Practical AI tooling, real implementation, and the skills to keep running it after we're gone. No hype, no transformation theatre. Just the work that earns its place.
Someone in the business has tried using AI, it sort of worked, and now they want to know what to actually do with it.
We typically work with:
A useful way to think about AI in a business: it earns its place where the cost of being wrong is low and nobody needs to own the outcome personally. It doesn't earn its place where the cost of failure is high, or where the work depends on a human being genuinely accountable for what happens next.
That sounds abstract. In practice it usually looks like this.
AI is excellent at the patient, repetitive work that used to eat hours. Drafting management reports from raw data. Triaging incoming emails by topic. Pulling a first-pass draft of a board paper or a pitch deck. Reconciling line items across systems. Producing the kind of routine document that needs to exist, doesn't need to be brilliant, and should never have been a human's full job in the first place.
Done well, this kind of automation gives the team back hours every week. Done well, it also doesn't break, because the output gets checked, the cost of being wrong on any single document is small, and nobody's career rides on whether the model hallucinated a number.
Cold outreach to people you actually want to win. Volume AI outreach is fine for low-stakes blasts: event announcements, generic updates. But the prospect you really want, a high-value client, a senior buyer, a key partner, has already deleted thirty AI-drafted "I noticed you're growing your team" emails this week. Their instinct for AI prose is sharper than the AI's instinct for them. A short, slightly imperfect message from a human, or a phone call, beats a polished AI draft every time.
Client conversations involving stakes, ambiguity, or trust. AI handles a password reset well. It handles a frustrated client badly. The words come out right, but the customer leaves feeling heard and not helped. Companies that pushed customer service all-in on AI have publicly walked it back, because the moment a situation requires judgement, emotional weight, or someone to actually own the outcome, automation breaks down. The cost of being wrong is too high, and the cost can't be undone by an apologetic follow-up message.
The interesting question isn't "can AI do this?" Technically, it almost always can. The interesting question is "what's the cost when it gets it wrong, and who carries that cost?" That's the filter we apply to every deployment. Most of the work that pays back is in the first category. Most of the trouble comes from forcing things into the second.
Engagements vary in shape and length, but the work is usually some combination of:
The shape varies more than the Fractional CFO work does, because the right engagement depends entirely on what's already in place and how much the team can absorb at once.
AI is not a full solution to anything. It is, at this moment in the cycle, the most useful new tool in a long time, and worth learning to use well. Both things are true, and the firms that pretend otherwise tend to disappoint their clients in opposite directions.
We're not the firm that will tell you AI is going to transform your business. We're also not the firm that will tell you to wait and see. The honest position is that the businesses learning to use these tools properly right now are quietly building real advantages, and the businesses waiting will spend the next two years catching up to where they could be. Neither hype nor hesitation pays back.
Most consulting engagements end with the team needing the consultant to come back to make the next change. We work the opposite way. Every engagement ends with the team understanding what we built, why we chose it, and how to extend it.
This is partly principle and partly self-interest. The work that sticks is the work the team can run themselves. The work that doesn't stick gets blamed on the consultant who set it up. We'd rather build something the business can actually maintain than something that requires us to be on retainer to keep alive.
When the team grows past what we set up, we'd rather they call us back to do the next interesting piece of work than because the old work fell over.
Anywhere from two weeks for a focused outcome to three months or more for broader work. The honest answer comes out of the first conversation, once we understand what's already in place and what the team can absorb. Most businesses underestimate what's achievable in two weeks.
No. We don't do AI deployment to reduce headcount. The work we do gives the team back hours they were spending on manual tasks, so they can spend them on work that actually requires a human. If you're looking for a redundancy plan, we're the wrong firm.
Whatever fits the job. We use Claude, ChatGPT, and the major automation platforms regularly. We're not tied to any one vendor, and we'll often recommend the cheapest tool that works rather than the most impressive one. For smaller operations, that often means staying inside Google Sheets or the existing accounting system rather than adding new platforms.
Taken seriously. Before deploying any tool that touches client data, we work through the policies of the relevant platform, what's stored and where, and what your obligations are under Australian privacy law. We won't deploy something that creates a privacy or compliance problem to fix a workflow problem.
It will. The tools we recommend today won't be the tools we recommend in two years, and some of them won't exist. The work we do is designed to survive that: clear workflows, documented processes, and team skills that transfer between tools. The underlying question, what's worth automating and what isn't, doesn't change as quickly as the platforms do.
Understand your business, where AI might actually help, and where it probably won't. If we're not the right fit, we'll tell you.