Reframing and Reshaping Business Analysis

Welcome back to The Corner!

We’re starting 2026 with an important, practical, three-part series called "Reframing and Reshaping Business Analysis" that extends our challenge to the global community from 2025 to develop not just our mindset (although that is critical), but also our approaches and roles in the AI-influenced world we all now operate in. Doing this well requires critical thinking—as our work always does—but also developing new ways of adapting our skills and experience to change.

I’m grateful to Filip Hendrickx for authoring the first (and third) editions in our new series. Filip is a talented teacher, speaker, coach, and consultant based in Belgium. He led IIBA’s Brussels Chapter for many years, founded and co-leads the BA & Beyond annual conference in Europe, and joined the IIBA global Board of Directors in 2025.

Filip’s contributions to the business analysis community have been recognized with the IIBA EMEA Region Volunteer of the Year Award in 2022 and 2024. He is also a published author and visiting professor, co-authoring Brainy Glue with Ian Richards, as well as Digital Product Management and Cycles, a book, method, and toolkit for faster innovation.

— Delvin

From Strategy to Reality, Now and in the Future

The business analysis community has been actively exploring two critical questions: "How can AI improve business analysis?" (AI4BA) and "How can business analysis shape AI adoption?" (BA4AI). These are important conversations.

But beyond improvement through AI and shaping AI adoption, there's a third question—the elephant in the room—that we must confront: What will AI do to business analysis? How will we prepare and respond when AI transforms business analysis (i.e., now)?

The comforting motto, "AI may not replace you, but someone who uses AI will," might actually miss the most important point entirely. The real question isn't just about using AI; it's about whether business analysis will remain relevant in the future.

To answer this and prepare ourselves, we need to examine, reframe, and reshape the role of business analysis in delivering change.

Understanding the Role of Business Analysis in Delivering Effective Change

Effective change is delivered through four interconnected stages:

• Trend exploration is where we observe what's happening in the wider business or more specific product or service environment and make sense of it, moving from confusion to clarity. This is the foundation of strategic awareness, where we detect signals and patterns that others might miss. It’s also where business analysis provides sense-making to stakeholders and organizations.
• Solution ideation transforms exploration insights into action paths. This is where sense-making becomes sense-giving: we shape potential solution pathways that address the trends, challenges, and opportunities we've identified, helping stakeholders understand possibilities and make choices.
• Solution implementation moves from concept to reality through design, specification, development, testing, and roll-out. Solutions encompass both software and non-software elements, requiring coordination and collaboration across multiple domains and disciplines.
• Benefits realization closes the loop as we determine whether initiatives succeed or fail, i.e., whether initiatives are helping us achieve the intended outcomes, feeding insights back into exploration for continuous improvement.
  
  

AI's Transformative Impact on Business Analysis Capabilities

To understand AI's potential to reshape our profession, consider how it's already affecting key business analysis capabilities in change delivery, automating and augmenting them.

For example, take elicitation. AI can transcribe and summarize stakeholder interviews, from which it derives user stories and use cases as input for further analysis. But it can also now prepare, perform, and consolidate hundreds of interviews in less time than it takes a human business analysis professional to conduct one.

This isn't merely about speed. AI enables us to capture more nuances, identify patterns across larger data sets (such as different cultures), and uncover insights that might elude individual practitioners working at a human scale.

It's not just faster; it's potentially better. It's augmentation beyond mere automation and acceleration.

Additionally, documentation is being revolutionized through AI-enabled knowledge hubs. We can now collect and curate any kind of relevant information, in any shape or form, and consolidate it into LLM-powered, queryable project and product knowledge repositories. These function almost like a second brain or digital twin at the project or product level, making organizational knowledge accessible and actionable, even simulatable, in ways that were previously impossible.

Similar augmentations are occurring across specification, verification, and validation. Each capability is being augmented and, in some cases, potentially replaced by AI systems that work faster, more consistently, and at greater scale than human practitioners. This is AI4BA in action: leveraging AI to improve and enhance business analysis work.

The Uncomfortable Questions

But what if AI doesn't just augment the value chain? What if it removes entire stages, transforming the way change is delivered and the role business analysis plays (i.e., "alteration")?

