AI Strategist, Senior Manager

What matters most

• Turn discovery into a phased AI roadmap

  The role interviews stakeholders, maps processes and pain points, assesses data and AI readiness, then ranks AI, GenAI and agentic use cases by value, feasibility, risk and dependencies. Prepare to explain a structured route from discovery findings to adoption recommendations.

• Make the value case measurable

  The posting calls for benefit hypotheses, cost estimates, ROI models, KPI baselines and value-tracking plans, developed with finance, delivery and industry specialists. Prepare to show how assumptions and measures would be made explicit.

• Shape implementable, responsible PoCs

  Translate requirements into solution concepts, PoC scopes, work plans and proposals with technical specialists. Recommendations must also address governance, security, privacy, human oversight and change management.

• Lead client-facing delivery

  Manage consulting and PoC workplans, junior team members, client meetings, actions and quality. On-time, executive-ready assessments, roadmaps, business cases and proposals are stated performance measures.

• Contribute beyond individual engagements

  The role supports account pursuits, develops reusable use-case content and demos, and shares lessons across the APJ AI Builder community. Influenced pipeline and bookings, funded next steps, reusable assets and team upskilling are stated measures.

Likely challenges to think through

• AI ambition versus operational readiness

  Preparation hypothesis: an attractive GenAI or agentic use case may depend on data, security controls or process changes that are not yet ready. Prepare to explain how you would surface those dependencies, narrow an initial scope and sequence a roadmap without losing sight of business value.

• A credible ROI before benefits are proven

  Preparation hypothesis: discovery-stage benefit estimates may be uncertain even when stakeholders want a decision. Prepare an approach that distinguishes hypotheses from baselines, tests cost and adoption assumptions with specialists, and defines KPIs for checking value after a PoC.

• Delivery quality alongside pursuit and reuse

  Preparation hypothesis: managing live engagements while supporting pursuits and reusable assets could create competing demands. The posting measures both timely deliverables and contributions to pipeline and shared assets. Prepare to describe how you would prioritise work, delegate to junior team members and preserve the quality of client-facing outputs.

Topics to prepare

• AI solution patterns

  Posting: Practical understanding of GenAI, agentic AI, LLMs, RAG and context engineering is expected. Suggested exercise: For one hypothetical business problem, explain when a retrieval-based assistant, an agentic workflow or no AI is appropriate, including limitations and human oversight.

• Enterprise AI readiness

  Posting: The role assesses processes, data and AI readiness and needs familiarity with enterprise data and cloud AI platforms. Suggested exercise: Draft a readiness checklist covering data availability, quality, access, integration and operational constraints for a hypothetical client.

• Use-case evaluation principles

  Posting: Use cases are evaluated by value, feasibility, risk and dependencies before phased roadmaps are recommended. Suggested exercise: Define what each criterion means and explain how a dependency could change the order of three hypothetical use cases.

• Business-case and measurement concepts

  Posting: Benefit hypotheses, cost estimates, ROI models, KPI baselines and value tracking are part of the role. Suggested exercise: Explain the difference between a forecast benefit and a measured outcome, and list the assumptions needed to test each.

• Responsible adoption

  Posting: Recommendations must incorporate governance, security, privacy, human oversight and change management. Suggested exercise: Review a hypothetical AI recommendation and identify one decision or control needed in each area before a PoC proceeds.

Skills to revise

• Stakeholder discovery and process mapping

  Posting: The role interviews stakeholders and documents processes and pain points. Suggested exercise: Draft questions for a senior stakeholder, then turn hypothetical answers into a current-state process map with pain points and open questions.

• Prioritisation and roadmap creation

  Posting: The role develops prioritisation models, phased roadmaps and adoption recommendations. Suggested exercise: Score three hypothetical use cases against value, feasibility, risk and dependencies, then produce a short sequence of proposed phases.

• ROI and KPI worksheet

  Posting: Business cases include costs, ROI, KPI baselines and value-tracking plans developed with specialists. Suggested exercise: Build a small illustrative calculation showing assumptions, estimated benefits and costs, baseline measures, and how results would be checked after implementation.

• PoC scoping and proposal writing

  Posting: The role translates requirements into solution concepts, PoC scopes, work plans, assumptions, risks and proposal content. Suggested exercise: Write a one-page hypothetical PoC brief with a business objective, scope boundaries, success measures, responsibilities and unresolved specialist inputs.

• Engagement leadership and executive briefing

  Posting: The role manages workplans, junior team members, meetings, actions, quality and executive-ready outputs. Suggested exercise: Create a two-week illustrative workplan and an executive update that states decisions needed, owners, risks and next steps.

• Reusable use-case content

  Posting: The role creates reusable use-case content and demos, supports pursuits and shares lessons across the APJ AI Builder community. Suggested exercise: Turn the hypothetical PoC brief into a reusable one-page use-case summary with applicability, expected value, prerequisites and lessons to capture.

• Prepare a discovery walkthrough

  Choose a relevant example, if available, showing stakeholder interviews, process mapping, pain points and data-readiness findings. Bring a one-page summary of how those findings became AI use cases; label any hypothetical example as such.

• Practise prioritising use cases

  Create a sample decision matrix comparing AI, generative AI and agentic AI options by value, feasibility, risk and dependencies. Turn the ranking into a phased roadmap with adoption assumptions.

• Build a sample business case

  Draft benefit hypotheses, cost assumptions, a simple ROI model, KPI baselines and a value-tracking plan. Show which inputs would need validation with finance, delivery or industry specialists.

• Scope a proof of concept

  Prepare a short solution concept and PoC plan covering requirements, success measures, work, assumptions and risks. Be ready to explain when LLMs, RAG or agentic approaches fit, and what architects, engineers and data specialists would need to confirm.

• Add responsible-adoption decisions

  Annotate the proposed roadmap or PoC with governance, security, privacy, human-oversight and change-management considerations. Distinguish known constraints from questions requiring client input.

• Rehearse an engagement-lead example

  If available, select an example of leading a workstream or small team with senior stakeholders. Prepare an executive-ready summary showing how you managed meetings, actions, quality, ambiguity and delivery; otherwise, use a clearly hypothetical scenario.

• Show how work could be reused

  Draft a sample one-page use-case asset or demo outline that another team could adapt. Explain how you would capture lessons and support an account pursuit, both responsibilities named in the posting.

• Bring questions about success and scope

  Ask how this role divides time among client delivery, pursuits and reusable assets; how progression from discovery to funded work is assessed; and how the senior AI Builders and APJ community collaborate.