Working with Agentic AI: Business Analyst
1. Working with Agentic AI: Business Analyst
How Human Skills and Agentic Skills Dynamically Combine Across business analysis Responsibilities
1.1. business analysis as Professional Work
A business analyst works at the intersection of business needs, organizational processes, information, technology, stakeholders, and solution development. The role involves understanding what an organization or stakeholder is trying to accomplish, identifying business problems and opportunities, eliciting and analyzing requirements, examining existing processes and systems, identifying gaps between current and desired conditions, evaluating possible solutions, documenting requirements and process models, analyzing data and information flows, supporting solution design and implementation, facilitating communication among business and technical participants, and evaluating whether implemented changes satisfy intended objectives. business analysts may also contribute to business cases, risk and impact assessments, testing, change management, process improvement, technology transformation, data governance, and other initiatives in which business needs must be translated into actionable requirements and coordinated organizational changes. Their responsibilities therefore extend beyond documenting requirements: they continuously translate information among business objectives, stakeholder needs, operational processes, data, technology capabilities, organizational constraints, risks, costs, and proposed solutions while applying professional knowledge and judgment to determine what information matters, what problems should be addressed, what changes are feasible, and how organizational value can be achieved.
1.2. Working with Agentic AI
Agentic AI can participate in many parts of business analysis by combining the capabilities of increasingly advanced large language models and other foundation models with task instructions, contextual information, external knowledge sources, tools, software systems, and reusable or dynamically constructed agentic skills. Large models can provide broad parametric knowledge together with capabilities such as language comprehension, reasoning, comparison, generation, coding, evaluation, classification, and multimodal interpretation. When these capabilities are organized into goal-directed agentic workflows, an AI agent can perform more than an isolated response to a prompt: it can interpret a task, decompose it into subtasks, retrieve relevant information, select appropriate skills or tools, perform intermediate operations, evaluate partial results, respond to changing conditions, and continue working toward a defined objective.
Within business analysis, these capabilities can be applied to activities such as processing stakeholder information, extracting and organizing requirements, analyzing business processes, examining data and process flows, comparing current and future states, identifying gaps and dependencies, generating preliminary process models, evaluating solution alternatives, preparing business cases, analyzing risks and impacts, maintaining requirements traceability, supporting testing, preparing stakeholder communications, and consolidating information across organizational functions. Their practical value does not depend on transferring an entire professional responsibility to AI. Instead, agentic skills can participate selectively at the level of individual tasks and subtasks, allowing work to be distributed according to the capabilities, information, reliability, authority, and judgment required at a particular moment.
1.3. Human Skills and Agentic Skills
Human skills refer to the professional capabilities that a business analyst can apply when performing work, including business knowledge, analytical reasoning, experiential judgment, contextual interpretation, stakeholder communication, facilitation, negotiation, organizational understanding, situational awareness, problem formulation, responsibility for recommendations, and the ability to evaluate consequences within a specific business and organizational context. Human skills also include many information-processing capabilities that AI can perform, such as analysis, comparison, classification, explanation, and problem solving. Human and agentic skills should therefore not be treated as completely separate capability categories.
Agentic skills are task-oriented capabilities that an AI agent can apply during task execution. They may include information retrieval, semantic analysis, summarization, classification, requirement extraction, process analysis, comparison, reasoning, planning, generation, data analysis, coding, tool use, consistency checking, traceability analysis, evaluation, monitoring, and iterative refinement. These skills can draw upon the capabilities and parametric knowledge of underlying models while also using external documents, databases, applications, business information, system information, organizational knowledge, instructions, and other authorized resources. As models, tools, and agent architectures evolve, the scope, reliability, and complexity of available agentic skills can also change.
The important distinction is therefore not that a particular cognitive activity inherently belongs to either the human or AI. Both may be capable of performing overlapping activities. The practical question is which available human or agentic skill, or combination of skills, should perform a particular task or subtask under the current conditions.
1.4. Dynamic Allocation of Tasks and Subtasks
A professional responsibility such as requirements elicitation, process analysis, solution evaluation, or stakeholder management is usually composed of multiple tasks and subtasks rather than being a single indivisible activity. These subtasks may require different combinations of comprehension, information retrieval, business analysis, technical analysis, judgment, communication, generation, evaluation, decision-making, and execution. The participation of human skills and agentic skills can therefore vary substantially within the same responsibility.
