Digital Transformation and Artificial Intelligence

Strategies for Realising Return on Artificial Intelligence Investment

An applied course in return on AI investment built around the decisions practitioners actually face.

  • 5training days
  • 4modules
  • 8sessions
  • 32topics
Strategies for Realising Return on Artificial Intelligence Investment
5days

About the programme

Course Overview

The constraint on return on AI investment is rarely the model or the platform — it is the data and the operating discipline behind it. The distance between a working prototype of AI investment and a system the business can depend on is where most budgets disappear. Sessions alternate between guided analysis of the wider digital and data-driven work agenda and supervised application. The outcome is a practitioner who can hold a position on return on AI investment and revise it on evidence. It is pitched for practitioners with responsibility for this area of digital and data-driven work, not for observers of it. Teams frequently over-invest in documenting AI investment and under-invest in testing it. It concentrates on the parts of return on AI investment that determine outcomes and treats the rest proportionately. Mature organisations treat AI investment as a standing capability rather than a project that finishes. The final session converts the week's work on this part of digital and data-driven work into commitments with owners and dates.

Expected Learning Outcomes

  • Design the integration points between return on AI investment and the systems already in production.

  • Plan the migration path for AI investment without an extended outage or a parallel-running trap.

  • Structure records of return on AI investment so that they answer the questions an auditor will actually ask.

  • Specify the data AI investment depends on, where it originates and who is accountable for its quality.

  • Select indicators that show whether return on AI investment is improving, and reject those that only look useful.

  • Establish monitoring that detects model or service degradation in AI investment before users report it.

  • Distinguish the parts of return on AI investment that must be standardised from those that require judgement.

Who Should Attend

  • Public sector digital leads applying return on AI investment under procurement and privacy rules.

  • Vendor and contract managers overseeing suppliers involved in AI investment.

  • Chief information officers accountable for the investment in return on AI investment.

  • Solution architects designing how AI investment fits the existing estate.

  • Project and programme managers whose delivery depends on return on AI investment.

  • Managers with direct responsibility for AI investment within the transformation programme.

Course Modules

Open any module to see its sessions and topics.

01

Return on AI investment: vendor selection and avoiding lock-in

2 sessions · 8 points

Session 1The governance return on AI investment needs and the governance it does not

  • Set the metrics that will show whether return on AI investment is drifting from its intended behaviour.
  • Classify the data in AI investment and apply access controls that match the classification.
  • Test the procedure for return on AI investment against a realistic scenario.
  • Anticipate the objections AI investment will raise and prepare the answers.

Session 2Who answers for AI investment, and to whom

  • Assess the regulatory obligations return on AI investment triggers in each jurisdiction.
  • Remove steps in AI investment that add effort without adding assurance.
  • Arrange the handover of return on AI investment so capability survives staff changes.
  • Establish what evidence demonstrates AI investment is under control.
02

AI investment: governance, ethics and explainability

2 sessions · 8 points

Session 1What to measure in AI investment and what to ignore

  • Map the handovers in return on AI investment between functions and secure them.
  • Plan how AI investment is versioned and how a bad release is rolled back.
  • Measure the current quality of the data feeding return on AI investment before assuming it is usable.
  • Prepare the response for the most likely failure in AI investment.

Session 2Where AI investment touches systems nobody wants to change

  • Record what was learned when return on AI investment did not go as planned.
  • Plan the sequence in which improvements to AI investment will be introduced.
  • Review whether return on AI investment is aligned with the objectives of the transformation programme.
  • Distinguish symptoms from causes when AI investment underperforms.
03

AI investment: people, skills and the change that follows

2 sessions · 8 points

Session 1Choosing a supplier for AI investment without being captured

  • Prepare the summary of return on AI investment that senior management will read.
  • Agree the smallest change to AI investment that would be visibly useful.
  • Identify the skills the team lacks to operate return on AI investment independently.
  • Set the review interval for AI investment and who attends.

Session 2Escalation and decision rights in return on AI investment

  • Record the rationale for each significant choice made about return on AI investment.
  • Identify every system AI investment must read from or write to.
  • Confirm the retention and deletion rules applied to data inside return on AI investment.
  • Define the exit route from the supplier supporting AI investment.
04

AI investment: measuring benefit and retiring what it replaces

2 sessions · 8 points

Session 1What breaks first when AI investment meets real volume

  • List the data sources return on AI investment consumes and confirm each has a named owner.
  • Agree who is on call for AI investment outside working hours.
  • Design the pilot for return on AI investment so that a negative result is still useful.
  • Define the service level AI investment must meet and what happens when it is missed.

Session 2Proving AI investment paid for itself

  • Decide which legacy process return on AI investment retires, and set the date.
  • Build the user briefing that explains what AI investment does and does not decide.
  • Build the internal briefing that explains return on AI investment to those affected.
  • Test AI investment against edge cases drawn from real historical records.

Choose the package that suits you

Silver Package

At least 3 people

USD1,250
  • Workshop or Program Participation
  • Airport Transfers
  • Customized Badge
  • Expert Mentorship (Private Sessions)
  • Supervision & Secretarial Services
  • Accredited Certificate of Participation
  • Complete Training Kit
  • Coffee Break
  • Closing Ceremony

Gold Package

At least 3 people

USD1,850
  • 5-night stay in a 5-star hotel
  • Workshop or Program Participation
  • Airport Transfers
  • Customized Badge
  • Expert Mentorship (Private Sessions)
  • Supervision & Secretarial Services
  • Accredited Certificate of Participation
  • Complete Training Kit
  • Coffee Break
  • Closing Ceremony

Complete your registration

We will contact you within one business day to confirm.

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