Five Common AI/ML Project Mistakes

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Companies of all sizes and across all verticals continue to embrace artificial intelligence (AI) and machine learning (ML) for myriad reasons. They’re eager to leverage AI for big data analytics to identify business trends and become more innovative, while also improving services and products. Companies are also using AI to automate sales processes, marketing programs and customer service initiatives with the common goal of increasing revenue.

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But the unfortunate reality is that 85% of AI and machine learning projects fail to deliver, and only 53% of projects make it from the prototype to production. Nevertheless, according to a recent IDC Spending Guide, spending on artificial intelligence in the United States will grow to $120 billion by 2025, representing growth of 20% or more.

As such, it’s important to avoid five common mistakes that often lead to the failure of AI and ML projects.

1. Understand the resources needed to train ML algorithms

While it might sound great to say that you’re utilizing AI and ML to revolutionize your company’s processes, the reality is that 80% of companies find those projects more difficult than expected.

For these projects to succeed, you need to clearly understand what’s needed in terms of both resources and personnel. One of the most common errors is not understanding how to obtain the correct training data – something that’s not only vital to the success of such initiatives, but also something that requires a great deal of effort and expertise to do successfully. Most companies who wish to undertake AI/ML projects lack access to the number of participants or the diversity of the group required to ensure high quality, unbiased outcomes.

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However, failing to do so often creates overwhelming obstacles to success, resulting in soaring project costs and plummeting project confidence.

2. Don’t depend upon data brokers for one-size-fits-all training data

There’s no lack of training data available for companies to purchase. The problem is that just because a company can easily purchase large amounts of data at cut-rate prices doesn’t mean that it’s high-quality training data, which is what’s needed for successful AI and ML initiatives. Instead of simply purchasing one-size-fits-all data, companies instead need data that’s specific to the project.

As such, it’s important to make certain that the data is representative of a broad and diverse audience in order to reduce bias. The data also needs to be well annotated for your algorithm, and it should always be vetted for compliance with requirements for data standards, data privacy laws and security measures.

3. Don’t misunderstand the circuitous path of AI development

Training ML algorithms is not a singular process. Once training has begun and the data model becomes better understood, changes must constantly be made to the data that’s being collected. However, it’s not easy to know what data you’ll actually need until the algorithm training process begins. For instance, you may realize that there are issues with the training set or in how data is being collected.

This is another problem that many companies run into when working with data brokers: they often severely limit amendment policies or don’t allow amendments at all. The only recourse is to purchase an additional training set to meet the new requirements. In doing so, though, a negative cycle begins that overwhelms budgets, delays timelines and reduces efficiency.

4. Always integrate quality assurance (QA) testing

All too often, QA testing is considered to be an add-on or a formality to ensure a product works correctly versus being viewed as a must-have tool used to optimize products across all iterations. The reality is that QA testing is a vital component to successful AI development. Outcome validation should be integrated into every stage of the AI development process to drive down costs, accelerate development timelines and ensure the efficient allocation of resources.

5. Schedule frequent reviews

While it might be daunting to think about, the reality is that AI projects are never really complete. Even if the project exceeds accuracy and performance expectations, the data used to do so reflects a point in the past. Moreover, algorithms learn to make decisions based on things that are constantly changing – opinions, dialogues, images and more. For an AI experience to be successful both now and in the future, it must be retrained on a rolling basis to adjust for new social attitudes, technological developments and other changes that impact data.

Ultimately, failure is driven by the fact that companies underestimate the effort and programmatic approaches needed to ensure top resources, best practices, and highest quality from the start of the project. In fact, companies that see the most positive bottom-line impact from AI adoption follow both core and AI best practices and spend on AI more efficiently and effectively than their peers. This includes doing things like testing the performance of AI models before deployment, tracking performance to see that outcomes improve over time and having good protocols in place to ensure data quality.

By developing a strong program approach to developing AI, companies can avoid these common mistakes and ensure the long-term success of their AI and ML initiatives.

About the author: As the AI and voice lead at Applause, Ben Anderson is responsible for a virtual team of AI and voice experts across some of Applause’s largest accounts, along with leading the global sales go-to-market program for the company’s AI and voice practices. A veteran of the sales organization at Applause, Ben works with global accounts where he evangelizes digital quality and crowd-powered feedback to provide the best possible customer experiences for some of the world’s top brands.

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