This doesn't have to be science fiction. When intended outcomes, scope, and guardrails are sufficiently clear, super-fast, automated iterative development by AI becomes entirely possible. We could see AI remove human software development, testing, and even continuous validation and improvement from the value chain.

The technical barriers to this are falling rapidly. The question for business analysis is not if this will happen, but where and when.

In this future, business analysis isn’t about prompt engineering. It's about context curation: selecting and specifying the context (intended outcome, scope, and guardrails) so that a generative AI system can iteratively build and deploy a successful implementation.

But wait, there's another unsettling question to ask ourselves! What if AI "removes" business analysis itself from the change delivery, blending it into other roles and responsibilities? What if the role of the business analysis professional becomes automated, optimized, augmented, and ultimately, unnecessary?

Will we become irrelevant? How do we prepare for such an unknown future?

Three Dimensions of Professional Reinvention

Rather than retreat in fear, we can prepare strategically by understanding the business analysis value chain in three dimensions. These dimensions represent opportunity spaces for professional growth and adaptation.

1. Horizontal Expansion: Moving Left and Right

As AI automates various parts of the change delivery chain, we can expand horizontally, both to the left and to the right, along the chain itself.

Today, many business analysis professionals find themselves concentrated in the second half of ideation and in implementation, essentially in the requirements and delivery space. But there's significant room to expand.

We can move left into deeper trend exploration, becoming more involved in observing emerging patterns and making sense of strategic ambiguity. We can also move right into benefits realization, ensuring that delivered solutions actually achieve intended outcomes and feeding that learning back into the system.

As AI takes over more routine activities in the relatively clear specification and implementation space, we have the opportunity (and the time!) to occupy these spaces where human judgment, strategic and analytical thinking, and systemic understanding become increasingly essential (and where these skills will be expected and pressured to shape how our organization makes a difference).

2. Vertical Expansion: Becoming More Strategic

Change delivery doesn't exist at just one level: it operates across multiple layers of organizational activity. From strategic to tactical to operational. From portfolio to program to project. From product portfolio to product roadmap to product increment.

We can broaden our role vertically, moving beyond project-level requirements work to influence strategic decisions at portfolio and program levels. This means developing the capability to connect big-picture organizational objectives with granular implementation details, operating fluently across different organizational levels.

As AI handles more operational and tactical execution, the strategic levels become increasingly critical and our new playing field. Those who can operate at these higher levels, translating between strategy and reality through context curation, become more valuable, not less.
 

3. Transversal Expansion: Crossing Domain Boundaries

Different domains are being impacted by AI at radically different speeds. Business domains like retail, pharmaceuticals, or energy face unique AI disruption patterns. Technical domains like data analytics or cybersecurity are evolving along their own trajectories.

We can broaden our expertise across these domains, achieving two critical objectives. First, we safeguard ourselves from irrelevance in any single domain: if AI disrupts one area, we have others where we remain valuable. Second, we become conduits of knowledge, bringing lessons about AI's impact from one domain to others.

This cross-pollination becomes a strategic advantage. A business analysis professional who understands how AI is transforming customer service in retail can bring those insights to healthcare. Someone who sees how AI is revolutionizing data analytics in the service industry can help traditional manufacturing sectors understand what's coming and prepare accordingly.

Another example is crossing role boundaries: specific AI-augmented business analysis activities may not disappear but rather blend into other activities. And we may broaden our scope by embracing these adjacent roles and activities.

What's Next?

These three dimensions give us a framework to think about, or analyze (pun intended), how we can reframe and reshape business analysis—both the profession and our individual roles—for maximum impact in a future reshaped by AI, other influential technologies, or paradigm shifts.

This article was inspired by conversations with and feedback from Fabrício Laguna, Howard Podeswa, Jas Phul, Michael Augello, Robert Snyder, Terry Roach, Tim Coventry, Tina Lovelock, Stijn Van Schoonlandt, and Delvin Fletcher. It was co-authored with Claude AI.

 — Filip

Up next: In Part 2 of our series, Angela Wick, one of the foundational contributors to the intellectual property of the business analysis profession, will join us to share some of her lessons and perspectives on how teams and leaders need to respond to the Reframe and Reshape challenge.