Implemented AI
From AI potential to operational capability.
There is no shortage of AI strategy, experiments or impressive demos. What is scarce is AI that runs inside a real business process, is owned by the business, and produces a result someone can measure.
The last mile
Most AI stops before it starts paying.
The route from strategy to value has five stages. The first three get the attention and the budget. The last two — where AI becomes part of how work is actually done — are where most initiatives stall. That is where we work.
- Strategy
- Experiment
- Proof of concept
- Operation
- Measurable value
Where we work
What “operational” means to us
- It runs inside a real business process — not beside it.
- It is owned by the business, not by an innovation team.
- Data, integration, security and governance are sorted.
- The people who use it trust it, and use it.
- The value is measured, not assumed.
Where we help
Pilot to production
Moving a proof of concept that worked in the demo into daily operation.
Choosing what goes live
Deciding which AI initiatives deserve to become operational — and which don’t.
AI in core systems
Getting AI to work inside ERP, CRM, supply chain and other existing platforms.
Stalled AI implementations
The same recovery logic as Software Recovery, applied to AI.
Same principle, earlier stage.
Software Recovery deals with technology that got stuck on its way to operation. Implemented AI helps new technology get there in the first place. The common thread is delivery.
Implemented AI is a developing practice within Pointing Fingers. If you have an AI initiative that should be producing value by now, the conversation starts the same way.
Have a project that should have been live by now?
Let’s establish what’s actually preventing it.
Initial conversations are confidential and exploratory. No obligation, no sales pitch.