It's one thing to hear about a buzzy technology like agentic AI. It's another thing entirely to figure out how to use it within your own organization.
Google's making the process of incorporating AI into your workflows a bit easier, regardless of whether your organization currently uses other Google AI products, by offering Gemini Enterprise: a new framework for building and deploying autonomous agents across your tech stack.
If you were on the fence about whether to give agentic AI a try, having an agentic AI product built right into the systems your team uses every day can make the decision easier. You don't have to go out of your way to embrace the next step in enterprise AI.
You can use Gemini Enterprise to create agents capable of taking a unified view of your data and systems, analyzing the information across all silos, and taking direct actions (within guardrails) based on its analysis. That's a big deal.
For those just starting out, this post will guide you step-by-step in how to deploy Gemini Enterprise at your organization. Let's get started.
Gemini Enterprise is Google's platform for creating, managing, and deploying custom AI agents. If your organization uses Google Workspace, the agents you create are automatically connected to your larger Google Workspace environment, meaning they can access all your relevant files and data.
Gemini Enterprise isn't just for organizations on Google Workspace, though. It also integrates with a number of other services commonly used by enterprise organizations, like Salesforce, Jira, and ServiceNow, helping you deploy agentic AI across all your critical business systems.
However, if you are a Workspace user, then you've probably already been using Google Gemini, the large language model included within Google Workspace, and you may wonder what's different about Gemini Enterprise.
While generative AI tools like Gemini can create content and aid users in completing tasks, Gemini Enterprise takes AI a step further by creating agents that can autonomously perform tasks — including multi-step actions.
When you build a task-specific AI agent, it can potentially handle full workflows and evolve with use to provide better results over time. Some possible use cases include:
As with any technology, how much value you'll get from agentic AI depends entirely on how you use it. To successfully launch your first AI agent with Gemini Enterprise, follow a few best practices.
Choose one manageable, meaningful use case to pursue as a pilot — ideally a repetitive, decision-heavy workflow that spans tools.
Bring in IT, security, relevant department leads, and end users early to align on goals, workflows, and data governance from the start.
Configure triggers, define rules, connect data sources, and train your agent on company context — keeping the scope tight and testable.
Launch to a limited group, gather honest feedback, monitor performance data, and refine before expanding further.
Expand to new departments and use cases with monitoring, logging, and feedback loops in place — and a culture of transparency around how agents work.
If you're excited about the potential of Gemini Enterprise, you may be tempted to jump into multiple projects at once. Instead, we recommend starting small: choose one manageable, but meaningful use case to pursue as a pilot.
Treating your first Gemini Enterprise project as a pilot doesn't mean you shouldn't take it seriously. Choose a real business problem you want to solve.
To narrow down your choices, look for an issue that involves a repetitive, decision-heavy workflow that spans tools. That gives you an opportunity to take advantage of some of the distinct benefits Gemini Enterprise offers, since it can handle multi-step workflows and access your internal data across multiple products and systems (if you choose to let it).
To give you some ideas, a few use cases we've seen clients choose the first time they deploy Gemini Enterprise include:
If all of those projects sound promising, don't worry. Once you gain experience from your first project, you can move onto others. But to start, just pick one.
Part of what's great about Gemini Enterprise is that it easily moves beyond silos and can handle workflows that span multiple departments — and multiple systems. That means effectively building your first AI agent will likely require bringing employees from different departments on board.
Engaging employees from IT and security, along with the relevant department leads and end users early in the process is important for a few reasons:
Collaboration is essential to making sure the agent you build fills a real need, and does so without creating any new security risks.
Once you've clarified what you want your first AI agent to do, it's time to create it. For more thorough instructions, check out our post on creating an agent in Gemini Enterprise. But to give you a brief idea of what's involved in the process, here are some of the basic steps:
Keep your first agent focused and testable. You don't want to overbuild on day one and have to start over. Start simple, and you can always add complexity later once your pilot proves successful.
Now the hardest part's done, and you want to make sure it works as intended. Launch your new agent in a limited group or function. Ask all your early users to provide their honest feedback, and monitor performance data to measure how well the agent is meeting your established goals.
Based on your initial feedback, refine your prompts, logic, and actions as needed to make it better. And make note of what you learn for future reference.
Now you're ready to move on to a larger user group and your next AI agents. Start expanding to new Gemini Enterprise use cases and departments, taking your time with each. Set up monitoring, logging, and feedback loops for each agent, so you can determine if they're working as intended and look for opportunities to make improvements. And build an internal framework for collecting AI notes and additional resources for governance and innovation.
As you introduce agents to new users, focus on trust and transparency. Your team should understand how they work and the intended purpose for each one. Train employees to approach agents with a collaborative mindset, not just a reactive one.
And make it clear that you're open to their feedback. People will respond better to working with AI agents if they know that their input matters, and any suggestions or complaints they have will be considered.
To deploy Gemini Enterprise effectively, the most important tips to keep in mind are to start small, involve the right people, and commit to learning and improving as you go. You may be surprised how easy it is to join the ranks of businesses embracing agentic AI.
If you set your agents up right, you can offload busywork, unlock productivity, and start seeing better results across your organization. But getting started right isn't a given — you'll increase your odds if you work with experts who already understand Gemini Enterprise.
Promevo offers a Gemini Enterprise Accelerator program. This structured effort gives you access to our AI experts for the entirety of your rollout, from concept to measuring the ROI of employee engagement.
The Gemini Enterprise Accelerator includes:
By the end of the Accelerator program, you'll have an agentic AI-enabled organization. Get in touch to speak with our AI experts today.
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