Ting Guides · For the COO
Why do AI pilots fail to reach production?
The short answer
Because a pilot proves the model can do the task, while production needs everything around it. Pilots tend to run on clean samples, outside the real systems, with a keen team checking every output. Production needs messy live data, integration with the systems of record, acceptance tests, a named owner, a plan for errors and a budget to run it.
Reviewed by Abdullah Al-RashedUpdated
What it depends on
Whether the problem was framed with the business
RAND's interviews with 65 experienced data scientists and engineers found misunderstanding or miscommunicating the purpose of a project to be the most common reason AI projects fail. A pilot built to answer "can AI do this?" rarely answers "can we run this every day, and is it the problem we have?"
Where the pilot's data came from
Hand-picked samples flatter the model. If the pilot never saw scans, missing fields, duplicates and edge cases from live traffic, its results don't transfer.
Whether it touched the real systems
A pilot that reads an export and writes to a spreadsheet has skipped the hardest part: authenticated, logged reads and writes to the ERP, CRM or messaging channel.
Who owns it after the demo
A live system needs a business owner who answers for its results and a technical owner who keeps it running. Pilots run by an innovation team often have neither on the operating side.
What success was agreed in advance
Decide before the pilot what result would justify production, and who makes that call. Without it, every pilot ends as "promising".
What changes between pilot and production in Saudi operations
General information, not legal advice. Each regulatory point summarises the official source cited beside it, as checked on the date shown under Sources; confirm how it applies to your company with your legal adviser.
Real data brings the PDPL with it
Pilots often run on samples or anonymised data. Production processes real personal data every day, and the Personal Data Protection Law applies to any processing of personal data in the Kingdom (Article 2), including its conditions for transfer outside the Kingdom (Article 29). A hosting choice that was fine for a demo can block a launch.[3]
Arabic at full variety
Live traffic brings dialect, voice-note transcripts, Arabizi and poor scans that a pilot's sample rarely had. Re-test on a live sample before scaling.
SDAIA counts the transition
SDAIA's AI Adoption Framework lists tracking the transition of projects from pilot to production as an indicator of an entity's maturity. Count your pilots that reached production, and write down why the others didn't.[2]
Pilot versus production, stage by stage
ExhibitWorked example
Rasikh Industries, the HVAC plant of the fictional Rasikh Holding, piloted turning shift supervisors' WhatsApp reports into maintenance work orders. What the pilot had, and what production needed: every gap became a work item.
The decisionSix gaps, six work items. The pilot proved the idea; production needed every gap closed and tested before go-live.
What Ting does and doesn't do here
What we do
- We build the solution defined in an approved Blueprint into your systems, test it against the acceptance criteria, deploy it with monitoring and access controls, give your team the runbooks, and stay accountable for how it performs.
- We send low-confidence items to a person, log every action, and make changes reversible.
- We re-run the acceptance tests on every change before it ships, so the live system stays as tested as the day it went live.
Where Ting helps: Custom AI Implementation
What we don't do
- We don't build without a paid, approved Blueprint. That is where production requirements are fixed.
- We don't take a pilot to production as it stands, ours included. The Blueprint can reuse what the pilot learned.
- We don't hand over a system without runbooks and knowledge transfer to the team that will run it.
Sources
Primary sources only, each read in full on the date shown.
- The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed (RR-A2680-1, 2024)RAND Corporation · checked
- AI Adoption Framework, version 2 (May 2025)SDAIA · checked
- Personal Data Protection Law (Royal Decree M/19 of 9/2/1443H, amended by Royal Decree M/148 of 5/9/1444H)SDAIA · checked
