Cory McNeley is a Managing Director at UHY Consulting.
Artificial intelligence (AI) has transformed the business landscape and changed how we work. Its capability to automate tasks, analyze extensive datasets efficiently and provide concise business insights facilitates both the speed and quality of business operations.
“Artificial intelligence” is often used to describe other technologies, such as machine learning (ML) and deep learning (DL). However, each of these technologies is distinct, and those differences impact which solution is right for your specific challenges. Understanding the high-level differences between each and the challenges that remain with implementation and adoption can help you have more meaningful and direct conversations about the role of these technologies in your organization.
Defining Artificial Intelligence And Machine Learning
AI is centered on programs that replicate common human-like skills. AI can solve problems, perform advanced calculations, and make decisions through the use of statistical models, neural networks and programmed rules. AI is an umbrella term that also includes various subsets of technology like ML and DL.
ML allows programs to identify patterns from data, which is used to enhance the program’s performance over time without the need for explicit programming. Common learning models include supervised, unsupervised and reinforcement learning techniques. This subset of AI is especially useful for data-driven decisions with extremely large data sets, such as sales forecasting. DL uses neural networks, a technique to replicate the human brain that is commonly found in image recognition and detection systems, as well as advanced AI applications such as autonomous vehicles.
Business Applications Of AI And ML
The world of communications, marketing and customer service is experiencing major disruption as a result of advancements in AI. Commercially available and custom-developed AI tools are helping companies provide high levels of customer service by employing advanced chatbots with more knowledge and flexibility than traditional chatbots. They can dissect and resolve complex inquiries without the need for human intervention. The natural language processing (NLP) aspect of modern AI allows these tools to provide customized marketing and communications that are reactive and continually evolving.
Common applications of ML technology include hyper-segmented customer profiling, predictive maintenance and fraud detection. Each of these is based on labeled (structured data), unlabeled (unstructured data) and reinforcement learning, where prior outputs are evaluated and used as inputs to adjust and refine ML’s results.
Profiling customers based on previous purchasing habits, location, household income, etc., is nothing new, but combining this data with commuting route data, weather forecasts and social media activity could yield more valuable insight and recommendations.
In predictive maintenance, the mean time to failure by specific machine and physical location in the building—even down to floor orientation—along with machine models with common parts, operator assigned and forecasted demand help management address problems proactively and optimize scheduled downtime.
In fraud detection and prevention, customer profiling, institutional data, travel plans and social media help find potential fraud. Previously, major credit card processors used only a few dozen measures to predict fraud. Today, using ML, the number of parameters the card processor considers is far higher, likely reaching into the hundreds.
Challenges And Learning Curves
There are challenges with implementing any of these technologies. Data quality ranks as the No. 1 issue. Similar to humans, bad information drives poorly informed decisions from AI. Businesses that plan on implementing any advanced AI tools need to review, catalog and cleanse their data to minimize potential issues with the tool.
Another major issue revolves around acquiring the right talent to work with these tools. According to the Bureau of Labor Statistics, data scientist jobs are projected to increase 36% from 2023 to 2033. With the high demand for expertise in this field, the difficulty in finding skilled and qualified talent to build and deploy could be increasingly difficult with the rising trends of adoption.
Several misconceptions about AI are also prevalent in organizations. While some solutions could be deemed plug-and-play, the vast majority require continual refinement and fine-tuning. This results in unrealistic expectations of what AI can and cannot do for your organization. Before you embark on your AI journey, clearly define your goals and objectives. Then, complete a detailed analysis to ensure the tool you are deploying will yield the expected results. Failed implementations could lead to cynical thinking about AI’s capabilities.
Conclusion
Whether AI or ML is right for your organization depends on context, and today’s tools are advancing rapidly. At the core of the matter is data. These solutions need quality data to operate effectively. Is your organization ready?
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