A bad system

What are the issues with implementing AI and RPA too fast?


Digital transformation has been on the horizon for many different size businesses and corporations. Digital Transformation has many different meanings, but AI (Artificial Intelligence) and RPA (Robotic Process Automation) have been on the radar for quite some time.  With Covid-19, digital transformation plans have been accelerated. Initially, digital transformation was a part of business plans to find a way to stay up with customer behavior, technology innovation, and finding faster ways to do things.  The fundamental factors are still a part of the transformation plans. The acceleration is needed to find the right ways to support remote work plans, safety for employees, and implementation of new processes due to new regulations. 

There are increasing demands to keep the customers and employees happy in this new world, but this comes with more cost that will cut into revenue profits.  Many business owners and leaders think that implementing RPA or some type of AI will solve many of their business problems and help with costs.  These types of technologies do produce positive results in the form of cost savings, increasing ROI, better processing or cycle times, and increase revenue. But if you do not take the time to plan correctly, implementing RPA and AI can become costly and time-consuming.  Business owners and leaders find out quickly that they have more complications after the implementation than before they started.

During my past 13 years of experience with any type of technology transformation and Continuous Improvement, if a business does not take time to plan, issues come up with the implementation.  These issues will cause the transformation to take longer than expected, which means it costs more money than expected.

What are the issues?

1. Wrong reasons

AI and RPA projects fail because there is a lack of clarity on the project from the start.  When a business starts implementing these types of projects quickly, the wrong problem is being solved.  The right kind of questions are not asked, which leads to the wrong reasons and project goals.  Without honestly knowing what you are solving for, the project will fail right from the start.

2. Process issues are seen faster

There are different arguments on whether you should fix a process first or throw technology at the problem. I see too many businesses throw technology at the issues, only to watch the process completely break down.  AI and RPA will reveal how bad a process is faster and at more cost. After these failures, the technology is removed, and the projects become failures. When these failures start to happen, businesses begin to lose momentum, and the business leaders lose all faith within the technology. 

Putting the human back into technology

3. Culture and workforce issues

Employees often fear AI and RPA because they are afraid they are going to lose their jobs. People fear what technology will do to do their jobs.   When you combine this with also processes that are broken by the technology, the culture becomes an issue. When your employees are unhappy, this will affect

  • Quality with the services and products
  • Production and cycle times begin to be elongate

Employees will not be on board with any future changes, and the workplace will become hostile.  Business leaders need to remember that when their employees are unhappy, their customers are usually going to be dissatisfied as well. Businesses cannot lose the people side of things if they wish to implement AI and RPA successfully.

4. Dissatisfied customers

Process issues and unhappy employees contribute to dissatisfied customers.  During these ever-changing times, it is vital to ensure customers are as happy as possible and do not feel alienated.   If customers are not satisfied, they start to lose faith, and they take their business to somewhere else. Without customers, a business cannot survive.

5. ROI (Return on Investment) will not be delivered

In 2018, Gartner stated that about ~85% of AI projects would not deliver the impact that CIOs wanted. One of my clients lost $2 M in costs, rework, and revenue with implementing RPA too fast.  One of my prospective clients lost ~$5 M in costs and had cultural issues due to implementing RPA too quickly.  It is important to note these types of cases because it helps to bring the hard reality when AI and RPA should not be applied too fast. These costly mistakes can be avoided by planning.

What’s next?

During these times, everything is changing so fast, but while it may seem moving fast is right the right things to do. But the right move is to move slowly to move fast.  But how do you plan to use AI or RPA right the first time? Each time I work with a client who wants to use AI or RPA, I apply Continuous Implement.  We start with setting goals, planning, leveraging Lean Six Sigma, and then apply AI or RPA. After everything is in place, we set up a Continuous Improvement Plan.  The plan to ensures that the company is always improving and can pivot as needed for growth, market demands, and customer changes. 

What are your thoughts?  Have you experienced this in your business or organization?
If you would like to help solve these issues, let’s set up some time to talk.

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