The allocation of work should be understood as dynamic rather than fixed. A subtask may initially be performed by an agentic skill, transferred to the business analyst when contextual judgment is required, returned to AI for additional analysis after new information is obtained, and later validated by the business analyst before the result is used for a consequential decision. Other subtasks may be performed independently by the professional or independently by an AI agent when their capabilities and authority are sufficient. Allocation can change according to task complexity, consequences, available information, model capability, tool availability, output reliability, stakeholder expectations, organizational policies, intermediate results, and changing task conditions.
A useful way to understand this structure is:
Occupation → Responsibility → Task → Subtask → Human and/or Agentic Skills → Evaluation → Possible Reallocation
The purpose of integrating agentic skills is therefore not to eliminate traditional professional skills. It is to expand the set of capabilities available for performing work and to enable the professional to determine, continuously and contextually, how human and agentic capabilities can be combined most effectively.
1.5. Business Analysis Responsibilities
1.5.1. Business Needs and Problem Discovery
Business needs and problem discovery involves identifying what an organization, business unit, or stakeholder is trying to accomplish, what problems or opportunities exist, why existing conditions are insufficient, and what outcomes would constitute meaningful improvement. Human skills are particularly important when conducting exploratory conversations, interpreting organizational context, distinguishing symptoms from underlying problems, recognizing conflicting interests, understanding stakeholder motivations, and determining which issues require deeper investigation. Agentic skills can process meeting transcripts, reports, operational records, previous analyses, system documentation, policies, and other authorized sources to extract stated problems, identify recurring themes, connect related evidence, distinguish known information from unresolved questions, and surface possible information gaps. The business analyst may formulate an initial understanding, AI may analyze supporting evidence, new findings may reveal additional questions, and subsequent stakeholder discussions may further change the problem formulation.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Conduct exploratory discussions, interpret organizational context, distinguish symptoms from underlying problems, understand stakeholder concerns, and validate actual business needs. | Process documents and discussions, extract problems and objectives, identify recurring themes and relationships, summarize evidence, and detect information gaps. | Human discovery generates context for agentic analysis; AI findings can reveal additional questions or relationships; subsequent stakeholder input can refine the problem definition. |
1.5.2. Requirements Elicitation
Requirements elicitation involves obtaining information about stakeholder needs, business rules, functional expectations, non-functional requirements, constraints, priorities, workflows, data needs, and desired outcomes through interviews, workshops, observation, questionnaires, document analysis, and other methods. Human skills are central when establishing productive stakeholder interactions, asking context-sensitive questions, recognizing implicit or contradictory requirements, managing disagreement, and determining when additional clarification is necessary. Agentic skills can analyze transcripts and documents, extract candidate requirements, classify them, identify duplicates or contradictions, detect missing information, generate possible follow-up questions, and maintain structured records of evolving requirements. Elicitation can therefore become iterative, with human interaction producing information for AI-supported analysis and that analysis informing subsequent human inquiry.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Facilitate interviews and workshops, ask context-sensitive questions, interpret implicit needs, resolve ambiguity, and validate stakeholder intentions. | Analyze transcripts and documents, extract and classify requirements, identify contradictions or gaps, and generate possible follow-up questions. | Human elicitation produces evidence for AI analysis; AI-detected gaps can guide subsequent discussions; new stakeholder information can then update the requirements set. |
1.5.3. Requirements Analysis and Documentation
Requirements analysis and documentation transforms collected information into structured, understandable, testable, prioritized, and usable representations of what a solution or organizational change should accomplish. Human skills contribute interpretation, business judgment, contextual understanding, prioritization, and the ability to determine whether documented requirements accurately represent stakeholder intent. Agentic skills can organize requirements, normalize terminology, classify requirement types, identify duplicates, detect inconsistencies, generate preliminary specifications or user stories, connect related requirements, and check documents for completeness and consistency. The business analyst can review and correct generated representations, introduce additional context, and delegate revised documentation or consistency analysis back to the agent.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Interpret stakeholder intent, determine priorities, resolve ambiguity, validate requirements, and ensure documentation reflects actual business needs. | Structure requirements, normalize terminology, classify items, identify duplication or inconsistency, generate preliminary documentation, and check completeness. | AI can organize and draft requirements at scale; human review corrects meaning and priorities; revised information can trigger further agentic restructuring and validation. |
