Three quarters of large organizations now have a Chief AI Officer. Most companies under a thousand people cannot justify a $400,000 hire for the role — so the decisions land on whoever has time, and the pilots quietly go nowhere. We do the job on a fraction of the schedule and a fraction of the cost.
Every figure here is sourced and dated. Check them — the argument only works if they are true.
of organizations have now established a Chief AI Officer — up from 26% a year earlier.
IBM Institute for Business Value, 2,000+ CEOs across 33 countries, May 2026
of enterprise GenAI pilots delivered no measurable P&L impact at all.
MIT, The GenAI Divide: State of AI in Business, 2025
of agentic AI projects are expected to be cancelled before the end of 2027.
Gartner, 2025 forecast
average US base salary for a full-time Chief AI Officer, before equity or benefits.
Glassdoor CAIO salary data, 2026
When 95% of pilots produce nothing, the instinct is to blame the models. That is not what the research found. MIT's conclusion was that the gap sits between adoption and transformation — companies bought tools, ran demos, and never changed the process the tool was supposed to serve.
McKinsey put a number on the same split: roughly 80% of companies bolt AI onto existing workflows and see no profit impact, while the small minority who redesign the workflow first capture 5% or more in EBIT. The difference is not the software. It is whether someone senior owned the decision about what to change.
That ownership is the whole job. Which use case first and why. What gets built versus bought. What data can safely be exposed. Who is accountable when an agent gets something wrong. How you know within a quarter whether it worked. Nobody in most companies has that on their objectives.
Six responsibilities. None of them are “pick a chatbot vendor”, and all of them are the reason pilots either land or die.
Which use cases get funded, in what order, and why. We rank candidates by value, feasibility, and risk, and we kill the ones that will not pay — usually before anyone has spent money on them.
MIT found externally built tools succeed about twice as often as internal builds. Knowing which side of that line a given problem falls on saves more money than any negotiation on price.
An acceptable-use policy people will actually follow, data handling rules, human-in-the-loop requirements, model and vendor review, and an answer ready for the client who asks whether you put their data into a model.
The failure mode is not that the tool broke; it is that people went back to the old way in week three. Training, workflow redesign, and named owners are the difference between a deployment and a demo.
Consolidating overlapping subscriptions, negotiating terms, and pushing back on roadmap promises. Most companies we start with are paying for three tools that do the same thing.
One concrete metric per initiative, baselined before launch, reported on a schedule. If a project cannot name its metric, that is a finding in itself — and usually a reason not to start.
We do both, and they are sold separately on purpose. Buying the wrong one is a common and expensive mistake.
This page — the leadership layer
For companies that do not yet know what to automate, in what order, at what risk, or how they would prove it worked. An executive owns those answers and stays accountable for them.
The build team
For companies that already know what they want automated. Agents, workflow automation, LLM integration, and data pipelines — designed, built, and monitored, with a first automation live in two to four weeks.
If you have one clear automation target and a budget, skip this page and go straight to the build team. The leadership layer earns its fee when there is a portfolio to run, not a single job to do.
The specifics vary by business, but these are the patterns that pay back fastest across the companies we work with.
Extraction, coding, matching, and approval routing, with a human on exceptions only. Hackett Group reports AI-enabled AP programmes hitting 60% touchless processing and 59% faster cycle times.
Every email, form, and ticket read, classified, enriched, and routed to the right owner with a draft response attached. Usually the single highest-volume manual task in the business.
First-pass review against your own checklist, with deviations flagged for a human. Turns a two-hour read into a ten-minute confirmation without removing the person who signs.
Every engagement is scoped after a call, because a 40-person services firm and a 400-person manufacturer need very different amounts of this.
Companies that need to know where AI actually fits before spending anything on it.
Companies running an AI portfolio who need someone senior accountable for it.
Companies where AI is central to the operating model and needs weekly executive attention.
For reference: a full-time Chief AI Officer averages $353,220 in base salary in the US, and $400,000 to $750,000 all-in once equity, benefits, and a six-to-nine month executive search are counted. Fractional engagements exist because most companies need the function long before they can justify that.
The second column costs us work. It is still the right answer for those companies.
The hub covers the case. These cover the work, the entry point, the risk layer, and the hiring decision.
THE WORK
Thirty-four automations across six departments — what the AI does, where the human stays in the loop, and the metric that proves it worked.
Read moreSTART HERE
The four-week assessment that produces your ranked roadmap, your tool audit, and your policy — whether or not you continue with us.
Read moreRISK
Acceptable use, data handling, human-in-the-loop rules, vendor review, and the shadow-AI problem nobody wants to look at.
Read moreTHE DECISION
What each actually costs, what you get for it, and the point at which hiring a permanent CAIO becomes the better call.
Read moreWhat companies ask before bringing in an AI executive.
Mostly, decide — with enough context to be right more often than not. Which use case first, what gets built versus bought, what data can safely be exposed, who is accountable when an agent is wrong, and how you will know within a quarter whether it worked.
Your team could learn all of that. The question is whether they can learn it while doing their existing jobs, on a timeline that matters, and whether the mistakes made along the way cost more than the engagement. For most companies the answer is that the first three decisions are the expensive ones, and those are exactly the ones made before anyone has experience.
AI Infrastructure builds things. You know you want inbound leads qualified automatically, we design and ship that system. It is scoped, project-based, and measured on delivery.
The Chief AI Officer role decides what should be built at all, in what order, under what rules, and whether it worked. It is ongoing and measured on outcomes across a portfolio. If you already know what you want built, you do not need this page — go to AI Infrastructure and save the retainer.
Yes, at the Operate tier and above. The role does not work at arm's length — the decisions that matter get made in rooms where trade-offs are being argued, and someone has to be there to make the AI case or kill the AI idea.
At the Orient tier the engagement is fixed-scope and ends with a presented set of findings, so there is no standing seat.
Very common, and it is usually where the first savings come from. Companies routinely find they are paying for three overlapping subscriptions, two of which nobody uses, and a per-seat contract sized for a pilot that never scaled.
The audit covers this. We are not going to tell you to throw everything out — some of what you bought is probably fine and just needs to be integrated or actually adopted.
The tool consolidation often covers a meaningful share of the fee within the first quarter, which is unglamorous but real. The larger return comes from not spending six figures on the wrong initiative, and that one is hard to prove because it is a cost you never incur.
What we will commit to is that every initiative we greenlight has a named metric and a baseline before it starts. If you cannot see the return, you should stop paying us.
It depends entirely on choices you are making right now, most likely without a policy. Staff pasting customer information into consumer chatbots is the most common exposure we find, and it is usually already happening before anyone engages us.
Governance is a named part of every tier for that reason: acceptable-use policy, data classification, human-in-the-loop requirements, and vendor review. It is the least exciting part of the job and the one most likely to matter to your clients.
Many companies do, and part of this job is telling you when that point arrives and helping you hire for it. If AI becomes central to how you operate, an internal executive with full-time attention and organisational authority will beat a fractional one.
The Embed tier includes a succession plan for exactly that. A fractional engagement that quietly extends forever, long after the company outgrew it, is a failure we would rather avoid.
Book a call. We will walk through what you have already bought, what your team is quietly doing with AI, and where the realistic wins are — and tell you honestly if you do not need us yet.
