10 Things Government Leaders Should Know About GenAI
1.) GenAI Has Two Core Parts: Model + Platform
All GenAI systems are built from two key components, the model and the platform.
The model (e.g., GPT-4, Claude, Gemini) is the intelligent brain that understands and responds. The platform is a secure environment where users interact, control access, and integrate workflows. Examples of these platforms include:
- ChatGPT is OpenAI’s secure platform for businesses and agencies, offering tools like memory, file handling, team controls, and access to custom GPTs.
- Microsoft Copilot for 365 is built on OpenAI models but integrated into Microsoft’s ecosystem. It connects with email, documents, and calendars to assist with tasks inside Office tools.
- Anthropic Claude is a platform that allows organizations to use Claude safely, with enterprise controls and collaboration features.
- Greystones’ Soleite CoPilot is a mission-driven platform built specifically for government workflows. It uses any commercial-grade model from the cloud or locally (like GPT-4o) and adds secure orchestration, agentic workflows, and role-based task automation.
Understanding the separation between platforms and models is foundational for deploying GenAI in mission-critical environments.
2.) Models Handle Language, Not Workflows
The model can generate text, summarize policy, write code, and answer questions. But it doesn’t manage tasks, remember sequences, or interact with systems unless paired with a platform. Agentic workflows are what make AI feel more like a teammate and less like a simple chatbot. Instead of just answering one question at a time, AI can follow a sequence of steps to complete a task, much like a human would. Here’s what that means in everyday terms:
- Plans actions: The AI can figure out what steps are needed to complete a task. For example, “To process this staffing request, I need to check availability, pull up forms, and notify the right supervisor.”
- Sequences tasks: It puts those steps in order and knows what comes first, second, and third.
- Calls tools or APIs: It can use other systems or software behind the scenes, like pulling data from a personnel database or submitting a form in an HR system.
- Adjusts based on outcomes: If something changes (like someone being unavailable), the AI can reroute or make a new decision based on the updated situation.
All of this doesn’t happen inside the AI model (like GPT-4 or Claude). It’s the platform, the infrastructure around the model, that manages the tasks, tools, and decision-making flow.
In a government setting, this means the AI can do more than just answer questions. It can coordinate staffing actions, route documents for review, or assess mission readiness, without a human needing to tell it every next step. It’s like giving the AI a playbook and a mission, and letting it carry it out, securely and in context.
3.) AI That Can Act Needs Clear Rules
When AI systems go beyond answering questions and actually start taking actions , like sending alerts, assigning tasks, or submitting forms the stakes get higher. That’s why strong oversight is essential. Just like with any government process, we need to ask:
- Who gave the AI permission to act?
- What exactly did it do — and when?
- Can we trace and review those actions later if needed?
These rules and records aren’t handled by the AI model itself, they’re built into the platform that manages the AI. A secure platform ensures that AI actions are authorized, logged, and reviewable. Bottom line: With the right guardrails in place, GenAI can act responsibly, helping agencies move faster while staying fully accountable.
4.) Platforms Make GenAI Operationally Useful
While the model is the “brain” of the system, it’s the platform that makes GenAI practical for real work. The platform is where key features live that allow the AI to function securely and effectively within an organization. These include:
- Secure file handling: Users can safely upload and share documents with the AI, such as policy memos, staffing plans, or data reports, without risking sensitive information. The platform ensures files are protected and access is controlled.
- Role-based access: Not everyone in an agency needs the same level of access. The platform allows administrators to control who can see what, just like in any secure government system.
- System integrations: The platform connects the AI to other tools and databases, such as HR systems, case management platforms, or mission readiness dashboards. This allows the AI to work within existing workflows, not outside them.
- Memory and context retention: The AI can remember helpful context from past interactions, which saves time and allows for more personalized, consistent support over time.
Without a strong platform, GenAI is just a smart chatbot. With the right platform, it becomes a force multiplier, one that improves how work gets done, supports decision-making, and aligns with mission goals.
5.) Agentic AI Needs Guardrails
As GenAI becomes more capable, especially when it starts taking actions, not just offering suggestions, the need for strong oversight becomes even more important.
Government teams need to be able to answer key questions:
- Who gave the AI permission to act?
- What exactly did it do, and when?
- Can we trace its decisions if something goes wrong?
That’s why this kind of AI must run on a governed platform, one that builds in transparency, generates audit logs, and ensures decisions are authorized and accountable.
With the right guardrails, agencies can confidently adopt agentic AI while maintaining trust, control, and compliance.
6.) Customized AI Happens on the Platform
If you want an AI assistant that knows how to help with HR policies, contracting rules, or intelligence analysis, that capability doesn’t come from the AI model alone, it comes from the platform.
The platform is where you can:
- Customize the AI to focus on specific topics or job functions
- Add memory, so it remembers helpful context over time
- Set up workflows that match how your team gets work done
In other words, the platform is what turns a general-purpose AI into a role-specific assistant, one that can understand your mission, support your staff, and adapt to your organization’s needs.
7.) Cost Control Must Be Built In
Agentic workflows can save time, but if left unmanaged, they can also drive-up compute costs. Platform-based cost controls ensure you can cap usage, track team activity, and align budget with mission value.
8.) AI Is Most Powerful When It Works with Your Existing Systems
For AI to be truly useful in government operations, it needs to do more than just answer questions, it needs to work with the systems and data your team already uses.
This means the AI should be able to:
- Pull information from secure internal databases, not just search the internet.
- Check systems like HR tools, case files, or readiness reports.
- Send results back into your normal workflows, like routing documents or updating dashboards.
To make this possible, you need a platform that’s built for federal data integration, one that understands security, legacy systems, and how government work actually gets done.
That’s how AI moves from being helpful to being mission ready.
9.) The Model Can Change, The Platform Is the Constant
As model capabilities evolve, your platform should stay stable, continuously supporting security, agentic tasking, and cross-model orchestration.
Think modular: swap the brain, keep the backbone.
10.) Agentic AI Is Changing How Government Work Gets Done
Agentic AI isn’t just a buzzword, it represents a new, smarter way of working. When built securely and aligned to real mission needs, this type of AI can:
- Take repetitive tasks off your team’s plate, like sorting documents, tracking requests, or sending updates.
- Monitor changing data in real time, and flag issues before they become problems.
- Take action automatically, like routing a report, assigning a task, or alerting the right person when something needs attention.
In short, agentic AI helps government teams work faster, stay focused, and make better decisions, all without adding more to their workload. For government teams, this is how GenAI moves from concept to mission acceleration.