ANSWER

The statement “We don’t need a Business Analyst—we have ChatGPT” reflects a common misunderstanding of both the Business Analyst (BA) role and what large language models are actually good at. It equates producing text with doing analysis. In reality, tools like ChatGPT change how BAs work, not whether they are needed.

Misunderstanding the Business Analyst role

The notion that a BA can be replaced by ChatGPT assumes that the primary value of a BA lies in drafting requirements documents. In most mature organizations, this is only a small portion of the role.

A Business Analyst is accountable for activities such as:

• Framing the right problem - Determining what should be solved and why, rather than simply documenting a pre-decided solution. This involves understanding strategy, constraints, and success metrics.

• Aligning stakeholders - Bringing together business, operations, technology, compliance, legal, and others to reach a shared understanding of goals, scope, and trade-offs. This is less about wording and more about negotiation, facilitation, and conflict resolution.

• Managing ambiguity and risk - Identifying assumptions, dependencies, edge cases, and potential negative impacts before they become costly failures in production.

• Enabling change - Supporting the transition from current state to future state through communication, training input, process changes, and feedback loops.

These responsibilities are about sense-making, decision support, and organizational alignment. Generating text is helpful, but secondary.

What ChatGPT can actually do

ChatGPT and similar tools are very good at:

• Drafting and rephrasing text (e.g., initial user stories, acceptance criteria, emails, meeting summaries).
• Brainstorming ideas, edge cases, or alternative process flows.
• Providing quick overviews of concepts or domains, which can then be validated against reliable sources.

In other words, AI tools excel at generating plausible language based on patterns in data. They are accelerators for certain BA tasks, especially those that are repetitive or text-heavy.

However, they do not:

• Understand the organization’s politics, culture, or history.
• Know unwritten rules, informal workarounds, or legacy constraints.
• Take responsibility for trade-offs, compliance, or impacts on real customers and staff.

They provide content, not context or accountability.

The risks of “AI instead of BA”

Treating ChatGPT as a replacement for a Business Analyst creates specific risks:

• Context-free requirements - Requirements produced by an AI may look polished but still be misaligned with the organization’s processes, systems, or regulatory environment. Without a BA to validate and tailor them, teams may implement the wrong solution very efficiently.

• Plausible but incorrect rules - Language models can “hallucinate” – producing confident but inaccurate statements about business rules, policies, or calculations. If these are accepted uncritically, the result can be financial errors, compliance breaches, or poor customer outcomes.

• Missing edge cases and unintended consequences - AI-generated text often covers the “happy path” well but may miss subtle edge cases, failure modes, or cross-domain impacts that emerge only through stakeholder interviews, workshops, and deep domain questioning.

• No ownership or learning loop - When a solution causes harm or underperforms, the organization needs someone who can explain the decisions, trace them back to stakeholder needs and constraints, and adjust the approach. That learning loop and ownership typically sits with roles like the BA, not with a tool.

BA × AI: a better model

A more realistic and effective stance is not “BA vs. ChatGPT” but “Business Analysts using ChatGPT well.” In this model:

• AI handles mechanical work: drafting, summarizing, rephrasing, generating initial lists of scenarios or questions.

• The BA handles critical thinking:
  • Validating AI output with domain experts and stakeholders.
  • Prioritizing and refining requirements based on real constraints and strategy.
  • Ensuring traceability from business goals to solution features and test cases.
  • Communicating decisions in a way that builds trust and alignment.

This combination can make analysis faster and more thorough, while preserving human judgment, ethics, and organizational understanding.