Executive Summary
A practical guide to separating AI substance from sales spin
There’s a growing confidence gap in the Australian C-suite when it comes to AI. Leaders know AI is becoming more important to strategy, operations and competitiveness. At the same time, many AI initiatives still fail to deliver meaningful value. One reason is that the people approving investments don’t always have enough visibility into what they’re actually buying.
That creates a problem. When internal confidence is low, slick vendor pitches can fill the gap. Tools are presented as transformative, demos are tightly controlled, and technical complexity is often smoothed over in the name of simplicity. The result is that leaders may find themselves buying a compelling story rather than a well-grounded solution.
The good news is that you don’t need to become technical to ask the right questions. You need a clearer framework for evaluating claims.
Here are three questions that can help separate a credible solution from an impressive-looking pitch.
Question 1. Is this a differentiated solution or a packaged interface?
Many tools currently being marketed as AI products are built on top of foundation models from providers such as OpenAI or Anthropic.
That is not necessarily a problem. In many cases, it’s a perfectly sensible approach. The real question is whether the vendor is offering meaningful additional value through proprietary workflows, domain expertise, integrations, data assets or governance controls - or simply packaging a general-purpose model inside a nicer interface.
A useful way to test this is to ask:
Which foundation model or models are you using?
What proprietary data, workflows or logic make this solution different?
Where does your value sit beyond the interface?
If a vendor can’t clearly explain what genuinely differentiates the product, it may be worth looking more closely at whether the pricing and positioning are justified.
Question 2. How well does this work with real enterprise data?
Many AI demos look smooth because they’re built on clean, structured, carefully prepared sample data.
That tells you very little about how the solution will perform in the real environment your teams work in every day.
A stronger test is to ask how the product handles messy, inconsistent or unstructured information: old documents, fragmented records, scanned PDFs, inconsistent naming conventions, duplicated entries, or incomplete metadata.
Helpful questions include:
Can you show how this performs on unstructured or inconsistent enterprise data?
What data preparation is required before this can work effectively?
How much effort is needed to make our environment usable for the solution?
If the answer involves a lengthy remediation phase before any value can be realised, that doesn’t necessarily mean the solution is poor. But it does mean leaders should understand the true implementation burden, rather than being guided only by the demo's smoothness.
Question 3. What happens when the AI is wrong, unsure or incomplete?
This may be the most important question of all.
Vendors often talk confidently about autonomous agents, end-to-end automation and intelligent decision-making. But in most enterprise environments, the more important issue isn’t what happens when the AI works perfectly. It’s what happens when it doesn’t.
That is where mature solutions usually distinguish themselves.
Ask:
Where does human review sit in the workflow?
What happens when confidence is low, or the output is ambiguous?
How are exceptions surfaced, routed and resolved?
What visibility do we have into errors, overrides and escalation paths?
A credible vendor should be able to explain the human-in-the-loop model clearly. They should show where exceptions go, how risk is managed, and how the organisation stays in control. Confidence without controls is not maturity, it’s exposure.
The Leadership Responsibility
Executives don’t need to understand every technical detail about AI, but they do need enough fluency to exercise sound judgement. That means understanding what the solution depends on, what risks it introduces, what governance surrounds it, and what assumptions sit behind the commercial case.
AI-savvy leaders look beyond the interface. They probe the data foundations. They examine implementation effort, governance, oversight and total cost of ownership. They want to know not only what the tool can do, but what it will take for it to work reliably in their environment.
When those questions are asked early, weaker propositions tend to unravel quickly. Stronger partners, by contrast, are usually willing to engage in the detail.
Why Leadership Oversight Matters
As AI buying accelerates, the quality of leadership judgement becomes more important, not less.
The organisations that make better AI decisions are unlikely to be the ones chasing the boldest claims. More often, they’ll be the ones asking better questions, testing assumptions properly, and looking past surface-level polish to understand where the real value sits.
That is what gives leaders more confidence, and gives organisations a better chance of investing wisely.
Need an independent view of an AI vendor claim?
Vervio helps organisations assess technical fit, delivery risk and architectural implications before major decisions are made. Find out more here. https://www.vervio.com.au/services/technical-consulting
Meet the authors
Martin
FOUNDER & CEO
Martin is a visionary Founder with a passion for innovation and entrepreneurship and well-written code.
