Manager, AI Transformation – Advisory

What matters most

• Redesign service-delivery workflows

  The role analyzes current workflows across Kroll practices, identifies where AI can automate or augment work, and translates pain points into target-state workflows and solution designs.

• Build tools practitioners can use

  The Manager builds mock-ups, pilots, and workflow automations using tools such as LLMs, orchestration platforms, or low-code platforms; refines them with practitioner feedback; and partners with technology teams or vendors when a more robust solution is appropriate.

• Make the impact measurable

  Initiatives must connect to productivity, quality, risk, or revenue. The role quantifies potential time savings, error reduction, and quality improvements, then turns the analysis into business cases and executive-ready recommendations.

• Carry changes through adoption

  The role helps define transformation milestones and standard operating procedures, supports training and documentation, tracks early adoption and performance, and recommends refinements.

• Preserve rigor and reusable practices

  The team supports AI governance, risk, and control frameworks while maintaining independence and defensibility. Its work also includes standardizing data capture, knowledge reuse, and review frameworks, and codifying lessons into playbooks.

Likely challenges to think through

• Balance speed with defensibility

  Preparation hypothesis: A quick AI prototype may demonstrate time savings but still need review steps and controls suitable for high-stakes advisory work. Prepare an example of how you would test output quality, identify risks, and retain human review without losing the workflow benefit.

• Adapt across distinct practices

  Preparation hypothesis: Reusable tools and standards may need to accommodate different workflows in valuation, investigations, cyber, restructuring, and compliance. Prepare a method for interviewing practitioners, separating common steps from practice-specific requirements, and deciding what to standardize.

• Bridge pilot success and sustained use

  Preparation hypothesis: A working pilot may not become a dependable process that professionals adopt. Prepare to explain how you would use practitioner feedback and early metrics to decide what to refine, document, or hand off for more robust development.

Topics to prepare

• Workflow decomposition and AI leverage points

  The role calls for analyzing end-to-end service-delivery workflows and identifying automation or augmentation opportunities. Revise by breaking a hypothetical document-review process into inputs, decisions, handoffs, review steps, and candidate AI leverage points; explain which steps should remain with practitioners.

• AI applications in professional-services workflows

  The posting names generative AI, NLP, document automation, and workflow tools. Revise by comparing where each could help with a repeatable, document-heavy task, including limitations and the review needed before outputs are used.

• Governance and defensibility

  Kroll emphasizes independence, rigor, defensibility, and AI risk-and-control frameworks. Revise by outlining the questions you would ask about source traceability, review responsibility, and error handling in an AI-assisted workflow. These questions are preparation suggestions, not a specified control checklist.

• Impact measurement and business cases

  Initiatives must connect to productivity, quality, risk, or revenue; the role also quantifies time saved, error reduction, and quality uplift. Revise by defining a baseline, a pilot comparison, and a calculation for a hypothetical time-saving opportunity, noting what evidence would be needed to substantiate the result.

• Target-state processes and adoption

  The role helps define target-state workflows, standard operating procedures, rollout plans, and adoption metrics. Revise by describing how a pilot becomes a documented process and which early usage and performance signals would prompt refinement.

Skills to revise

• Map a workflow from stakeholder input

  Practice a short stakeholder interview for a hypothetical valuation workflow, then draw its current-state steps, pain points, and proposed target state. This mirrors the posting’s interviews, workshops, and workflow-design responsibilities; the example is for practice, not a stated project.

• Build and refine a lightweight prototype

  Mock up an AI-assisted step using an LLM, AI API, orchestration tool, or low-code approach of your choice, then test sample outputs and revise the prompts or workflow based on practitioner feedback. The posting calls for hands-on prototypes and iteration but specifies no product or platform.

• Translate requirements into a solution design

  Turn a hypothetical pain-point statement into a one-page design showing inputs, processing steps, practitioner review, outputs, and configuration requirements. This directly practices the stated ability to convert business requirements into logical workflows and solution designs.

• Present an executive-ready recommendation

  Prepare a concise presentation for the hypothetical prototype covering the problem, proposed workflow, feasibility, measurable impact, risks, and next decision. The posting requires executive-ready deliverables and business-case recommendations.

• Draft rollout materials and a feedback loop

  Write a short standard operating procedure, user FAQ, and pilot-feedback form; specify how you would track early adoption and performance. These are hands-on versions of the posting’s documentation, training-support, and refinement duties.

• Map a service-delivery workflow

  Prepare an illustrative current-state map for one Kroll practice named in the posting, such as valuation or investigations. Mark manual steps, review points, and a possible AI automation or augmentation opportunity; label assumptions rather than presenting the map as Kroll’s actual process.

• Walk through a relevant project

  Select a real project you can discuss and prepare a concise account of the problem, stakeholders, structured analysis, your contribution, and the resulting deliverable or measurable outcome. Connect it to the role’s emphasis on turning workflow pain points into usable changes.

• Sketch an AI-enabled prototype

  Using the illustrative workflow, outline inputs, outputs, user actions, and where an LLM, document automation, API, or workflow tool might fit. Explain how you would test feasibility, gather practitioner feedback, and decide whether to involve engineering or a vendor.

• Build a measurable business case

  Prepare a one-page example with baseline assumptions, a method for estimating time saved or error reduction, and a proposed quality or risk measure. Turn it into a brief executive-ready recommendation that distinguishes estimates from verified results.

• Plan controls and rollout

  Draft a target-state workflow showing human review and how outputs could be checked for rigor and defensibility. Add proposed milestones, an SOP or playbook outline, training materials, a feedback loop, and early adoption and performance metrics.

• Organize evidence against the stated requirements

  Prepare specific examples of AI or automation exposure, prototyping, translating business needs into solution designs, and executive-ready communication. For each, note what you personally did and what evidence or artifact you can discuss.

• Write questions for the interviewers

  Ask how practice leaders prioritize workflows, what makes a pilot ready for broader use, how AI governance is applied, and which adoption and outcome metrics matter most. These questions reflect the posting without assuming a particular Kroll process.