AI and machine learning are sweeping the globe — no industry is immune to these disruptive technological advancements. AI in the workplace is fundamentally changing the way businesses function. A recent survey found that an overwhelming majority (92%) of firms are confident their organization will adopt this technology within three years. Companies that harness the power of AI stand to propel themselves into a whole new stratosphere of success. Still, for every AI success story, countless other AI projects crumbled before they even began. Even AI experts make mistakes. What can you do to make sure your AI project does not fail?
Why do AI Projects Fail?
There are quite a few good reasons why the AI project can fail. Some of them are unavoidable, and some, on the other hand, are not. You can work around them and avoid project failure by knowing these reasons.
Business leaders expect too much, too fast.
When your team does not understand the reality of AI, it is easy to get swept up in the hype. You may imagine that it is possible to create an artificially intelligent system overnight — but even with teams of data scientists and significant investments in research and development, most AI projects take years to reach fruition.
Business leaders often overestimate what AI can do for their organizations, thinking that AI will help them achieve new insights quickly because computers can simply crunch through more data than humans can. But data scientists must spend a great deal of time preparing datasets so computers can make sense — data is often messy and unstructured, which impedes its ability to be easily analyzed.
Leaders do not invest enough in AI
This issue has two components: money and time. Too often, companies allocate too little funding to AI projects, which means they are not given enough resources to succeed. It can also mean that companies do not dedicate enough time to the project, meaning they do not give it the attention it needs to flourish.
Data is often structured incorrectly
For AI to function at its best, you need to have a lot of good data — and that data needs to be formatted correctly. Many companies have data in incompatible formats or stored in incompatible places without the right tools or expertise. The upshot: Data scientists spend too much time performing data–wrangling instead of using their expertise for more critical tasks.
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Data silos impede data sharing
For organizations to take full advantage of AI, different departments must be able to share data freely. Unfortunately, many organizations still rely on data silos — separate databases where information is inaccessible to other teams in the organization. Leaders are unclear about what AI problems to solve. Many leaders do not know exactly how they want artificial intelligence and machine learning to help them achieve their goals. As a result, they do not know which problems would be most beneficial for their organization to solve or how best to track progress toward those goals.
Leaders are unclear about what AI problems to solve
Too many companies jump into AI simply because they think they should be doing it without really knowing what problem it will solve for them. Even when a company does have a clear idea of its needs, the wrong problem is often chosen first to be solved by AI: A low-impact problem is solved instead of a high-impact one. Or the scope of a problem is too large so that it cannot be fully solved in a reasonable amount of time. Or the problem is poorly defined or understood from every angle. Or it is not clear how solutions would fit into existing workflows and processes.
There is a lack of trust between business, AI, and IT teams
A lot of the time, teams are coming at an AI project from different perspectives. For example, the business team might want to build an AI model to get more customers; but if we are not measuring the right metrics, we will not be able to show that we have delivered on it. Or the IT team might say they cannot deliver what the business is asking for because it is not technically feasible.
Teams are not aligned around their goals
Suppose you are trying to build an AI model that has a massive impact on your business. In that case, you will need buy-in from all teams across the organization — from top management down to the data analysts who collect and validate data. If your teams are not aligned around common goals, you will not be able to achieve impressive results with AI.
Collaboration across departments does not happen
For most companies, a successful AI project requires collaboration across multiple departments and disciplines, including IT, data science, finance, and sales. But the silos that naturally form in many organizations can prevent this collaboration from happening. As a result, the project moves forward with incomplete information about how its data should be used and its goals. Without this information, an AI project is destined for failure.
Companies are not set up for data-driven decision-making
Companies’ first mistake is assuming people will embrace a new way of working just because it is better. For example, with AI, it is often considered that once you have good data and algorithms, you can start making smart decisions. But people do not work like this — they tend to stick with the approaches they know, even if they are suboptimal.
How to prevent the failure?
We know that a lot of AI projects fail. But the question is, how can we minimize the risk of failure of AI projects in our case? Here are some tips for you about preventing the failure of AI projects.
Understand the business problems that you want to solve
If your company is like many, you have probably been thinking about how AI can help you improve your products and services. But what are the specific business problems that AI can help you solve?
Consult with your team about how AI can affect the company’s current strategy and operations
For several reasons, it is important to think through issues like these before you begin implementing AI in your business. If handled poorly, AI could do more harm than good for your company.
Create a framework for reproducing the results of AI models
When reviewing data science work, it is not enough to simply check if a model works well. It is also important to understand how these models were built and what data was used.
Learn from other companies
Most businesses are not starting from scratch when it comes to implementing AI. Some have already tested new AI products or existing integrated ones into their existing processes — and some of these companies are even willing to share their experiences with other businesses that are just getting started with AI implementation.
Identify key stakeholders who will be involved in AI implementation
Before you begin any AI project, you need to identify who will be involved in the process. If multiple stakeholders are involved, there may be conflicting views about what needs to be accomplished. Ultimately, these stakeholders need to agree on a specific goal before you can get started with an AI project.
Gather all the data you need in one place before getting started on an AI project
It is important to have all the data before beginning an AI project. You do not want to discover halfway through your project that there is some additional data you require, and now you will need to go back and start over again from scratch. It can happen if stakeholders are not properly identified at the beginning of the project!
Start Small and work your way up for the bigger things
While it might seem enticing to slap AI onto any project, the reality is that taking the time to build or test with a smaller model will be far better than doing so with a massive one. Doing the grunt work first will help produce better results and increase user confidence in the project; it is not just about being lazy. So if you are a business leader, do not jump into using AI for every project out there. Test on something small first, then build up to the more significant projects.23
Business leaders and upper management are in an excellent position to oversee the implementation of AI solutions in their companies and prevent the negative outcomes that can often result from these implementations. These leaders should consider what went right and wrong with past deployments to ensure that the following projects go smoothly. However, those implementing AI solutions must have a consistent understanding of what they hope to accomplish through its implementation. At the very least, they should be sure they understand how they plan to use the system once it has been implemented.
Shivani is a talented CS manager with the skillsets to elicit, scope and manage end-to-end B2B SaaS project delivery. She has a keen interest in depicting her learnings in customer success by writing resourceful blogs and articles.
Published April 28, 2022, Updated August 04, 2022