Why do AI projects fail? The answer is multi-faceted and includes both human and technical factors.
According to recent research, almost one-third of global IT professionals say their business is now using artificial intelligence (AI). Are you considering implementing AI at your own organization? If so, you may be wary about moving forward, especially after hearing about the high failure rates of such projects.
Today, we’re diving into what goes on behind the scenes, how the best-laid efforts go awry, and what your organization can do to avoid such roadblocks.
The Role of AI in ERP
AI technology is rapidly changing the ERP industry. AI is increasingly incorporated into ERP software, helping business leaders manage and process data in new and innovative ways.
This is an exciting change because, for decades, ERP workflows have remained mostly the same: Once employees input data into the system, they work together to use that data in support of various business operations. Once those steps are done, the system delivers results that team members can analyze to make decisions, create forecasts, and perform basic functions.
While many of these processes have become automated, many are still highly dependent on human interaction and intelligence. With AI, most of these mundane tasks can be handled in seconds. This can benefit organizations on an enterprise-wide level, helping leaders across departments create, process, report on, and share business information.
We have multiple software expert witnesses available for provision of reports, depositions, and testimonies.
Think of the ERP functions that require a user to open a screen and enter data into the system. Now, imagine conversational bots taking care of those jobs, responding to user prompts to perform jobs, such as:
- Generating orders
- Updating job statuses
- Confirming warehouse inventory
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 a few reasons why this is the case.
Why do AI Projects Fail? [6 Reasons]
1. Problems with System Integration
Many AI failures occur because it can be exceedingly difficult to integrate this type of sophisticated 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 will we need to employ to make the best use of this new technology? Do we need to improve any current processes? How will AI affect and disrupt our current workflows?
Answering these questions is critical to making sure you not only have a functioning system at your go-live date, 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 small subsets 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 is no longer capable of working 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, making sure to 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.
For some companies, all the software development can be handled fully in-house. When you go this route, you have total control over the end system, but this can come with hefty overheads in terms of organizational and administrative resources.
If you don’t have such expertise in-house, then you may consider outsourcing your AI project to a third party. This way, you can rest assured that the team handling the project has the expertise required to develop the software and ensure it works with your existing system.
While this option might sound ideal, keep in mind that giving the project over to a third-party vendor usually means forfeiting your creative rights to the system. Plus, you’ll be dependent on the vendor if you ever need customer support or software troubleshooting.
4. Inadequate Staff 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 some aspects of human work, it doesn’t replace the need for end-user training and support.
One important aspect of this 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.
By focusing on these strategies early, you can prevent breakdowns and bottlenecks down the road. You also prevent any type of change resistance you might encounter when employees are faced with new workflows and processes that differ from the ones they’re used to.
End-user training is most effective when it is part of a comprehensive organizational change management strategy. This is a strategy that goes beyond training and focuses on key 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 programmed in such a way that it can 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 that currently exist in your cybersecurity strategy and brainstorming ways to reinforce them.
If you don’t have the internal resources to help you complete this step, there are third-party resources that can test your cybersecurity protections and identify any areas that need improving.
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. Form a project team, test in the right environments, and make sure the solution is flexible and scalable. In addition, be sure to look for risks, vulnerabilities, and other issues that could make your system less secure.