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.

Building Your AI Muscle in Financial Services

Building the AI Muscle

In a world where technology is rapidly changing the landscape of financial services, building your 'AI muscle' can make all the difference. I recently had the opportunity to witness this transformative journey firsthand at a financial services company during an agentic OS onboarding session.

Onboarding: A New Way of Thinking

Entering the room, I was greeted with eager faces ready to dive into the realm of AI. The initial energy was infectious. However, it soon became evident that while enthusiasm was high, confidence in using AI tools was shaky. Questions flooded in every few minutes, each one directed at me. The habit of asking a human first was deeply ingrained.

Our objective was clear: to transition from an apprehensive team to a confident, independent group capable of leveraging AI to its full potential. The key was straightforward yet profound—ask AI first. Like exercising a new muscle, it was a skill that needed practice to strengthen.

Transforming Questions into Learning Opportunities

The initial phase was challenging. Each time a question arose about workflow enhancements or financial modeling, I gently redirected them, encouraging the team to ask the AI tools first. This was about developing a new reflex; a decisive push towards empowering them to trust their AI companions.

Slowly but steadily, the questions to me began to dwindle. Not because the team had disengaged but because they were discovering the answers themselves, becoming more confident and autonomous in their roles. They were building their AI muscle.

Outcomes: Confidence and Innovation

What we witnessed was more than a success in AI adoption. It was a shift in how the team approached problems—strategic before tactical. Their newfound confidence resulted in improved problem-solving capabilities and innovative solutions that had previously been beyond their reach. They were staying in their zone of genius, focusing on tasks requiring human creativity and judgment while relying on AI for data-driven analysis.

According to a McKinsey report, 63% of financial services firms have adopted AI to enhance business processes.
A Deloitte survey highlighted that 45% of finance professionals report increased productivity in financial analysis processes due to AI.
Companies with mature AI strategies reportedly realize up to 30% higher revenue growth.

More Human Per Hour

The cumulative effect of building the AI muscle was clear: more human per hour. With AI handling routine tasks, the team could redirect their energies towards innovation, developing creative solutions tailored to complex problems, and ultimately propelling their company's growth.

Continuous Growth: Beyond Onboarding

This onboarding wasn't a one-time event but the beginning of an ongoing journey. As the team became accustomed to querying AI tools first, the cultural shift became palpable. An 'AI-first' mindset was taking root, fostering a culture of continuous learning and adaptation pivotal in navigating the evolving financial landscape.

In the financial services sector, where efficiency and decision-making define competitive advantage, fostering the AI muscle is essential. This approach not only ensures staying ahead of technological curves but also enriches human outcomes—confidence, efficiency, and innovation.

The success of AI integration in businesses is as much about tools as it is about people. So next time you face a challenge, remember to ask the AI first. The more you do it, the stronger your AI muscle becomes, empowering you to lead with outcomes over activity.