Program facts
- Study fee
- Free
- subjects
- 5
- lessons
- 15
- Estimated time
- 4.5 hours
- Access period
- 60 days
- Study language
- Arabic and English
About the program
The program starts with the question that precedes technology selection: what outcome should improve, for whom, and with what evidence? Five advanced modules move from problem definition and use-case portfolios through decision accountability, operating models, data and suppliers, work and skill redesign, and finally pilot evaluation. Fictional enterprise cases support comparison of realistic alternatives without requiring confidential information. You learn to explain an investment decision, its expected benefit and limitations, and the evidence needed before committing further resources.
Practical relevance
Apply the reasoning to a process you know: define a baseline, compare alternatives, document decision boundaries and propose a limited, reversible pilot. Learners without a suitable workplace case can use the included fictional cases. Exercises do not require buying a model or uploading organizational data.
Why this program
A small technical pilot can succeed without producing sustainable organizational value. Time saved may shift into reviewing unreliable outputs, while responsibility may be dispersed among teams with no authority to stop deployment. This program connects decisions often divided among management, technology and workforce teams. Scaling is not assumed to be the correct outcome: evidence may justify revising the scope, delaying investment or selecting a simpler solution.
Career relevance
The material relates to transformation, operations, portfolio, product and governance responsibilities. It develops judgment and cross-functional communication; it does not promise employment, promotion or financial return, or confer a regulated professional authorization.
What you will learn
- Evaluate an AI portfolio using eligibility gates, weight-sensitive scores and a net-benefit case.
- Design governance specifying decision accountability, risk records, oversight and stop/resume boundaries.
- Evaluate sourcing and operations that preserve data permissions and demonstrate vendor acceptance, change control and exit.
- Redesign work, learning and incentives while measuring competence, adoption and stakeholder impacts.
- Evaluate trial evidence, subgroup rates and net value to justify scaling, revision or stopping.
Learning outcomes (5)
- Evaluate an AI portfolio using eligibility gates, weight-sensitive scores and a net-benefit case.
- Design governance specifying decision accountability, risk records, oversight and stop/resume boundaries.
- Evaluate sourcing and operations that preserve data permissions and demonstrate vendor acceptance, change control and exit.
- Redesign work, learning and incentives while measuring competence, adoption and stakeholder impacts.
- Evaluate trial evidence, subgroup rates and net value to justify scaling, revision or stopping.
Who it is for
- Transformation, operations, product and governance leaders with relevant foundational experience
Prerequisites
- Experience managing and improving a process, team or project
- Ability to interpret performance, cost, risk and basic data concepts
Curriculum
- Subject 1 Open for free
AI Value Strategy and Use-case Portfolio
Select AI investments by moving from a measurable problem to a defensible portfolio.
- Subject 2
Governance, Accountability and Risk Boundaries
Determine who decides, which evidence is needed and when use must stop or escalate.
- Subject 3
Operating Model, Data and Vendor Decisions
Turn an investment decision into a service that can be tested, monitored, changed and exited.
- Subject 4
Work Redesign and Capability Adoption
Redesign tasks, build capability and measure adoption without obscuring impacts on people.
- Subject 5
Value Measurement and Responsible Scale
Interpret trial evidence and decide whether to scale, revise or stop within its limits.
Subjects unlock in sequence as you progress.
Strengths
- Enterprise trade-offs connecting value, governance and operations
- Fictional exercises with feedback explaining decision reasoning
- Integrated assessment and a distinct synthesis for each advanced module
Evidence basis
Concepts are grounded in scoped primary sources, including the NIST AI Risk Management Framework and OECD principles. Source guidance is distinguished from teaching assumptions and fictional numerical examples. Reading the public ISO description is not equivalent to reviewing the full standard or obtaining certification against it.
Reference organizations
- NIST
- OECD
Frameworks used
- NIST AI Risk Management Framework — 1.0
Methodology
Concepts are grounded in scoped primary sources, including the NIST AI Risk Management Framework and OECD principles. Source guidance is distinguished from teaching assumptions and fictional numerical examples. Reading the public ISO description is not equivalent to reviewing the full standard or obtaining certification against it.
Program questions
Do I need programming experience?
Exercises do not require coding, but assume basic familiarity with data and the AI system lifecycle. You need to interpret how technical decisions affect processes, responsibilities and risks. If these concepts are new, complete foundational preparation before tackling the advanced trade-offs.
How do the modules connect?
The first module selects the opportunity, the second defines boundaries and accountability, and the third designs its operation. The fourth addresses changes in tasks and skills; the fifth returns to evidence for the scaling decision. The final assessment connects these decisions rather than testing isolated tool names.
What will I produce in the exercises?
You develop elements of an enterprise decision: a measurable outcome, a portfolio ranked after eligibility checks, an escalation path, supplier conditions and an exit plan, and a pilot with multiple measures. Feedback explains the reasoning and the conditions under which another option could become more appropriate.
How does the final assessment differ from practice?
Practice offers open-ended application with guided solutions. The final uses automatically graded case questions with defined answers covering all five modules. It does not include an essay project assessment.
Does the certificate provide external accreditation?
The credential records completion of the platform’s internal Professional Mini-Master route. It is not a university degree, ISO certification or legal or professional license. Learning is free and credential issuance is optional under central policy. When issued, the certificate is accompanied by a module transcript showing the five modules and the results of their questions in the accepted final attempt. These are breakdowns of the integrated assessment, not separate module examinations or grades assigned before the learner takes the assessment.
Can I use workplace data?
Use fictional or appropriately de-identified information that you are authorized to use. The included data are sufficient to complete the program, and no exercise requires submitting secrets or personal information to an external service. Before applying the method at work, check data permissions, responsibilities and local constraints.