for one leg, but the handoffs may stall or slow down other work.  Redesigning your workflows can remove blockers like these in a few ways:  Strategic direction: Move from human-in-the-loop validation (where a person checks each step) to a human-in-the-lead model. Agents take on the work, and people set the direction and own the outcomes. Parallel dependencies: One agent can handle every dependency at once instead of passing work through a multi-person queue. For example, an agent can check product availability, credit limits, and contract paperwork at the same time without waiting on each step to finish. Manage by exception: Once the process is set up this way, the agent absorbs routine deterministic transactions, routing only ambiguous or high-judgment scenarios to human review. Cross-team stalls disappear, and your team stays in control. 2. Turn hesitation into ownership In our experience, the biggest barrier to adoption is trust. Many employees worry about their value if an agent handles most of their day-to-day tasks. Others are concerned with who takes the blame if an agent makes a mistake. We encourage teams to treat agents the way they'd treat a junior team member: Active delegation: Frame agents as digital coworkers. That means assigning tasks to them, defining their scope, and checking their work for accuracy. Clear responsibilities: Set boundaries around which decisions an agent can make and which require human sign-off. When team members know exactly where accountability sits, they can better direct agents. Comfort through exposure: Confidence grows through familiarity. We recommend starting with low-stakes use cases (like scheduling internal meetings) to help motivate teams before moving to high-stakes operations (such as financial forecasting).    3. Manage hybrid teams of people and agents Next, it’s important to support collaboration when you bring agents into your team. Let’s look at how this might work in practice, Focus on results: Collaborate across teams to break down workflows at both the tool and task level. Cross-functional perspectives reveal an end-to-end view of the process, helping you pinpoint where agentic capabilities belong and how to prioritize ideas by technical feasibility and business impact.  Reinforce behaviors: Top performers on a hybrid team are the people who guide their agents well. Reward team members for auditing an agent's reasoning and catching when its logic drifts from company goals. Coach your teams: Show your teams how to work with agents so people can spend more time on creative work and decision-making. 4. Build agentic literacy through experimentation Your teams need to understand how agents solve problems. People who know where an agent's logic breaks down (and why) can guide and judge its output. Beyond the tool: Literacy covers both writing a good prompt and auditing the result. Agents choose the most likely path instead of following deterministic rules, so people need to know when to trust an agent's output and when to check it. Safe learning zones: Give business roles the same sandbox access that technical teams get to build agentic literacy across the whole organization. Celebrating intelligent failure: Experimentation is how teams learn. In our experience, we’ve seen that when teams build an agent that fails because the data wasn't accurate, that team then understands how important data readiness is. Even if it didn’t work out, this should still be encouraged so your teams keep experimenting.  5. Govern agents with a hub-and-spoke model To roll agents out across an enterprise, you need central guardrails that don't slow down the teams closest to the work. A hub-and-spoke model does both: it gives teams a shared foundation to build on and room to move at their own pace. The hub: Establish a central AI council team that sets guidelines, maintains ethical guardrails, and manages a library of approved tools. That way, teams don't rebuild the same things.  The spokes:

Extras afișat conform termenilor RSS ai publicației · 150 cuvinte · textul integral și figurile sunt pe site-ul publicației