Blog Common AI Mistakes: Optimizing AI for Your Business
By Carmen Taglienti / 22 Feb 2023 / Topics: Data and AI Generative AI
In a previous blog, our expert provided a guide for managing data more effectively to support Artificial Intelligence (AI) initiatives. An annual survey conducted by Foundry and commissioned by Insight recently found that while 85% of companies are already using AI to drive business insights, 57% have only just started their journey or are looking to mature their practices.1 In this blog, we’ll explore common AI mistakes and how to overcome them for optimized results.
Are you asking the right questions?
Sometimes an AI journey can get off the ground before the purpose is fully defined, leading to insufficient results that are not aligned with the business need. Even if an AI project has already begun, it’s never too late to revisit or recalibrate the purpose to make sure you are asking the right question. As a matter of fact, the data science lifecycle encourages frequent hypothesis testing, reinforcement learning and adaptability. Part of this evaluation should also be to ensure that the data you have and want to use is adequate: Do you have the type, scale and quality of data that you need for this AI project to be successful?
Additionally, the question you ask may need to be nuanced to get the outcome that will either answer your question or provide a better understanding of how to refine your pursuit of the answer. For example, a company may be looking for more insight into their customer's behaviors — but it’s important to define which behaviors they’re trying to understand. If the company wants to dig into the buying behavior of customers to determine store hot spots, that will look different than if they wanted to analyze the behavior differences between new and returning customers.
Coming to the right conclusion
Even when you have a good understanding of the question(s) you are seeking answers to, results from AI techniques must still be interpreted in the context of the model's ability to provide an answer that is unequivocal. Essentially, in the world of algorithmic learning and intelligence, probabilities and correlations are rarely 100%. First, it’s important to evaluate the conditions around the project: Is there confidence in the model that was created? If the model is based on low-quality or outdated data, or the wrong types of data, then the result might not be answering the question at all. This is a chance to evaluate if the model and results might be biased by incomplete data or presumptions. At the end of the day, AI can be a transformational tool for your business — but it’s not magic. We can instead think of AI as a powerful tool that when wielded by domain experts that understand its strengths, can augment the “business intelligence” of the organization.
The importance of a feedback loop
One of the key lessons of AI is that it’s a continuous process. Implementing AI is not a “set it and forget it” technology. It is most successful when part of an iterative process. One of the best ways to ensure you’re asking good questions and coming to correct conclusions is to maintain an active feedback loop for your AI project to re-engage and recalibrate as necessary. Besides the fact that you may have been asking the wrong question or coming to an incorrect conclusion, you may just want to completely shift the purpose of your AI. Sticking with the customer behavior example, you may find after some time that there is a different or more pressing customer behavior you want to learn about and adjust accordingly.
Placing it in the real world
The consequences of AI that does not meet business expectations can be frustrating; it might be a strain on financial or infrastructure resources, or it can result in missing out on critical business insights that hurt ROI. With this in mind, it’s important organizations don’t just implement AI, but embrace it. For example, a national convenience store chain that wanted to get business insights from its security cameras opted to use computer vision technology for the solution. By using computer vision, the client could feel confident in the mature model to provide it with the inventory and loss prevention visibility it wanted. Additionally, the project’s success set the stage for the chain to look toward future applications of the technology to answer other questions about the business. This example demonstrates the power of purposeful AI and the value that can be gained even from existing assets.
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Carm Taglienti
Chief Data Officer & Data/AI Portfolio Director and Distinguished Technologist, Insight
Carmen has more than 25 years of experience as a cloud computing, data science, data analytics and data management expert. As a principal cloud and AI architect for Insight, he focuses on cloud-based business solutions across multiple industries and technical domains. He also serves as an adjunct professor at Northeastern University Khoury College of Computer Sciences and the College of Professional Studies teaching graduate courses in data science, cloud computing and analytics. Carmen is a published author and speaks at industry and technology events.
Sources:
1 MarketPulse Research by Foundry Research Services. (February 2023). The Path to Digital Transformation: Where Leaders Stand in 2023. Slide 45. Commissioned by Insight.
