Building AI Businesses Around Customer Value.

A business owner rarely starts the day thinking about artificial intelligence models. They think about the enquiry nobody answered yesterday, the quote that is still unfinished, the document a client has not sent and the work waiting for a team that is already stretched.

They want customers served, invoices paid and fewer things falling through the cracks.

That is where I see the real opportunity in applied artificial intelligence. Not in the technology itself, but in the work businesses still struggle to do.

At a glance

• Customers do not buy artificial intelligence. They buy the removal of a problem they are already paying for in time, errors or lost revenue.
• A product earns its place when the value it creates can be measured, including the effort required to review, maintain and support it.
• The largest gains come from connecting a whole process, from enquiry to quote to job to invoice, rather than improving one step in isolation.
• Launching a product is the start of learning. Continued use, customer feedback and commercial performance decide whether it deserves further investment.

Start with a problem worth paying to solve

Most Australian businesses are still early in adopting artificial intelligence. The Australian Bureau of Statistics reported that 12 per cent of businesses used it in 2024 to 2025, with adoption far higher in information and professional services than in construction, agriculture or transport.

That gap is not explained by a lack of technology. It is explained by a lack of products that fit the way these businesses actually work, at a price that makes sense to them.

So at Sharktech Global, my first question is never “what can this model do?” It is:

• What work is taking too long?
• Where is information being copied between systems by hand?
• Which decisions are delayed because nobody has a clear view of what is happening?
• What is that costing the business today?

The answers determine what we should build. If we cannot describe the problem in the customer’s own words and put a cost against it, we are not ready to build anything.

Value has to survive contact with real work

Saving time is valuable only when that time can be put to better use. Faster follow-up matters when it improves the customer’s experience. Better reporting matters when someone can act on it.

A product also has to be honest about its full cost. An automation that saves two hours of administration but creates an hour of checking, correction and maintenance delivers less value than its demonstration suggests. I want to measure the net result, not the headline.

These are the questions any serious investor will ask of an artificial intelligence business: does the customer get a measurable return, can the product reach customers affordably, and does the cost of serving each customer fall as the business grows? I ask them of our own products first.

Build around how the business operates

Sharktech brings artificial intelligence, automation, data analytics, software, cybersecurity and information technology support together because, in a real business, those parts depend on each other.

An intelligent agent needs reliable information and appropriate access. A workflow needs a person responsible for exceptions. A dashboard needs measures the team understands and trusts.

Consider a single enquiry moving through a small business. It becomes a quote, then an approval, then a job, then an invoice. Improving the quoting step helps. Connecting the entire process removes repeated work across every step, reduces errors at each handover and gives the owner a clear view of what is happening.

The right solution still has to fit the people using it. They should understand what happens automatically, what needs their review and how to recover when something fails. Technology that people do not trust does not get used, and technology that does not get used creates no value.

Launching a product is not the same as building demand

I have led the launch of four products in Australia: Flagman.ai for industrial safety, VCPility for business growth, Launch Your Dream.ai for industry-specific growth platforms and Accrual OS for accounting practice workflows. They address different customers and different problems.

What I have learned is that a launch creates an opportunity to learn. It does not prove anything on its own.

Demand is proven by different evidence:

1. Continued use. Customers return to the product because it has become part of how they work.
2. Customer feedback. Users tell us what is missing, and their requests point to a clearer problem rather than a wider feature list.
3. Commercial performance. Customers pay a price that covers the cost of serving them, and that cost falls as we learn.

Each product has to earn further investment through that evidence. That discipline matters more in a portfolio, because time and capital spent on one product are not available for another.

What this means for founders and business leaders

For anyone building or buying artificial intelligence products, a few principles hold:

• Name the customer and the problem precisely before choosing the technology.
• Measure the current cost of the problem so that the improvement can be proven.
• Design the whole process, not just the impressive step.
• Count the full effort, including review, maintenance and support.
• Let evidence decide where to invest next.

My aim is to build products that become useful parts of everyday work. That requires a clear customer, a specific problem and a measurable reason to keep using the product.

For me, the opportunity in artificial intelligence begins there: with work people already need to do, and a better way to help them do it.

For conversations about investment, strategic partnerships or technology CEO opportunities, contact Dainu Devis.

About the author

Dainu Devis is an Australian emerging technology CEO. As Chief Executive Officer of Sharktech Global and Divine Lab Worx, he brings a decade of international business consulting and political strategy advisory experience to building emerging technology businesses. His work spans engineering, operations, product development and economic strategy, with concurrent product and process design at its centre.