Why most AI projects fail & what can you do about it

Imagine this: it’s 2023, and AI is suddenly everywhere. From the release of ChatGPT to the constant chatter about automation, companies are scrambling to get on board. The promise? A revolution. Faster workflows, fewer errors, and innovation on a scale we’ve never seen before.

But fast forward to today, 2024, and reality has set in. What was once a rush to implement AI has become a quiet struggle. Excitement has been replaced with frustration, and instead of success stories, the headlines ask, “Why are so many AI projects failing?”

So, what happened? Why did so many companies fall short of AI success? More importantly, how can you make sure that your AI projects don’t just survive but thrive?

The truth behind failed AI projects

Let’s be real - getting AI to work well is tough. What seems impressive in a demo often crumbles when it's time to go live. In fact, according to a recent Gartner report, at least 30% of generative AI projects will be abandoned after the proof-of-concept stage by the end of 2025. Why are so many AI initiatives hitting a wall? There are a few reasons, and they’re more common than you think:

• High costs
  AI is expensive - there’s no getting around that. From maintaining infrastructure to updating models, the costs can spiral quickly, leaving companies questioning whether the return on investment is worth it. Gartner estimates that building or fine-tuning a custom generative AI model can cost between $5 million and $20 million, plus $8,000 to $21,000 per user per year. Additionally, using a generative AI API might cost up to $200,000 upfront and an additional $550 per user per year.

• Low accuracy 
  Building a prototype that works at 75% accuracy is relatively straightforward, but businesses need AI that performs at 90% or higher. That last 15%? Achieving it is significantly harder and takes far more work than expected. It's the difference between an AI that occasionally assists and one that consistently delivers value.
• Low adoption
  Even if the AI works, getting teams to embrace it is a different story. People are creatures of habit, and AI can feel like an unwelcome disruption. That's why, despite the hype, % of employees consistently using AI tools like Copilot is low. Resistance to change, lack of understanding, and fear of job displacement all contribute to low adoption rates.
  

These hurdles mean that AI projects often end in disappointment. Instead of celebrating, many teams are left explaining why their solutions aren’t delivering the expected results.

The AI reality check: only 7% of people are truly proficient

Here’s a number that might surprise you: just 7% of the workforce can be classified as truly proficient in using AI to make a real difference. These people save 30% of their time, unlock AI’s potential, and drive productivity. That leaves 93% of people either experimenting or not using AI at all. This gap is a significant factor in why AI projects fail. AI tools alone won't automatically lead to success. You need the right talent and training to make AI initiatives work. Without it, AI remains just another buzzword.

Beyond the obvious challenges

Beyond the obvious hurdles, there are underlying issues that sabotage AI projects:

• Data quality and availability

AI thrives on data, but not just any data. High-quality, relevant, and well-structured data. Many companies underestimate the effort required to collect, clean, and maintain datasets. Poor data leads to poor AI performance, and without proper data governance, projects are doomed from the start.

• Lack of clear objectives

What's the problem you're trying to solve with AI? Vague goals like "improve efficiency" aren't enough. Successful AI projects have specific, measurable objectives. Without a clear target, even the most advanced AI can miss the mark.

• Over-reliance on technology over people

AI is powerful, but it's not a magic solution. Companies often focus on technology and neglect the human element - the people who use, manage, and are affected by AI systems. Ignoring organisational culture, change management, and user training leads to resistance and failure.

How to succeed where others fail

Don’t just throw technology at the problem and hope for the best. AI success requires more than just good software - it needs the right talent, tools, and approach. Here’s what you can do to avoid the common pitfalls.

1. Build a custom AI stack

Off-the-shelf AI models might look good in demos, but they often fall short in real-world applications. Tailoring AI solutions to your specific industry - whether that’s finance, healthcare, or another field - delivers deeper insights and better accuracy. For instance, an AI model trained on general language data might not perform well in the medical domain without specialised training.

What does building a custom AI stack involve?

Building a custom AI stack involves creating a tailored architecture that integrates multiple AI components, such as models, data pipelines, and deployment frameworks - designed specifically for your business’s needs. This approach considers your industry’s unique data requirements, regulatory constraints, and operational workflows.

Why is a custom AI stack better?

Pre-built models often rely on general-purpose data and algorithms, meaning they might lack the nuance needed to solve specific, industry-driven problems. A custom AI stack, by contrast, allows you to create models that directly address the challenges unique to your domain. For instance, in finance, custom models can be trained to recognise specific patterns in transaction data, improving fraud detection accuracy far beyond what a generic AI model could achieve. Additionally, having full control over your stack enables continuous refinement and adjustment as new data or business requirements emerge.

How does it future-proof AI strategy?

A custom AI stack allows for modularity and flexibility, meaning you can integrate newer models or update components without rebuilding the entire system. As new technologies like quantum computing or advanced neural networks become accessible, a custom AI stack can be upgraded to incorporate these innovations, ensuring your AI infrastructure remains adaptable and competitive in the long term.

2. Implement guardrails for control

AI can sometimes feel unpredictable. Implementing guardrails helps ensure that AI stays aligned with your business needs, providing control over outputs without constant developer intervention. This includes setting ethical guidelines, compliance checks, and validation processes to prevent undesired outcomes.

What are AI guardrails?

