Reframing and Reshaping Business Analysis

Welcome back to The Corner!

We’re wrapping up the first three Corner articles of 2026 with the final instalment in our series called "Reframing and Reshaping Business Analysis.” Our goal here has been to extend 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 delighted to welcome back Filip Hendrickx—consultant, teacher, coach, and IIBA Board Director—to explore how the profession is evolving in an AI-influenced world.

— Delvin

Learning From Past Transformations

While we've been exploring AI's impact on the business analysis profession and the implementation of successful change, it's important to recognize that AI isn't the first or only force reshaping business analysis. 

We've weathered transformative waves before: agile methodologies, the #noprojects movement, the shift from project-to-product thinking, and the rise of DevOps and continuous integration and delivery in software development—with clear implications for the business. Each brought disruption, resistance, partial adoption, and valuable lessons.

What can we learn from these past evolutions about navigating the AI transformation that’s currently unfolding? How can we not just shape the future of business analysis but effectively realize it?

The Self-Organization Parallel

Consider an intriguing parallel: Designing guidelines and constraint systems ("context curation") for autonomous AI agents is remarkably similar to empowering people and shifting toward self-organization. Both require us to move from direct command and control to indirect value creation through the nimble capability of sense and respond, from prescription to principles, from
micromanagement to guardrails... and trust.

The agile movement taught us that self-organizing teams need clarity on boundaries, not detailed instructions. They need the "what" and "why," not the "how." When organizations got this right—defining clear outcomes, establishing meaningful constraints, and trusting (accountable!) teams to navigate within those boundaries—transformation succeeded. When they imposed "agile
processes" from above without genuine empowerment and rationale, transformation failed, leaving cynicism ("agile doesn’t work here") in its wake.

The same pattern applies to AI agents taking up role responsibilities beyond simple task automation. An AI system with clear objectives and well-designed constraints can operate autonomously and effectively within the provided (curated) context. One with vague direction or over-specification will either fail or require constant human intervention, negating the benefits of automation and augmentation.

The business analysis skills we've developed in facilitating self-organization, like clarifying objectives, defining boundaries, and establishing feedback loops, are directly transferable to designing AI systems. This isn't coincidental. Both represent a fundamental paradigm shift from command and control to enabling, guiding, and empowering. 

Business analysis professionals who have mastered the shift from analyzing to facilitating with human teams are well-positioned to repeat this with AI systems.

Patterns of Adoption: What's Different This Time?

History shows us patterns in how transformations unfold. Agile promised faster delivery and better alignment. Some organizations achieved this, while others simply renamed their waterfall processes "sprints," and their releases "MVP 1," "MVP 2," etc. 

Project-to-product thinking promised continuous value delivery. Some organizations genuinely transformed, while others added product titles without changing funding models or decision-making structures. CI/CD enabled rapid, reliable releases. Some teams achieved a high deployment frequency driven by business feedback loops, while other businesses still release quarterly despite having CI/CD pipelines with daily deployment. 

All of these are examples where business analysis can and should be highly influential! The pattern? Surface-level adoption without fundamental change delivers surface-level results. True transformation requires changing not just practices but mindsets, structures, and power dynamics.

So how will the AI shift be different? In some ways, it won't be. We'll see the same adoption curve: early enthusiasts, pragmatic majority, and resistant holdouts. We'll see superficial adoption: organizations using AI tools without changing how they make decisions or create value. We'll see the same debates about whether this changes everything or nothing.

But in crucial ways, AI is different. Previous transformations required organizational consensus and cultural change. AI can be adopted incrementally and individually. A single business analysis professional can use AI for research, documentation, analysis, and even software generation without waiting for organizational buy-in. This democratization accelerates adoption but also risks increasing fragmentation. 

Moreover, AI's capabilities are improving at unprecedented speed. The gap between agile methodologies in 2001 and 2010 was evolutionary. The gap between AI capabilities in 2023 and 2025 is revolutionary. By the time organizations complete their "AI transformation programs," the technology will have evolved beyond their plans.