1.5.4. Stakeholder Analysis and Management
Stakeholder analysis and management involves identifying people and groups affected by an initiative, understanding their interests, influence, responsibilities, concerns, information needs, and relationships, and determining how they should participate throughout the work. Human skills are particularly important for understanding organizational dynamics, building trust, recognizing sensitive relationships, negotiating conflicting expectations, and adapting communication to different stakeholders. Agentic skills can consolidate stakeholder information, analyze documented interests and concerns, summarize interaction histories, identify unresolved topics, organize engagement information, and prepare stakeholder-specific meeting materials. Agentic assistance can improve preparation and continuity while consequential relationship management remains strongly dependent on direct human participation.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Understand stakeholder motivations, manage relationships, negotiate conflicting expectations, facilitate communication, and exercise situational judgment. | Consolidate stakeholder information, summarize interactions, identify unresolved concerns, organize engagement records, and prepare stakeholder-specific materials. | AI-prepared context can strengthen human stakeholder interactions; information obtained through those interactions can update subsequent agentic analysis and preparation. |
1.5.5. Business Process Analysis and Mapping
Business process analysis and mapping examines how work is currently performed across people, activities, systems, information, decisions, and organizational boundaries. Human skills are important for understanding why activities are performed in particular ways, distinguishing formal procedures from actual practice, identifying organizational constraints, and interpreting the practical significance of inefficiencies or exceptions. Agentic skills can process process documentation, interviews, logs, system information, and other evidence to identify activities, actors, inputs, outputs, decision points, dependencies, handoffs, bottlenecks, and possible inconsistencies and to generate preliminary structured process representations. Human validation can correct incomplete or misleading process models, while revised observations can be returned to AI for further analysis.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Interpret how work actually occurs, understand exceptions and organizational constraints, identify meaningful inefficiencies, and validate process representations. | Extract process elements, identify actors and dependencies, analyze flows and bottlenecks, organize process information, and generate preliminary process representations. | AI can construct preliminary models from available evidence; human validation can correct contextual errors; corrected models can then support deeper agentic process analysis. |
1.5.6. Data Analysis and Business Insights
Data analysis allows business analysts to examine operational, customer, financial, system, process, or other organizational information to understand existing conditions and support business decisions. Human skills are important for determining what questions should be investigated, whether available data adequately represents the business situation, how results should be interpreted, and whether observed patterns are practically significant. Agentic skills can assist with data retrieval, cleaning, transformation, querying, coding, statistical analysis, pattern identification, visualization preparation, and generation of preliminary interpretations. Human evaluation can then determine whether those findings are meaningful within the organizational context and whether additional analysis is required.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Define meaningful analytical questions, interpret results in business context, evaluate significance, and determine implications for decisions. | Retrieve and transform data, perform analytical operations, identify patterns, generate summaries, and prepare preliminary visualizations or interpretations. | Human questions guide agentic analysis; AI findings can reveal unexpected patterns; professional interpretation can then generate additional analytical questions. |
1.5.7. Current-State and Future-State Analysis
Current-state and future-state analysis establishes how an organization, process, system, information flow, or operating model functions now and how it is expected to function after a proposed change. Human skills contribute organizational understanding, stakeholder interpretation, practical judgment, and the ability to determine whether a proposed future state is realistic and aligned with business objectives. Agentic skills can consolidate information from multiple sources, identify current-state components and relationships, compare existing and proposed conditions, generate preliminary future-state representations, and identify assumptions requiring validation. This type of analysis is especially relevant to transformation initiatives involving new platforms, security controls, infrastructure, data management, or operating models.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Interpret current organizational conditions, define meaningful desired outcomes, evaluate feasibility, and validate whether the proposed future state reflects business needs. | Consolidate current-state evidence, model relationships, compare existing and proposed conditions, generate preliminary future-state representations, and identify assumptions. | AI can construct and compare preliminary state models; human validation can modify assumptions or desired outcomes; AI can then regenerate and reassess the future-state model. |
1.5.8. Gap Analysis and Improvement Identification
