Do I need to know what should be automated?

No. Most people arrive with a feeling rather than a specification — something takes too long, or only one person can do it, or it gets dropped whenever the week is busy. That is enough to start from. If you can point at one job you would gladly stop doing by hand, we can work with that. If you can’t point at one yet, start with the free diagnostic instead: it finds the constraint first, and the workflow follows from it.

What happens with the first workflow?

We build it. You describe the job the way you would explain it to somebody starting on Monday — what comes in, what you would check, what should go out, and what you would never let leave without reading it. That becomes a working workflow, connected to the tools you already use, running on your account. You watch it run on real work before you rely on it. One workflow, start to finish, so what you are judging is a thing that works rather than a proposal for one.

What happens after that?

It runs. Keeping it running is the recurring part — hosting, the models it calls, monitoring, and the fixes for when something upstream changes its mind about a field name. Most people then add a second workflow, and it is usually one they spotted themselves while watching the first one work. There is no bundle to commit to up front. The next workflow is a decision you make when you want it, and not before.

Can my team eventually build workflows themselves?

That is the intended end state, and the training exists for it. Once your team has watched a few workflows get built, the skill turns out to be the describing — knowing what the work actually involves and where the judgment sits — which is the part your team already has. Some businesses would rather we kept building, and that is a legitimate choice rather than a failure. Most want at least one person inside who can change something without picking up the phone.

Do I become dependent on hmn.plus?

It is the right question to ask any vendor, and the honest answer is that you can be for exactly as long as it is useful. Three things are deliberate: the workflows are described in plain language rather than locked in a format only we can read, they run on infrastructure that can sit in your own name, and we teach the building instead of keeping it. Being the only people who can change your systems would be a business model. It is not ours.

Can we eventually own the system?

Yes, and it is an option rather than a destination — plenty of businesses would rather we kept running it, and that is a reasonable thing to want. When you do want it, the workflows, their definitions and the infrastructure transfer to you, and your team keeps the training that makes them usable. We would rather you stayed because staying is worth it.

The first automation is a fixed price, so the first decision is a small one: NZ$500 + GST to map it and get it built. After that, the platform it runs on is NZ$150 + GST a month for each person who builds workflows — everyone else who uses it is free — and that covers hosting, running your workflows, monitoring and support. Workflows you build yourself cost nothing extra; the ones we build are quoted one at a time, on what each actually involves. No annual lock-in, and nothing larger to sign before you have seen something of your own working.

Why not use ChatGPT, Claude, Zapier or n8n?

They answer a different question. Chat tools put something capable on the other end of a conversation, and then nothing happens unless a person types — every session starts again, and what the business knows ends up living in whoever writes the best prompt. Zapier and n8n wire apps to each other; the wiring holds until a field name changes, and it never makes a judgment call. Both leave the work depending on a particular person. What we build has a fixed shape: the same steps in the same order, your rules written in, a record of every action and the reason for it, and a person standing where a person should stand. The thinking inside a step can be as sharp as it needs to be. The path through it does not change because somebody phrased something differently. That is the difference between a tool one person is good at and a system a business runs on.

Where should AI actually be used?

On the judgment inside a step, not on the whole step. Reading an enquiry and working out what kind it is: yes. Drafting a reply in your voice, from your own past replies: yes. Moving money, or telling a client something binding, with nobody looking: no. What we build to is that anything with consequences waits for a person, and everything in front of it does not have to. A faster version of a bad process is still a bad process, which is why deciding where AI belongs happens before anything gets built, and is most of the value.

From execution to orchestration: AI UX insights

The changing landscape: From execution to orchestration

Speaking at the UX Christ Church meetup was a rewarding experience filled with engaging conversations and insightful discussions. The topic at hand—the transition from executing tasks to orchestrating workflows with AI—highlighted a shift in the skill sets that the market now demands.

Gone are the days when mere execution suffices. Today, the industry cries out for architects and orchestrators. The role of execution is now almost a given. Instead, the conversation is moving towards strategy, creativity, and that distinctly human interconnection layer that machines alone can't replicate.

Understanding the human value in orchestration

The heart of the discussion revolved around the human value of orchestration. It’s not just about creating workflows; it’s about understanding customer needs deeply and engaging in meaningful customer discovery. By co-creating personalized experiences through app and website interactions, we can harness technology to deliver remarkable results.

During the event, we delved into why the critical decision has shifted. It’s no longer purely about what we execute but more about what we choose to build and why. This shift is fundamental for ensuring reliable and consistent user experiences.

Scalability and the importance of system building

Building the right system is crucial. Only then can we achieve scalability and manage high volumes effectively. For instance, during my talk, I emphasized how AI agent memory handling—while perceived as complex—can be streamlined. Workflow building and execution are contextual, not necessarily memory-heavy, as context is offered when needed, avoiding reliance on exhaustive semantic data from vector databases.

Starting points and insights for AI workflows

For those new to workflow creation and AI agents, I recommended starting with technologies like Claude. It’s a practical entry point for understanding capabilities and building simple workflows. However, a word of caution: while Claude is an excellent learning tool, it’s not designed as a scalable, persistent system and may lead to vendor lock-in if not used judiciously.

A fantastic event fostering engagement and growth

The event was more than just a seminar—it was a collaborative exploration. Discussions spanned across different spectrums with students, professionals, senior personnel, business owners, and UX designers all contributing to the dialogue. The sheer potential of AI across industries and businesses was evident, empowering UX designers and other professionals to lead the charge rather than playing catch-up with the AI wave.

My thanks go to the organizers for facilitating such a fantastic event. It showcased not just the future of UX design but the transformative power of AI in business and professional spheres. I left the meetup energized and optimistic about the role orchestration will play in our technological evolution.