Artificial intelligence (AI) is rapidly transforming the business world. From healthcare to finance to transportation, AI is being used to solve complex problems and uncover new insights.
However, as with any business technology, the implementation process can be fraught with challenges. The failure of AI projects is common, especially when an organization doesn’t anticipate the impending challenges.
In this blog post, we will explore common reasons why AI projects fail and provide tips on how to avoid these pitfalls.
The Consequences of Failed AI Projects
When AI projects fail, the consequences can be far-reaching. For example, it can lead to:
• Financial losses: AI projects can be expensive to develop and implement. If a project fails, it can result in a significant financial loss for the organization.
• Time delays: AI projects can also take a long time to develop and implement. If a project fails, it can delay the organization’s ability to achieve its goals.
• Reputational damage: If an AI project fails publicly, it can damage the organization’s reputation. This can make it difficult to attract customers and partners.
• Loss of business: In some cases, the failure of an AI project can lead to the loss of business. For example, if a healthcare organization’s AI system fails to diagnose a patient’s disease accurately, it could result in a lawsuit.
While the rate of AI project failure is high, many organizations are finding ways to succeed. They’re taking steps such as:
• Carefully planning and defining the project goals.
• Using a realistic and achievable timeline.
• Assembling a team of technology experts with software expert witness experience.
• Ensuring high-quality data.
• Monitoring the project closely and making adjustments as needed.
By understanding the risks of AI project failure and taking steps to mitigate them, organizations can avoid having to hire a software failure expert.
Contemplating litigation?
We have multiple software expert witnesses available for provision of reports, depositions, and testimonies.
The Role of AI in ERP
Despite these risks, AI technology is rapidly changing the ERP industry. Artificial intelligence is increasingly incorporated into ERP software, helping business leaders manage and process data in new and innovative ways.
Here are some examples of how AI is being used to improve ERP workflows:
• Predictive analytics: AI can be used to analyze ERP data to identify patterns and trends. This information can then be used to make more accurate predictions about future demand, inventory needs, and other key business metrics.
• Optimized production and supply chain management: AI can forecast demand, predicting disruptions and recommending optimal inventory levels. This can help businesses reduce costs and improve efficiency.
• Improved customer service: AI can provide personalized recommendations, automate customer support, and predict customer churn.
• Enhanced security and compliance: AI can identify and prevent fraud, detect anomalies, and automate risk assessments.
Here are some more specific examples:
• A manufacturing company might use AI to optimize its production schedule. The AI system would analyze quality data to identify patterns and trends in demand. It would then use this information to recommend a production schedule that would minimize costs and maximize efficiency.
• A retail company might use AI to predict customer churn. The AI system would analyze customer data, such as purchase history and customer support interactions, to identify customers who are at risk of churning. The company could then use this information to target these customers with special offers and other retention programs.
• A healthcare company might use AI to improve its fraud detection capabilities. The AI system would analyze insurance claims data to identify patterns that may indicate fraudulent activity. This information would then be used to flag suspicious claims for further investigation.
It’s an exciting prospect, but AI projects often run into challenges before they even get off the ground. Let’s take a look at some reasons why this is the case.
Why do AI Projects Fail? [6 Reasons]
Wondering why the statistics show that many AI projects fail? Our computer software expert witnesses can attest to some common issues companies encounter:
1. Problems with System Integration
Many AI failures occur because it can be exceedingly difficult to integrate this type of technology with existing systems. Not only does this require top-tier AI technology, but it also means that the current infrastructure must also be efficient and fully functioning.
While it’s easy to focus on the bells and whistles of AI solutions, it’s important to consider how AI will work with your existing tools – and not just from a technical perspective.
You should also consider . . .
• What new processes you need to employ to make the best use of the new technology.
• How you need to improve your current processes.
• How AI will affect and disrupt your current workflows.
Answering these questions is a critical factor in making sure you not only have a functioning system at go-live, but one that actually enhances your current setup.
2. Unscalable Technical Performance
When development teams first work on AI solutions, they usually do so with a small amount of data. These early testing models only use up a fraction of the computing resources required to run a full-fledged solution.
When the solution expands into larger production systems, many organizations have a rude awakening. The solution that performed adequately just a short time ago no longer works because there isn’t enough computing power allocated for it.
To prevent this from happening, companies deploying AI should test often and in a near-production environment. They should also consider how scaling up will affect the system.
When creating a near-production environment, we recommend focusing on all the different interactions a user might have with the AI model.
3. Lack of Project Management
A successful AI project will take more than the right hardware and software. A solid project team is also necessary. This is the linchpin between efforts that take off and those that don’t.
Without the right people in place, it can be nearly impossible to visualize how AI fits into your existing business model and system infrastructure.
To avoid this challenge, you need to build a team with the skills and knowledge necessary to integrate this technology into your existing setup.
If you don’t have expertise in-house, then you may consider outsourcing your AI project to a third party.
4. Inadequate Employee Training and Support
An AI system is only as efficient as the staff who use it. This is why training is an essential component of an implementation plan.
While AI eliminates many aspects of human work, it doesn’t replace the need for end-user training and support.
One important aspect of training is preparing your workforce to take over in the event that a customer-facing AI channel experiences a technical issue.
For example, your employees should know the protocols to follow if a phone answering service breaks down and hundreds of frustrated customers start emailing you.
End-user training is most effective when it is part of a comprehensive organizational change management strategy. This should go beyond training and focus on success factors, such as communication and resistance management.
5. System Inflexibility
It’s no secret that business processes and workflows aren’t static. Even if you’re not doing business process reengineering on a regular basis, you’re likely making incremental improvements to your processes.
As such, you need an AI system that’s capable of keeping pace. The solution you install should be able to easily justify inputs, process data, and provide insights even in the face of shifting business requirements.
These are important factors to consider as you build your production environment. Otherwise, issues could arise whenever the system encounters real-world scenarios that it didn’t learn about in training and testing.
To prevent this from happening too often, it’s important to consider hypothetical situations that could exist outside of your plans and designs. This way, you can develop technical and operational contingencies that you can deploy if necessary.
6. Lack of Attention to Data Security
As you develop your AI solution, don’t forget to account for the element of cyber risk. Any time you introduce new technology into your organization, you open it up to security vulnerabilities. This can even make your existing cybersecurity solutions less secure and more vulnerable to outside attack.
Before your project takes off, we recommend taking the time to develop a risk-based approach. This includes identifying any existing points of weakness in your cybersecurity strategy and brainstorming ways to reinforce them.
If you don’t have the internal resources to help you, there are third-party resources that can test your cybersecurity protections and identify any areas that need fortification.
Finding Success in Your AI Project
Artificial intelligence has the potential to radically transform the way that almost every business operates. Instead of asking, “Why do AI projects fail?”, ask yourself how your digital transformation can be the exception to the rule.
While the points above can help you understand what goes wrong, the key is getting it right from the beginning. Our ERP consulting services focus on all types of enterprise software, including AI. Request a free consultation below to connect with our ERP implementation consultants.
