Why do AI projects fail? And what you can do to stop it from happening.
Hardly a day goes by without an article or LinkedIn post touting the benefits that Artificial Intelligence can bring to the table. So, it’s typical for companies, big and small, to start adopting AI to get a competitive edge. But, it’s not that easy. According to a Gartner report, 85% of AI projects fail to deliver, and only 53% make it from prototype to production. So why do AI projects fail?
When you think of AI projects, the first thing that probably comes to mind is computers and data centers. But these endeavors often fail not because of the technological factor but the human element.
If you want your project to succeed, you need to get your employees on board. Focus on developing a data-driven culture, show them why data-driven decisions are better, and encourage data sharing within the organization. Consider it a change management initiative and implement it as such. If you want a more detailed set of steps, we’ve written an extensive article on how you can get your employees on board with Artificial Intelligence.
Garbage in, garbage out. This is a crucial mantra you need to have in mind. Data is the basis of a successful AI project. Without it, you can’t go forward. Any sound AI system needs both historical data and real-time data to provide the types of insights that can help you make efficient business decisions. So, you need to be prepared.
Most companies have data coming in from multiple sources, usually in different formats and often incomplete. It also tends to be siloed, meaning that only specific departments have access. Having a data governance framework in place that can help you clean it, structure it and use it in your AI initiative can be of tremendous help. You also need to talk to relevant stakeholders to ensure that you have the correct data for the business challenges you want to solve.
For example, suppose you are working on a system to identify your clients with a higher probability of up selling. In that case, you don’t need to have information such as phone numbers or the name of the CEO clogging up the system.
When you have a nice big shiny piece of tech, you might feel the urge to initially point it at a large and complicated business challenge. And this is a surefire way to fail. At first, focus on smaller and specific business issues that you might have that could be automated or improved with the help of AI. Look at what business problem needs to be solved and how you will measure success. Based on our years of experience, we’ve developed a handy checklist to help you select the right processes that could benefit from artificial intelligence.
By spending more time choosing the proper process, you increase your chances of success, and you can also show ROI. Which is what matters to top management.
AI implementation in a company is best-achieved step by step. Start with smaller projects such as predicting shopping intent or tailoring communication. As both you and the company become accustomed to using Artificial Intelligence, you can move on to bigger and more complex ones. Or even begin to experiment to create new products, solutions, or business models.
Governance and monitoring
After being deployed, AI projects need maintenance. You need to understand why the system made a particular decision and look at the data it used to arrive at it. There are plenty of examples that highlight what can go wrong with AI when it receives bad data or has biased algorithms. Just look at Tay, a chat bot developed by Microsoft that got shut down after just 16 hours because it started posting inflammatory and offensive tweets. Or the Amazon recruiting tool that showed bias towards women.
Also, the market changes over time and with it the data and business challenges that companies face. So, your AI needs to be recalibrated every time this happens.
How can we help
QTeam has developed AI solutions for both SME and large multinational companies. We can help you craft AI solutions based on your needs and budget. Get in touch with us, and let’s talk AI.