Artificial intelligence is no longer a fringe topic, yet many businesses are still unsure how to engage with it meaningfully. In our advisory work, we see the same tension play out repeatedly: some organisations rush headlong into AI because everyone else seems to be doing it, while others dismiss it as hype and choose to wait it out. According to Factional CTO and Senior Technology Implementation advisor Shane Williams, both approaches lead to the same outcome: failure.
Shane is a client we’ve been working with for over a year through our commercial advisory and mentoring work. As someone who regularly helps family owned manufacturing businesses select and implement technology, he has a clear view of what works, what fails, and why. We recently spoke with him because we’re seeing more businesses struggling to make confident, value-driven decisions about AI.
The first approach Shane encounters is FOMO-driven adoption (Fear of Missing Out). These businesses invest because everyone else is doing it. Decisions are rushed, objectives are vague, and the scope is often far too large. Shane shared an example of a business that spent over $250,000 on an AI system simply because their competitors had one. The result? No measurable improvement in operations. Shane likens it to buying expensive exercise equipment that ends up as a clothes rack. Action was taken, but without intention or outcomes, resulting in a waste of capital and no measurable value.
The second group sits at the opposite extreme: the sceptics. These businesses assume AI is just hype and choose not to engage at all. While this might feel safe in the short term, it creates a growing risk. As competitors adopt AI to reduce costs, improve decision-making, and move faster, sceptics are rapidly losing ground. The danger here isn’t wasted investment; it’s falling behind without realising it until the gap is too wide to close easily.
Shane advocates for a third path, and it’s the one we consistently recommend to our clients: start small, start with problems, and prove value before scaling.
This approach has a few simple steps:
1. Start with real operational problems, not technology features.
Focus on friction areas in the business, for example: time-intensive workflows, data hand-offs, reconciliation issues, reporting delays, or repetitive manual tasks. These are often the lowest-risk, highest-impact places for AI to deliver measurable improvement.
2. Define what success looks like before anything is built.
Every AI initiative needs clear KPIs, anticipated ROI, a named owner accountable for outcomes, and agreement on what “better” actually means. If success can’t be clearly articulated, the initiative isn’t ready to proceed.
3. Run small, tightly scoped pilots.
Implement one narrow use case and evaluate its impact within a short, disciplined window, no more than 90 days. Small pilots cap cost, limit disruption, and make it far easier to isolate cause and effect. Remember, it’s okay to fail.
4. Prove value first, then scale deliberately.
Once a pilot demonstrates measurable ROI, it becomes a repeatable template. Scaling then becomes a controlled expansion of what already works, rather than another leap of faith.
Shane has seen this approach deliver dramatic outcomes, including reducing operational downtime by months. The takeaway is simple: AI isn’t something to chase blindly or ignore entirely. The real opportunity sits in the middle, where clear objectives, disciplined execution, and measured scaling turn AI from noise into a genuine business advantage.
Our Director, Taural Rhoden, has been working closely with Shane on building more predictable revenue through disciplined commercial decision-making. If you’d like to speak with Taural about commercial advisory, contact us to arrange a meeting.