Prime Ai Solutions
Read time: 3 minutes
Leader, welcome back.
Most AI implementations fail, and it is not the models.
It is the shape of what you bought. Here is the fork, the fix, and a prompt that audits your worst process for you.
LATEST INSIGHT
The two dead ends
A company leader gets pitched AI in two shapes, and both quietly burn money.
One: the horizontal assistant.
Hand everyone Copilot or Claude Cowork and let them build.
Three months on you have a fat usage bill and a graveyard of half-built agents nobody owns.
And there is a quieter cost.
Microsoft's own CEO, Satya Nadella, warned this week that with AI you pay twice, once in cash and once in the know-how you feed it.
Every correction your team makes, every "no, we do it this way," trains a model you do not own! Unless you know what you’re opting into and how controlled this is!
Two: the point solution.
One AI tool for AP, another for the close, another for expenses. None of them knows your AP process has seven steps not four, or how your exceptions really get handled, so your team logs into a dozen tools for half the gain.
MIT studied 300 enterprise deployments this year and found 95% delivered no measurable return, and the cause was the approach, not the technology.
The same research found outside partnerships work about twice as often as internal builds, and that the biggest returns hide in back-office work like finance.
The shape that works is neither.
It is one layer sitting across the systems you already run, doing the work the way your team would, and flagging only the calls that need a human.
Takeeaway: You do not have an AI problem, you have a glue problem.
The value is in the person copying a number between screens, chasing the email when two figures do not match, escalating when nobody replies.
Automate that and the close shrinks.
Buy another tool and it does not!
STEAL THI
Audit your worst process in 20 minutes
Pick the one finance process everyone dreads and map what actually happens, not what the SOP claims.
The gap is where the money hides.
Jot down how it really runs including the workarounds, the monthly volume, what a single error costs and how often one slips through, and how exceptions get handled and in how many formats.
Then let AI do the analysis.
Paste your notes into this:
You are a finance operations analyst.
Below are my rough notes on how one of our processes actually works, including the messy bits and workarounds: [paste your notes].
Do three things.
First, lay it out as clear numbered steps, separating the pure pattern-matching steps like lookups, matching, routing, and posting from the few that need real human judgment.
Second, flag which steps an AI agent could safely take over and which must stay with a person.
Third, score the process on time or money saved, revenue impact, and risk reduced, and give a one-line verdict on whether it is worth automating first.
Keep it specific to my notes and do not invent steps I did not describe.
The audit tells you what is worth automating.
Turning that into a working agent that keeps your data inside your own walls is the part most teams get wrong, and the part worth getting help with.
SIGNAL / NOISE
Signal: Fable 5, Anthropic's top model, is free on paid plans only through July 19, then it starts costing per use. This week is the free window to test it on your real work.
Signal: GPT-5.6 became the default model inside Microsoft 365 Copilot this month. If your team lives in Excel and Outlook, the engine changed under them, and every prompt they type feeds a model you do not control. Ensure you adjust your settings accordingly, we previously covered this. Worth knowing.
Noise: the "new model wins by three points" leaderboard race. Nobody swept the board, and for finance it barely matters!
This year's best models still score only around half on real analyst tasks in the our AI benchmarks, so do not hand the close to one unsupervised!
Where we come in
Here is the uncomfortable part.
Even where official AI projects stall, MIT found around 90% of staff already use personal AI tools for work, mostly unsanctioned. So your institutional knowledge is already walking out the door, correction by correction, and nobody is capturing it.
This is the work we do at Prime AI Solutions.
We build, run, and advise on AI that live inside the tools you already use, and we train your team to work alongside them, so the learning compounds as your asset instead of the vendor's.
Two ways to start, both just a reply.
- If you want to audit your processes and see where an agent would actually help, tell me the one that hurts most.
- If you want your team to properly understand AI in finance, ask me about the training (self paced or live workshops)
-Umar, Prime AI | primeai.solutions