This demands a different approach: instead of planned transformations with defined end states, we need continuous adaptation with flexible frameworks. The three-dimensional expansion model—horizontal, vertical, and transversal—provides exactly this: a framework for ongoing value-driven evolution rather than one-time change.

Applying Historical Wisdom

The lessons from past transformations suggest clear strategies for the AI era, at the same time challenging us to really change.

Start with problems and opportunities instead of solutions. 

Just as the best agile adoptions began with real pain points rather than methodology mandates, effective AI integration should start with specific business challenges, not technology fascination. 

But: Don't get stuck in analysis paralysis and generic AI critiques. Leverage quick, contextual, AI-enabled iterative analysis and development and the powerful feedback loop it offers. Be driven by analysis and delivery pain points and inspired by AI opportunities.

Build capability before scaling. 

Successful CI/CD adoption built engineering capability before automating everything. Similarly, business analysis professionals should develop AI literacy and experiment with AI tools before attempting large-scale integration. 

But: Don't limit yourself to mere automation and productivity gains. Pursue augmentation and true alteration. Don't stop at automating existing processes as they are; instead, re-think them. Leverage AI across all three dimensions—horizontal, vertical, and transversal—of the change delivery value chain.

Maintain the human core. 

Project-to-product thinking succeeded when it stimulated and enhanced (not replaced) human judgment about priority and validation of human value. AI should augment business analysis capabilities without substituting for our understanding of organizational context, stakeholder needs, and business value. 

But: Don't be fooled by the oversimplified "AI isn't empathetic" myth and similar ones. Rather, curate the context so that the human and AI perspectives complement each other.

Create feedback loops. 

The most successful transformations built rapid feedback mechanisms. The same principle applies to AI: implement, learn, adjust, repeat. 

But: Don't stop at solution feedback loops. Build outcome feedback loops. Thanks to AI, these feedback loops have never been faster.

The three-dimensional framework of horizontal, vertical, and transversal expansion is a model for professional adaptation. It helps us respond to any transformative trend by expanding our practice rather than defending our current position, and by blending analytical thinking into other areas of change rather than reinforcing rigid borders between practices. It embodies the learning from decades of transformation: stay flexible, build broad experience enabled by foundational capabilities, and focus on enduring value rather than temporary techniques.

From Threat to Opportunity

The future of business analysis will be shaped by how we harness technological and organizational change. We choose whether we retreat into increasingly narrow specializations or embrace broader, more strategic roles.

The business analysis profession has always been about ensuring connection: between business and technology, between strategy and execution, between what is and what could be. As AI transforms the landscape, this essential value proposition will shift, potentially becoming more impactful and thus critical. More than ever, organizations need professionals who can make sense of complexity, give sense to stakeholders, and ensure that technological capability and opportunity translate into valuable business responses.

Yes, AI will change business analysis profoundly. Some of what we do today will be automated. Some activities and even roles will disappear. But new opportunities will emerge for those who can expand horizontally into exploration and realization, vertically into strategic altitudes, and transversally across diverse domains.

The question isn't whether AI will change our profession. It will. The question is whether we'll be passive subjects of that change or active architects of our professional future. By reframing and reshaping business analysis and understanding where we can expand and evolve, we ensure that our profession remains essential in an AI-transformed world.

AI is reshaping our field, but it doesn’t have to dictate its direction. We have the opportunity to lead that change. Now is the time to reinvent ourselves, both as professionals and as a community. Are we up for it?

— Filip

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.

Up next: Based on the feedback I’m seeing and lots of discussion in our community, I think we’ll be continuing to explore this “reframing and reshaping” theme! In our next edition, I’ll be joined by Dr Terry Roach to share some of what he has been learning (and working on) over the past few months on the intersectional areas between business analysis and business and enterprise architecture—including what some of those lessons are for leadership and the next generation of our craft.