AI Consulting FAQ | Alyra Technology
What a fractional Chief AI Officer costs, why AI pilots stall, how to pick an AI consulting firm, and what middle-market companies should ask before they spend.
What does a fractional Chief AI Officer cost?
Across the market, fractional Chief AI Officer retainers generally run from about $5,000 to $30,000 a month, with senior independent operators charging more. That is roughly 20 to 40 percent of the all-in cost of a full-time hire, where salary, equity, benefits and recruiting together typically land between $400,000 and $750,000 a year.
The range is wide because the work is not one thing. A standing advisory rhythm with your executive team costs a fraction of days each week inside the operation. We price against the intensity the season actually calls for, and we say which one we think you need before you ask for a number.
What does a fractional Chief AI Officer actually do?
A fractional Chief AI Officer owns the AI outcome the way a full-time executive would: setting strategy, owning the roadmap, deciding what gets built and what does not, pressure-testing vendor and build decisions, governing the risk, and answering for what goes live once it is carrying real work.
The part most arrangements leave out is that last one. Leadership and governance are the same job here, because the person deciding what gets built is the right person to answer for how that system behaves in production.
When should we hire a fractional CAIO instead of a full-time one?
When AI is already strategic but does not yet justify, or cannot yet fill, a full-time seat in the C-suite. The usual signals are AI tools spreading without anyone owning them, pilots that worked once and never compounded, a CTO absorbing AI on top of a full job, vendor decisions stacking up, and board questions nobody can answer cleanly.
It suits companies large enough that the details matter and small enough that leadership still sits close to the work. If you already have someone senior whose whole job is AI, you do not need us for this.
Why do most AI pilots stall after the first one?
Because the operating model never caught up with the technology. In RSM's 2026 Middle Market AI Survey of 1,030 companies, about half of those that ran pilots in the prior two years called the results moderate or limited, and the reasons they gave were data quality at 53 percent and integration at 47 percent, not model performance.
The same survey found 86 percent of middle-market companies have adopted AI in some form, while only 36 percent have it embedded across core processes. The first pilot usually works. The next ten do not compound, because nobody measured how the work actually ran before building, and nobody stayed to find out whether anyone used what was built.
What should we ask an AI consulting firm before signing?
Ask who will actually do the work, and whether they are in the room when you scope it. The gap between the senior people who sell an engagement and the team who delivers it is where most middle-market AI disappointment comes from.
Then ask four more: how do you assess whether our data is ready before recommending anything, what happens to this system after launch and who owns it, what did you believe about AI a year ago that you have since changed, and what went wrong on your last engagement. The last two are the ones that separate a real practitioner from a deck.
How long before we see a return?
A fixed-scope diagnostic returns something you can act on in weeks, not quarters: a ranked list of where AI moves a cost, a cycle time or a win rate in your operation, with the evidence underneath it. That is deliberately the first thing, because it is also the cheapest place to find out the answer is no.
Build timelines depend on what the work touches. What we will not do is quote a return before measuring how the work runs today. A number produced before that is a guess wearing a spreadsheet.
Do we need to fix our data before we start?
Usually not before you start, but almost always as part of the work. Data quality is the single most common reason middle-market AI stalls, cited by 53 percent of companies whose pilots underdelivered, so treating it as a later phase is what turns a working pilot into a dead end.
We assess data readiness as part of the first engagement rather than assuming it. Sometimes the finding is that a workflow needs fixing before any model is worth pointing at it.
How do you handle AI governance and compliance?
Governance is designed in, not added afterward. Access rules, audit trails and human review go in at the design stage, because someone will eventually ask how a system reached an answer: a regulator, an auditor, or your own board.
It is not sold separately either. Regulatory readiness, policy authoring, model oversight, drift detection, bias testing and incident response are part of the fractional Chief AI Officer engagement, for the simple reason that whoever decides what gets built should answer for how it behaves.
What size company do you work with?
Middle-market companies, typically in insurance, law, finance, life sciences and manufacturing. The fit is less about revenue than about three things: operations complex enough that the details matter, leadership close enough to the work to act on what gets found, and enough volume that a system either holds up on a normal Monday or does not.
We are a small senior team and we take on a limited number of engagements at a time. That is a real constraint, and it is why we say no to work we are not the right firm for.
What if AI is the wrong answer for our problem?
Then we tell you, in the first conversation, before there is a scope to sign. Some problems are not AI problems, and some are not worth the spend this year.
That costs us work and it is why clients come back. A firm that has never talked a client out of a project is a firm whose advice is worth less than it charges.
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