AI guardrails are crucial frameworks that ensure AI operates within predefined ethical, legal, and operational boundaries. These are not just technical controls but can also include governance policies that outline how data is collected, processed, and utilised. For example, in sectors like autonomous driving, guardrails ensure that the AI-controlling vehicles adhere to safety standards, only making legally and ethically sound decisions. Without these constraints, AI models could behave unpredictably, posing risks to both users and companies.

How do guardrails provide better control over AI outputs?

Guardrails create checkpoints in AI workflows that help detect and mitigate errors or deviations before they have a significant impact. By embedding mechanisms such as bias detection, anomaly identification, and real-time monitoring, you ensure the AI is producing reliable and consistent outputs. For instance, a financial AI system might flag a transaction as suspicious and immediately trigger a compliance review. These controls prevent incorrect outputs from cascading into larger issues and ensure that AI operates in alignment with business goals and regulatory requirements.

How can guardrails prevent AI risks?

The risks associated with AI range from unintended biases to breaches in regulatory compliance, especially in sectors like healthcare, finance, and autonomous technologies. Guardrails help mitigate these risks by enforcing strict guidelines on how the AI should behave under various circumstances. Take AI in healthcare: guardrails can be implemented to ensure patient data is anonymised and securely processed to prevent data breaches. In the financial industry, guardrails might involve strict adherence to anti-money laundering (AML) protocols, ensuring the AI doesn't approve transactions that could potentially violate legal standards.

3. Invest in skilled teams

A major reason AI projects fail is the lack of the right talent. Bringing in AI specialists, whether it’s data scientists, engineers, or project managers, is crucial to moving from prototype to production. But don't stop there. Upskill your existing workforce. Provide training and resources to help your team understand and leverage AI effectively.

Why do AI projects need specialised teams?

AI is complex and requires expertise across multiple disciplines, including machine learning, data science, software engineering, and domain-specific knowledge. A specialised team ensures that each stage of the AI lifecycle - from data collection to model training and deployment - is handled by experts who understand the technology and the industry in which it operates. For instance, data scientists must be proficient in selecting the right algorithms, while engineers must scale models and integrate them into production environments. Meanwhile, domain experts ensure the AI addresses real-world problems effectively, reducing the likelihood of failure.

Why is upskilling your existing workforce important?

AI is not just a tool for technical teams; it’s an enabler for the entire organisation. Upskilling your workforce ensures that various departments understand how to use AI in their roles, from automating routine tasks to making data-driven decisions. For example, a marketing team that understands AI can use predictive analytics to improve customer segmentation, while a logistics team can utilise AI to optimise supply chain management. Training your existing employees allows you to fully leverage AI across your operations, ensuring that the technology is not siloed but integrated into everyday workflows.

How does investing in talent de-risk AI efforts?

AI is evolving rapidly, and so are the skills needed to work with it. By investing in your team, you ensure that they can handle current AI technologies and are prepared to integrate and utilise emerging innovations.

4. Choose developer-friendly tools

Select tools that streamline the integration process, helping your team easily deploy and manage AI solutions. Consider solutions that are agnostic towards large language models (LLMs) or AI providers, allowing you the flexibility to choose or switch between different technologies as needed. This approach prevents vendor lock-in and ensures you can test AI models available.

What does an agnostic approach mean?

An agnostic approach involves creating AI solutions that are not dependent on any specific AI model or provider. This strategy gives you flexibility and adaptability, allowing you to leverage the best available technologies as they evolve.

Why flexibility matters?

The AI industry moves fast, and committing to a single provider can slow down your ability to innovate. By adopting an agnostic approach, you’re free to integrate multiple AI models tailored to specific tasks without the limitations of a one-size-fits-all solution. You stay agile, capable of switching to better-performing models as soon as they’re available.

Is it the key to future-proofing AI?

An agnostic approach allows you to optimise performance and manage costs by selecting the best model for each specific need. You also reduce the risk of service disruptions tied to a single vendor, ensuring your operations are stable and resilient.

5. Seamless integration

AI should complement existing systems, not complicate them. Developer-friendly tools and streamlined processes help make integration smoother, ensuring that AI deployments are effective and efficient. APIs, microservices architecture, and modular design can facilitate this integration.

A shift in management and investment

As AI becomes more embedded in business operations, it's not just the technology that needs to evolve - your approach to product development and team management has to change, too. The old ways of working in silos won't cut it anymore.

• Embrace cross-functional teams
  Truly cross-functional teams bring together domain expertise and AI proficiency. When data scientists, engineers, and business analysts collaborate, they can bridge the gap between technical capabilities and business needs.

• Cultivate an AI-driven culture
  Foster an environment where experimentation is encouraged, and failure is seen as a learning opportunity. Leadership buy-in is essential. Leaders should champion AI initiatives and model the behaviours they wish to see in their teams.

• Invest in talent development
  It's not enough to hire a few costly AI experts. You need to upskill your existing workforce, giving them the training and confidence to become part of the AI class. This investment pays off in increased innovation and a more agile organisation.

To illustrate how these principles come together in practice at Pwrteams, have a look at how we helped a fintech company build an AI-powered price prediction engine that benefits businesses across industries.

Ready to thrive with AI?

AI is here to stay, but succeeding with it isn’t automatic. It takes the right strategy, the right technology, and, most importantly, the right team. With Pwrteams, you get all three. We’re here to help your AI projects not just survive - but thrive.

Let’s make AI work for you. Ready to take your next step? Get in touch with us today and find out how we can help your business succeed with AI.