AI-Augmented Operations
Not a roadmap. A running system inside the workflow that is costing you money.
- Timeline
- First workflow in production within a quarter
- Engagement
- Fixed-scope per workflow
- Built for
- A COO or GM who owns an operational number
The problem
You have had the workshop. You have a slide with twelve use cases and a maturity curve. Nothing is in production.
Who this is for
- A COO or GM who owns an operational number
- Organisations past the strategy phase and short on shipped systems
Who this is not for
- Portfolio-level AI strategy with no single process owner
- Teams looking for a maturity assessment rather than a working system
How it runs
- 01
Find the money
Process mapping against real cost and cycle time, so the first candidate is chosen on value rather than on enthusiasm.
- 02
One workflow, in production, this quarter
Narrow scope, real users, measurable. Breadth comes after the first one works.
- 03
Measure against the baseline
Captured before the change, so the delta is arguable by your CFO rather than by us.
- 04
Data foundation, only where needed
We fix the data the next workflow depends on — not the whole estate, not up front.
- 05
Governance and rollout
Usage policy, ownership and the pattern for the next process.
What you get
- Prioritized opportunity map with expected value per process
- One automation shipped into production
- Measured delta against a pre-change baseline
- Internal ownership handover
- AI use policy and governance model
AI-Augmented Operations: questions we get asked
Where should a business start with AI?
With one process that already has a number attached to it — a cost, a cycle time, an error rate. Starting from a use-case inventory produces a slide; starting from a number produces something you can prove worked.
How do you pick which process to automate first?
We map processes against real cost and cycle time, then choose the one with the best ratio of value to blast radius. The first automation should matter enough to be worth doing and be contained enough to be safe to get wrong.
How soon can we see a measurable result?
One workflow in production within a quarter, measured against a baseline captured before the change. If it has not moved a number by then, the approach was wrong and you should know that early.
Do we need to fix our data first?
Only the data the workflow you are shipping next actually depends on. Enterprise-wide data programmes ahead of any working system are the most reliable way to spend a year with nothing in production.
Delivered in your region.
- GDPR
- UAE PDPL
- ISO 27001 practices
- Data residency in the EU, the UAE, or your own cloud account
APPINE L.L.C-FZ, Dubai
$ appine assess --fixed-fee
Two weeks. Fixed fee. You end with a decision, not a deck.
A system and codebase audit, a risk register ranked by severity, an engineering baseline, and an explicit recommendation — keep, harden, rebuild, or don't do it at all.
If the answer is “don't hire us,” we'll write that down too.