How AI Is Changing Business Analysis and Business Systems Analysis in Practice
AI is becoming a practical part of everyday enterprise systems, not a future concept. For Business Analysts and Business Systems Analysts, the shift is less about disruption and more about evolution—new tools, new ways of working, and better outcomes when used correctly.
Rather than replacing the role, AI is increasingly embedded within the systems analysts already work with.
In business and systems analysis, AI works best when it supports human judgement rather than replacing it. Many organisations are using AI to enhance existing activities such as:
• Analysing large volumes of operational and system data
• Identifying patterns, anomalies, or trends that would be difficult to spot manually
• Supporting forecasting, prioritisation, and early risk identification
The analyst’s role remains critical in interpreting outputs, validating assumptions, and ensuring insights are relevant to the business context.
Smarter Requirements and Process Design
AI tools are increasingly used to support requirements engineering and process analysis. For example:
• Analysing historical change requests or incidents to identify recurring issues
• Suggesting process improvements based on usage data
• Supporting scenario modelling for future-state workflows
These capabilities allow analysts to move faster while maintaining structure and accuracy.
AI Within Enterprise Systems
AI is now embedded in many enterprise platforms, from ERP and HRIS systems to CRM and case management tools. Business Systems Analysts help define how these capabilities are configured and used by:
• Ensuring AI-driven recommendations align with business rules
• Validating outputs through UAT and real-world scenarios
• Defining governance, escalation paths, and human oversight
This ensures AI enhances system performance without introducing unintended risk.
Data Quality, Governance, and Trust
AI relies heavily on data. Analysts play a key role in defining data standards, validation rules, and reporting requirements that make AI outputs reliable and auditable—particularly in regulated environments.
By embedding these controls into system design, analysts help organisations build trust in AI-enabled systems.
New Ways of Working
As AI matures, the analyst role increasingly involves:
• Continuous improvement driven by data insights
• Faster iteration of system changes
• Closer collaboration with engineering, data, and compliance teams
This creates more adaptive systems that evolve alongside business needs.
Conclusion
AI is changing how business analysis and systems analysis are performed, but the fundamentals remain the same: clear thinking, structured analysis, and alignment with real-world outcomes. When used effectively, AI becomes a powerful extension of the analyst’s toolkit—enhancing insight, efficiency, and decision-making across enterprise systems.
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Author
Peter Adebanjo
Business Systems Analyst / IT Business Analyst
Focused on enterprise systems, digital transformation, and integrating AI-enabled capabilities into practical, well-governed business solutions.
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