Lead, Operations AI Enablement

What matters most

• Scale an agentic AI operating model

  The role designs an Operations-wide model combining RPA, AI reasoning and decision support. It works with subject-matter experts to select journeys where agents should execute, assist or escalate, then gathers requirements with the PACS technology team.

• Roll out useful, reusable co-pilots

  The role leads co-pilots for Operations workflows and analytics, including insights and forecasting for workload planning and potential issues. It also develops AI use cases that can be reused across platforms.

• Guide value-led investment

  Senior leaders will look to this role for advice on where AI adds material value and on investment and design decisions supporting Operations transformation, rather than a proliferation of tools.

• Set decision rules across journeys

  The Operations AI rulebook must define decision boundaries, human-in-the-loop requirements and consistent agent behaviour across journeys.

• Own AI assurance

  The role establishes an Operations QA framework for accuracy, stability, bias, drift monitoring and escalation, aligned with Group AI governance, ethics principles and regulatory expectations.

Likely challenges to think through

• Scaling without flattening workflow differences

  A likely challenge is making bots reusable across functions and platforms while preserving the requirements of each operational journey. Prepare to explain how you would identify common functionality with SMEs and distinguish it from journey-specific decisions; this is a preparation hypothesis, not a reported problem.

• Balancing autonomy with human control

  Agents may execute, assist or escalate, so greater automation can conflict with clear accountability and consistent decisions. Prepare a way to reason through decision boundaries and escalation triggers within the stated rulebook remit; this trade-off is inferred from the duties.

• Demonstrating value while maintaining quality

  Efficiency and forecasting gains need to be weighed against accuracy, stability, bias and drift as use cases scale. Prepare to describe how you would assess operational value alongside ongoing QA and escalation; the posting assigns both responsibilities but does not report a current failure.

Topics to prepare

• Agentic AI operating model

  The role designs and scales bots combining RPA with AI reasoning across Operations. Revise the distinction between automated execution, AI-assisted decisions and escalation; as a preparation exercise, sketch how those modes fit together in a hypothetical Operations journey.

• Decision boundaries and human oversight

  The Operations AI rulebook must cover decision boundaries, human-in-the-loop requirements and consistent behaviour across journeys. Draft a sample decision policy specifying what an agent may execute, when a person must approve and when the case must escalate.

• AI assurance and governance

  The posting calls for a QA framework covering accuracy, stability, bias, drift monitoring and escalation, aligned with Group AI governance, ethics principles and regulatory expectations. Revise what each measure detects, then outline sample checks and responses for a hypothetical bot.

• Scalable use-case selection

  The lead advises senior leaders on where AI adds material value and on investment and design decisions, while avoiding fragmented use cases. As a preparation exercise, compare two hypothetical Operations use cases on expected value, decision risk and reuse across platforms.

• Operational analytics and forecasting

  The posting calls for co-pilots that generate intelligence and forecasting models for workload planning and potential issues. Revise how forecast error and changing workload affect planning; explain how you would check whether a hypothetical forecast is useful.

Skills to revise

• Map journeys with subject-matter experts

  The role works with SMEs to identify priority journeys for agent execution, assistance or escalation. Practise drafting a journey map for a hypothetical Operations task, marking decision points, exceptions and the proposed agent role at each step.

• Gather bot requirements

  The posting requires gathering requirements for base bot functionality and partnering with the PACS technology team. Write a short requirements brief covering inputs, expected outputs, decision boundaries, exceptions and acceptance criteria for the hypothetical task.

• Plan a co-pilot rollout

  The lead designs and rolls out co-pilot bots for Operations workflows. Prepare a sample rollout plan showing the workflow change, SME feedback points, quality checks and criteria for expanding use; this is a preparation exercise, not a stated company process.

• Test and monitor AI behaviour

  The role owns Operations AI quality assurance and escalation. Build a small hypothetical test set with routine and exception cases; record how you would assess accuracy, stability and bias, detect drift and route failures for review.

• Present an investment recommendation

  The posting emphasizes communication, influence and advice to senior leaders. Practise a brief recommendation for one hypothetical AI use case, explaining operational value, trade-offs, governance controls and why it should or should not scale.

• Prepare a leadership project walkthrough

  Select an AI and operations project you led or supported. Document your role, stakeholder decisions, delivery challenges and measured outcomes; the posting seeks experience leading AI use cases and projects.

• Map a candidate Operations journey

  For a hypothetical journey, draft a one-page map showing where an agent would execute, assist or escalate. List questions for subject-matter experts, the requirements to gather and what the PACS technology team would need for development.

• Sketch a reusable bot and co-pilot design

  Prepare a simple design showing how RPA and AI reasoning would work together, how a co-pilot would support an Operations workflow, and what could be reused across platforms. Label it as a proposed design, not a PACS system.

• Work through an analytics example

  Outline how an Operations co-pilot might surface insights, forecast workload and flag potential issues. State the data and validation you would need rather than assuming those data are available.

• Draft a sample AI rulebook and assurance plan

  Write example decision boundaries, human-review triggers and escalation paths, then a QA plan covering accuracy, stability, bias and drift. Note that the posting calls for alignment with Group AI governance, ethics principles and regulatory expectations.

• Rehearse an executive recommendation

  Prepare a short comparison of two hypothetical use cases, explaining expected operational value, quality and customer-outcome measures, risks and investment trade-offs. Use it to practise advising senior leaders without claiming knowledge of PACS priorities.

• Bring targeted questions for the employer

  Ask how PACS prioritises Operations journeys, divides responsibilities among SMEs and technology teams, assesses reusable use cases, and applies Group governance to the Operations AI rulebook and QA framework.