Smart Start: Don't Waste on Failed AI

Artificial Intelligence has quickly become the centrepiece of modern business transformation narratives. Whether you’re attending a boardroom briefing or browsing LinkedIn posts, there’s a near-universal consensus: AI is going to change the game. And yet, for most organisations — especially small-to-midsize operators — the game hasn’t changed. At least, not in the way they hoped.

A recent report by RAND cuts through the buzz with a dose of hard truth. Despite 84% of business leaders stating they believe AI will significantly impact their business — and 97% admitting that urgency around AI adoption has grown — only 14% say their organisations are fully ready to integrate it. That gap between intent and implementation isn’t just a mild teething issue. It’s a chasm. One that has seen more than 80% of AI projects end in failure, a rate that’s twice as high as non-AI IT projects. The question we should all be asking is: why?

To answer this, RAND did something refreshingly practical — they spoke directly to the builders. Their team interviewed 65 data scientists and engineers with at least five years of real-world experience deploying AI/ML models across a range of industries. What emerged was a clear, pattern-based understanding of why AI projects tend to fall apart, and — more importantly — how that outcome can be avoided. For SMEs, the findings are gold.

At SrvdNeat, we’ve worked shoulder-to-shoulder with Australian business owners navigating AI adoption. We’ve seen firsthand how easily things can go off track when well-intentioned decisions are made in the absence of operational context or long-term thinking. RAND’s insights echo what we see on the ground every day — and validate the core philosophy we’ve built into NeatAudit, our AI readiness and deployment platform.

The first major failure point highlighted in the report is one that many in the industry quietly know to be true: most projects start by solving the wrong problem. The team at RAND found widespread evidence of mismatched expectations between technical and business teams. Models were trained and deployed only to realise they were optimising for metrics that had little relevance to the actual pain point the business was trying to address. In practice, that might look like a retail business developing an AI pricing model when the real margin leakage is in logistics. Or a professional services firm installing sentiment analysis tools when the issue is client churn due to delayed deliverables. Misalignment here isn’t just expensive — it’s fatal.

This is why, before any line of code is written, we invest in deeply understanding not just the business, but the real operational friction points. Our intelligence layer doesn’t “do AI” for the sake of it. It triages, scopes, and structures the problem first — so that any eventual automation or agent deployed is laser-focused on the outcome that matters most.

The second issue RAND outlines is a lack of usable, structured data. While the AI discourse tends to focus on models and capabilities, the unspoken reality is that no model can perform without fuel. Many organisations simply don’t have the clean, consolidated, and relevant datasets needed to make AI viable. Worse still, some don’t know that they don’t have it — entering into AI projects with blind confidence in the quality or availability of their internal information.

In the SME world, this is particularly pronounced. Data is often stored in silos — a spreadsheet here, an old CRM there, some financials in Xero, customer queries in email. Bringing that together into a coherent dataset is often more work than building the model itself. And if the data is missing, misleading, or fragmented? Any AI layered on top becomes a house built on sand. That’s why we designed NeatAudit to surface not only AI opportunities but also infrastructural gaps. If the data isn’t there — or isn’t good enough — we don’t mask it. We map it. Then we help teams build the foundational hygiene needed to make AI actually deliver.

The third pitfall RAND names is one we see far too often in fast-moving sectors: chasing tech for tech’s sake. It’s easy to get caught up in the momentum of the latest tool or model release — to assume that using GPT-4 or building with LangChain or vector databases must be the answer. But as RAND makes clear, the best AI projects don’t start with the technology. They start with the problem. Successful teams resist the temptation to let the tail wag the dog. They’re clear about what needs to be solved — and only then do they explore what technology best serves that outcome.

We’ve seen this firsthand. SMEs trial bleeding-edge platforms that promise low-code AI automation or agent orchestration — but after the first week, the results don’t quite align with reality. Why? Because the tools were selected based on novelty, not need. That’s why our approach is problem-first and tech-agnostic. We match capability to context. If a simple logic rule or webhook is the best fit, we’ll use that over a generative agent every time.

Then there’s the matter of infrastructure — or lack thereof. RAND rightly points out that even when a model is working well in a controlled environment, it often dies in production. SMEs, in particular, don’t have the MLOps muscle or DevSec pipelines of a large tech firm. That means even good ideas struggle to reach execution. Worse still, once deployed, models often require monitoring, updating, and refinement — tasks that are rarely planned for in SME budgets or timelines.

This is where many platforms leave SMEs stranded — promising intelligence but delivering complexity. At SrvdNeat, we’ve built our delivery flow to avoid exactly that. Whether it’s a lightweight automation or a more involved intelligent agent, our outputs are designed to integrate into your current systems and workflows. Not beside them. Not above them. Within them — cleanly, quietly, and without adding maintenance overhead your team can’t manage.

Finally, RAND touches on something that isn’t said enough: not every problem can be automated. Some tasks are fundamentally unsuited to AI. They require human nuance, ethical consideration, or real-time contextual judgement that no model — however large — can yet provide. AI is powerful, yes. But it’s not a panacea. And in the wrong hands or for the wrong task, it can create more problems than it solves.

This is why our approach isn’t about AI maximalism. It’s about targeted transformation. We deploy AI where it delivers tangible, measurable value. Where it doesn’t, we say so. And if the business isn’t ready — structurally, culturally, or operationally — we make that clear, too. It’s not about deploying the most AI. It’s about deploying the right AI — and doing so in a way that supports, not disrupts, the way your team works.

The RAND report is a timely reminder that AI isn’t failing because the tech is flawed. It’s failing because businesses are being sold a version of AI that’s disconnected from their actual needs. They’re being promised magic when what they need is clarity. They’re being offered tools when what they need is capability.

At SrvdNeat, we’re building that capability — quietly, thoughtfully, and purposefully — for the seven million Australians powering the economy through small and medium business. If you’re AI-curious but hesitant, or if you’ve already had a failed attempt and want to do it properly this time, let’s talk. Because AI that works doesn’t start with hype. It starts with understanding.