Advanced AI and Transformation Delivery Programme

Managing AI and Digital Transformation Projects

Lead AI-enabled and digital-transformation initiatives from use-case definition and experimentation through governance, adoption, scaling and measurable value.

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Course overview

Deliver AI and digital transformation as organisational change—not technology installation.

Managing AI and Digital Transformation Projects develops the capability to define, govern and deliver initiatives in which technology, data, operating processes, roles, controls, behaviour and value must change together.

The course is distinct from AI-Enabled Project Management. It does not focus primarily on using AI to improve the project manager’s own tasks. Instead, it focuses on managing the initiative that introduces AI-enabled or digital capability into the organisation.

Participants work through a complete transformation case covering use-case definition, data readiness, experimentation, responsible governance, vendor and model risk, adoption, scaling, value evidence and human accountability.

Learning outcomes

What participants will be able to do.

By the end of the course, participants should be able to:

  • Distinguish AI-enabled transformation projects from conventional technology implementation and from the use of AI as a personal productivity tool.
  • Define an AI or digital use case in terms of purpose, users, workflow, value, constraints, risks and stakeholder consequences.
  • Assess data readiness, process readiness, platform dependencies and organisational conditions before committing to implementation.
  • Design a controlled experimentation approach with hypotheses, boundaries, evidence requirements and explicit decision gates.
  • Establish proportionate governance covering privacy, security, fairness, transparency, safety, model risk and human oversight.
  • Evaluate vendor, platform, model and integration choices without surrendering organisational accountability.
  • Integrate technical delivery with workflow redesign, role change, capability development, adoption and operating support.
  • Assess readiness to scale across data, workflow, ownership, governance, support, infrastructure and measurement.
  • Define baselines, balanced value measures and evidence sufficient to support expand, redesign, narrow, pause or stop decisions.
  • Develop a practical implementation and scale roadmap with owners, gates, controls, adoption actions and human-accountability boundaries.

Who should attend

Designed for leaders responsible for delivering AI-enabled and digital organisational change.

  • Project and programme managers leading digital or AI initiatives.
  • Digital-transformation and innovation leaders.
  • AI product owners and business use-case owners.
  • PMO and portfolio professionals governing transformation investment.
  • Technology, data, risk, compliance and assurance professionals.
  • Business leaders accountable for adoption, operating change and realised value.

Prerequisites

Advanced, practical and transformation focused.

  • Meaningful experience of projects, programmes, change or digital delivery is recommended.
  • No programming, data-science or machine-learning qualification is required.
  • Participants should understand basic project governance, stakeholder and risk concepts.
  • Corporate cohorts may use a current or realistic AI-enabled initiative as the principal application case.

Course structure

Five integrated modules from use-case definition and experimentation to adoption, scaling and value.

The programme follows the full AI-enabled transformation lifecycle and uses one evolving case to connect business purpose, technology, governance, change and evidence.

1

Module 1

Use-case definition, value and transformation framing

  • AI and digital transformation project characteristics.
  • Business purpose, users, workflow and stakeholder consequences.
  • Strategic fit, value drivers and success conditions.
  • Use-case boundaries, assumptions and dependencies.
  • AI initiative canvas and initial decision frame.
2

Module 2

Data readiness, experimentation and delivery architecture

  • Data availability, quality, access, privacy and ownership.
  • Process and operating-model readiness.
  • Hypotheses, prototypes, pilots and bounded experiments.
  • Technical, platform and integration architecture.
  • Experiment evidence and decision-gate design.
3

Module 3

Responsible governance, model risk and vendors

  • Impact, consequence and risk classification.
  • Human oversight, decision rights, escalation and override.
  • Bias, fairness, transparency, safety and security controls.
  • Vendor due diligence, model limitations and contractual dependencies.
  • Monitoring, incidents, audit trails and evidence packs.
4

Module 4

Workflow redesign, adoption and operating transition

  • Task assistance, workflow redesign and role change.
  • Human-AI allocation, review points and exception handling.
  • Manager, user and support-team mobilisation.
  • Capability development, trust and resistance.
  • Operating transition, support and adoption measures.
5

Module 5

Scaling, value evidence and portfolio decisions

  • Readiness across workflow, data, platform, ownership and governance.
  • Controlled scaling, waves and entry-exit criteria.
  • Baselines, benefits, costs, risks and confidence.
  • Expand, redesign, narrow, combine, pause and stop decisions.
  • Implementation roadmap, operating rhythm and organisational learning.