Gap analysis identifies differences between current conditions and desired outcomes and determines what capabilities, processes, technologies, information, controls, or organizational changes may be required to close those differences. Human skills are important for evaluating which gaps matter, determining their business significance, understanding organizational constraints, and deciding which improvements deserve priority. Agentic skills can systematically compare current- and future-state information, classify gaps, identify dependencies, group related deficiencies, generate candidate improvement areas, and update the analysis as conditions change. Human judgment can then refine priorities and redirect agentic analysis toward the most consequential areas.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Determine the significance and priority of gaps, interpret organizational constraints, and decide which improvements are practically valuable. | Compare current and desired conditions, classify gaps, identify dependencies, group related issues, and generate candidate improvement areas. | AI can identify gaps systematically; human evaluation determines their significance; revised priorities can guide further agentic investigation and comparison. |
1.5.9. Solution Analysis and Evaluation
Solution analysis and evaluation involves examining alternative ways of addressing identified business needs and determining how effectively different options satisfy requirements, constraints, risks, costs, and expected outcomes. Human skills contribute business understanding, professional judgment, organizational awareness, and the ability to evaluate trade-offs that cannot be reduced to predefined criteria. Agentic skills can retrieve relevant information, map requirements to solution capabilities, generate alternatives, compare options against specified criteria, identify assumptions and dependencies, and produce structured preliminary evaluations. For initiatives involving secure data storage, for example, alternatives might include VDI, enhanced access controls, or other secure data-management approaches rather than assuming a single technical solution in advance.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Determine appropriate evaluation criteria, interpret trade-offs, assess organizational fit, and validate whether alternatives adequately address business needs. | Retrieve solution information, map capabilities to requirements, generate alternatives, compare options, and identify assumptions, gaps, risks, or dependencies. | AI can generate and compare candidate solutions; human judgment can change criteria or priorities; revised criteria can trigger renewed agentic evaluation. |
1.5.10. Business Case and Recommendation Development
Business case and recommendation development translates analysis into a reasoned justification for a proposed action, investment, solution, or organizational change. It may incorporate expected benefits, costs, risks, implementation considerations, adoption requirements, alternatives, dependencies, and other decision-relevant factors. Human skills are essential for determining which considerations matter, evaluating uncertain or qualitative consequences, understanding organizational priorities, and taking responsibility for recommendations. Agentic skills can consolidate supporting evidence, structure alternatives, perform preliminary cost-benefit comparisons, identify assumptions, generate scenario analyses, and prepare draft business-case materials. The specialized business analyst role used as an occupational reference explicitly includes developing recommendations and a supporting business case incorporating risk, cost, and adoption considerations.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Evaluate strategic relevance, weigh qualitative and quantitative considerations, understand organizational priorities, and validate recommendations. | Consolidate evidence, structure alternatives, perform preliminary comparisons, identify assumptions, generate scenarios, and draft business-case materials. | AI can assemble and analyze supporting evidence; human judgment determines the defensibility of the recommendation; additional questions can trigger further agentic analysis. |
1.5.11. Risk and Impact Analysis
Risk and impact analysis examines how proposed changes may affect business operations, systems, data, security, stakeholders, costs, compliance, adoption, and other organizational conditions. Human skills are important for interpreting consequences, recognizing risks that are difficult to represent formally, understanding organizational tolerance for uncertainty, and determining appropriate mitigation priorities. Agentic skills can process large bodies of evidence, classify risks, identify dependencies, compare scenarios, map affected processes or systems, generate preliminary impact assessments, and maintain risk information as conditions change. In technology-oriented business analysis, this may include examining data sensitivity, storage practices, access controls, system dependencies, data and process flows, and migration risks.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Interpret consequences, evaluate organizational risk tolerance, recognize contextual risks, prioritize mitigation, and validate consequential assessments. | Identify and classify risks, analyze dependencies, compare scenarios, map affected components, and generate preliminary impact assessments. | AI can systematically surface potential risks and impacts; human evaluation determines their significance; newly identified concerns can trigger deeper agentic analysis. |
1.5.12. Requirements Validation and Traceability
