Digital Transformation and AI Adoption Strategy Training
A one-day programme for the people who have to decide what the organisation actually does about AI, rather than the people who will use the tools. Most Malaysian organisations are currently sitting somewhere between a scattering of unsanctioned personal ChatGPT accounts and a proof of concept that impressed everyone in the demo and never reached production. Both states are expensive, and neither one is a strategy.
The day works from business problem to roadmap. It covers how to tell a real AI use case from a merely interesting one, how to size value before committing budget, when to buy a product rather than configure a platform or build, what the total cost looks like once data work and change effort are counted honestly, and the governance guardrails that have to exist before deployment rather than after an incident. Participants leave with a prioritised use case shortlist and a twelve-month roadmap for their own organisation, not a generic maturity model.
HRD Corp SBL-Khas Claimable
Programme Agenda
9:00 AM - 9:15 AM
Welcome and Programme Overview
Introduction to the session, objectives, and housekeeping.
9:15 AM - 10:15 AM
Separating Digital Transformation From AI
What is genuinely new, and what is ordinary digitisation wearing a newer label. The four things AI is currently reliable at in a business context: language work, classification, extraction from unstructured documents, and prediction from clean historical data. The categories that consistently disappoint and why. Where Malaysian organisations are seeing measurable returns today, and where budget is quietly being burned.
10:15 AM - 10:30 AM
Break
10:30 AM - 11:30 AM
Finding and Qualifying Use Cases
Starting from process pain rather than from technology looking for a home. Qualifying a candidate against volume, repeatability, tolerance for error, availability of data, clarity of the current process, and whether anyone owns the outcome. The high-volume, low-stakes rule for a first deployment. Scoring use cases on value against feasibility and plotting the portfolio so the sequence is visible. Participants generate and score candidates from their own operations.
11:30 AM - 12:30 PM
Data Readiness and the Honest Cost
Why most AI projects turn out to be data projects. Assessing what the organisation actually holds, where it lives, who owns it, and whether it is fit for the purpose being proposed. Documents, systems of record, and the integration reality behind a clean-looking demo. Total cost of ownership: licences, integration, data preparation, human review, change and training, and ongoing evaluation. Why the pilot is almost always the cheapest part of the programme.
12:30 PM - 1:30 PM
Lunch
1:30 PM - 2:15 PM
Buy, Configure or Build
The decision that determines most of your eventual cost. Off-the-shelf and embedded AI inside tools you already pay for, platform configuration with retrieval over your own content, and genuine custom build. Vendor evaluation for AI products: what to ask about data handling and training use, model version changes, evaluation evidence, lock-in and exit. Reading the Malaysian vendor market, and telling a capable integrator from a reseller with a new deck.
2:15 PM - 3:15 PM
Governance, Risk and the Guardrails
The policies that need to exist before anything is deployed: acceptable use, data classification and what must never leave the organisation, human oversight for consequential decisions, disclosure to customers and staff, and evaluation both before and after go-live. PDPA obligations when personal data meets an AI system, the emerging Malaysian and regional regulatory direction, and how all of this connects to the risk and internal audit functions you already have. Shadow AI read as a governance signal rather than a discipline problem.
3:15 PM - 3:30 PM
Break
3:30 PM - 4:15 PM
Operating Model, People and Sequencing
Who owns AI in the organisation, and why a small central capability plus embedded business owners usually beats either a pure centre of excellence or a free-for-all. Building capability rather than buying it repeatedly. The roles that change rather than disappear, and how to say that honestly to staff. Sequencing the portfolio so early wins fund credibility for the harder work. Defining success measures that survive contact with finance, and killing a pilot cleanly when the evidence says so.
4:15 PM - 4:45 PM
Roadmap Workshop
Participants draft a twelve-month roadmap for their own organisation: prioritised use cases, data and governance prerequisites, ownership, decision gates and success measures.
4:45 PM - 5:00 PM
Wrap-Up and Q&A
Key takeaways, next steps, and close.
Key Outcomes
- Distinguish genuine AI opportunity from ordinary digitisation and vendor narrative
- Qualify and score use cases on value, feasibility, data readiness and tolerance for error
- Estimate total cost of ownership beyond the licence, including data preparation and change effort
- Make a defensible buy, configure or build decision and evaluate AI vendors properly
- Put governance guardrails, PDPA controls and human oversight in place before deployment
- Leave with a prioritised use case shortlist and a twelve-month adoption roadmap
Training Mode Physical / Online / Hybrid / e-learning
HRD Corp SBL-Khas Claimable
Level Intermediate to advanced, suitable for senior management, heads of department, IT and digital leads, transformation and strategy teams, and board members with a technology remit
Duration 1 Day (8 Hours) | 9:00 AM to 5:00 PM
Venue In-house at the client's premises, or delivered via the client's preferred platform (Microsoft Teams, Zoom, or equivalent)
Assessment A twelve-month AI adoption roadmap plus a 10 to 15 question knowledge assessment
Certificate Certificate of Completion issued to all participants upon full attendance