Why do so many tech projects fail?

Behind every successful innovation lies plenty of failure.

As any technologist will tell you, failure is part of the learning process. Without it, much of what we take for granted today would never have existed.

Failure, when recognised and learned from, can be a powerful teacher. But the scale of failure in today’s IT projects goes far beyond productive learning. The numbers are sobering, and nowhere is this more evident than in business transformation and AI initiatives.

According to BCG, more than two-thirds (70%) of large-scale tech programmes fail to be delivered on time, on budget, or within scope. Bain’s 2024 research found that 88% of business transformations fail.

Meanwhile, the State of AI Report 2025 shows that 95% of organisations see no measurable business impact from AI, despite $30-40 billion in investment.

So, what’s really going wrong?

Looking beyond execution

Project failures are often blamed on poor project management – scope creep, unclear requirements, weak stakeholder engagement, communication gaps, skills shortages, or quality issues. Yet, awareness of ‘best practices’ has never been higher. Certifications in project management, architecture, and business analysis are at record levels, and still, failure rates remain stubbornly high.

This suggests the problem doesn’t lie in execution. It begins earlier, in a stage often underestimated in its impact: the discovery or planning phase.

In our experience, four areas tend to be overlooked or insufficiently addressed:

• Understanding the current technology landscape
• Defining the true problem to solve
• Selecting the right solution and validating it early
• Recognising how psychological bias can distort decisions

Let’s unpack each one.

1 | Understand existing systems

Projects that aim to modernise or replace legacy technologies often fail because they skip a crucial step: fully understanding the systems already in place.

We’ve seen organisations underestimate hidden dependencies, small 'workarounds' built over years that nobody documented. When ignored, they reappear later as integration issues, delays and cost overruns. Modernisation without full system understanding isn’t transformation, it’s disruption disguised as progress. Why does this happen?

Legacy infrastructure is often seen as outdated or irrelevant. Documentation may be incomplete or missing. Key experts might have left the organisation. Or perhaps integration data is simply hard to trace.

Ignoring this context leads to weak integration, inconsistent data, and loss of functionality. Adoption rates drop, delays multiply, and costs spiral. Success depends on truly understanding how the business operates. That means reverse-engineering data models, mapping dependencies, and identifying the ‘invisible’ conveniences built into legacy systems over time.

2 | Make sure you’re solving the right business problem

If you don’t identify the real problem, every solution seems right, until it doesn’t.

Many companies have invested millions in chatbots to 'improve service”, only to discover that the true issue was slow case resolution caused by inefficient internal routing, not response times. Once the routing process was improved, customer satisfaction increased, even without the chatbot going live.

Many projects fail because teams never properly define the actual problem they’re trying to solve. Often, they’re not even aware of it.

A recent use case from Mindera Consulting illustrates this point clearly. A global food producer approached us with what appeared to be a straightforward request: to design a new online shop.

However, as discovery progressed and the right questions were asked, a much broader opportunity emerged. This project was not just about the shop, but a comprehensive digital strategy to consolidate stock, centralise order fulfilment, and define an operating model for scalable, global ecommerce growth. This client was attempting to build an entire ecommerce operation without any operational sustainability in place.

The project shifted from a technology build to a business transformation initiative, helping the client redefine their business plan before a single line of code was written.

Differentiating between surface symptoms and root causes is no easy task. When the discovery phase is rushed, the wrong solution often follows, leading to rework, shifting requirements, and missed deadlines. Until the underlying issue is uncovered, success remains elusive.

3 | Identify the best possible solution

Once the problem is clear, the next step is to validate the solution quickly through a Minimum Viable Product (MVP). Yet, many teams misunderstand what an MVP is, leading to feature overload, endless iteration, and ballooning budgets.

A true MVP is not a half-finished product. It’s a focused version built with only the essential features needed to test the concept, learn from users, and guide further development. This approach accelerates learning, reduces rework, and improves ROI, but it requires a balance of design thinking, prototyping, and early validation.