Requirements validation and traceability ensure that requirements remain connected to their sources, business objectives, stakeholder needs, solution components, implementation decisions, and verification activities throughout an initiative. Human skills are important for confirming that requirements remain meaningful, resolving conflicting interpretations, approving changes, and determining whether requirements still support intended business outcomes. Agentic skills can maintain relationships among large requirement sets, detect missing links, identify inconsistencies, compare changed versions, trace requirements to supporting evidence or solution components, and flag potentially affected items when changes occur.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Validate requirement meaning, resolve conflicts, approve changes, determine business relevance, and confirm alignment with intended outcomes. | Maintain traceability relationships, detect missing links, compare versions, identify inconsistencies, and flag requirements affected by changes. | AI can continuously maintain and inspect traceability; human decisions about requirement changes can automatically trigger renewed agentic consistency and impact analysis. |
1.5.13. Solution Testing and User Acceptance Testing Support
business analysts frequently support solution testing and user acceptance testing by connecting requirements and business processes with scenarios that demonstrate whether a proposed solution performs as expected. Human skills are important for defining meaningful business scenarios, interpreting user feedback, recognizing whether technically correct behavior actually satisfies operational needs, and determining whether unresolved issues are acceptable. Agentic skills can translate requirements into preliminary test scenarios, generate test cases and expected outcomes, organize testing evidence, classify defects or feedback, compare results against requirements, and identify areas requiring additional validation.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Define meaningful business scenarios, interpret user feedback, evaluate practical acceptability, and determine whether requirements have been satisfied. | Generate preliminary test cases, map tests to requirements, organize results, classify issues, compare outcomes with expectations, and identify missing coverage. | AI can prepare and analyze testing materials; human users and analysts evaluate practical results; observed issues can trigger additional agentic analysis and revised tests. |
1.5.14. Cross-Functional Collaboration
business analysts collaborate with business stakeholders, product teams, project managers, engineers, architects, data professionals, security specialists, operations teams, compliance functions, and other participants to ensure that needs, requirements, constraints, risks, decisions, and responsibilities are consistently understood. Human skills are central to facilitating discussions, resolving disagreements, negotiating priorities, communicating contextual information, and participating in decisions involving organizational authority or accountability. Agentic skills can summarize meetings, consolidate authorized information, identify unresolved questions, organize action items, prepare briefings, connect related information across documents or systems, and maintain accessible project or initiative context. Cross-functional collaboration is particularly important where recommendations must be validated across business, security, governance, and technology functions.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Facilitate cross-functional discussions, resolve disagreements, negotiate priorities, communicate context, and participate in organizational decisions. | Summarize discussions, consolidate information, identify unresolved issues, organize actions, and maintain structured initiative context. | AI-maintained context can support human collaboration; decisions and new information from cross-functional discussions can subsequently update the context available to AI. |
1.5.15. Change and Implementation Support
Change and implementation support helps translate approved recommendations and requirements into operational changes. business analysts may assist with implementation planning, migration roadmaps, process transitions, stakeholder readiness, documentation, adoption considerations, and clarification of requirements as implementation proceeds. Human skills are important for understanding organizational readiness, communicating changes, interpreting stakeholder concerns, resolving unforeseen issues, and adjusting plans when implementation conditions differ from expectations. Agentic skills can organize implementation dependencies, maintain action and requirement information, generate preliminary migration or transition plans, identify inconsistencies, summarize implementation feedback, and analyze emerging issues. The referenced business analyst role, for example, includes supporting a migration roadmap and related change-management considerations for secure data-storage and VDI initiatives.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Interpret organizational readiness, communicate changes, address stakeholder concerns, resolve contextual issues, and adapt implementation decisions. | Organize dependencies, maintain implementation information, generate preliminary transition plans, summarize feedback, and analyze emerging issues. | AI can support planning and continuous information management; human responses to implementation conditions can change priorities or plans; updated conditions can trigger renewed agentic analysis. |
1.5.16. Reporting and Stakeholder Communication
Reporting and stakeholder communication translates analytical findings, requirements, risks, alternatives, decisions, and recommendations into forms appropriate for different audiences. Human skills are important for understanding what stakeholders need to know, determining appropriate levels of detail, communicating uncertainty, presenting recommendations persuasively but responsibly, and responding to situational questions. Agentic skills can consolidate analytical results, generate preliminary reports and presentations, adapt explanations to different audiences, identify supporting evidence, summarize complex material, and check communication artifacts for consistency. business analysts can then validate the content and determine how findings should be presented within the organizational context.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Determine communication objectives, adapt messages to stakeholders, present findings, explain implications, and respond to questions or concerns. | Consolidate findings, generate preliminary reports, summarize complex information, adapt content for different audiences, and check consistency. | AI can prepare substantial communication materials; human judgment determines what should be communicated and how; stakeholder responses can generate new information for subsequent analysis. |
