AI in Clarity and the Shift Toward Usability
And why the real opportunity is more practical and more meaningful than most teams initially expect.
There is a noticeable shift happening in conversations around Clarity right now. It came through clearly in our recent Rego Refresher Series session, led by Bob Werner, Senior Director of Sales Engineering, and Brian Toplicar, Senior Solutions Consultant at Rego Consulting, where the discussion reflected what teams are experiencing day to day.
It does not feel driven by hype or a rush to adopt something new. If anything, the tone feels more grounded in how teams are approaching it. It is less about, “How do we use AI?” and more about, “Why does this still take so much effort?”
Even in well-structured environments, the same points of friction continue to surface. And even when the data is technically available, it does not always feel accessible in a way that supports confident decision making.
Within that context, AI enters the conversation not as a pursuit of innovation for its own sake, but as a response to a growing expectation that the system should begin to meet users halfway. The expectation is not simply for more, but for a better experience of using what is already there, with less effort required to access, interpret, and act on information.
AI Is Becoming Part of the Experience, Not an Add-On
One of the more important shifts happening within Clarity, particularly with Broadcom’s introduction of Vaia, is that AI is no longer being positioned as a separate capability.
It’s being embedded directly into how the system works.
Vaia introduces AI as a contextual layer within Clarity, designed to generate summaries, interpret data, and assist users in real time as they move through their work. Instead of navigating to analyze information, users can begin to interact with it more naturally, with AI helping to translate complexity into something more usable.
That distinction matters, because historically, most enterprise tools have approached AI as something you “go use.” Another dashboard. Another feature. Another place to click into. AI is starting to function less like a destination, and more like a bridge between data and decisions.
From Generative AI to Agent-Based Support
A lot of early AI conversations focused on generation like summaries, content, and quick insights. That’s still part of the story, and it’s valuable. But it’s not where things stop.
Broadcom is building with AI agents in Vaia signals a deeper shift.
These agents can be configured within specific areas of Clarity—at the workspace or object level—where they can pull from structured data, reports, and even documents to generate context-aware insights. Instead of producing generic outputs, they operate closer to where work happens, supporting users in a more targeted way.
In practical terms, that means AI is no longer just summarizing what has already happened. It’s beginning to participate in how work is understood and managed in real time. Because it moves Clarity away from being purely a system of record, and closer to being a system that helps users interpret what they’re seeing.
The Reality Most Teams Run Into
There’s also a more grounded side to this conversation, one that came through clearly in our discussions. AI doesn’t automatically make the system better. It makes it more visible.
If your Clarity environment is already well-structured with consistent data, clear governance, and aligned processes, then AI can accelerate understanding in a way that feels immediately valuable. But if those things aren’t in place, AI tends to surface the gaps rather than solve them.
Inconsistent data becomes more obvious, and misalignment shows up faster. That’s not a limitation of technology. It’s a reflection of how dependent AI is on the quality of what it’s working with. Which is why some of the most successful organizations are treating AI as an amplifier.
Prompt Quality Is the Originator of Value
One of the more practical takeaways is how much prompt quality shapes the outcome. AI in Clarity is about asking for the right thing.
A vague prompt like:
Summarize this project
will almost always produce something generic.
But a more intentional prompt:
Summarize key risks, blockers, and next steps for executive review
You’re asking AI to think in a specific frame. Over time, teams that see the most success are getting better at interacting with it. They’re learning how to:
Because in practice AI quality is prompt quality.
Where AI Starts to Make a Difference
When it’s introduced into a stable environment, the impact is less dramatic but more meaningful.
One of the first areas where teams feel it is in status and reporting. Instead of manually pulling together updates or trying to interpret large volumes of information, AI can help synthesize what’s already there into something clearer and more digestible. That doesn’t eliminate the need for oversight, but it reduces the effort required to get a useful view.
Another area is decision support. AI can begin to highlight patterns, surface potential risks, or point out inconsistencies that might otherwise go unnoticed. It’s not making decisions on behalf of teams, but it does help ensure that fewer things fall through the cracks.
And then there’s a quieter impact: reducing administrative overhead. The small, repetitive tasks that tend to accumulate, like status formatting, data interpretation, routine updates, all start to feel lighter. Over time, that adds up to something more significant: more time spent on actual work, and less time managing the system itself.
Taken together, these changes point to something broader.
Clarity is gradually shifting from a platform that requires users to do the work of interpretation, to one that begins to assist with it.
Directionally, it’s moving toward a model where:
That may not sound dramatic, but in practice, it changes how people experience the tool day to day.
Where to Focus Now
For organizations exploring AI in Clarity, the most effective approach tends to be practical rather than ambitious.
Instead of trying to apply AI everywhere at once, it’s more useful to focus on areas where friction already exists in places where teams are spending time but not necessarily getting proportional value.
Reporting bottlenecks. Portfolio visibility gaps. Communication that feels heavier than it should. Those are often the clearest entry points. At the same time, it’s worth stepping back and asking a more foundational question: Is the current environment structured in a way that AI can support?
Because the long-term value of these capabilities will depend less on how advanced they become, and more on how well they’re grounded in the underlying system.
The Bottom Line
AI in Clarity isn’t about making the platform smarter for the sake of it. It makes it easier to use, easier to trust, and easier to act on. From a system that asks users to do the heavy lifting
to one that starts to share it. That’s what customers have been asking for all along.
Learn More with Rego
The Rego Refresher Series is designed to help teams revisit the fundamentals of Clarity and unlock new efficiencies in project, portfolio, and resource management.
Tune in for our last session AI in Clarity or sign up for the next session on April, 21 2026, 9:00am MDT on Financials in Clarity.
If you missed the live session or want to learn more about Portfolio and Program Management, contact Rego Consulting at info@regoconsulting.com or visit regoconsulting.com.
About the Author: Liz Palisin
- AI in Clarity and the Shift Toward Usability
- AI Is Becoming Part of the Experience, Not an Add-On
- From Generative AI to Agent-Based Support
- The Reality Most Teams Run Into
- Prompt Quality Is the Originator of Value
- Where AI Starts to Make a Difference
- Where to Focus Now
- The Bottom Line
- Learn More with Rego
- About the Author: Liz Palisin
