Artificial Intelligence is quickly becoming one of the most discussed topics in business today.
Organizations are exploring AI to automate processes, improve decision-making, enhance customer service, and reduce operational costs.
In many conversations, however, I hear the same question:
"How can we use AI?"
I believe that is the wrong place to start.
A better question is:
"What business problem are we trying to solve?"
This might sound simple, but it is often the difference between a successful AI initiative and an expensive experiment.
The good news is that business analysts already have a framework for approaching these initiatives. The BABOK® Guide provides many of the principles needed to ensure AI projects deliver value rather than technology for technology's sake.
I recently observed several automation initiatives where stakeholders became excited about AI capabilities before defining the underlying business problem.
The conversation started with the technology rather than the objective.
Instead, organizations should begin with questions such as:
Once the need is clearly understood, the appropriate solution becomes much easier to determine.
Sometimes AI is the answer.
Sometimes it isn't.
A strong business analysis approach helps distinguish between the two.
One of the biggest lessons from automation and AI initiatives is that technology rarely fixes poor data.
If source information is incomplete, inconsistent, or unreliable, AI will simply process bad data faster.
Before discussing models, prompts, or automation workflows, organizations need to understand the quality of the information being used.
This is a traditional business analysis challenge.
Current state analysis, stakeholder engagement, process analysis, and root-cause investigation remain just as important in AI initiatives as they are in any other change initiative.
AI discussions often become focused on capabilities.
Can it summarize?
Can it classify?
Can it predict?
Can it generate content?
These are interesting questions, but they are not business outcomes.
Instead, business analysts should define measurable success criteria from the beginning.
Examples include:
Without measurable outcomes, it becomes difficult to determine whether the initiative was successful.
Another common misconception is that AI will completely replace human decision-making.
In reality, many business processes still require judgment, accountability, and risk management.
For this reason, business analysts play an important role in designing appropriate controls.
Some questions worth asking include:
These considerations are often more important than the technology itself.
I believe AI is creating a significant opportunity for business analysts.
Organizations need professionals who understand both business needs and solution capabilities.
Business analysts are uniquely positioned to bridge that gap.
They can help organizations:
These responsibilities align closely with the core principles of BABOK and remain relevant regardless of how technology evolves.
Despite all the excitement surrounding AI, the fundamentals of business analysis have not changed.
Organizations still need to understand problems before pursuing solutions.
They still need stakeholder alignment.
They still need clear success measures.
And they still need to evaluate whether expected value was achieved.
AI may be new, but the discipline required to implement it successfully is not.
The organizations that achieve the greatest benefits from AI will likely be those that combine innovative technology with strong business analysis practices grounded in BABOK principles.