1.5.17. Continuous Process and Solution Improvement
business analysis often continues after an initial solution or process change has been introduced. Continuous improvement involves examining operational outcomes, stakeholder feedback, performance information, emerging problems, changing requirements, and new opportunities to determine whether further adjustments are needed. Human skills contribute interpretation of organizational significance, experiential judgment, prioritization, and understanding of changing business conditions. Agentic skills can continuously process performance information, feedback, issue records, process data, and other authorized sources to identify patterns, compare outcomes with expected objectives, detect recurring problems, and surface potential improvement opportunities.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Interpret changing business conditions, evaluate improvement opportunities, determine priorities, and decide which changes are organizationally appropriate. | Process performance information and feedback, identify recurring patterns, compare outcomes with objectives, and surface possible improvement opportunities. | AI can continuously identify patterns and deviations; human evaluation determines their practical significance; selected improvement areas can become new inputs for agentic investigation. |
1.5.18. Domain, Business, and Technology Knowledge
business analysts need to maintain knowledge of the organizations and domains in which they work, including business processes, operating models, technologies, data practices, regulations, industry developments, organizational policies, and emerging approaches that may affect business needs or solution possibilities. Human skills contribute professional understanding, experiential knowledge, interpretation of relevance, and the ability to connect new developments with specific organizational circumstances. Agentic skills can retrieve and summarize documentation, organize policy or technology updates, compare new information with existing knowledge, explain unfamiliar concepts, filter information according to current priorities, and surface developments relevant to particular initiatives. business analysts working in technology-intensive environments may also need to learn specialized domains rapidly, such as data governance, data classification, security and compliance frameworks, endpoint technologies, access controls, VDI, or AI-enabled workplace systems.
| Human Skills | Agentic Skills | Dynamic Human–Agentic Interaction |
|---|---|---|
| Develop professional understanding, determine relevance, connect domain knowledge to organizational situations, and apply experience when evaluating needs and solutions. | Retrieve, filter, summarize, compare, and organize business, technology, policy, regulatory, and domain information. | AI can continuously surface relevant knowledge; professional evaluation determines its significance; emerging business needs can guide subsequent agentic exploration. |
1.6. Human–Agentic Involvement Across business analysis
The relative involvement of human and agentic skills differs not only between responsibilities but also among the tasks and subtasks contained within the same responsibility. The following matrix therefore represents common tendencies rather than fixed assignments.
| Responsibility | Typical Human Involvement | Typical Agentic Involvement | Common Interaction Pattern |
|---|---|---|---|
| Business Needs and Problem Discovery | High | High | Iterative human–agentic discovery |
| Requirements Elicitation | Very High | High | Human-led elicitation with agentic analysis |
| Requirements Analysis and Documentation | High | High | Agentic structuring with human validation |
| Stakeholder Analysis and Management | Very High | Medium–High | Human-led relationships with agentic preparation |
| Business Process Analysis and Mapping | High | High | Collaborative process modeling and validation |
| Data Analysis and Business Insights | High | High | Agentic analysis with contextual human interpretation |
| Current-State and Future-State Analysis | High | High | Iterative modeling and professional validation |
| Gap Analysis and Improvement Identification | High | High | Agentic comparison with human prioritization |
| Solution Analysis and Evaluation | High | High | Collaborative and iterative evaluation |
| Business Case and Recommendation Development | Very High | High | Agentic analysis with human-led recommendation |
| Risk and Impact Analysis | High | High | Agentic identification with professional interpretation |
| Requirements Validation and Traceability | High | High | Continuous agentic traceability with human validation |
| Solution Testing and User Acceptance Testing Support | High | High | Agentic test support with human acceptance judgment |
| Cross-Functional Collaboration | Very High | Medium–High | Human coordination supported by persistent AI context |
| Change and Implementation Support | High | Medium–High | Human-led change with agentic planning support |
| Reporting and Stakeholder Communication | Very High | High | Agentic preparation with human-led communication |
| Continuous Process and Solution Improvement | High | High | Continuous agentic analysis with human prioritization |
| Domain, Business, and Technology Knowledge | High | High | Continuous agentic information processing and human learning |
1.7. Why the Division of Work Changes
The boundary between human and agentic participation should not be treated as permanent. It can change from one organization, initiative, stakeholder interaction, task, subtask, or moment of execution to another.
