The 6 Biggest Pitfalls That Companies Must Avoid When Implementing AI

The age of AI is upon us and many companies begin to start their AI journey and reap the full potential of AI in their respective industries. But, some still consider AI as an immature technology with plenty of ways for it to go wrong. Therefore, before starting your long AI journey, there are some pitfalls you should avoid in implementing and developing AI solutions. They’re a result of the anecdotal, personal and published experience of AI projects that could have gone better.

1. Building AI systems that have become industry standards

Reinventing the wheel, that’s the reasonable words to describe building an AI system that has become an industry standard. It is a waste of your company’s time and resources. Instead, buy it from a company that has done research and development for years, and has launched a product that has been used and trusted by ample of users. Embrace their solution because this buy decision can get you high-quality AI services at a fraction of the cost and time that it would take to develop in-house. Because building an AI system in-house is a costly and risky endeavor, only do it if the AI system is quite specialized to your business and allow you to build a unique defensible advantage, something that can differentiate your company from its competitors.

2. Using AI to automate jobs

Most existing AI systems have a narrow capability, i.e. it is programmed to perform a single task — whether it’s detecting the position of vehicles on the road, recognizing faces, predicting the weather, or analyzing the movement of NBA players. As a result, these systems don’t perform outside of the single task that they are designed to perform. Thus, you shouldn’t expect a single AI system to automate a job.

A job normally consists of several tasks that are associated with each other. What you can do, at the beginning, is to select a single task to be performed by an AI system. Find a task that is the key driver, the main pain point, or the bottleneck of the business workflow.

Despite its narrow capability, we shouldn’t underestimate it. AI systems can improve our work productivity and efficiency due to their ability to process data and complete tasks at a significantly quicker pace than any human being can. Consequently, the bottleneck can be reduced, the workflow can be continuing smoothly, and boring, routine, and mundane tasks can be downsized.

3. Lack of data and poor data quality

Most of AI systems use deep learning as their foundational technology. Its main limitation: it requires thousands, millions, or even billions of training examples in order to perform a particular task. If the AI system that you need is an application that relies on supervised learning, you need to ask, at the very least, the following questions:

1. Do you have the data to train the AI system?
2. Are the quality and the amount of data that you have adequate to achieve the expected performance from the system?
3. If the existing data is inadequate qualitatively and/or quantitatively, do you have the capacity and capability to acquire more data?

If the answer to one or more of the above questions is no, you should rethink about your own AI initiative. The quality and amount of data oftentimes determines the performance of deep learning systems. Thus, when you don’t have enough quality training data, deep learning would fail miserably and might not be your ideal solution.

4. Having unrealistic AI expectations

I blame sci-fi movies for causing people to have unreasonable expectations towards AI. AI has indeed surpassed human in performing particular tasks, making the impression that AI will take over the world soon. But, to think AI can surpass human in any tasks is irrational. Similarly, to think AI can cure any problems encountered by your company is unrealistic. So, it’s important to know what’s currently feasible and what isn’t.

“Anything you can do with one second of thought, we can probably now or soon automate”

- Andrew Ng

AI tends to work well when the task is simple and there is a lot of data available. To give you a rule of thumb, anything you can do with one second of thought, we can probably now or soon automate with AI. So, it’s still a long way until AI can write you 80 pages market research report. The reason is because AI needs tonnes of data and loads of tries to succeed on very specific problems, and it is difficult to generalize its knowledge on tasks very different to those trained upon. AI can learn, but it won’t suddenly learn all aspects of human intelligence and outsmart us.

Knowing what the limitation of AI is crucial for succesful implementation of AI. Don’t expect AI to solve everything and be realistic about what AI can and can’t do

5. Failing to align AI projects with business goals

Don’t make AI implementation as the primary goal as it can derail your business from its original goals. Thus, start an AI initiative by combining AI knowledge and domain knowledge, i.e. select projects that can be done with AI and valuable for your business.

It’s like combining AI knowledge and domain knowledge and finding a sweet spot between those two. AI knowledge is mainly possessed by your AI and machine learning engineers and domain knowledge by your business associates. Therefore, don’t count solely on machine learning engineers to come up with the use cases of AI. Instead, pair engineering talent with business talent and work cross-functionally to find feasible and valuable projects.

6. Expecting AI initiative to work at the first try

Machine learning algorithms, particularly neural networks, are often thought of as black boxes due to the convoluted nature of the processes between their input and output. Input data undergo complex transformations in multiple layers of the algorithm, which cause the model to behave in complex and unpredictable ways. If our model doesn’t work as expected, we can’t manually tuning its parameters to fix the problem. What we can do is by feeding the model with more fine-quality data and/or tune the model hyperparameters.

Uncertainties are inherent in an AI project. Thus, don’t expect an AI project to work the first time. Instead, plan AI development to be an iterative process, with multiple attempts needed to succeed.

Due to its infancy, the majority of AI projects fail. Knowing the pitfalls of AI implementation is one of the important first steps in your company’s AI journey. Attention to the pitfalls will help us in solving any problems along the way and achieve a succesful AI initiative.