The Challenge: Making AI and Internal Audit Work Visible Without Creating More Complexity

The organization had two clear needs.

Managing Audit Work as Projects or Investments

The first challenge was around internal audit work. An internal auditing team was managing roughly 30 to 40 audit efforts each year and needed a better way to track that work as projects or investments. Because the team was relatively small and had a lot to deliver, they needed visibility into resource allocation, capacity, and workload.

Understanding the Real Effort Behind AI

The second challenge was newer, broader, and very familiar to many organizations right now: AI.

Like many enterprises, the organization needed a way to understand how artificial intelligence initiatives were performing across the portfolio. It was not enough to know that a project had “something to do with AI.” Leaders needed a way to see whether work was AI-related, where that work lived, how much effort it required, and how it affected cost and capacity.

Before the update, the main gap was visibility. As Dakota explained, the organization had “no visibility into the effort and costs related to AI work.” The request came from strategic partners and executive-level stakeholders who needed the information, but there was no clear way to get it.

For AI, visibility matters because the work is often scattered. A chatbot initiative, for example, may appear as one AI project, but the work behind it can touch network teams, operations teams, support teams, infrastructure teams, and other groups. Some of that work may be project-based. Some may be operational. Without a consistent way to flag and quantify AI-related effort, the cost and impact are easy to miss.

AI was not just a technology problem. It was a portfolio visibility problem.

The Solution: A Simple, Scalable Way to Capture AI Effort in Clarity

Rego helped the organization extend Clarity in a practical way: by adding targeted attributes at both the project and task levels. 

The solution combined two forms of AI tracking:  

That structure gave the organization a more detailed view of AI effort without asking users to work outside the system or build an overly complicated process. Each level served a different purpose.

Project-level tracking established the portfolio view. The organization could identify to what degree an initiative was connected to AI, distinguishing full AI initiatives from projects that had only partial AI involvement.

For example, a project to implement an AI chatbot might be considered fully AI-related. But supporting work from infrastructure, networking, or operations might only be partially related to that initiative. Capturing both levels gave the organization a more realistic view of the work behind AI adoption.

Task-Level AI Tracking

Task-level tracking added greater precision. Rather than marking an entire project as AI-related and calling it done, teams could identify specific tasks that supported AI work and assign a percentage of each task. This empowered the organization to capture AI-related effort where it happened, even when that work lived inside another team’s operational or project budget.

AI initiatives often depend on work that does not look like AI on the surface. Network changes, performance updates, system administration, integration work, and support changes may all contribute to an AI outcome. Task-level tracking helps make hidden work visible.

Blueprint Rules and a Better User Experience

The most important technical piece was Clarity blueprint rules.

Rego used blueprint rules to control field behavior, field-level security, required fields, and conditional sections. If a user marked work as AI-related, Clarity will guide them to the additional information they need to provide. If the work was not AI-related, users were not forced through irrelevant fields.

That kept the experience cleaner for users while still helping the organization capture the data leaders needed.

As Dakota explained, the “magic” was in using blueprint rules to control behavior and required fields without relying on custom JavaScript or complex workflow processes. The result was a simpler configuration with less maintenance and less overhead.

The Implementation: Advisory-Led, Native Clarity Configuration, and Built to Adapt

One of the strongest parts of this engagement was its simplicity.

Enterprise portfolio systems can become brittle when every new requirement turns into a custom process. Here, the goal was the opposite: solve the business problem in a way that the organization could maintain and adjust over time.

Rego did not need to build a highly customized solution or write a pile of scripts to solve the problem. Instead, the team used native Clarity capabilities, thoughtful configuration, and a consultative approach to help the organization define what needed to be captured and how users should interact with it.

Rego’s value extended beyond configuring Clarity. The team helped the organization translate the executive requests into practical questions, that the solution needed to answer:

Rego’s advisory role was especially useful here. The organization had a business need that did not come with a perfect out-of-the-box answer. Rego helped translate that need into a Clarity design that was simple enough to use and structured enough to report on.

The Results: Greater Visibility into AI Effort, Costs, and Audit Capacity

The biggest result was visibility.

The organization now has a way to capture AI-related work at both the project and task levels. That gives strategic partners and executive stakeholders a clearer view into where AI work is happening, how much effort it requires, and how that work connects to broader financial impact.

This is especially important because many organizations are investing in AI before they can fully prove short-term value. As Dakota noted, organizations may move forward with AI because they believe it is where the market is headed, but they still need to track the financial impact of that work.

Clarity now gives the organization a clear foundation for answering those questions.

Business Outcomes

The Road Ahead: Extending Clarity as Business Priorities Evolve

For the organization, the work shows how a mature Clarity environment can continue to support new business priorities long after the initial implementation.

As AI becomes more embedded in enterprise work, organizations will need better ways to track not only AI projects, but also the supporting work that makes those projects possible. The organization now has a practical foundation for doing that in Clarity.

The same is true for internal audits. By extending Clarity to teams outside the traditional project management function, the organization can continue building a more connected view of work, capacity, and enterprise priorities.

The main takeaway is that Clarity does not need to be reinvented every time the business changes. With the right advisory partner and thoughtful configuration, it can adapt.

Rego Consulting Capabilities Demonstrated

This engagement highlights how Rego helped the organization extend and optimize Clarity to meet emerging portfolio management needs.

• Clarity optimization
• AI work tracking
• Project and task-level attributes
• Blueprint rules
• Portfolio visibility
• Executive reporting enablement
• Audit work management
• Resource allocation and capacity planning
• User experience improvements
• Configuration-first solution design
• Strategic advisory support

About Rego Consulting

Rego Consulting helps organizations get more value from Clarity and AI through practical, expert-led strategic portfolio management, project portfolio management, technology business management, and cloud consulting services.

Ready to get more from Clarity? Explore Rego Consulting’s Clarity services and see how the right expertise can help you scale with confidence.