| Allocation Factor | Effect on Human–Agentic Work |
|---|---|
| Task complexity | Complex work may require decomposition, repeated reasoning, specialist involvement, or greater human supervision. |
| Task consequence | Decisions involving significant operational, financial, legal, security, technical, or organizational consequences may require stronger human validation or authority. |
| Available information | Agentic performance depends on access to sufficiently complete, current, relevant, and authorized business and technical information. |
| Model capability | More capable models may perform subtasks that previously required greater human effort, while weaker models may require closer supervision. |
| Agentic skill availability | The existence of appropriate reusable or dynamically constructed skills affects what can be delegated effectively. |
| Tool access | Access to documentation systems, databases, analytics platforms, process tools, project systems, code execution, search, communication systems, or other tools can substantially change agentic capability. |
| Output reliability | Weak, inconsistent, unsupported, or uncertain outputs can cause work to be returned to the business analyst or another specialist. |
| Intermediate results | New findings can change the problem definition, reveal constraints, create additional requirements or subtasks, or alter the appropriate division of work. |
| Stakeholder context | Sensitive, ambiguous, strategic, political, or relationship-dependent situations may require greater direct human involvement. |
| Organizational policy | Governance, security, confidentiality, compliance, data-access, and internal approval rules may restrict particular forms of delegation. |
| Authority and accountability | Analytical capability does not automatically provide organizational authority to approve requirements, make commitments, accept risks, or authorize consequential changes. |
These factors can operate simultaneously. A task that is largely agentic under one set of conditions may become human-led under another, while improvements in models, agentic skills, information systems, and organizational processes may gradually change the practical boundary over time.
1.8. Developing business analysis Capability with Agentic AI
The increasing availability of agentic skills does not eliminate the need for traditional business analysis capabilities. Business understanding, requirements reasoning, stakeholder communication, contextual judgment, facilitation, process understanding, organizational knowledge, analytical thinking, and professional accountability remain important because they help the business analyst understand what should be accomplished, determine whether generated results are appropriate, and connect analytical work with stakeholder and organizational objectives.
At the same time, professional capability increasingly includes the ability to work effectively with agentic systems. A business analyst may need to determine which tasks can be delegated, provide the necessary business context and constraints, select appropriate agentic skills, evaluate intermediate and final results, distinguish evidence from unsupported assumptions, request refinement, combine outputs from multiple agentic activities, recognize when stakeholder or specialist intervention is necessary, and incorporate validated results into subsequent professional action. Competence therefore increasingly includes not only performing an analytical task directly but also orchestrating how that task should be performed across available human and agentic capabilities.
This creates a broader model of professional capability:
Professional Capability = Human Skills + Agentic Skills + Human–Agentic Coordination
The proportions of these components will differ across responsibilities and will continue to change as technology, organizations, business environments, and professional practices evolve.
1.9. The Evolving Boundary Between Human and Agentic Skills
The relationship between a business analyst and Agentic AI is best understood as an adaptive division of work rather than a fixed separation between human tasks and AI tasks. Some subtasks may be performed largely through agentic skills, especially where large amounts of information must be retrieved, filtered, classified, summarized, compared, structured, generated, checked, traced, or repeatedly updated. Other activities may require substantial human participation because they involve stakeholder relationships, contextual interpretation, organizational dynamics, negotiation, accountability, prioritization, sensitive communication, or consequential recommendations. Between these areas lies a much larger collection of tasks in which human and agentic skills can interact through delegation, information exchange, review, correction, evaluation, refinement, escalation, and repeated reassignment.
The resulting work arrangement is therefore better represented as an adaptive human–agentic workflow than as a simple sequence in which either the professional or AI performs an entire responsibility independently. At one stage of an initiative, an AI agent may perform much of the information-processing work while the business analyst supervises and validates the result. At another stage, the business analyst may lead stakeholder discussions while AI performs only supporting retrieval, documentation, process analysis, or requirements organization. A subtask can move from human execution to agentic execution, return to human review, and later be delegated again as requirements change, intermediate results emerge, stakeholder priorities shift, new information becomes available, or different skills become necessary.
The long-term value of Agentic AI in business analysis therefore lies not only in automating individual analytical activities. It lies in expanding the set of usable capabilities available to the professional and enabling those capabilities to be dynamically selected, combined, evaluated, and reallocated according to the evolving requirements of the work. business analysts who understand both the enduring value of professional human skills and the rapidly developing possibilities of agentic skills can use this changing division of work to improve analytical coverage, efficiency, consistency, traceability, adaptability, and professional effectiveness without reducing the occupation to either traditional human execution or fully automated AI performance.