Only 35% of businesses reported using AI in 2022. However, more than 40% of the remaining companies said they were considering implementing it.

AI can bring significant benefits to your organization. Unfortunately, AI implementation failure is common. This failure is even more damaging when it’s part of your larger ERP implementation.

Learn more about common causes of artificial intelligence project failure and how to avoid them.

7 Triggers of AI Implementation Failure

1. Not Going “All In” on AI​

Successful implementation of artificial intelligence requires commitment.

AI is expensive. You need a significant investment in infrastructure, personnel, and training. A partial attempt can lead to wasted resources and poor results.

AI alone can’t fix your business operations. It must be part of a larger process improvement strategy. Not committing to improving or reengineering your business processes as part of your AI project can lead to software implementation failure.

AI solutions also need ongoing maintenance, like staying current with updates and ensuring ongoing scalability.

A Failed Payroll System Implementation

Panorama’s Expert Witness team was retained to provide a forensic analysis and written report to the court regarding the failed implementation of a major software developer’s ERP/payroll system.

2. Lack of Clear Business Goals​

Before you start any AI projects, identify the problem you’re trying to solve. Questions to consider include:

• Is AI the best solution for this problem?
• Which parts of our company need or don’t need AI?
• What are the criteria for project success?

Each AI initiative needs specific and measurable objectives. You should identify some use cases with high ROI. Look for applications that will have the largest influence on your KPIs.

3. Insufficient Expertise

Organizations had almost 800,000 open AI-related jobs in 2022. However, the skills gap makes filling those positions challenging.

You need a multi-disciplinary team for a successful AI implementation. Employees with the expertise to manage AI-related data are especially critical.

Hiring professionals with the expertise you need is one strategy. You can also upskill your existing personnel through education and training. Enterprise software consultants or a technology expert can help you bridge any remaining gaps.

4. Ignoring Change Management and AI Literacy​

Successfully implementing AI requires significant changes in your organization. Processes, workflows, and employee roles shift. These changes can fuel workers’ misconceptions that AI technology will lessen or replace their roles.

A change management strategy can help reduce this apprehension. Explain that new AI workflows usually offload tasks from employees, and don’t completely take their jobs.

Employees should understand what AI can do for them.

For example, a more streamlined workflow can make parts of their job easier.

Change management also involves training, which in this case means building AI literacy. Start by explaining the strengths and weaknesses of the technology.

For example, AI is only as good as the data it uses. It doesn’t innovate. Therefore, employees shouldn’t rely on AI decisions without understanding how the decision was reached.

Most applications of AI still require human involvement. New workflows have employees as trainers. People are the ones who handle the exceptions that don’t fit easily into AI parameters.

5. Not Involving the Right Stakeholders

AI projects require collaboration across multiple teams in your organization, including:

• IT
• Data science
• Business strategy
• Operations (engineers, plant operators, warehouse managers, etc.)
• Legal

You need the IT and data science teams to create and configure the system. Without input from people who understand the business context, though, you risk AI implementation failure.

Siloed decision-making leads to unsatisfactory results. Involving all stakeholders helps you identify your highest-priority business goals. Then, you can identify the AI requirements to meet those goals.

6. Misaligned Expectations​

AI can be transformative, but it won’t fix all of your business’s problems. Unrealistic expectations can lead to disappointment and incomplete implementation.

Stakeholders should understand the capacities and limitations of AI. This helps stakeholders understand the criteria for project success as they work toward implementation.

7. Poor Data Management​

Successful AI solutions require a significant amount of high-quality data. Poor quantity or quality can lead to AI implementation failure and may require the help of a software development expert.​

Data Understanding​

The amount of data your AI system will need depends on factors like the type of project and the algorithms. Considerations before starting the project include:

• How much data you have
• Whether the data is in-house or third-party data
• Whether additional data will be necessary
• Data access

Investing time upfront to analyze your data requirements will save time, money, and resources. Discovering that you’re missing critical data after the implementation is underway can delay or stop your project.

Data Quality

Even a sufficient quantity of data won’t prevent AI challenges if the data is of poor quality. You need to clean your data, modify third-party data, and use human-involved labeling where necessary.

If data is incomplete, inconsistent, or biased, the AI predictions may be incorrect or unreliable. A consistent representation of data and data relationships is essential.

Avoid AI Implementation Failure​

You can avoid AI implementation failure. Allocating enough resources and ensuring you have personnel with the right expertise is essential. You also need to clearly define your business goals for AI projects.

Panorama Consulting Group can help you avoid artificial intelligence project failure. Our ERP implementation services can help ensure that your AI solutions align with your business goals. Contact us below for a free consultation.

About the author

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As Director of Panorama’s Expert Witness Practice, Bill oversees all expert witness engagements. In addition, he concurrently provides oversight on a number of ERP selection and implementation projects for manufacturing, distribution, healthcare, and public sector clients.

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