ChatGPT Enterprise training for leadership & strategy teams in Singapore
ChatGPT Enterprise training for leadership teams in Singapore: one hands-on day, onsite, on your own files. Decision briefs. AI opportunity assessment.
ChatGPT Enterprise for leadership & strategy teams
Leadership teams are usually the ones deciding what a custom GPT is allowed to do. This programme builds one, then works through the governance questions that decision raises.
Where the work happens
- File analysis
- Custom GPTs
- Projects
- Canvas
The distinguishing exercise is building a custom GPT on your own files that a whole team can reuse, plus the verification standards that keep its answers trustworthy.
Who this programme is for
Executives, department heads and transformation leads. Teams building shared internal tools.
- Executives who need to brief, decide and set direction from long material
- Department heads choosing which AI use cases their teams should pursue
- Transformation and strategy leads building the case for AI investment
- Leadership teams agreeing acceptable use, ownership and approval points for AI
Not the right fit
- Teams looking for a vendor demo or an awareness keynote rather than hands-on practice
- Anyone expecting a ready-made AI strategy rather than the skills to judge one
- Technical architecture or platform-selection projects — this is applied assistant training
What the day covers
Module 1: Decision briefs
Decision briefs are synthesised from uploaded reports, with alternatives and evidence gaps drawn out.
Exercise. Synthesize approved reports into a decision brief with alternatives, evidence gaps and questions for the team.
What you leave with. An executive briefing template and evidence checklist.
Module 2: AI opportunity assessment
Candidate use cases are compared by effort, value, data readiness and review requirement.
Exercise. Map team workflows and compare candidate use cases by effort, value, data readiness and review requirements.
What you leave with. A prioritised opportunity shortlist with measurable pilot criteria.
Module 3: Governance and adoption
Acceptable use, ownership and approval points are written into a custom GPT's own instructions as a worked example of policy-as-configuration.
Exercise. Define ownership, acceptable use, human approval points and a practical adoption plan.
What you leave with. A governance checklist and 30-day team adoption plan.
A worked example: Scoring candidate use cases
One exercise from the day, as it runs on your own material in ChatGPT Enterprise.
- What goes in
- Short descriptions of eight processes your teams have proposed for AI.
- What is asked
- Score each one for value, effort, data readiness and review burden, and say which scores rest on assumptions.
- What comes out
- A ranked shortlist with the assumptions listed.
- What the reviewer checks
- The leadership team overrides the scores where they know better; the point of the exercise is the conversation, not the ranking.
Singapore rules the exercises respect
Singapore has published more practical AI governance guidance than most markets, and the leadership day uses it directly.
- IMDA's Model AI Governance Framework, and its 2024 edition for generative AI, give a working structure for accountability, testing and disclosure.
- AI Verify offers a testing approach the organisation can adopt as it moves from pilots to production.
- The PDPC's advisory guidelines on personal data in AI recommendation and decision systems set expectations on consent and explanation.
- For financial institutions, MAS's FEAT principles on fairness, ethics, accountability and transparency apply to how AI output is used.
What this day does not cover
Scope is agreed before the session so the day stays hands-on.
- API work, function calling or anything that requires writing code. This is a day for the people doing the business task.
- Admin configuration, workspace provisioning or SSO setup.
- Fine-tuning. The programme builds custom GPTs on your own files, which is a different thing and the one most teams actually need.
Before the session
- A licensed ChatGPT Enterprise seat for each participant
- Familiarity with your own strategy, operating priorities and current AI activity
- Anonymised or sample working files — a recent report or board paper, a list of candidate use cases
- No coding background required
Format and schedule
- Delivery
- On site in Singapore or remote, delivered live. Cohorts are kept small enough that every participant works on their own files rather than watching a screen share.
- Duration
- Typically one full day, or two half-days scheduled around leadership calendars. Module order is adjusted to your priorities and tool stack.
- How each module runs
- Each module follows the same pattern: a short walkthrough of the technique, a hands-on exercise on your own data, then a review of what the assistant got wrong and how to catch it.
- Afterwards
- Participants leave with the prompt patterns, templates and review checklists produced during the session, so the work continues after the training day.
How your data is handled
- Exercises run inside your own tenant and your own licences, so your data does not leave your environment for the training.
- Files shared with the trainer in advance are anonymised extracts; board material, financials and strategy documents stay inside your own tenant and licences.
- Every module includes verification: checking output against the source, confirming what the assistant left out, and marking what still needs a human read.
- Decisions stay with the leadership team. The programme teaches AI-assisted analysis and preparation, and how to set the approval points your teams will work within.
Who runs the session
Abhijit Ghosh
Founder, Eagentix · Data & Generative AI Practitioner · Singapore
The programme is delivered by Abhijit Ghosh, who has spent 25 years in data and AI, including roles at Accenture, IBM, Deloitte, Cloudera, Tibco and Wipro. Sessions are run by the practitioner, not handed to a junior facilitator.
Questions about ChatGPT Enterprise for leadership & strategy
Who should own a custom GPT once it exists?
A named person, which is the point the third module makes concrete. The exercise writes acceptable use, ownership and approval points into the assistant's own instructions, so the policy lives where the work happens.
What is a custom GPT and why does the day centre on building one?
It is a reusable assistant configured once with your own files and instructions, then shared with the team. It matters because it turns a good prompt one person wrote into a repeatable standard everyone uses — including the review checkpoints written into its instructions.
Is our uploaded material used to train the model?
Enterprise plans are sold on the basis that business data is not used for training, and your administrators can confirm the terms that apply to your workspace. We cover what to upload and what to keep out regardless, because access scope inside your own organisation matters as much as the vendor terms.
Which Singapore AI governance frameworks does the session reference?
IMDA's Model AI Governance Framework, including the 2024 generative AI edition, AI Verify, and the PDPC's advisory guidelines on personal data in AI systems. Financial institutions also cover MAS's FEAT principles. These are used as working structures, not as legal advice.
What does a leadership team leave with?
A shortlist of use cases with owners, an acceptable-use position and the approval points that apply before anything reaches production.