Author: MindArc, July 28, 2026
When AI makes it possible to build almost anything, the question isn't just whether you can — it's whether you should. Building gives control but carries security and maintenance overhead. Buying is faster but creates vendor dependency. Borrowing via APIs is cheapest upfront but trades data control for speed. The right call depends on your use case, data sensitivity, and team capability.
In our first episode of Signals by MindArc, Co-Founder and Director Sean Pieres sat down with Head of Technology John Certeza-Vella to talk through a question many e-commerce leaders are asking right now, with AI making it possible to build almost anything. When building your own tool is within reach, how do you decide what's worth building, what's worth buying, what's worth renting for now, and what's worth leaving alone entirely?
Their conversation drew on real problems MindArc has solved and seen with AI to date. Here's what that experience has taught them about the framework itself, the risks worth watching, and how to prioritise where to start.
What are the cost differences between building, buying, or borrowing AI tools?
Buy: Purchase the software outright. Best suited to tools you'll use exactly as they come, with no real need to customise.
Borrow: Pay an ongoing licence or subscription. Makes sense for anything that isn't core to how your business runs day-to-day.
Build: Invest the time to create the tool yourself, with no ongoing cost beyond infrastructure. Only worth it once you can name the specific gap a generic tool leaves open, and you have access to the expertise, in-house or through a partner, to close it properly.
Most tools don't cover everything a business needs. There's usually a gap, and until now, building your own to close it was too expensive for most teams. AI is changing that. Building is now a real option, even for businesses without a big dev team.
That doesn't mean building is always the right call. It just means it belongs in the conversation. Here's what's worth thinking through before you start.
What security risks come with building your own AI tools?
AI has lowered the barrier to building something that works. It hasn't lowered the barrier to building something that works safely.
Security and infrastructure gaps are easy to miss when the focus is on getting something functional fast. A well-known example involves an API key left exposed on the public-facing side of a website. Once it's discovered and scraped, anyone can use it, and the resulting bill can run into the tens of thousands before anyone notices.
For any CTO or technical lead, anything built quickly through AI still needs a proper security review before it touches live infrastructure or customer data.
What is the scraping trade-off when borrowing AI capabilities?
E-commerce businesses carry a second, more specific risk. Product and pricing data sitting on a public website is now easy for AI tools to scrape and act on automatically. That could be a competitor tracking pricing, or something more direct, like products being relisted elsewhere with orders routed straight back to the original site the moment a sale is made.
Locking that down protects the data. It's worth knowing the trade-off. The same access that exposes a site to scraping is often what makes its products discoverable to AI shopping tools in the first place. There's no simple fix here, only a decision about which risk matters more for your business right now.
What should ecommerce teams watch out for when evaluating AI tools?
Much AI content online is built to generate attention rather than convey anything useful. Bold claims and lists of "top prompts" rarely survive real testing, and most are forgotten within a day or two of being posted.
What's worth paying attention to is proof. A workflow that's been properly tested and shown working, often shared through an open source project or a screen-recorded walkthrough, carries far more weight than a long post nobody reads past the first line.
The same logic applies to picking a model. Testing a few rather than committing to one is worth the time, since each tends to have a strength, whether that's research, writing or image generation. Benchmarking tools now exist that check a model's output against a historic baseline every time it updates, so a shift in capability shows up as data rather than a guess.
What hidden costs do ecommerce brands miss when adopting AI tools?
AI pricing has moved to usage- and quota-based models, which means the tool and the model chosen for a task change what it costs to run. Reaching for the most capable model available for a simple task burns through usage far faster than necessary, when a lighter option would do the job just as well.
How a task gets done matters just as much as which model does it. A general-purpose AI agent manually working through a report can cost significantly more than pulling the same data through a connection built specifically for that purpose, for the exact same result. That gap tends to widen as usage scales, which makes it a budgeting risk, not just an efficiency one.
What MindArc learned building its own tool
MindArc has been putting this into practice internally, building its own AI tools rather than just talking about the possibilities. One example is a tool that automatically sorts incoming email, built because no off-the-shelf option could handle the specific rules needed to separate personal messages, client work, and billing. It started with broad, general rules, and had to be refined repeatedly over time to reach a workable level of accuracy.
The lesson applies well beyond email. Letting a tool run fully unsupervised before it's proven reliable carries real risk. One cautionary example that circulated widely involved an AI assistant that began deleting messages after a safeguard was lost during a routine update. Keeping a human check in place until a tool is consistently accurate is a sensible starting point.
Where should an ecommerce brand start with AI tools?
The lowest risk entry point is still the simplest one. Using AI for content, reporting and internal documentation is a safe way to start testing what it can do for your business.
Getting a personal or internal tool off the ground from there is achievable for most teams. Scaling that reliably across a whole business, or beyond it, is a different challenge, and it's usually the point where bringing in the right technical expertise makes the difference between a good idea and something that truly holds up.
Want the full conversation? Listen to the first episode of Signals by MindArc, where the team unpacks build, buy or borrow, the risks of AI-built tools, and what they've learned putting this into practice.
→ Watch the full episode here.
Want to know more?
Weighing up a similar decision for your own business? Reach out to the MindArc team at hello@mindarc.com.au or contact us below.
