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Sample Brief · representative example, not a real user
AI requirements copilots, BRD drafting, and BPMN generation — Lucid AI, Microsoft Visio AI, Atlassian Intelligence — now produce the routine analysis artefacts at usable quality (vendor 2024 releases; Anthropic Economic Index 2025). Pure-requirements roles are contracting; what holds is the change-impact call under stakes, the synthesis of stakeholders who want contradictory things, and the audit-trail documentation a regulator can follow to a named person.
The market context and capability dimensions that frame this role today — generated by the LLM intelligence layer against Runway's universal capability dimension registry.
BA hiring remains broadly stable across financial services, insurance, government, and large enterprise tech — sectors where regulatory change and legacy system transformation sustain demand. Specialist BAs with domain depth in areas such as payments, core banking, ERP migration (particularly SAP S/4HANA), or NHS/public sector digital command salary premiums 15–25% above generalists. Generalist BA roles in mid-market and startup environments face compression: product-oriented teams increasingly absorb discovery and requirements work into product manager or UX researcher remits, shrinking pure-BA headcount in those contexts. Tools like Jira, Confluence, Miro, and Visio are table stakes; BAs who can query data directly (SQL, Power BI) are meaningfully more hireable. AI is accelerating documentation and process-mapping grunt work — first drafts of BRDs, user stories, and process diagrams — but has not displaced the stakeholder trust and conflict-navigation work that defines senior BA value. The strongest near-term risk is role absorption in agile product teams; the opportunity is in transformation programmes where BA rigour is a compliance and governance requirement.
Top capability dimensions
Computed against the user's actual capability evidence — not a generic archetype score. The V13.2 pipeline maps each task in the user's task allocation into the dimension coordinate system, then compares against the role's required weights.
Top gaps to close
Strengths to deepen
No dimensions where the representative user's evidence meets or exceeds the role's required weight.
What this means: the gap analysis is computed against the user’s actual capability evidence — not a generic archetype score. The V13.2 pipeline maps each task in the user’s task allocation into the dimension coordinate system, then compares against the role’s required weights from the LLM intelligence layer. Your numbers will look different.
The three-section synthesis a real user receives, grounded in the role intelligence and gap analysis above.
changed
AI tools now produce first-draft BRDs and user stories at speed, compressing the time cost of written output but raising the bar on precision and review competence. BAs who cannot critically evaluate AI-generated artefacts against actual stakeholder intent ship ambiguous requirements downstream.
Evidence: Anthropic Economic Index 2025
Process diagram generation via AI (Miro AI, Visio Copilot integrations) reduces drafting time for as-is flows, shifting BA effort toward validating accuracy with process owners and identifying exceptions that AI models miss. The drafting gap narrows; the facilitation gap widens.
Evidence: Gartner Hype Cycle for Business Process Management 2024
Lightcast data shows SQL and Power BI appearing in 34% more BA job postings in 2023–2024 than in 2021–2022; BAs who query data directly rather than waiting on analysts are materially more competitive. AI-assisted analytics lower the floor for basic reporting but increase expectations for self-service insight.
Evidence: Lightcast 2024
Structured analysis dimensions are rated AI-accelerant, meaning AI can surface patterns across documentation sets faster than manual review. The BA role shifts toward framing the right comparison baseline and arbitrating conflicting source quality — tasks where structured thinking matters more than throughput.
Evidence: Brynjolfsson, Li & Raymond 2023
AI accelerates the synthesis of benchmarking data and cost modelling templates, but business case credibility in regulated sectors (financial services, NHS) still hinges on the BA's ability to defend assumptions to senior stakeholders and governance boards. The human-facing validation step is not compressible.
Evidence: Lightcast 2024
stable
Active listening, probing questions, and the ability to surface unstated needs in workshops remain outside AI's functional reach. Elicitation quality depends on trust built over repeated interactions with wary or politically cautious stakeholders — a dynamic that does not transfer to automated tooling.
Evidence: O*NET 4.A.2.b.3
Conflict navigation between business and technology stakeholders, and between competing programme priorities, is rated AI-neutral across all major labour market analyses. Senior BA value in transformation programmes is indexed to this capability more than any documentation output.
