AI Strategy & Digital Transformation
Before anything gets built, someone has to decide which decisions are worth handing to a machine and prove the money adds up. That is what this piece of work does, and it is the cheapest part of any AI programme to get right.
A roadmap your finance team will actually sign.
- Your executive team has committed to AI but nobody can say which problem it solves first.
- You have run trials that impressed people in the room and never made it into daily use.
- You need a business case that will survive finance, procurement and internal audit.
- You are about to commit budget and want an independent opinion before you do.
Most AI strategy work in the region ends in a document nobody can act on. It lists ideas with no data behind them, no honest figure for what it will cost to run, and no way of telling six months later whether any of it worked. It reads well in a board pack and dies in procurement.
We treat it as an engineering problem. We write down the decisions your people make over and over, work out which of them are expensive to get wrong, and check whether you already hold the information needed to make them better. Only then do we rank anything. An idea with obvious value and no usable data is not an opportunity. It is a data clean-up project wearing a costume, and we will say so.
Everything in the plan is costed: what it costs to build, what it costs to run, and what you pay every time the system answers a question, which is the number almost nobody works out until the first invoice arrives. Where the numbers do not work, our advice is to stop. A few weeks of work that ends in "do not build this" has done its job, and it is worth more to you than a roadmap written by someone with hours to sell.
This is normally where an AI programme in Abu Dhabi should start, particularly if you answer to a board, a regulator or a procurement committee who will ask where every number came from.
What you get
Opportunity map
Every idea we looked at, ranked on what it is worth, whether your data can support it, and how likely it is to go wrong on the way. The ones we advise against are listed too, with the reasons written next to them.
Data inventory
What information you hold, who owns it, what condition it is in, and what has to be fixed before any AI can use it. This is usually what really decides how long the programme takes.
Business case
What it costs to build and to run, set against how the work performs today, including what you pay every time the system answers and the hours your own staff will have to put in.
Sequenced roadmap
What to do first and why, with the things that have to happen before other things spelled out, and points along the way where stopping is a perfectly good answer.