Oakes Consulting
AI
The tools are not the hard part.
Getting people to change how they work is. That is the part I was trained on, and it is why AI engagements here start with your people and your processes rather than a product demo.
AI belongs in the logistics, not in the relationships
The tools change monthly. The judgment about where they belong does not.
I use these tools every day, at depth, in my own work. They are genuinely good at the mechanical parts: drafts, summaries, first passes, follow-ups, the reporting nobody has time to build. Used well, they hand your people back hours for the work only people can do.
If AI makes you uneasy, that instinct is worth keeping. The organizations getting this right are not the ones handing everything over. They decide, on purpose, which work is mechanical enough to delegate and which work stays human on purpose. A thank-you note that drafts itself is a gift to your team. A hard conversation that drafts itself is a problem.
And most AI trouble is not a tool problem. It is an adoption problem: something new arrives with excitement, and a few months later nobody is using it. Adoption is the sixth dimension of the framework behind all of my work, and it is where a doctorate in industrial and organizational psychology earns its keep, because getting people to take up a new way of working is a science, not a pep talk.
01
Training and Workshops
Hands-on, starting from wherever your team actually is, including zero. The goal is not enthusiasm. It is a team that knows what these tools are for, what they are not for, and how to tell the difference on a normal working day.
- Leadership briefings: what this is, and what it is not
- Staff workshops on your real work, not canned demos
- Room to practice, with every question welcome
02
Implementation
AI put to work inside the systems you already run, on the work you already do. Built in the open, like everything else here, so you can see what it is doing and redirect it while redirecting is cheap.
- Drafting, summarizing and reporting inside real workflows
- Automation with AI in the loop, and a person at the end of it
- Connected to the software you already pay for
03
Policy and Guardrails
Your people are already using AI, whether or not anyone has said so out loud. A written policy turns that from a quiet risk into a decision: what it may be used for, what never goes into it, and where a person stays accountable by name.
- An AI use policy people can actually read and follow
- Data and privacy lines: what never leaves the building
- Where a human signs off, written down by name
04
Tool Selection and Rollout
The vendor noise is deafening, and most of it is the same three capabilities wearing different logos. I cut the list to what fits your work and your budget, then roll it out the way anything should arrive: with training behind it and a check afterwards.
- A short list that fits how you actually work
- Rollout with training behind it, not an announcement
- A check a month later on whether anyone is using it
Bought alone, like everything else
Plenty of organizations start with a leadership briefing and nothing further, and some just want the policy written. Any of these can be bought on its own, and an engagement runs the same four stages as everything else: that is on how it works, and the rest of what I do is on what I do.
Curious where AI actually fits your organization?
That is a conversation, not a commitment. Bring your skepticism; it will be useful.