Most AI projects in Saudi Arabia do not fail at the demo. They stall in the six weeks after it.
Short answer: the hard parts are where the system may run, how it handles real Arabic, who approves its actions, and how you prove it works. Plan for those on day one and the demo becomes a product.
1. Where the data may go
A demo runs on a public API. Production often cannot. Personal data falls under the PDPL, sensitive sectors have their own controls, and many organisations require hosting inside the Kingdom or inside their own tenant. Decide this first, because it narrows which models and services you can use.
2. Real Arabic, not demo Arabic
Demo questions are clean. Real ones mix Arabic and English, use dialect, abbreviate names and quote scanned PDFs. Build a test set from real material early. See Arabic NLP.
3. Integration with the systems you already run
Value comes when the AI reads from and writes to your ERP, document store or ticketing system. That needs access, permissions and an owner on the IT side. Agents should act with scoped permissions and leave a record, as in our agentic AI work.
4. Approvals and accountability
Someone has to own the system. Decide which actions need a human checkpoint, and write it down. SDAIA's AI ethics principles expect exactly this; our working guide turns them into a checklist.
5. Measuring before and after
Without a baseline you cannot show the system helped. Measure how long the work waits today, then measure again after release. Every system we build ships with an evaluation harness.
A realistic sequence
Scope one workflow, decide hosting and data, build a test set, ship to a small group, measure, then widen. If the right answer is not an agent, say so early. Start with one workflow.