Let’s face a difficult reality that’s common in the modern enterprise: Your employees are using AI, whether you have approved it or not.
This is referred to as “shadow AI.” And for the enterprise, it is a nightmare scenario for governance.
What is shadow AI and what are the governance risks for enterprises?
Shadow AI can occur when users take internal information—strategy documents, roadmaps, proprietary code, and so on—and paste it into public versions of ChatGPT or Gemini. They aren’t doing this to be malicious. They are doing it because the output is incredibly valuable to them as individuals. They see the immediate productivity gain, while the risk to the organization feels nebulous and distant.
Shadow AI exposes intellectual property, personally identifiable information (PII), and other sensitive corporate data to public models—environments in which you have zero control.
The answer isn't to ban AI. The answer is to bring that utility inside the firewall, but with a level of context and control that generic models cannot provide.
The problem with pre-packaged "skills"
When enterprise software vendors try to solve this, they often miss the mark. Look at the current landscape of AI integration in strategic portfolio management (SPM). Most vendors offer what they call "skills."
What are the limitations of pre-packaged AI skills offered by SPM vendors? These are hard-coded AI agents designed to do one specific thing. They know where to look for data because the vendor hard-coded the path. That works fine if your needs match their package exactly. But the moment you need to answer a question that falls outside that pre-defined box, you are stuck. You cannot tweak the skill. You cannot force it to look at a different dataset. You have to ask the vendor for a new feature.
This rigidity stifles innovation. It assumes the vendor knows your business better than you do.
Context is the cure for hallucinations
The approach we have taken with Clarity by Broadcom is fundamentally different. We don’t rely on canned skills. Instead, we give the customer champion—the person who actually understands the organization—the ability to designate exactly what data the AI agent should use.
You can point the AI agent at a specific grid or body of data and say, "Answer this question using only this context." This does two things. First, it pulls data in real time, not from a stale cache. Second, it drastically reduces the potential for hallucinations. By constraining the AI agent within a governed context, you stop it from inventing facts.
Your culture. Your LLM. Your control.
There is also the issue of ownership. We don’t believe in forcing you into a specific AI culture. You shouldn’t be required to use a branded bot that feels alien to your organization.
Clarity allows you to bring your own large language model (LLM). If you have a corporate standard, plug it in. If you want to resell Google Gemini through us, you can do that too. You can brand the AI to match your internal culture, so adoption feels natural.
We even go a step further into the FinOps side of AI management. We give you control over the temperature (creativity) and token limits of the models. This might sound trivial, but it has massive implications for cost management and outcome measurement. If you don't need AI to write a verbose, creative novel, you shouldn't be paying for the computing power to generate one. (For more information, see our post on tracking AI spending.)
Real enterprise AI isn't about a chatbot summarizing an email. It is about handing the AI model a relevant, governed slice of your SPM system and asking it to solve a complex problem using real-time data. That is how you turn usage of AI into a strategic advantage.
What is "shadow AI" and why is it a problem for my organization?
Shadow AI is the practice of your employees using public AI models (like ChatGPT or Gemini) for work tasks. Often, this involves users pasting internal, proprietary information into their prompts. This is a problem for governance because it exposes intellectual property (IP) and personally identifiable information (PII) to public models where your organization has no control.
How is Clarity's approach different from AI solutions in other SPM offerings?
Most vendors offer rigid, pre-packaged "skills" that can only access a fixed data set and perform one specific, hard-coded task. Clarity’s approach is fundamentally different. Clarity enables the customer champion to designate exactly what governed data the AI agent should use. When prompts are submitted, the agent accesses real-time data.
How does controlling the context reduce AI hallucinations?
AI hallucinations occur when models invent facts. By constraining the AI agent to a governed slice of your SPM data, you drastically limit AI’s ability to invent content or pull data from extraneous sources. This forces the AI to generate answers using only real-time, approved information.
Does Clarity force me to use a specific AI model?
No. Clarity allows you to bring your own LLM. If you have a corporate standard, you can plug it in. You can also resell models like Google Gemini through Clarity. Finally, you can brand the AI interface to match your internal culture, which helps with user adoption.
