I have spent my career at the intersection of finance, technology and commercial decision-making. As finance director for a listed digital company in Saudi Arabia that builds market research- and AI-driven products, I have seen firsthand how promising ideas can lose their way long before they reach the market.
The irony is that most AI products don’t fail because the algorithms are weak. They fail because the financial and commercial foundations beneath them are fragile. The real challenge is not building the AI model – it is building a business around the model.
Flexible budgets
AI development rarely follows a straight line. Models need training, retraining and more data than anyone expects at the start. But the biggest financial risk is not the cost itself, it is the absence of a disciplined budget framework. Without clear financial guardrails, even a strong technical team can unintentionally burn through resources.
A small dose of reality early on can save millions later
A successful AI product requires three financial parameters:
- a defined cost envelope
- stage-gated spending
- a clear threshold for when to pivot or stop.
Technology development can be flexible. Budgets cannot.
The ROI blind spot
AI teams often focus on model accuracy, not commercial viability. But accuracy does not pay salaries – customers do. Many AI products fail because the capital burn rate grows faster than the path to revenue. The team may be improving the model, but the business case remains unchanged.
There are a few questions I always insist on asking early:
- Who will pay for this?
- Why will they pay for it?
- How soon can we test willingness to pay?
- What is the smallest version of this product that can generate revenue?
If there are no answers to these questions, then the product is not ready, no matter how impressive the technology.
Unrealistic assumptions
In digital and AI businesses, commercial assumptions are often made with the best intentions. I have seen teams assume that ‘the market is ready’ or that ‘competitors aren’t doing this yet’.
Yet when we finally test the product with real customers, the response is often more cautious than expected. Commercial success requires evidence, not optimism. This is why I push for early market validation, even if the product is still rough. These early validations directly influence growth planning, pricing models and the commercial roadmap. A small dose of reality early on can save millions later.
Board blind spots
The boards of listed companies are under pressure to show innovation. AI is attractive, exciting and often misunderstood – a combination that can create blind spots. I have been in leadership meetings where the enthusiasm for AI overshadowed practical questions such as the long-term cost of maintaining a model, emerging regulatory risks or even whether the product aligns with the commercial identity of the business.
Innovation has to survive long enough to become profitable
AI investments must align with long-term revenue growth and sustainable unit economics. Good governance is not about slowing innovation down but ensuring that innovation survives long enough to become profitable.
The missing link
Companies that succeed are the ones that build bridges between the language of AI teams, finance teams and commercial teams. Controlling costs is not the only valuable contribution a finance leader can make – translating the technical potential into commercial outcomes is equally important. That means:
- helping product teams understand financial constraints
- helping the board understand technical realities
- helping commercial teams shape pricing and positioning
- ensuring the entire organisation moves with a shared understanding of value.
AI products don’t fail because they lack intelligence. They fail because they lack alignment.
The future of AI will be shaped not only by engineers and data scientists, but also by leaders who understand how to turn innovation into sustainable business models. The most successful AI initiatives are the ones where:
- financial discipline guides creativity
- commercial insight shapes development
- governance protects long-term value
- leadership connects the dots.
Technology may power the product, but commercial strategy and leadership determine whether the business grows – or fails.
