AI transformation consulting
AI transformation consulting that ends in the P&L.
AI transformation consulting redesigns how a company’s work gets done around what AI can now do, and the result has to show up in the P&L. Codos does it for companies where that has to happen this year, not in the next pilot: McKinsey-style transformation at the speed of an AI lab, for the cost of software. A 30-day diagnostic finds where AI changes your cost base, then the same team builds the automations and stays until your finance team verifies the result.
- Start
- 30-day diagnostic. Either side can stop at day 30.
- Then
- One core workflow rebuilt end to end every two months
- Fee
- Monthly retainer plus a success fee on realized impact
- Proof
- Baseline and result signed off by your finance team
Why companies call us
AI transformation is existential. We help you treat it that way.
“Progress, but not fast enough.”
The internal program has a roadmap, a few tools in use, and a board asking when the number moves.
“We have piloted everything.”
A dozen AI tools, several vendors, no change in how the company actually runs.
“We cannot staff this in-house.”
No one who has done a transformation before, and no budget for a fleet of consultants.
“The pilots never showed up in the numbers.”
Hours were saved somewhere; finance cannot find them.
If it does, the rest of this page shows how the engagement runs, what it produced for companies like yours, and how the result gets counted.
Case studies
What AI transformation consulting produced for companies like yours.
Anonymized from Codos delivery work. Our clients run 300 to 5,000 people; financial services and payments first, retail chains and software next.
17 FTE returned from one sales workflow.
A financial-services company brought us in with a 12-month cost target. The partnership sales team was rebuilt around a fully agentic onboarding flow, returning 17 FTE and opening new revenue with service quality up.
Engineering output tripled. A third of the workforce got two hours a day back from the Company Brain. The annual target was passed six months early.
Hundreds of operations mapped in weeks.
Dozens of AI voice interviews produced a map of several hundred operations, each with the employee’s own account, the tool actually used, and a time estimate.
Department heads reviewed the heaviest ones. Every finding was classified as automate, change the process, fix the structure, or leave alone. The first pilot came from a process the team understood well enough to change.
An application becomes a draft order.
Merchant onboarding collected the same documents twice and shipped terminals to a registered address instead of the shop.
We rebuilt the flow: documents at the right point, delivery address separated from legal address, applications routed by who owns the checks, a draft order generated from every completed application for a person to review. Then we followed activation time, drop-offs, and what was still manual.
The numbers mean the same thing.
Commercial reporting drew on a CRM, analytics, partner settlements, and bank statements, with the same merchant linked differently in each and three definitions of “active”.
Codos delivered on-demand dashboards, then took on reconciling the records and writing the definitions down, so that an agent preparing the weekly review knows which customers count, what period a figure covers, and which source supports it.
Why not a strategy firm
The deck is not the deliverable.
The people who run the diagnostic build the automations. No handover from partners to a delivery bench, no roadmap left on your desk.
Part of our fee is a success fee on impact your finance team has verified. If the number does not move, we do not get paid in full.
The Company Brain runs on infrastructure you control, with your sources, definitions, and access rules. It keeps working after we leave.
A McKinsey-style team and engineers from frontier labs, priced like a software contract, not a partner-led program.
After the diagnostic
A Chief AI Officer function, running.
After day 30, Codos runs as your Chief AI Officer function: one core workflow rebuilt end to end every two months, a weekly impact number in front of the CEO, and an AI Office review where failures, access, training, and the next fix get decided.
Every workflow has an owner inside the function that runs it. In one engagement, automations lost momentum the week the visiting team left. The fix was an internal transformation owner plus a named owner per workflow, with use checked in the first week.
The CEO’s job is to make the priority explicit. The manager’s job is to make the new process work on an ordinary Tuesday. We write both into the plan. The full program, cycle by cycle, is on the enterprise AI transformation page.
How we count impact
Connect the improvement to a business result.
Agree the baseline before building. For onboarding: completed applications, activation time, manual handling, errors. For a sales team: pipeline handled per person. Then follow what changes economically.
Capacity released.
The team clears a backlog or handles more customers with the same people. Measure the extra output and check that quality holds. Hours returned to a team are capacity; finance still has to establish any cash saving.
Cost changed.
A contractor invoice falls, a planned hire becomes unnecessary, an existing expense ends. Avoided spending stays separate from realized savings. An annualized saving is labelled a run rate, with the period and assumptions visible.
Revenue improved.
More customers activate or a location opens sooner. Establish the evidence linking the change to the workflow. Model, software, engineering, and review costs go into the return. Forecasts stay separate from observed results.
Who it is for
Companies where AI has to hit the P&L this year.
Typically 300 to 5,000 people, privately or PE-owned, business-led, with margin pressure and a thin internal AI team. Large enough to feel the cost of fragmented work. Focused enough to change the operating model in a quarter. Not sure you are there yet? Take the self-serve AI readiness assessment.
- Sponsor
- CEO, PE operating partner, head of AI transformation
- Functions
- Sales, operations, finance, support, engineering
- Need
- Verified financial impact, not more pilots
Questions buyers ask
Before we begin.
AI transformation consulting redesigns how a company’s work gets done around what AI can now do: which workflows to change, in what order, with what data, and how the result shows up in the P&L. Most firms deliver that as strategy: an operating model and a roadmap deck. Codos delivers it as deployment: the diagnostic, the Company Brain, and the automations in production, from one team.
Runs the 30-day diagnostic: AI-led interviews with the workforce, in-person interviews with executives, a check of the real systems. Ranks the opportunities with your process owners, agrees the baseline with finance, then builds the automations with our engineers and stays through daily use. The team comes from McKinsey, Meta, and Tesla.
Three ways. The same team diagnoses and builds, so nothing is handed over as slides. The Company Brain and the automations run on infrastructure you control and keep working after we leave. And part of our fee is a success fee on impact your finance team has verified, so we carry risk on the result.
30 days from kickoff, once access is agreed. We schedule the interviews in week one. By day 30 you have the workflow map, the ranked opportunities with owners and value, a baseline signed by finance, and the first cycle plan. Either side can stop at day 30.
The diagnostic is a fixed-price 30-day engagement. The transformation after it is a monthly retainer plus a success fee on realized, finance-verified impact. For reference, fractional AI leadership runs $5,000–30,000 a month on the market and large-firm programs run to seven figures a year. We quote on the first call and confirm it in writing the same day.
No. You need an executive sponsor, process owners, access to the relevant systems, and someone in finance to agree how impact is measured. Codos brings the product and the engineers. An existing AI team works with us, with ownership made explicit.
On infrastructure you control. We agree the connected sources, access rules, and model providers before rollout. Any external model or integration call is written into that setup, not discovered later.
The workflow, measured before and after: handling time, throughput, quality, operating cost. Freed capacity, avoided spending, realized savings, and revenue impact are reported separately. Finance agrees the baseline and validates any financial result attributed to the work.
Start with a real problem
Which process has to change this year?
Bring one process, one recent case that got stuck, and the number it is holding back. We will scope a 30-day diagnostic around the people, systems, and economics behind it. If your AI program is on track, you do not need us. If it has to hit the P&L this year, book the call.
founders@codos.ai