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.

Meeting Clients Where They Are in the AI Journey

Understanding the client's AI journey

When I launched HMN.plus as an AI consultancy, my aim was to empower businesses with the tools and knowledge they needed to leverage AI effectively. Over time, this evolved into a product—a scalable AI automation system. But a recurring challenge has been the varying stages of AI adoption among clients. Some are just starting out, exploring basic tools like ChatGPT. Others are advanced users, integrating AI agents and using sophisticated platforms.

This diversity in client experience prompted a key realization: success in the AI sector is not solely about pushing a product. It's about offering the right guidance based on where clients are in their AI journey.

The value of a flexible approach

My recent conversation with a prospective client reinforced this perspective. People need support that aligns with their current understanding and experience of AI. I've developed a scaling funnel, which is less about selling and more about educating. The goal is to equip clients with the knowledge to make informed choices—whether or not that leads to adopting our product.

Clients must be aware of the potential risks and benefits of AI approaches. For instance, AI agents can seem attractive, but they aren't always the most cost-effective solution, especially when AI tokens are subsidized. Then there's the question of hallucinations—those unpredictable instances when AI systems output incorrect information—and how they affect decision-making and accountability.

Why systems matter

To build trust and foster genuine relationships, I've embraced a systems perspective. AI systems are deterministic in nature, meaning they don't necessarily produce consistent results without proper configuration and oversight. It's crucial for clients to understand this as they navigate their AI journey.

When we meet clients where they are, we're not just offering a product. We're building a relationship based on understanding their needs and providing honest guidance. This approach doesn't just scale my business; it lays the foundation for trust and long-term collaboration.

Building trust through informed decisions

Ultimately, the best approach to offering AI solutions is one that is transparent and client-focused. By providing insights into the complexities of AI—from the functionality of AI agents to the implications of determinism—we empower clients to choose what's best for them. This could mean opting for our product, or it might mean choosing another platform. What's important is that they make an informed, confident decision.

While AI technology continues to evolve quickly, the principles of good business remain steady. Meeting clients where they are fosters loyalty and helps them succeed. In the fast-paced world of AI, there's no better strategy.