Analysis Debt, AI Fluency, and Why the Requirements Phase Is Over

When Angela Wick talks about AI fluency, she draws a line that's worth paying attention to.

Fluency isn't knowing how to use the tools. It means understanding what AI can do: its capabilities, its limits, and how it can be embedded into the systems and processes an organization runs on. That's a meaningfully higher bar, and Angela makes the case that it's the bar business analysis professionals need to clear right now.

Angela recently published in the Journal of AI, Robotics, and Workplace Automation on the critical role of business analysis professionals in AI initiatives, and IIBA brought her in for an open Q&A session based on that work. What followed was one of the most direct, practically grounded conversations I've been part of on this topic.

Here are a few of the ideas that have stayed with me.

Continuous analysis has replaced the requirements phase

Angela's argument here cuts against habits that many of us built our careers on. The model where business analysis professionals produce detailed requirements documentation before delivery begins is becoming obsolete. Requirements now happen in parallel with development: continuous analysis running alongside prototypes and working products, often inside the same tools developers are already using.

The most common mistake Angela sees right now is holding on to the dedicated requirements process—defending the artefact, the phase, the handoff. The shift she describes moves from "I create this document and pass it on" to "I facilitate this structured conversation and keep the context alive in decision-making." It's a theme Angela also explores in her Corner piece.

It's the same skills applied in a different way of working.

Analysis debt, and why most organizations don't know they have it

This was the concept that landed hardest for me. Angela defines analysis debt as the gaps in an organization's analysis capabilities that get in the way of effective AI implementation. When organizations rush to adopt AI without first preparing their processes, governance, and documentation, the foundational analysis work doesn't get done. And once AI systems are deployed, those gaps grow.

Business analysis professionals are well positioned to address this. Identifying what's missing, putting the right governance structures in place, and doing the analytical groundwork that makes AI implementations function is exactly the type of work this profession is built for. Organizations that skip it tend to find that what looked like a faster path creates significantly more complexity down the line.

Orchestration: a role that's emerging in practice

Angela uses the word "orchestration" to describe something business analysis professionals are already starting to do, even when they're not calling it that. As AI agents become part of how work gets done, someone needs to define the work, set the context, and establish the boundaries within which those agents operate. Someone needs to ensure humans and AI systems understand their respective roles and that governance structures are in place.

Business analysis has always sat at the intersection of people, process, and technology. Orchestration is what that intersection looks like when one of the parties is an AI agent, and it's a natural extension of what experienced practitioners already do.

Staying visible in the work

The thread running through everything Angela said is visibility. Business analysis professionals who are actively present in co-creation (working alongside product owners, developers, and stakeholders in real time) demonstrate their value naturally. Those who hold on to phased, deliverable-driven ways of working can become harder to see, even when the underlying skills are strong.

Angela's overall message is clear: the business analysis role isn't being phased out so much as redefined, and that redefinition calls for an honest look at how we work. Letting go of ownership over documents and phases, and instead owning a place inside the continuous, collaborative work. No other role combines the knowledge, skills, and relationships needed to connect strategy, governance, and delivery in the way business analysis professionals can.

For those willing to build their AI fluency, close their organization's analysis debt, and stay visible in the room where decisions are being made, Angela's conversation is an invitation, one she believes the most adaptable professionals in the field are already accepting.

I think she's right. The recording is worth your time.

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