How to Deploy Gemini Enterprise in Your Organization

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.

What Is Gemini Enterprise?

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 uses cases include:

• Creating meeting summaries, and taking basic actions discussed in a meeting (like adding deadlines or follow-up meetings to the calendar)
• Auto-generating project timelines after analyzing factors like the main steps required, the typical time each step takes, and the calendars of the employees involved
• Handling IT tickets that involve a standardized process (like device approvals), or determining when they should be escalated to a human and routing them to the right person
  
  

5 Steps to Deploy Gemini Enterprise at Your Business

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.

Step 1: Identify a High-Impact Use Case

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:

• Employee onboarding or offboarding
• Internal ticketing support
• Sales opportunity follow-ups
• Document review or compliance processes

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. 

Step 2: Get the Right Stakeholders Involved

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:

• Employees that handle the processes you're planning to automate can provide input on how their current workflows work, and which tasks would most benefit from outsourcing to agentic AI.
• Department heads can help define goals for the agent that align with business priorities.
• Your security team can make sure data access and governance policies are considered from the beginning, so the agent's design ensures compliance with all relevant policies.

Collaboration is essential to making sure the agent you build fills a real need, and does so without creating any new security risks. 

Step 3: Build & Train Your First Agent

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:

1. Log into Google Workspace and open the Gemini Enterprise dashboard. You can choose between using pre-built agents within the Agent Gallery, or creating an agent with Agent Designer.
   
   
   • Pro tip: Consider leveraging Gemini and the Vertex AI Agent Builder within Gemini Enterprise to help you design the agent logic.
     
     
2. Fill out the fields as prompted. Be prepared to fill in your agent's name, primary function, and goal. You can also customize the agent's tone and personality.
   
   
3. Configure your agent. At this stage, you'll want to define the agent's triggers and the actions it should take in response to each (e.g. creating tickets, sending notifications, summarizing updates, etc.). Clarify the rules you want it to follow, and the escalation paths it should take. And connect it to any Google Workspace services and third-party apps it will need to access.
   
   
4. Train your agent by providing it with all the input sources it needs to understand your company's culture, the guidelines it needs to follow, and any other information it needs access to. Input sources can include emails, Google Docs, chats, and data from third-party systems.

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.

Step 4: Pilot & Iterate

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.

Step 5: Scale & Operationalize Gemini Enterprise

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. 

Step into the Future with Agentic AI

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, unload 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. 

Lucky for you, Promevo is offering Gemini Enterprise Pilot Workshops. That means we won't just build working agents for you, we help your team learn the skills needed to build successful agents themselves moving forward.

Gemini Enterprise Pilot Workshops include:

• Use Case Discovery: We'll help you identify and evaluate the best use cases for deploying Gemini Enterprise at your organization, and we'll offer guidance on the best one to choose for your pilot.
• Implementation Support: We'll then help you set up seamless integration with any services and tools relevant to the use case you selected, and will help set up an AI agent that fits into your existing workflows.
• Training & Optimization: We'll also help introduce Gemini Enterprise to your teams, so they know how to work with your new AI agents effectively in order to maximize Gemini Enterprise’s capabilities.

By the end of the four-week pilot period, you'll have your first AI agent, initial data on how well it's working for your team, and the knowledge your team needs to move forward with additional Gemini Enterprise use cases. Get in touch to sign up for a workshop today.