Evidence: Anthropic Economic Index 2025
UAT coordination is execution-heavy and human-dependent: scheduling business users, managing defect triage conversations, and maintaining sign-off accountability require ongoing relationship management that AI tooling does not replace. Demand remains steady in ERP and core banking programmes where UAT is a regulatory checkpoint.
Evidence: Lightcast 2024
watching
In agile product teams, backlog ownership is migrating toward product managers, compressing pure-BA involvement in this task in mid-market and startup contexts. BAs in transformation programmes retain the task but should monitor whether AI-assisted prioritisation tooling (e.g., Jira AI scoring) shifts ownership further toward PMs.
Evidence: Lightcast 2024
Problem framing is flagged as a growing trajectory dimension; solution evaluation is its downstream expression. As AI tools produce faster option comparisons, the BA's differentiator moves toward structuring evaluation criteria that reflect organisational constraints and risk appetite — a framing task, not a data-gathering one.
Evidence: Brynjolfsson, Li & Raymond 2023
Demand for BAs who can define measurable outcomes rather than just capture requirements is emerging in postings tied to digital transformation and operational improvement programmes. This task sits at the intersection of structured analysis and data literacy — both dimensions under active market pressure.
Evidence: Lightcast 2024
AI requirements-elicitation copilots, BRD generation, and BPMN drawing tools (Lucid AI, Microsoft Visio AI, Atlassian) at growing parity for routine BA artefacts.
High-stakes change-impact assessment, stakeholder synthesis under conflicting requirements, audit-trail documentation, and judgment on solution evaluation remain weakly automatable.
Pure-BA / pure-requirements roles are contracting; product-analyst, product-ops, and BA-with-data-science scopes are expanding.
Signals quoted verbatim from Runway’s career-moves rubric (V11.1). Each references peer-reviewed research, independent benchmarks, or large-N labour-market data — see citations on each move below.
⚠ Placeholder · Sample brief — confidence band shown is a representative midpoint, not an evidence-grounded computation. Take the assessment for a calibrated number.
Verbatim from the career-moves rubric. Each move ships an observable artefact — not self-report.
Move 1 · Defensibility
Why it works · codified process knowledge becomes the org's reference; durable across reorgs.
Time budget
4h / week
Prerequisites
cross-functional access, BPMN tooling
Verifiable artefact
≥5 mapped processes in version control with cycle-time baselines, dashboard with named owner, monthly review log.
Citation: O*NET 4.A.2.a.3 (Analyzing Data or Information)
Move 2 · Defensibility
Why it works · regulated business cases require a named accountable human and audit-trail rigour; the artefact compounds.
Time budget
5h / week
Prerequisites
regulated initiative on the roadmap, executive sponsor
Verifiable artefact
business-case document version-controlled with executive + internal-audit sign-off; dated trail of approvals; outcome reviewed at programme close.
Citation: O*NET 4.A.2.a.2 (Evaluating Information to Determine Compliance with Standards)
Move 3 · Skill build
Why it works · BAs who can run causal analysis are rare; the methodology depth raises authority with finance and product.
Time budget
5h / week
Prerequisites
SQL fluency, basic Python or R
Verifiable artefact
notebook or repo with reproducible synthetic-control or diff-in-diff study; methodology memo circulated; ≥1 business decision references the analysis.
Citation: Athey & Imbens 2017 — Econometric causal inference
Sample data, not a real user. The tracking layer in the product follows actual move-completion artefacts and recomputes defensibility from observed signals.
Hannah completed Move 1 — “Build a documented business-process measurement programme with cycle-time KPIs” — over 6 weeks. Tracking-layer artefact recorded: ≥5 mapped processes in version control with cycle-time baselines, dashboard with named owner, monthly review log.
Sample numbers. Real movement is computed from your task map + verified move artefacts when you complete an assessment.
Get your own Brief
This sample is the archetype for a business analyst. Your specific tooling, environment, and tenure produce a different Task Exposure Map — and a different ordered list of moves. About 10 minutes, free, no card.