I talk to a lot of business owners who come to me saying, “We need to build an AI solution.” When I ask them what problem they’re trying to solve, about 80% of the time the answer is something that an existing tool already handles perfectly well.

That’s not a criticism. The AI hype cycle has been relentless, and it’s genuinely hard to separate what needs a custom solution from what’s already been solved by someone else. So let’s work through it.

Start With the Problem, Not the Technology

This sounds obvious, but the number of businesses I see starting from “we should use AI” instead of “we have a problem that AI might solve” is remarkable. The first approach leads to expensive solutions looking for problems. The second leads to practical outcomes.

Before you even think about custom versus off-the-shelf, write down the specific business problem in plain language. Not “we need AI-powered customer insights” but “we’re losing 15% of customers after their first purchase and we don’t know why.”

That specificity matters because it determines the type of solution you need. Customer churn analysis? There are dozens of existing tools. Automated invoice processing? Plenty of options. Predicting equipment failure in a specific type of machinery that’s only used in Australian mining operations? Now you might be in custom territory.

When Off-the-Shelf Works (Which Is Most of the Time)

Here’s my honest assessment: for 80-85% of small and medium business AI use cases, an existing tool will do the job. Probably not perfectly, but well enough to deliver real value at a fraction of the cost of building something custom.

The common categories where off-the-shelf tools excel:

Customer service chatbots. Tools like Intercom, Zendesk AI, or Freshdesk’s Freddy AI handle the vast majority of customer service automation needs. They integrate with existing help desk workflows, support common CRM platforms, and improve constantly. Building a custom chatbot that outperforms these is expensive and rarely justified for an SMB.

Content creation and marketing. ChatGPT, Claude, Jasper, and dozens of others handle content drafting, email copy, social media posts, and basic analysis. Unless your content needs are highly specialised (medical, legal, technical), these tools work well with the right prompting.

Data analysis and reporting. Tools like Tableau, Power BI, and Google’s Looker now include AI-powered features that handle natural language queries, automated insights, and predictive modelling. For most business reporting needs, they’re more than sufficient.

Document processing. Extracting data from invoices, receipts, contracts? Services like Google Document AI, Amazon Textract, or Australian startup Sypht handle this at scale, trained on millions of documents.

Sales forecasting. CRM platforms like HubSpot and Salesforce have built-in AI forecasting that’s surprisingly accurate for businesses with enough historical data (typically 12+ months of sales records).

The total cost for a comprehensive suite of AI-powered off-the-shelf tools? Typically $500-$3,000 per month. Compare that to building a single custom AI solution, and the economics are clear.

When You Actually Need Custom AI

Custom AI makes sense in a narrower set of circumstances. Here’s what I look for:

Your data is truly unique. If the problem you’re solving requires training on proprietary data that no generic tool has access to — your specific product catalogue, your customer interaction patterns, your manufacturing process parameters — then custom training or fine-tuning may be necessary.

Existing tools don’t fit your workflow. Sometimes the problem isn’t the AI capability but the integration. If you need AI embedded deeply within a proprietary system or unusual workflow, and no existing tool offers the right APIs or connectors, custom development might be justified.

Competitive advantage depends on it. If the AI capability you’re building will be a core differentiator for your business — not just an efficiency tool but something your customers actually experience — the investment in custom development can be strategically sound.

Regulatory or compliance requirements. Some industries (healthcare, finance, government) have data handling requirements that prevent the use of third-party AI tools. Data can’t leave certain boundaries, certain processing must be auditable, or specific certifications are required.

Scale makes it economical. If you’re processing millions of transactions and paying per-API-call for an off-the-shelf solution, there’s a crossover point where building and hosting your own model becomes cheaper. For most SMBs, that crossover point is far higher than they think.

The Evaluation Framework

Here’s a practical way to make the decision. Answer these five questions:

1. Has someone already solved this problem? Search for “[your problem] + AI tool” and spend an hour reviewing what’s available. You’d be surprised how many niche AI tools exist for specific industry problems. Agricultural yield prediction, real estate valuation, legal document review — there’s probably something out there.

2. What’s your budget reality? Custom AI development in Australia typically starts at $50,000-$100,000 for a minimum viable product, with ongoing costs of $2,000-$10,000 per month for hosting, monitoring, and updates. If that’s more than 5-10% of your annual revenue, it’s probably not right-sized for your business.

3. Do you have the data? Custom AI models need training data. If you don’t have a substantial, clean dataset relevant to your problem (think thousands of examples, not dozens), a custom model won’t perform well regardless of the development budget.

4. Who will maintain it? AI models aren’t set-and-forget. They need monitoring, retraining, and updating as your business and data evolve. If you don’t have someone capable of this, or a budget for ongoing external support, the model will degrade over time.

5. What’s the realistic ROI timeline? Custom AI projects typically take 6-12 months to deliver a production-ready solution and another 6-12 months to generate clear ROI. If you need results in three months, off-the-shelf is the only realistic option.

The Hybrid Approach

There’s a middle ground that works well for many SMBs: using off-the-shelf AI platforms with custom configuration. This includes fine-tuning existing models on your specific data, connecting pre-built AI tools to your systems via APIs, using no-code or low-code platforms to build AI workflows specific to your business, and working with AI consultants in Sydney or other metros who can customise existing tools rather than building from scratch.

This approach gives you 80% of the benefit of a custom solution at 20% of the cost. For most small businesses, that’s the sweet spot.

My Honest Recommendation

If you’re a business with fewer than 50 employees, start with off-the-shelf tools. Give them a genuine three-month trial. Track the results. Identify where they fall short.

If there’s a clear, specific gap that no existing tool addresses, and that gap represents a significant business opportunity or cost, then explore custom development with clear scope and budget boundaries.

But don’t build custom because it feels more impressive or because a vendor told you off-the-shelf isn’t sophisticated enough. In my experience, the businesses that get the most value from AI are the ones that start simple, learn fast, and only add complexity when the problem genuinely demands it.