The rapid development of artificial intelligence is prompting organisations to reconsider how work is performed, how decisions are made and how services are delivered.
Agentic AI is one example of this shift. Unlike AI tools that primarily generate information, AI agents may be able to interpret an objective, complete several activities and interact with business systems with varying levels of human involvement.
However, the introduction of more capable technology does not remove the need for structured analysis. It increases it.
Before an organisation can determine where AI should be used, it must understand the problem being addressed, the value expected, the people affected, the decisions involved and the risks that must be managed.
These are core areas of business analysis.
As organisations explore agentic AI, the role of the business analyst is expanding. Business analysts are increasingly required to connect strategic objectives, operational processes, technology capabilities and organisational change.
New technologies often attract attention because of what they are capable of doing. This can lead organisations to begin with the solution rather than the need.
A technology-led approach may result in multiple pilot projects without clear ownership, measurable objectives or a defined route to implementation.
Business analysis provides an alternative starting point.
It begins by examining the organisation’s objectives, current challenges and desired outcomes. It then considers whether technology can contribute to those outcomes and what wider changes may be required.
This distinction is particularly important for agentic AI. An AI agent may appear capable of automating a process, but the underlying process may be unclear, inconsistent or dependent on undocumented knowledge.
In these situations, introducing AI may automate confusion rather than resolve it.
The business analyst helps organisations identify where the real problem exists and whether the most appropriate response is process redesign, improved data, conventional automation, employee training or an AI-enabled solution.
Starting with the business problem
A strong business analysis approach begins with the purpose of the initiative.
The first question should not be, “Where can we introduce an AI agent?” It should be, “Which business outcome are we trying to improve?”
This outcome could include:
Once the outcome is clear, the business analyst can assess the current situation.
This includes identifying process delays, duplicated activities, information gaps, inconsistent decisions and dependencies between teams or systems.
The business analyst also helps determine whether the proposed initiative supports wider organisational priorities. A technically successful project may still provide limited value if it does not address a meaningful operational need.
This is one of the most important contributions of business analysis: ensuring that investment remains linked to a clear business purpose.
As technology becomes more autonomous, requirements must address more than functions and user interfaces.
Business analysts must help organisations define who or what is authorised to make a decision.
This includes determining:
These questions are essential because accountability cannot be transferred entirely to technology.
Even where an AI agent performs part of a process, the organisation remains responsible for the design of that process, the permissions granted and the controls applied.
By documenting these boundaries, the business analyst provides clarity for leadership, technology teams, risk functions and end users.
Automation should not be treated as a choice between fully manual work and complete autonomy.
There are several possible approaches.
A system may provide information to support a human decision. It may produce a recommendation or prepare an output for approval. It may complete defined low-risk actions while escalating exceptions. In limited circumstances, it may operate independently within a tightly controlled area.
The appropriate approach depends on the process and the organisation.
Business analysts can support this decision by assessing:
A gradual approach is often appropriate. Organisations can begin with decision support, monitor performance and increase automation only where there is sufficient evidence that it can be managed safely.
Governance is sometimes viewed as separate from business analysis or introduced late in a project.
In practice, governance requirements should influence the solution from the beginning.
Business analysts can help translate high-level principles, such as fairness, transparency and accountability, into specific operational requirements.
These may include:
The value of business analysis lies in making these controls practical and testable.
A statement that an AI solution must be transparent is difficult to implement without further definition. A requirement that users must be shown the information, rules and assumptions supporting a recommendation provides clearer direction.
This analytical detail helps move governance from policy into everyday operations.
Technology adoption is also an organisational change initiative.
Employees may be uncertain about how a new system will affect their responsibilities, whether its outputs can be trusted and what they should do when they disagree with a recommendation.
Business analysts help address these concerns by involving users throughout the initiative.
Stakeholder engagement allows organisations to understand practical needs, identify resistance and develop processes that reflect how employees work.
Users should understand:
This supports more informed use of technology and reduces both overreliance and unnecessary resistance.
A successful AI initiative should not be assessed only by whether the technology operates as expected.
The organisation must determine whether it has improved the business outcome that justified the investment.
Business analysts can help define measures such as:
These measures provide evidence of value while also identifying where the process or solution requires improvement.
They also support better decisions about whether automation should be expanded, restricted or redesigned.
Agentic AI is likely to change how certain tasks and processes are delivered. However, the value achieved will depend less on the availability of technology and more on the organisation’s ability to apply it appropriately.
Business analysis provides the structure required to make that distinction.
Business analysts help organisations move from broad ambition to defined outcomes. They examine how work is performed, identify the right opportunities, clarify decision rights, translate governance into practical controls and support employees through change.
As a result, business analysis should not be viewed only as a project delivery function.
It is a strategic capability that helps organisations make better decisions about transformation, investment and the future design of work.
As technology becomes more autonomous, the need for business analysis does not diminish. It becomes increasingly central to ensuring that change is purposeful, controlled and capable of delivering measurable value.
Grant Thornton supports organisations in assessing transformation opportunities, analysing business processes and defining practical requirements for technology-enabled change.
Our multidisciplinary approach brings together business analysis, process improvement, technology, data and governance expertise to help organisations move from initial ideas to sustainable business outcomes.
