Over the past 18 months, AI has moved from being a sideline topic in boardroom discussions to a main point on strategic agendas. Every company is considering how AI can help them work more efficiently, grow effectively, or solve ongoing operational issues. Yet alongside this wave of enthusiasm, there is a less-discussed reality: most AI projects fail, often without much recognition.
Research shows that 93% of AI transformations fail to achieve their intended goals. Additionally, 96% of large organisations have reported financial losses from unsuccessful AI initiatives, highlighting that the issue lies not in the technology itself but in the process behind its implementation.
Last month, we held a local seminar in Reigate titled “Avoiding the AI Trap”. I had the opportunity to speak with leaders from various sectors, and many in that room expressed a common concern: they feel pressured to adopt AI. Still, they are unsure how to implement it to minimise risk and maximise value.
What Causes Most AI Projects To Fail
Many believe AI projects fail because of unpredictable or untested technology. However, our experience with clients reveals that AI projects fail due to product-related issues long before they encounter technical problems. This insight is one of the most critical findings.
1. Starting with AI instead of starting with the problem
The conversation often starts in the same way. A senior stakeholder says, “We want to explore AI.” What can we do with it? Although this curiosity is valid, it is also the source of the problem.
When teams begin with solutions rather than needs, the same issues arise. Vague problem statements. Misaligned teams. Undefined workflows. No success metrics. This leads to what we call a hype-led adoption cycle rather than a strategy-led adoption cycle.
At Sonin, we address this through our discovery process, where we define the business objective, user needs, required data, and measurable indicators of value. If the problem is not well defined, AI cannot solve it.
2. Misreading organisational AI readiness
There is often a large gap between AI excitement and AI readiness. Many leaders believe their organisation is more AI-mature than it is. Some have lots of data but no clear use case. Some have clear use cases but unstructured or inaccessible data. Some have both but lack alignment across departments. Others still have teams running ahead while adjacent teams push back.
This leads to failed pilots, misinvestment and a sense that AI is hard to scale. But the real issue is a misunderstanding of starting conditions.
Understanding your AI maturity level is a critical step in the adoption journey. This is why we run AI readiness assessments with clients before any design or development begins.
3. Treating AI like traditional software
Traditional software is deterministic. The same inputs give you the same outputs. AI is not deterministic. It is probabilistic. It changes over time. It requires monitoring, safety constraints, success metrics and a clear feedback loop.
Many organisations deploy AI and then expect it to operate like a set-and-forget tool. This leads to inconsistent outcomes, model drift, workflow misalignment and user frustration.
This is one of the reasons why we emphasise product thinking in every AI project. AI needs governance, iteration and continuous optimisation, just like any other digital product.
What Businesses Need Before Adopting AI
At the seminar, we shared a simplified maturity model that shows the three factors essential for AI success.
- Clear business objectives
- Meaningful user/team needs and well-understood workflows
- Realistic understanding of AI and data capability
If any one of these is unclear or assumed, the risk of failure increases significantly.
Some organisations find themselves with an abundance of data but struggle to identify the right problems to solve. Others may have the correct issue to address, but lack a solid data foundation. Additionally, some have both data and the correct problems but lack alignment across teams. This model provides leaders with a straightforward way to identify gaps and determine where to focus their efforts first.
Why AI Requires Product Thinking
One statement that resonated with attendees was this: even with AI, you are still building a product. Technology does not determine success; value does.
Every successful AI product requires:
- Discovery
- User research
- Data mapping
- Hypothesis testing
- Clear success criteria
- A structured measurement plan
- Continuous iteration
This is how we determine whether AI is the correct answer and where it can add the most value. At Sonin, we regularly find that, after proper discovery, the solution is a combination of automation, workflow redesign, and, occasionally, AI. The difference is that the decision is evidence-based rather than hype-driven.
How To Escape the AI Pilot Loop
Many organisations we speak to find themselves caught in a cycle of pilot projects. They conduct AI experiments that show initial promise but fail to expand beyond the experimental stage. This stagnation occurs because the criteria for success are unclear, or the organisation has not established a clear definition of what constitutes a successful outcome beyond mere novelty.
To break this cycle, success must be tied to measurable business outcomes, not just model accuracy. Some examples include:
- Reduced manual review time
- Faster processing cycles
- Improved output quality
- Lower error rates
- Fewer support tickets related to a workflow
This is how we move clients from experimentation to adoption.
Start Small. Prove Value. Scale With Purpose.
One of the most practical takeaways from the event is this: do not begin by replacing an individual’s processes or workloads. Instead, start by removing their least valuable tasks.
Large AI initiatives can lead to unnecessary risks, fears, and resistance. By starting small, you can build trust, achieve early wins, lower costs, and provide the organisation with tangible evidence that the investment is worthwhile.
This approach is how we help businesses build momentum.
AI Is Not Set and Forget
One of the most prominent mindset shifts leaders must make is understanding that AI performance evolves. It does not stay fixed. It requires:
- Monitoring
- Evaluation
- Safety constraints
- Retraining
- Governance
- Adaptation as workflows change
At Sonin, we build refinement cycles into every AI project. This prevents degradation and ensures the AI solution matures with the organisation rather than drifting out of alignment.
In Summary
AI serves as a powerful amplifier. It enhances clear problems, effective processes, and well-defined workflows. However, it also magnifies weak foundations, which is why so many AI initiatives face difficulties.
If you want AI to deliver meaningful value, the answer is not more tools or bigger models. The answer is better product thinking.
- Start with clarity.
- Investigate the problem thoroughly.
- Understand your users.
- Assess your data.
- Define outcomes.
- Start small.
- Validate quickly.
- Scale with purpose.
This is the approach we take at Sonin. It is how we help businesses reduce risk, unlock value and build AI solutions that support long-term success.
The key question is not if you should use AI, but whether your company is prepared to develop AI products that yield measurable outcomes. Where do you currently stand on this journey?