Distinguish rule-based automation, machine learning and generative AI by the decisions each is fit to support.
Digital Transformation & Artificial Intelligence
AI Literacy for Managers Who Commission and Oversee AI Systems
Gives managers who commission and oversee AI systems the literacy, due diligence questions and oversight habits to approve, monitor and govern AI projects responsibly.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Many organisations now run AI systems that were approved by managers who never designed a model and never will. The real gap is not coding skill; it is AI literacy: knowing enough to ask a vendor a hard question, read a model card without help, and decide when a pilot should stop rather than quietly expand. This course gives managers who commission and oversee AI systems a working vocabulary for machine learning and generative AI, a method for building and approving a business case, and the oversight habits that keep a deployed model accountable. Teaching combines short technical briefings with practice on the participant's own AI proposals, vendor contracts and governance documents, distinguishing rule-based automation from predictive models and from generative tools throughout. Sessions cover due diligence questions for suppliers, risk classification under current AI regulation, and the monitoring needed once a system is live. Participants leave able to commission, question and govern AI systems without depending on the original project team for every judgement.
Expected Learning Outcomes
Evaluate a vendor's AI proposal using training data provenance, testing evidence and documented limitations.
Write a business case for an AI use case that names the decision it changes and the criteria for stopping it.
Classify a deployed AI system against recognised risk tiers and confirm the oversight each tier requires.
Set up a monitoring routine that catches model drift and falling accuracy before it reaches a business decision.
Draft contract and governance terms that assign liability, audit rights and a named accountable owner.
Lead staff through the role and workflow changes an AI system introduces, with a working feedback channel.
Who Should Attend
Department heads and functional managers who commission or approve AI-enabled projects.
Operations and business change managers overseeing an AI system after it goes live.
Programme and portfolio managers building a case for AI investment across several teams.
Risk, compliance and internal audit staff who must assess AI systems they did not build.
Procurement specialists negotiating contracts with AI and automation vendors.
Senior professionals moving into a role with AI oversight responsibility for the first time.
Course Modules
Select any module to see its sessions and points.
01Distinguishing Automation, Machine Learning and Generative AI Systems
2 sessions · 8 points
Session 1The Technology Landscape a Commissioning Manager Must Recognise
- Separate rule-based automation, predictive machine learning and generative AI by the type of decision each is fit to make.
- Read a model card to identify a system's intended use, training data sources and documented limitations before approval.
- Interpret basic accuracy, precision and recall figures well enough to challenge an optimistic vendor claim.
- Map where within a business process an AI system sits so accountability for its output is never ambiguous.
Session 2Working With Data Science and Engineering Teams Without Becoming One
- Ask a data science team what a model was trained on, what it was tested against and where it is known to fail.
- Recognise the signs of an overfitted or poorly validated model before it reaches a business decision.
- Translate a technical evaluation report into the plain-language risk summary a steering group can act on.
- Agree a shared glossary with technical teams so terms such as accuracy, confidence and error mean the same thing to everyone.
02Framing and Approving AI Use Cases
2 sessions · 8 points
Session 1Building a Business Case a Governance Committee Can Trust
- Write a business case for an AI use case that states the decision it changes, not just the technology it uses.
- Set success criteria and a stop condition before a pilot begins, so continuation is a decision, not a default.
- Estimate the ongoing cost of monitoring, retraining and human review, not only the cost of initial development.
- Score competing AI proposals against a shared feasibility, value and risk framework rather than internal advocacy.
Session 2Vendor Due Diligence and Contracting for AI Systems
- Question a vendor on training data provenance, update frequency and how model changes will be communicated.
- Negotiate contract clauses covering liability, audit rights and support when a supplied model underperforms.
- Check a vendor's claims of regulatory alignment against the AI system's actual risk classification.
- Define the exit plan and data portability terms before signing, not after the organisation depends on the tool.
03Overseeing Risk and Compliance in Deployed AI
2 sessions · 8 points
Session 1Applying Risk Classification to Systems Already in Use
- Classify each deployed AI system against the EU AI Act's risk tiers to identify which obligations apply.
- Confirm that high-risk systems carry the human oversight, logging and transparency measures the classification requires.
- Maintain a register of AI systems in use, including owner, purpose, data sources and last review date.
- Escalate an AI-related incident through the same channel as any other operational risk, not as a separate track.
Session 2Monitoring Performance and Managing Drift After Go-Live
- Set a monitoring cadence for deployed models covering accuracy, fairness indicators and data drift.
- Define the threshold at which falling performance triggers retraining, retirement or a return to manual process.
- Review a sample of AI-assisted decisions against outcomes so oversight rests on evidence, not the original approval.
- Hold the accountable owner, not the model, responsible for every decision an AI system's output feeds into.
04Governance Structures and Workforce Adoption
2 sessions · 8 points
Session 1Establishing Oversight Bodies and Documentation
- Set up an AI governance committee with clear authority to approve, pause or retire a system.
- Maintain model documentation that a new manager could use to understand a system without the original team.
- Assign a named accountable owner to every AI system, distinct from the team that built or bought it.
- Align internal AI policy with the ISO/IEC 42001:2023 AI management system standard without over-engineering it.
Session 2Leading People Through AI-Enabled Change
- Explain to affected staff how an AI system changes their role, decisions and the checks placed on their work.
- Identify tasks an AI system will absorb and redesign roles around the judgement work that remains.
- Build a feedback channel so staff using an AI system can report errors without the report disappearing.
- Plan the upskilling a team needs to supervise an AI system rather than simply comply with it.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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