It is also important to consider that an MVP only delivers real value when its objectives are explicitly aligned with long-term goals. Each iteration should validate the strategic direction and build the solution incrementally along that path, rather than shifting towards short-term optimisations. Establish a concise set of success metrics that are directly linked to your guiding objective and the outcomes outlined in the business case. If the MVP reveals that the current approach will not achieve those outcomes, that too is a form of success, as it conserves budget and allows for early course correction.

Recently, we helped a national sports federation reshape a digital initiative. They initially asked for an app focused on fixtures and results, without any foundation to understand or engage their fans. Discovery revealed no clear link between that build and their real goals. We audited systems across match day, content, grassroots, and commercial operations, then defined a phased roadmap focused on learning and validation.

The MVP became a Fan Identity and Data foundation with consented profiles, single sign on, and CRM integration, supported by simple content that would later enable stronger activation across partnerships, commerce, and ticketing. Instead of building another generic app, we validated real user and business value early and set the foundation for long term impact through verified fan records, repeat engagement, youth registrations, and greater business value.

Training teams in this mindset is key to avoiding the ‘build more’ trap.

4 | Recognise and overcome psychological biases

Even the best-run projects can derail when human psychology takes over. Two biases are especially common:

• Escalation of commitment – the tendency to keep investing in a failing project out of loyalty to past decisions.
• Loss aversion – the discomfort of admitting failure, which can make teams cling to costly initiatives longer than they should.

Acknowledging these biases helps teams act faster when projects start slipping. Strong discovery work, realistic budgets, and transparent governance can prevent them from taking root in the first place.

Several big companies saved millions simply by having the courage to stop a data platform that no longer served their strategy and start again smaller, smarter, and faster.

Courage to stop is often a greater sign of leadership than persistence to continue.

Preparation is everything

Agile practices and strong execution matter, but without solid discovery, even the most disciplined delivery will struggle. Reducing failure starts with:

• Clearly defining objectives and success measures.
• Mapping risks and dependencies early.
• Ensuring every team member understands the potential pitfalls.

Of course, even with perfect preparation, failure can still happen. What matters is how you respond.

Recognise it, own it, and learn from it.

Avoid denial.

Foster a culture where it’s safe to admit mistakes and course-correct quickly.

That’s how organisations evolve.

When things do go wrong, denial is the biggest threat. Teams that embrace transparency, communicate openly, and treat mistakes as learning opportunities recover faster and deliver stronger outcomes in the long run.

Do everything you can to avoid failure, but always be prepared to learn from it.

So, how would Mindera support your initiatives to reduce failure?

If most projects fail because discovery is shallow, problems are poorly defined, or solutions are disconnected from business reality, then the path to improvement lies in how we start.

At Mindera, we believe that successful technology projects begin long before development starts. They start with the right questions.

Our Business Consulting team ensures that every initiative begins with a clear business perspective, questioning assumptions, uncovering the real problem to solve, and defining a path guided by ROI, measurable value, and strategic intent.

This business thinking is then complemented by deep technological expertise, where design, architecture and engineering work hand in hand with consulting to build scalable, robust and future-ready solutions.

It’s this complementarity between business and technology that allows us to help partners not just deliver projects, but produce outcomes that truly match expectations, grounded in reality, driven by purpose, and built to last.

Contact our Business Consulting team today to take the first steps on the journey to reducing failure and driving success.

Key takeaways

• More than two-thirds (70%) of large-scale tech programmes fail to be delivered on time, within budget, or within scope. Bain’s 2024 research found that 88% of business transformations fail. What's the reason behind the consistently high number of failures?
• This suggests the problem doesn’t lie in execution. It begins earlier, in a stage often underestimated in its impact: the discovery or planning phase. In our experience, four areas tend to be overlooked or insufficiently addressed: Understanding the current technology landscape, defining the true problem to solve, selecting the right solution and validating it early, and recognising how psychological bias can distort decisions.
• Even with perfect preparation, failure can still happen. What matters is how you respond. Recognise it, own it, and learn from it. Avoid denial. Foster a culture where it’s safe to admit mistakes and course-correct quickly. That’s how organisations evolve.