Most GCC AI pilots never reach production.
We move one of yours into a daily-use workflow in 90 days. Audit, build, embed, transition. Your team trained to run it.
Saqr Solutions is the AI implementation arm of Saqr Academy, the KHDA-approved applied AI institute in Dubai Media City.
The shift
McKinsey's 2025 GCC survey found that 84% of regional organisations have adopted AI in at least one business function. Only 31% have actually scaled it. The bottleneck is the same in almost every case. Pilots run, vendors get paid, and then the work to integrate the system into how the business actually operates never quite happens.
The reasons are familiar. The data the model needs sits in three different systems. The team that ran the pilot has moved on. The change management piece was never properly resourced. Big consultancies sell the strategy and then leave before the embed phase. Software vendors sell the platform and assume someone else will integrate it.
The opportunity is in the embed layer. Most of the technology is mature. Most of the data exists. What's missing is the work of taking one specific use case from 'we tried it' to 'the team uses it daily.'
What we build
A production AI system embedded inside one operational workflow you've chosen.
Common patterns we deploy: a RAG layer over policy and document repositories, an agentic resolution wrapper over CRM and OMS, an exception-handler agent over ERP, an internal copilot for shared services teams. Built using tools we trust (Claude, agent frameworks, MCP-based integrations, vector search where needed) and configured for your specific systems.
In some workflows, the AI system becomes visible enough to name, assign, and manage like part of the operating model. It has a defined role, a human owner, escalation rules, and performance measures. We don't force that model, but when it fits, it helps teams treat AI as operating capacity rather than software on the side.
Then we train the people who will run it.
What counts as production
We use the term carefully. A system is in production when:
- 01
It has a real use pattern.
Daily, weekly, or event-triggered, the system is part of how the work gets done, not a demo people remember to revisit.
- 02
Named owners hold it.
There is a person whose job description includes the system, not just a steering committee that meets quarterly.
- 03
Escalation paths are documented.
When the system gets something wrong, the team knows who reviews it and when.
- 04
Performance is measured against pre-agreed metrics.
A monitoring dashboard your operations leadership can read.
- 05
Governance is in place.
Audit logs, access controls, and the documentation a regulator or internal auditor would expect to see.
Anything short of all five is a pilot, not a production system. Our 90-day engagement is structured to deliver all five.
What's included
An audit of your existing AI pilots and a Production Readiness Report on the one with the strongest case.
The full system built and deployed inside your environment, tested against your real data and workflows.
Integration with the systems of record: ERP, CRM, document repositories, identity, whatever the use case requires.
Governance posture mapped to PDPL, DIFC Regulation 10, ADGM data protection, and sector-specific requirements where relevant.
Training for the operators who will use the system daily and the supervisors who will oversee it, drawn from Saqr Academy's AI for Operations & Business program.
A runbook, monitoring dashboard, and transition plan with named internal owners.
How the engagement runs
- 01
Weeks 1 to 2
Audit.
We inventory your existing pilots, map the data, and identify the one use case with the strongest production case. You get a written report and a prioritised recommendation.
- 02
Weeks 3 to 7
Build.
Engineering sprint to take the chosen pilot to production. Weekly demos. Working software, not slides.
- 03
Weeks 8 to 10
Embed.
Two cohorts of users trained. Change management workshops. Monitoring dashboard live. Escalation paths set.
- 04
Weeks 11 to 12
Transition.
Hand over to your internal team.
- Next
After the system is live.
The natural next step for many engagements is an ongoing Fractional Chief AI Officer arrangement, where we extend the work into adjacent functions, hold AI strategy at the executive level, and own the governance posture as the AI estate grows.
Who this is for
Mid-large GCC organisations of 500 to 5,000 employees with at least one GenAI pilot already running and stalled. Banks, insurance, telco subsidiaries, retail conglomerate divisions, healthcare networks, real estate developers, large family-owned groups.
You'll get the most value if you have a clear sponsor at COO, CIO, or Chief Transformation Officer level, and a pilot or use case that already has internal momentum but hasn't reached production.
What makes this different
- 01
We deliver working systems, not roadmaps.
The people who design the system are the people who write the code. There's no partner-to-junior handoff.
- 02
We're independent of any single vendor stack.
We integrate with whatever ERP, CRM, and cloud you already run. We don't push you toward a platform we have a partnership with.
- 03
Training drawn from KHDA-approved curriculum.
The training inside the engagement uses the same curriculum that runs through Saqr Academy's AI for Operations & Business program. Your team finishes the engagement able to extend the system on their own.
Frequently asked questions
Strategy consultancies tell you what to build. We come in and build it. Our engagements deliver a production system at the end of 12 weeks, not a roadmap. The audit phase is two weeks, not three months. The work that strategy firms call "implementation handover" is what we do from week three.
In most cases, yes. McKinsey's 2025 GCC research shows that 84% of regional organisations have run AI pilots but only 31% have scaled them. The gap is usually in three places: data integration, change management, and governance. Our 12-week engagement is structured around closing those three gaps for one specific use case. The fit-check happens in the first two weeks. If your pilot can't be moved to production in 90 days for a structural reason (data residency, regulatory blocker, vendor lock-in we can't unlock), we'll tell you in week two and adjust scope.
Most data isn't ready when we start. The audit phase identifies what needs cleaning, what needs migrating, and what needs new permissions. Light data preparation is part of the engagement scope. Heavy data engineering (multi-system migrations, master data management) is scoped separately if the use case requires it.
Microsoft Azure, AWS, and Google Cloud, plus regional sovereign cloud providers including Core42 and G42 infrastructure where data residency requires it. We're independent of any single vendor.
Big 4 engagements are typically structured for enterprise-wide transformation, with timelines and fee structures suited to that scope. Our 12-week engagement is built for a different mandate: take one specific workflow into production, deliver it with senior practitioners, and leave the business with a system it can run. We're not trying to replace a global consultancy on the strategy layer. We're designed for the implementation layer where pilots stall.
DIFC Regulation 10, governing autonomous and semi-autonomous processing, has been in force since 2023. PDPL applies across the UAE more broadly. Both are mapped during the audit phase. The system we build includes the AI register, risk classifications, audit trails, and review workflows that compliance teams need to keep their AI estate explainable to regulators. For deeper regulatory engagements (regtech tooling, formal certification work), we bring in compliance partners and scope that as a separate engagement.
You can use whatever model fits the use case. We typically deploy Claude where reasoning depth and instruction-following matter, GPT or Gemini where the use case calls for it, and regional models like Falcon or Jais where Arabic or sovereign deployment is the priority. Model selection is decided in week one based on the workflow.
Yes, and we usually do. The engagements that deliver the most lasting value are the ones where your internal IT or engineering team is involved from week one. We hold the AI engineering and integration work; your team holds the institutional knowledge of your existing systems. By week twelve, they own the system.
Tell us about a pilot that's stuck.
Send us a short note about the situation. We'll read it, get back to you within two business days, and arrange a call to talk through it.