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 starts at NZ$200 a month, which covers hosting, running costs, monitoring and support. Further workflows are quoted one at a time, on what each actually involves — no seat count, 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.

What happens if AI is left unchecked?

AI: A tool or a potential wild card?

We all know someone who, when left to their own devices, pursues their interests unchecked. They're smart, maybe even brilliant—capable of driving projects that evoke both awe and fear. But without guidance, these individuals can drift into realms of selfishness, causing disruption not out of malice, but from an unchecked pursuit of their goals.

Artificial Intelligence is much like these individuals. When we create sophisticated algorithms and systems, we're essentially building tools that think faster and more efficiently than we do. There's potential, yes, but as with that brilliant, unguided individual, there's risk if left without oversight.

The human parallel

Imagine an unchecked AI acting like a super-intelligent but isolated person. Such individuals—without a compass, feedback, or accountability—could well maximize outcomes purely for themselves. AI, absent of oversight, could optimize tasks in unforeseen ways, potentially bypassing ethical boundaries or societal norms.

The potential pitfalls

Self-interested optimization: Like an unsupervised genius pursuing ambition, AI might prioritize efficiency over ethics, optimizing in ways that humans might consider morally ambiguous or even harmful.
Unintended consequences: A brilliant mind can hatch complex plans that achieve specific outcomes without considering collateral damage. AI, too, could achieve its goals without regard for unexpected repercussions unless we set boundaries.
Isolation from external feedback: Without guidance, both human and AI can labor in self-made echoes, unchallenged by alternative viewpoints or corrections, potentially leading to unexpected dead-ends or disasters.

Cultivating an oversight culture

At hmn.plus, we've learned that AI development requires more than just sophisticated algorithms—it demands a framework of checks and balances. Much like mentorship and constructive feedback guide even the most independent thinkers, AI systems benefit from a carefully built oversight mechanism.

Implementing effective oversight

Human ethics in AI: Designing AI with embedded ethical red lines ensures alignment with societal norms—a lesson from how we guide young prodigies in ethical decision-making.
Continuous human involvement: Regular checks on AI development foster ongoing dialogue, much like how feedback refines and recalibrates human focus and ambition.
Dynamic adaptability: As we grow people through new experiences and learning, AI too requires the ability to adapt with the input of diverse perspectives and situations.

Conclusion: AI in partnership, not isolation

Unchecked AI mirrors the unchecked genius: unpredictable and often in service of its own optimized goals. It's why, at hmn.plus, we emphasize oversight—balancing freedom with responsibility. By embedding ethical guidelines and fostering continuous human interplay, we can harness AI's power without falling prey to the pitfalls of unchecked potential. This isn't just theory—it's practice and experience. The future isn't about AI replacing us but working with us, responsibly and effectively.