Learning experience

Frame, test, govern, redesign and scale.

Participants learn by managing one complete AI-enabled transformation case through definition, experiment, governance, adoption, scale and value decisions.

Frame

AI initiative canvas

Participants define the use case, users, workflow, strategic value, stakeholder consequences and decision boundaries.

Test

Experiment-design laboratory

Teams develop hypotheses, pilot boundaries, evidence requirements and decision gates for a controlled experiment.

Govern

Responsible-use review

Participants classify impact, assign owners and design oversight, monitoring, escalation and evidence requirements.

Redesign

Workflow and adoption workshop

Teams redesign human-AI work, review points, roles, capability, support and adoption measures.

Scale

Readiness and portfolio challenge

Participants assess whether the initiative should scale, be redesigned, remain experimental, narrow, pause or stop.

Mobilise

Transformation roadmap presentation

Each team presents a staged roadmap with owners, controls, value evidence, adoption actions and human-accountability boundaries.

Course completion

Demonstrate responsible AI-transformation delivery judgement.

  • Participate actively in use-case, experimentation, governance and scale simulations.
  • Complete the principal AI initiative, governance, adoption and value outputs.
  • Contribute to the final scale-readiness and portfolio-decision presentation.
  • Prepare a practical implementation roadmap for workplace application.

Participant outputs

Typical course outputs include:

  • AI initiative canvas.
  • Data and organisational readiness assessment.
  • Controlled experiment and decision-gate plan.
  • Responsible-governance and human-accountability map.
  • Workflow redesign and adoption plan.
  • Scale-readiness assessment.
  • Value-evidence and implementation roadmap.

Delivery options

Choose the format that fits your AI and transformation environment.

Instructor-led

Live Classroom

Face-to-face delivery with use-case design, governance workshops, transformation simulation and executive challenge.

Instructor-led

Live Virtual Classroom

Interactive online delivery using collaborative canvases, breakout design sessions and real-time scale decisions.

Flexible

Blended Learning

A structured combination of preparation, live workshops, applied transformation assignments and follow-up.

Organisation-specific

Corporate and In-Company

Tailored delivery built around the organisation’s AI portfolio, governance, technology environment and transformation priorities.

Frequently asked questions

Course information and participation.

How is this different from AI-Enabled Project Management?

AI-Enabled Project Management focuses on how project professionals use AI within project and PMO work. This programme focuses on managing the project that delivers AI-enabled or digital organisational change.

Do participants need a technical AI background?

No. The programme is designed for delivery and transformation leaders. Technical concepts are addressed at the level needed for governance and decisions.

Does the course cover responsible AI?

Yes. Governance, human oversight, privacy, security, fairness, transparency, model risk, incidents and accountability are integrated throughout.

Can the programme use a current AI initiative?

Yes. Corporate delivery can use a live or realistic initiative, provided confidential information is protected and the case is suitable for learning.

Does course completion provide a professional certification?

The programme develops advanced AI and digital-transformation delivery capability and may provide an INDENTRA completion record where applicable. It is not presented as a third-party professional certification unless separately stated.

Take the next step

Convert AI experimentation into governed transformation and measurable value.

Discuss public delivery, a transformation cohort or a tailored programme built around your organisation’s AI-enabled project portfolio.

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INDENTRA may adapt sequencing, exercises and examples to suit the delivery format and participant profile while preserving the stated learning outcomes.