My work moves between people, brands, systems and writing. I help people and teams find a usable next step with AI, and I write about the identity, care and lived experience that cannot be reduced to a workflow.
It began with people, pressure, and the patience to stay with a problem long enough for the next clue to appear.
“The person in front of the problem is more important than looking technically impressive.”
A people-first background shaped the method: understand the pressure, reduce shame, reflect what is real, then help the next action become visible.
Hosting conversations, being interviewed, and representing Diggn'It in public strengthened the work I still rely on now: listen carefully, explain without performing certainty, and keep the person inside the problem.
AI advice often becomes too abstract too quickly. Try Next starts with the human moment around the task: what is happening, what feels unclear, and what would make one next move useful.
The public method is simple: Notice, Narrow, Try, Next. Make the moment small enough to try, give the tool useful context without giving away what should stay private, and keep your judgment in charge of what happens next.
Your starting point should be safe to say before it is polished into a plan.
AI should reduce confusion, repeated work, or decision drag, not create more homework.
Every session should create something useful: a decision, a draft, a prompt, a workflow, or a next action.
The handover should be clear enough for you or your team to keep using after the conversation.
Bring the thing that feels stuck. The first move is to make it clear enough to act on, then choose the right container only if deeper support makes sense.
