Driving Better Decisions: How Complex ML Models Create More Accurate Business Insights

Complex machine learning (ML) models, such as deep learning and neural networks, process extensive amounts of data and identify intricate patterns that simpler models might miss. They are game changers for predictive analytics, partly because they unlock the power of big data. These advanced models provide accurate and nuanced insights, helping businesses make better-informed decisions. Whether predicting customer behavior, optimizing supply chains or detecting fraud, complex ML models transform raw data into actionable business intelligence to drive innovation in business strategy.

ML’s Growing Role in Business

There are several factors shaping the ongoing evolution of ML models in business. For example, the development of deep learning speech recognition tools marks a significant milestone in the growth of ML models, as does the announcement of Google’s self-driving automobile project Waymo and the establishment of DeepMind, a pioneer in AI and deep learning.

Given this technological innovation and exponential growth, companies in every sector are increasing their tech spend on artificial intelligence (AI), ML and other emerging technologies. Financial institutions, for instance, are forecasted to double their spending on AI by 2027. According to International Data Corp. (IDC), sales of software, hardware and services related to AI systems will increase by nearly 30% in 2024, reaching $166 billion and surpassing $400 billion in 2027. IDC predicts­ that spending in the financial sector will more than double in that period, reaching $97 billion in 2027, with a 29% compound annual growth rate (CAGR).

Trends such as multimodal AI will continue to advance AI’s capabilities. Multimodal AI processes, comprehends and generates outputs for more than one type of data. This capability to go beyond traditional single-mode data processing signifies “a step toward mimicking the human ability to process diverse sensory information,” TechTarget’s Lev Craig recently wrote in a piece summarizing the top 10 AI and ML trends for 2024.

In that same article, Craig also touted agentic AI as “a significant shift from reactive to proactive AI.” Describing AI agents as “advanced systems that exhibit autonomy, proactivity and the ability to act independently,” Craig noted that these agents are designed to understand their surrounding environment, set goals and then reach those milestones without relying on human assistance. Craig cited examples such as making reservations, trip planning and connecting to other services.

These are just a few practical applications for which complex ML models could be used in a business environment, providing valuable insights while automating many tasks. Regarding customer service, for instance, complex ML models can be used to power the chatbots that address customer inquiries, provide information and even troubleshoot issues. This capability significantly improves customer service and makes the process more efficient and effective because these interactions help chatbots better understand human language, enhancing their responses over time.

ML models can also be developed for recommendation systems, especially in sectors such as e-commerce and entertainment, filtering information to provide the most relevant suggestions to the users. ML systems analyze user behavior, preferences and feedback to make product, service and content recommendations. This precision in making pertinent suggestions can help drive customer satisfaction, loyalty and, importantly, revenue. To power its personalized product recommendations, Amazon leverages matrix factorization methods, such as singular value decomposition (SVD) and alternating least squares (ALS), to power its user-item interaction platform.

Challenges and Concerns Using ML Models

As these examples illustrate, complex ML models offer more accurate predictions than more traditional learning models, make predictions based on larger data sets and quickly adjust to changes in data sets. ML models also possess several features that make them especially adept at handling big data’s volume, variety, velocity and complexity. For example, ML models are scalable and can learn hierarchical representations, and their advanced algorithms are effective at recognizing and predicting patterns. ML models can also be trained to respond to new data, which enables results that are geared to specific business needs.

Of course, challenges are associated with implementing complex ML models in a business context, including data availability and quality. By partnering with third-party vendors, companies can leverage specialized expertise and experience to harness data effectively. Standardizing data through application programming interfaces and enriching raw data can boost data quality and consistency.

In terms of scalability, it’s essential for organizations to be diligent about ensuring collaboration and communication across teams, maintaining a robust data governance system and choosing technology stacks that enable the company to adapt to future changes. To maximize effectiveness, it’s important that ML models seamlessly integrate into existing business processes. This requires integration that factors in business rules as overrides or thresholds on model prediction.

A practical example involving customer service optimization underscores the importance of integrating ML models into existing business processes for business rules and thresholds. In one case, a telecommunications company wanted to improve customer service by automatically categorizing customer complaints and inquiries received via email. The goal was to quickly route these emails to the appropriate department and ensure a timely and relevant response. To achieve this objective, the organization opted to implement an ML model that could classify emails based on their content, following a four-step process:

1. Build the ML model. Based on a data set of emails, data scientists built and trained an ML model to recognize different types of inquiries, including billing issues, service disruptions, technical support and general questions.
2. Incorporate business rules. By integrating business rules into the ML model, the company prioritized certain categories of customers and escalated emails regarding specific issues to senior management.
3. Integrate into business processes. To ensure that all emails were automatically classified and properly routed, the company integrated the business rules into its existing customer service platform. For example, general inquiries from regular customers were routed to the general customer service queue, whereas billing issues for VIP customers were flagged as priorities and routed to a specialized billing team. Service disruption complaints were escalated to senior management.
4. Establish a monitoring and feedback system. To track the performance of the ML model and effectiveness of the business rules, the company solicited feedback from customer service representatives and adjusted the ML model and business rules as needed.

There are controversial aspects to consider as well. Privacy concerns are one of the most worrying facets of ML technologies because of the potential for violating users’ privacy and technologies capable of depixelating images. ML models’ ability to share individuals’ intimate details and impersonate individuals raises ethical and privacy issues.

ML models also have the potential for job displacement; many workers are already anxious about this possibility. In a recent survey, more than 75% of U.S.-based employees said they were concerned about AI causing job loss. It’s important to recognize, however, that ML can assist human workers in becoming more efficient in their jobs and create new types of jobs. Concerns about ML’s short-term impact on employment remain, and they underscore the need to train, educate and prepare the workforce for a more automated future.

Harnessing Potential

The ML field is rapidly evolving and holds tremendous potential. To stay competitive, it’s imperative for businesses to invest in continuous learning to keep the workforce apprised of developments such as the democratization of technology, explainable AI and advancing algorithms. Organizations can also adopt scalable infrastructure and ethical AI practices. High-quality data is the cornerstone of effective ML models, and it’s critical that leadership prioritizes data governance and management to ensure the organization’s ML models are accurate, unbiased and capable of producing valuable insights.

ML is ultimately more than just a technological tool – the models are strategic assets that can dramatically transform business operations and strategy. Integrating ML models successfully means establishing clear objectives, obtaining sufficient high-quality data and aligning with business goals. Leadership teams play a critical part in this effort – defining the vision for the organization’s use of ML, allocating the needed resources for successful ML integration, and nurturing a culture that prizes innovation and data-based decision-making processes.