Spending a large amount of time straightening lines and moving boxes has been a rite of passage for many business analysts. Christina Lovelock explores how this might change in the future.
For decades, business analysts have invested considerable time and effort into producing visual models and diagrams. We use techniques such as process flows, data models and use-case diagrams to enable shared understanding, decision making and design. Many hours have been invested in mastering tools like Visio to create professional outputs, where a minor change can result in a major effort to rework!
Today, the rapid evolution of AI in business analysis is reshaping our approach to modelling. With many tools capable of generating entire process models, user journeys and even architecture diagrams from text descriptions or transcripts, BAs can now produce outputs in minutes that used to take hours. What does this mean for the profession?
How automation will shift focus
The future of diagram creation will see a massive reduction in time spent on formatting and layout. With AI generated business diagrams, BAs can focus more on analysis: understanding the business context, engaging more widely, exploring root causes, testing assumptions, facilitating discussion and resolving disagreement. It also allows further exploration of edge cases, error handling and alternative flows.
Is AI bad for business analysis?
Many BAs enjoy the manual process of creating a model . We derive real pleasure from taming the complex and representing the many decisions, activities and data classes in our ‘manual’ modelling tools. There is a sense of ownership and pride for a diagram that we slaved over and wrestled with, trying to create the optimum output which automated process modelling doesn’t deliver.
Perhaps this was never a good use of time, but it was often a learning opportunity — learning the detail of the business situation, the modelling notation and the tool. We still need to have a good understanding of different analysis techniques and different notations, and we still need to be able to elicit and identify information from various sources to be able to create accurate diagrams. We just don’t need the same level of knowledge of modelling software.
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This doesn’t seem like much of a loss. However, established BAs and BA leaders will need to create new opportunities for early career business analysts to learn and practise different techniques and notations, or they will not have the knowledge to review, refine and correct AI generated outputs.
How AI will enable business analysts
The increased speed of generation allows us to create multiple tailored outputs for different audiences. Not every stakeholder requires formal notations like BPMN or UML. By quickly generating multiple versions of a diagram in different formats or levels of detail, AI can help BAs provide the most suitable outputs to different audiences. This will enable more effective communication and decision making. AI also lowers barriers to entry; many BAs are fearful of modelling software and hide this skills gap by sticking to text-based outputs, but new methods may encourage more of them to try modelling.
The limitations of AI modelling
Often the diagram outputs created by AI cannot be edited, but this will become standard very rapidly. This will allow us to generate a good starting point and then make updates and corrections without further prompting and re-generation. Even with AI generated outputs the age-old problem of creating ‘flat’ diagrams as opposed to fully integrated architecture and analysis models still remains. The trade-off for enterprise-wide modelling tools has always been excellent in terms of re-use of elements and ability to perform impact and risk analysis, but terrible in terms of usability and training overheads!
When AI capabilities move from creating a compelling visual output to a fully integrated model of the enterprise, including its systems, process and data, this will represent a step change in our understanding of our organisations.
Conclusion
The ability to produce clear, professional diagrams remains a core skill https://www.ilo.org/resource/other/core-skills-age-ai for business analysts. While AI can accelerate diagram generation, it cannot replace the analysis, judgement and critical thinking that underpin meaningful models. While AI can enhance our ability to tailor visuals to different audiences, it’s still the BA who ensures the model represents reality and is a valuable visual output.
About the author
Christina Lovelock is a digital leader, coach and author. She is active in the Business Analysis professional community and champions entry level roles. She is the author of the BCS books Careers in Tech, Data and Digital and Delivering Business Analysis: The BA Service Handbook, which are both available in the BCS bookshop.
