ChatGPT for Digital Product Planning: A UAE Founder Workshop

ChatGPT can help a UAE founder organise a product idea, compare possible features, draft specifications and summarise feedback. It does not establish customer demand or make a prototype production-ready. This Digital Training Jet workshop approach, led by Parikshit Khanna, uses a fictional product idea to teach a disciplined path from problem definition to a reviewable product brief.

The useful outcome is a better set of questions and documents: who has the problem, what evidence exists, which assumptions need testing and what a small first version should do. A founder should leave the exercise able to distinguish a generated proposal from a validated business opportunity. Real users, commercial judgement and technical review remain part of product development.

Step 1: describe the problem before the solution

Use a fictional Dubai SME that struggles to organise customer enquiries from several channels. Ask learners to specify the user's role, current process, repeated difficulty and consequences. Avoid beginning with “build an AI platform.” A clearer problem might be that staff lose the status of an enquiry after a handover and cannot identify the next responsible person.

Act as a product discovery facilitator for this fictional SME enquiry process. Ask eight questions about users, current steps, handovers, frequency, evidence and constraints. Separate supplied facts from assumptions. Do not declare product-market fit, invent interviews or estimate demand. Produce an assumptions register and a plan for questions to ask real users.

The group reviews whether the questions are neutral or lead the interviewee toward a preferred answer. A useful interview plan explores the existing process and asks for examples. It should not rely on a generated persona as evidence that a real customer wants the product.

Step 2: scope a small first version

Give the fictional product a narrow goal: track an enquiry, owner and next action. Ask learners to distinguish essential functions from optional features. A first version may need a case list, status update and reminder view; it may not need automatic message sending, predictive scoring or a payment system. The product owner explains every inclusion.

Planning item	Example output	Review question
User problem	Enquiry ownership is unclear after handover	What evidence supports the problem?
First-version goal	Track owner, status and next action	Can the task be tested simply?
User story	Staff update case ownership	Are permissions and edge cases clear?
Success measure	Cases with a recorded next action	Is the measure observable?
Open assumption	Staff will maintain case updates	How will the pilot test this?

A practical ChatGPT method: plan the work, draft, analyse, revise and verify.

Step 3: create user stories and acceptance criteria

Ask for a story containing user, task and reason, then specify how the team will tell whether it works. For ownership transfer, acceptance criteria might require a visible new owner, timestamp and reason, plus rejection of unauthorised changes. Include missing values, duplicate enquiries and accidental edits in the test scenarios.

Write user stories for the approved fictional MVP scope. For each story, provide acceptance criteria, required input, permissions, error cases and open questions. Do not add features outside scope. Mark technical assumptions for a developer to review. Include tests for a missing owner, duplicate enquiry and unauthorised update.

Six practical prompting rules: task, context, evidence, constraints, format and verification.

Step 4: prepare a prototype brief

A prototype brief can describe pages, actions, states and sample data. Make the distinction between a click-through demonstration and a functioning system explicit. The founder can use the brief to discuss a build with a developer or evaluate a no-code tool. Real authentication, data storage and production deployment require technical work and approval.

Where a prototype uses AI-generated code, ask a qualified developer to review it for correctness, security and maintainability. Do not put credentials into prompts or treat a suggested payment integration as safe simply because it looks plausible. A learning demo should use test data and avoid real transactions until the appropriate reviews are complete.

Step 5: draft launch messaging from actual scope

Use the approved feature list as the source for a short product description and enquiry form. Ask for clear benefits expressed as intended use, without an invented customer count or performance percentage. The copy should state what the prototype does today rather than promote a future feature as available.

For a UAE audience, learners may produce an English draft and a language-review brief for an Arabic version. The commercial owner checks offer terms, data-collection statements and the call to action. Product claims should be supported by actual functionality or evidence gathered in the pilot.

Step 6: organise feedback without manufacturing evidence

Use fictional feedback for class practice and label it clearly. Ask ChatGPT to group comments, preserve dissenting views and list questions requiring follow-up. A common mistake is turning a few positive comments into proof of demand. The workshop should show how to retain the limitations of a small sample.

For real feedback, decide what may be uploaded and remove unnecessary identifiers. Projects can keep approved planning material together, subject to account and access settings. Maintain versioned briefs so an old assumption does not silently become a confirmed requirement.

What a founder should leave with

The exercise pack includes a problem statement, interview questions, assumptions register, MVP scope, user stories, acceptance criteria and pilot plan. The next step is to test the important assumptions with real users and review the build requirements. A practical workshop prepares that work; it does not guarantee a launch, revenue or product-market fit.

Frequently asked questions

Can ChatGPT validate my startup idea?

It can help organise assumptions and plan research. Validation requires evidence from real users, market checks and business judgement.

Is this a coding course?

The product planning lab focuses on discovery and specifications. Coding or integration can be agreed as a separate technical module.

Can a UAE founder join online?

Yes. A live online session can focus on a founder’s approved product-planning tasks or use fictional exercises.

Plan your training with Parikshit Khanna

Parikshit Khanna is the founder of Digital Training Jet and a corporate AI trainer based in Delhi NCR. His UAE-related experience includes online training for Malabar Group finance teams. Share your department, participant count, approved tools, preferred dates and two tasks you want to improve. Live online delivery can connect UAE teams; an onsite proposal depends on availability, travel and agreed arrangements. The quotation confirms scope, licences, follow-up and commercial terms.

parikshitkhanna@digitaltrainingjet.com | WhatsApp +91 99972 13177 | View current private coaching availability.

Related learning guides

• Choosing an automation course

• Business ChatGPT foundations

Editorial note: AI assisted research organisation and drafting. Practice scenarios are fictional and do not report client results. Product access can change; confirm account and workspace capabilities before the session. A qualified reviewer approves real workplace outputs.