Artificial Intelligence (AI) is one of the most transformative technologies of the 21st century. I think not many will be able to argue with that. The hype surrounding everything that has to do, even remotely sometimes, with AI is beyond imagination.
However, despite the hype and potential, many AI projects fail to deliver on their promises. Many studies have shown that a large number of AI projects do not meet expectations or are given up on completely. Why does this happen? What are the main mistakes that cause AI projects to fail?
In this article, we will look at the main reasons why AI projects fail, based on real-life examples and expert observations.
Lack of Understanding of the Business Context
One of the most common reasons artificial intelligence projects frequently fail is because of a lack of clear understanding of the business context. As this expert aptly put it, "All machine learning projects that turned into a disaster in my career have one common point: they didn’t understand the business context first, got overexcited about the tech, and jumped into coding too early." This sentiment is shared by a significant number of professionals who have been involved in artificial intelligence developments.
Teams often rush to use complicated AI solutions for problems that could be solved with simpler, rule-based methods. As an example, using a costly AI chatbot to answer simple questions like store hours or product information might be unnecessary. A static FAQ webpage or a rule-based bot could achieve the same result at a fraction of the cost. The main point is to resist the urge to use flashy AI tools without checking if you really need them.
To avoid this pitfall, it’s important to analyze the problem complexity, compare different solutions, and focus on the value that AI will bring to the business. Make sure that your AI project is aligned with your organization’s goals and addresses a genuine business need. Ask yourself: What is the business value here? Can this problem be solved with a simpler heuristic? By answering these questions, you can definitely increase the chances that your AI project is aligned with the organization’s goals and delivers tangible results.
Poor Data Quality and Data Engineering Challenges
Another major reason AI projects fail is poor data quality. Let's be precise here, data is undeniably the lifeblood of AI, and without high-quality data, even the most sophisticated algorithms will fail to deliver accurate results.
Data quality issues can present themselves in several forms: it may be incomplete, biased, or of low quality. Nonetheless, the challenges extend beyond just data quality. The essential tasks of data engineering—cleaning, preprocessing, and maintaining data—are of utmost importance.
Mistakes made during the data engineering phase can have far-reaching consequences. For example, if a data engineer fails to properly clean or preprocess data, it can lead to inaccurate predictions, biased outcomes, and wasted resources. Consider a scenario where a company is trying to predict customer churn using CRM data. If the data is skewed or incomplete, the resulting model will be unreliable, leading to poor decision-making.
The solution to this problem is twofold. First, invest in high-quality data collection and preprocessing. Go above and beyond to guarantee that your data is clean, complete, and representative of the problem you’re trying to solve. Second, hire skilled data engineers who can maintain data integrity throughout the project lifecycle. By addressing data quality and engineering challenges early on, you can set your AI project up for success.
Learn more about the dangers of inaccurate data and what you can do about it.
The Hype Surrounding Generative AI
With tools like ChatGPT, DeepSeek, Gemini, etc. generative artificial intelligence has spread across the world like wildfire. Still, the buzz around generative artificial intelligence can also be a two-edged blade. Executives sometimes feel that these technologies can magically change their businesses without fully realizing the time and cost needed to apply them successfully.
This phenomenon, often referred to as "shiny tech syndrome," leads organizations to adopt advanced AI tools for problems that could be solved with simpler, more cost-effective methods. For example, using a large language model to answer a simple question like "Where is my order?" might be overkill. In many cases, a rule-based system as we mentioned above could handle such queries more efficiently and at a lower cost.
To avoid falling victim to shiny tech syndrome, it’s important to focus on the user problem and evaluate whether AI will truly enhance the user experience or solve a major pain point. Don’t adopt AI for the sake of it—ensure that it adds real value to your business and consider simpler, more cost-effective solutions where appropriate.
Outdated Infrastructure and Lack of Resources
Projects involving artificial intelligence sometimes call for large computational resources and infrastructure. Many companies, meanwhile, battle with outdated technology that is clearly unable to meet the needs of modern artificial intelligence applications. For example, if your infrastructure is outdated or lacking the required tools, integrating a real-time data processing system might not be the best business idea.
The encouraging news is that modern artificial intelligence capabilities are now available on cloud platforms including AWS, Azure, and Google Cloud. Such platforms give access to innovative tools and frameworks, so lowering the need for companies to keep their own AI infrastructure in place. You will thus have to select the one that best suits your need since not all frontier models are available on every platform.
If your company is thinking about an artificial intelligence project, evaluate your tools and systems. Make sure your artificial intelligence projects can be supported by your storage, bandwidth, and computing capability. If your present setup falls short, think about closing the difference with cloud-based solutions.
Misalignment Between AI and Business Goals
Many AI projects fail because they are not aligned with the organization’s broader business goals. This misalignment can occur for several reasons. For example, the project might be driven by a desire to adopt the latest technology, rather than a genuine need to solve a specific business problem. Alternatively, the project might lack clear objectives or key performance indicators (KPIs), making it difficult to measure success.
To avoid this pitfall, ensure that your AI project is closely tied to your organization’s strategic goals. Define clear objectives and KPIs at the outset, and regularly evaluate the project’s progress against these metrics. By aligning your AI initiatives with your business goals, you can ensure that they deliver real value to your organization.
Lack of Skilled Talent
The availability of skilled talent is likely the most crucial factor in determining the success or failure of an AI project. AI is a specialized field that demands expertise in areas such as machine learning, data science, and software engineering. Many organizations face challenges in finding and retaining the right talent, which can result in less effective AI implementations.
The shortage of skilled talent can show up in different forms. A data scientist may not have the necessary domain knowledge to grasp the business context, while a software engineer might find it challenging to incorporate AI models into current systems. Even the most meticulously planned AI project can falter without the appropriate skills in place.
Organizations should focus on investing in training and upskilling their workforce to tackle this challenge effectively. Furthermore, think about collaborating with external specialists or consulting firms to address any areas where your team may need additional expertise. Establishing a solid base of skilled professionals can significantly enhance the chances of success for your AI project.
The Key Takeaway: How to Ensure the Success of Your AI Projects
AI can change businesses and encourage new ideas, but it has difficulties that you need to know about and don’t take lightly. As we’ve seen, many AI projects fail due to a lack of understanding of the business context, poor data quality, the hype surrounding generative AI, outdated infrastructure, a lack of skilled talent, and misalignment with business goals.
By addressing these common pitfalls, you can set your AI project up for success and unlock the full potential of this technology. Remember, AI is a powerful but just a tool that, when used wisely, can deliver significant value to your organization.
Read more about what every small business should know for successfully adopting AI.
How Solwey Can Help
Solwey is a boutique agency established in 2016 focusing on customers' success through excellence in our work. Often, businesses require simple solutions, but those solutions are far from simple to build. They need years of expertise, an eye for architecture and strategy of execution, and an agile process-oriented approach to turn a very complex solution into a streamlined and easy-to-use product.
That's where Solwey comes in.
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