For business analysts, GenAI offers powerful tools to accelerate insights and presents new considerations for human-centered analysis. Over the years AI has enhanced and improved fields like finance (fraud detection algorithms), customer service (chatbots), and medicine (computer vision assessing scans).

Using generative AI in business analysis

With Generative AI tools, like ChatGPT, offering enhanced capabilities for roles across all industries, what does it mean for business analysis?

Business analysts (BAs) have a varied role, with many interpretations. The International Institute of Business Analysis defines the role as:

“A Business Analyst bridges business and technology worlds, identifying needs and recommending solutions to deliver value and enable change.”

BAs by nature are human prompt engineers:

  • We ask questions
  • We bridge the technical and business
  • We help identify needs

The core skillset of the BA, asking the right questions in the right way at the right time, sits at the heart of how to use the new GenAI tools – that is prompt engineering.

Therefore, BAs are well suited to adopt GenAI, where needed. However, one must consider what is lost when adopting AI, as much as what is gained.

For example, BAs often create backlog items. A large part of their value is in the relationships built with stakeholders and the team whilst shaping them, fostering ownership and understanding.

Generative AI can create many stories at once, but it cannot create relationships and understanding.

Accelerating the basics

An advantage of adopting GenAI from a business analysis perspective is the time it frees to focus on building relationships and delivering value earlier.

An impactful use of AI in an internal project has been in pump-priming conversations much faster. For example, breaking down complex legislative comparisons, technical stack suitability, and query a proposed approach. The use of AI with the support of experts cuts the work from a week to hours.

As a result, the project teams can have meaningful conversations about our way forward, address key stakeholder questions, and begin technical spiking earlier than anticipated.

Exploring what this adoption means at BJSS has been fun. Asking “What if…” questions to see if we can get the tool to do what we need. There’s freedom in experimenting to find the limits and working out what that means for different use cases.

The freedom to play with AI tools is needed before the formal metric-based assessment takes place. This must be balanced with Responsible AI considerations such as privacy, bias, transparency, accuracy, and quality review.

When selecting AI tools, you’ll want to consider:

  • Organisational fit – Will these tools be used outside the BA space?
  • Cost and risk appetite – What policies and funding does your organisation have?
  • What do you intend to do – Is GenAI the right solution for that task? Do the features of the tool match your intended use case?

Steps for integrating AI into business analysis

  • Evaluate which tasks are best suited to AI and offer benefits compared to a human alone, other techniques, or simpler approaches
  • Create hypotheses – what will you gain or lose, how will you measure these?
  • Build an experimentation team
  • Select appropriate AI tools and technologies
  • Check your AI governance is in place, including your Responsible AI policy, and AI risk and impact assessments
  • Develop a pilot project to test your hypotheses – have fun here, ask “What if?” questions, push the envelope
  • Evaluate results, refine the approach, amplify across the organisation
  • Scale up AI implementation and employee training in AI across the organisation

Balancing innovation with intuition

GenAI can be a great companion tool for business analysts, but it is not a replacement for the skill, personal touch, and intuition that comes with human operators.

Care should be taken to ensure selection of tooling that fits with the long-term goals of the organisation; the risk and financial appetites; and gives appropriate feature coverage for the analysts.

When looking at the tools you plan to adopt, be brave and embrace the unknown. Experiment, explore, give yourself permission to fail if the tool doesn’t perform as expected. Be optimistic about what you can gain, but mindful about what you might lose. Consider whether those tradeoffs are worthwhile.

BAs are key stakeholders, enablers, and allies to the successful adoption of AI, so be vocal, share your insights, celebrate successes, socialise the blockers and collaborate on solutions.