S&C GN - TS&T – Digital Foundations - AI & Data Strategy - Manager

What matters most

• Identify measurable AI opportunities

  Conduct use-case assessments, value mapping, industry benchmarking and diagnostics to identify where AI can create measurable client value.

• Assess readiness and design foundations

  Evaluate data maturity, AI readiness, infrastructure and gaps; guide enterprise AI and data architecture, platform strategy, cloud architecture and product-development lifecycle design.

• Build fundable strategies

  Develop prioritized roadmaps, value frameworks and quantitative and qualitative business cases for executive decisions.

• Establish governance and responsible AI

  Define data ownership, standards, policies and operating models while addressing data quality, regulatory compliance and ethical AI considerations.

• Lead client and engagement delivery

  Run discovery workshops and C-level discussions, lead teams developing recommendations and proofs of concept, and manage engagement budgets, forecasts and financial proposals.

Likely challenges to think through

• Value versus readiness

  Preparation hypothesis: A high-value use case may depend on data or infrastructure the client does not yet have. Prepare an example of how to test value and readiness together, then sequence near-term opportunities and foundational work.

• Architecture choices versus existing constraints

  Preparation hypothesis: A scalable target architecture may conflict with a client's current technology landscape. Prepare to explain how you would compare product and platform options, make trade-offs explicit and phase a roadmap without assuming a preferred vendor.

• Speed versus governance

  Preparation hypothesis: Pressure to advance AI initiatives may outpace data ownership, quality controls or Responsible AI safeguards. Prepare to show how governance and compliance considerations can be built into prioritization and delivery rather than deferred.

• Executive ambition versus a defensible investment case

  Preparation hypothesis: Executives may seek a broad AI strategy before benefits and costs are well evidenced. Prepare to explain assumptions, distinguish quantitative from qualitative value, and connect a phased roadmap to budgets and decision points.

Topics to prepare

• AI approaches and value realization

  Suggested revision exercise: For one industry named in the posting, compare where traditional, generative and agentic AI could create measurable value, and where each would be unsuitable. The role calls for understanding these approaches and linking AI opportunities to business outcomes.

• Data supply chain, maturity and governance

  Suggested revision exercise: Sketch how data moves from source to AI use, then identify ownership, quality, standards and policy gaps at each stage. The posting calls for data capability assessments, governance frameworks and operating-model design.

• Enterprise AI and data architecture

  Suggested revision exercise: Explain the trade-offs among cloud and non-cloud foundations, data lakes, cloud data warehouses and AI platforms for an enterprise scenario. The role covers architecture, platform strategy, cloud migration and the product development lifecycle.

• Responsible AI and data privacy

  Suggested revision exercise: Take a proposed AI use case and identify ethical, regulatory and privacy questions that would affect its design or rollout. The posting calls for Responsible AI recommendations and regulatory-compliance discussions.

• Strategy prioritization and industry context

  Suggested revision exercise: Define criteria for ranking AI opportunities, then explain how industry benchmarks would test the ranking. The role requires prioritization approaches, benchmarking and strong understanding of a specific industry.

Skills to revise

• Build an AI opportunity assessment

  Suggested practice: Create a one-page assessment of three hypothetical use cases, mapping each to a business metric, value hypothesis and key uncertainty. The posting explicitly calls for use-case identification, value mapping and AI value diagnostics.

• Run a current-state diagnostic

  Suggested practice: Draft stakeholder questions and a findings template covering data maturity, AI readiness, infrastructure and gaps. The role involves client interviews and current-state assessments before recommending AI foundations.

• Compare architecture options and sequence a roadmap

  Suggested practice: Compare two plausible platform options against a client's constraints, then sequence foundation work, a proof of concept and scale-up decisions. The posting calls for evaluating products and frameworks, designing foundations and developing AI roadmaps.

• Construct a fundable business case

  Suggested practice: Build a small quantitative and qualitative case with stated assumptions, costs, benefits, sensitivity checks and a budget forecast. The role requires board-level business cases, value frameworks, budgeting and financial proposals.

• Facilitate an executive discovery session

  Suggested practice: Prepare a workshop agenda, probing questions and a brief C-level readout that turns pain points into decisions and next steps. The posting calls for discovery workshops, design sessions and executive engagement.

• Structure team and proof-of-concept work

  Suggested practice: Break a hypothetical strategy engagement into workstreams, assign review points and define what a proof of concept would need to demonstrate. The posting calls for managing teams, structuring analytical work and developing proofs of concept.

• Map evidence to the manager requirements

  Prepare a one-page evidence map for strategy consulting, enterprise AI and data solution design, and team leadership. The posting specifies 5–7 years in strategy consulting and 3+ years in each of the latter two areas; use only examples you can substantiate.

• Build a client project walkthrough

  Prepare a concise, anonymized account of an AI or data strategy engagement: client problem, your role, stakeholder discovery, analysis, recommendations and measured outcomes. The role emphasizes measurable AI value and executive decisions.

• Practice an opportunity and readiness assessment

  For a hypothetical client in an industry you know, draft a use-case shortlist, value map, data-maturity questions, infrastructure review and gap analysis. Explain how you would prioritize opportunities; this is a practice exercise, not a stated interview format.

• Sketch an AI foundation design

  Create a one-page architecture exercise covering data flows, AI and data platforms, cloud choices, product development lifecycle and integration with the existing technology landscape. Be ready to explain trade-offs rather than assume one vendor fits every client.

• Prepare a fundable roadmap

  Draft a phased roadmap with dependencies, a value framework, quantitative and qualitative business-case assumptions, and a simple budget and forecast. The posting calls for board-level strategies, financial proposals and engagement budgeting.

• Work through governance and Responsible AI decisions

  Outline data ownership, quality standards, policies, operating-model roles, privacy considerations and ethical AI controls for your practice scenario. Distinguish controls you would recommend from regulatory obligations that would require client-specific review.

• Rehearse executive and team-leadership examples

  Prepare examples of facilitating a discovery workshop, presenting a recommendation to senior stakeholders, structuring a team's analysis and guiding a proof of concept. State decisions, your contribution and results without disclosing client-confidential information.

• Write targeted questions for the employer

  Ask how this manager role balances strategy, architecture and delivery; how teams measure AI value realization; and what ownership managers have for client workshops, team development and engagement finances.