Careers
Artificial intelligence project manager
Choosing the use case that pays, assembling the necessary data, leading the team that builds it and measuring the real effect on the business: the AI project manager makes the difference between an impressive demo and a lasting gain.
Choosing what deserves to be built
The first task is one of sorting: out of ten ideas, only one has the necessary data, an identified user and a measurable gain. The project manager runs that selection with the business, then organises data access and governance. Then comes the build phase, run like a classic project but with acknowledged uncertainty on the outcome. Deployment comes with a measurement arrangement, and a clear decision on what stays validated by a human.
- Select a use case on available data and expected gain
- Organise data governance and access rights
- Lead a mixed team: data, engineering, business
- Measure the effect in production and decide on human validation
Organisations that are investing
Banking and microfinance
Risk scoring, anomaly detection, document processing automation: gains there are direct and measurable.
Operators and retail
Forecasting, segmentation, personalisation: abundant history and teams already organised around data.
Industry and infrastructure
Predictive maintenance, visual quality control, energy optimisation: instrumented assets already producing the data.
What the qualification commits to, figure by figure
These values come from the official catalogue filed with the ministry, and they can be recomputed. One credit stands for twenty-five hours of work, ten of them taught by an instructor. Multiply the credits by ten and you land on the hours shown below. That is the very calculation an equivalence office runs when it opens a file, and you can run it before you even apply.
- qualification level
- 6
- months of programme
- 12
- taught hours
- 800 h
- credits
- 80
- competency blocks
- 5
Manager and professional
Led by an instructor.
1 credit = 25 h of work, 10 of them taught.
Framework of the « AI Project Manager » qualification, code TP57.
The framework, block by block
The qualification is built from competency blocks. Each is assessed separately, on real output, and each carries its own hours. From day one you know what you are working on, in what order, and what each part weighs. The blocks add up exactly to the total shown above: both figures come from the same document.
- 01
Qualifying use cases
How to spot them, expected gain, full cost, feasibility, criteria for dropping — 200 taught hours.
- 02
Running data and AI projects
Dataset assembly, lifecycle, milestones, suppliers, data debt — 200 taught hours.
- 03
Pre-deployment evaluation
Business test set, accuracy, bias, edge cases, the decision to deploy — 210 taught hours.
- 04
Governance, compliance and oversight
Data protection, accountability, human oversight arrangements, logging — 140 taught hours.
- 05
Managerial conduct
Telling leadership an AI project should not happen, measuring real effect — 50 taught hours.
Steering artificial intelligence in business
The programmes below build the skills this role mobilises. Each row shows what the institute commits to: duration, credits, taught hours and fees in Guinean francs, read straight from the catalogue. You compare on the same basis as the admissions office, with exactly the same figures.
| Programme | Duration | Credits | Taught hours | Fees |
|---|---|---|---|---|
| Artificial Intelligence programme — Bac+5 level · Master's degree | 2 years | 120 | 1200 h | 7 800 000 GNF per year |
| Applied AI for Business Certificate · Professional certification | 4 months | 12 | 120 h | 3 200 000 GNF in total |
The points people ask us about
Do I need to be a data scientist?
The role requires understanding what a model can and cannot yet do, evaluating a technical proposal and talking on equal terms with the team. The AI in business certificate installs that literacy in four months, without leaving your job.
How is return on investment demonstrated?
By choosing an existing business indicator from the outset and measuring before, during and after: file processing time, share of anomalies detected, volume handled at constant headcount. The framework devotes a full block to that discipline.
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