2tasks in this job
43skills it uses
3tools named
The skills this job leans on most
- 1Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 2 tasks
- 2Planning delivery dependencies and the critical pathNeeded in 1 of 2 tasks
- 3Verifying benefits realisation and attributionNeeded in 1 of 2 tasks
ToolsMicrosoft Power BIMicrosoft ExcelMicrosoft PowerPoint
All 2 tasks, where people get stuck and all 43 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
All skills
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Verifying benefits realisation and attribution
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing decision-oriented research with customers, users or employees
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Presenting and defending evidence-based recommendations to decision-makers
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Variance analysis
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Coming soonDelivering Data and AI Projects with EvidenceManaging Data and AI Products
4tasks in this job
77skills it uses
4tools named
The skills this job leans on most
- 1Judging whether and how to use AI for a taskNeeded in 2 of 4 tasks
- 2Evaluating fitness and full cost of AI-assisted workNeeded in 2 of 4 tasks
- 3Evaluating responsible AI use risksNeeded in 2 of 4 tasks
- 4Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 4 tasks
- 5Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 4 tasks
ToolsMicrosoft Power BIOneTrustMicrosoft PowerPointMicrosoft Excel
All 4 tasks, where people get stuck and all 77 skills
Tasks
- Evaluate an AI use case for suitability, value and risk before adoption
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Maintain a data and AI risk register and record risk treatment and acceptance
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
All skills
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Judging whether and how to use AI for a task
- Maintaining a risk register with owners, triggers and responses
- Prioritising work by evidenced value, cost, risk and confidence
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Bounding and securing AI systems' tool and data access
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and comparing decision scenarios
- Designing and running evaluations of AI systems
- Designing decision-oriented research with customers, users or employees
- Designing testable data controls
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Variance analysis
- Verifying benefits realisation and attribution
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Coming soonDelivering Data and AI Projects with EvidenceManaging Data and AI ProductsApplied AI at Work
4tasks in this job
77skills it uses
4tools named
The skills this job leans on most
- 1Judging whether and how to use AI for a taskNeeded in 2 of 4 tasks
- 2Evaluating fitness and full cost of AI-assisted workNeeded in 2 of 4 tasks
- 3Evaluating responsible AI use risksNeeded in 2 of 4 tasks
- 4Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 4 tasks
- 5Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 4 tasks
ToolsMicrosoft Power BIOneTrustMicrosoft PowerPointMicrosoft Excel
All 4 tasks, where people get stuck and all 77 skills
Tasks
- Evaluate an AI use case for suitability, value and risk before adoption
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Maintain a data and AI risk register and record risk treatment and acceptance
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
All skills
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Judging whether and how to use AI for a task
- Maintaining a risk register with owners, triggers and responses
- Prioritising work by evidenced value, cost, risk and confidence
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Bounding and securing AI systems' tool and data access
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and comparing decision scenarios
- Designing and running evaluations of AI systems
- Designing decision-oriented research with customers, users or employees
- Designing testable data controls
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Variance analysis
- Verifying benefits realisation and attribution
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Coming soonDelivering Data and AI Projects with EvidenceManaging Data and AI ProductsApplied AI at Work
About the role
Accountable for product across the enterprise and for how product investment serves the business strategy. Sits on the executive team and answers to the chief executive or board for product results. Decides which product and AI bets the enterprise makes and the risk it accepts in making them.
Also called CPO (product)