Business Analytics and AI: Separating Promise from Practicality

In the world of business, discussions around AI often paint it as the ultimate solution to almost any problem, sparking enthusiasm and a bit of skepticism. Are we truly at the threshold of an AI-powered future, or is this just another case of “fear of missing out” (FOMO) driven by hype?

The Evolution of Analytics in Business

While AI has recently captured the spotlight, the foundation of analytics in business goes back much further. Since the 1950s, businesses have used analytical techniques, which today fall under three primary types within business analytics:

1. Descriptive Analytics (and Diagnostic Analytics): Focused on analyzing past data to understand why events occurred, this approach has been around since the 1950s. By examining historical data, managers can uncover factors that contributed to successes or failures.
2. Predictive Analytics: Emerging in the 1980s, predictive analytics uses historical data and algorithms to forecast potential future outcomes. This model trains computers to detect patterns, helping businesses anticipate trends and make proactive decisions.
3. Prescriptive Analytics: Popularized in the 2010s, this approach not only predicts what may happen but also suggests how to optimize decisions and processes. By running scenarios, prescriptive analytics identifies the potential outcomes of various actions, guiding businesses toward the most efficient decisions.

While these approaches aren’t new, technological advancements in data processing, machine learning, and cloud computing have made it possible for AI to perform more complex analyses, expanding its role in business today.

Avoiding the Trap of Over-Reliance on AI

Before rushing to adopt AI to solve intricate business problems, it’s essential to simplify existing processes and frameworks. For example, if an organization struggles with unpredictable demand or supply chain issues, AI alone may not be the answer. Instead, a well-structured supply chain model, grounded in segmentation and planning principles, can provide a more sustainable foundation.

When AI does become part of the solution, it must be implemented with a focus on people and collaboration. Technology is only as effective as the people who use it.

AI Use Cases in Supply Chain Management

AI has proven useful in supply chains, though examples may be rare, as many industries are cautious in adopting new technologies. Here are some promising applications:

• Predicting Purchase Order Delays: AI-driven regression models can forecast supply delays, offering insights based on historical events and supplier performance.
• Commodity Tracking and Price Prediction: Time series forecasting models help predict price trends and supply issues for key commodities, providing actionable insights for procurement teams.
• Supply Chain Disruption Alerts: Classification models sift through massive datasets to identify events that could disrupt supply chains, enabling companies to respond faster to potential threats.
• Supplier Network Visibility: Graph-based recommendation models allow businesses to visualize their suppliers’ locations, improving risk management and transparency.
• Natural Disaster Simulation: Simulation models project the impact of weather events or natural disasters on supply chains, enabling businesses to better prepare and respond to disruptions.

Although AI can enhance supply chain management, it’s rarely a magic bullet. Rather than relying on AI to create the “perfect” operational schedules or routes, it’s more effective for AI to provide planners with alternative options supported by data. This collaborative approach allows experienced managers to make informed decisions while considering various constraints and variables.

Choosing the Right AI Solution

The key to successfully integrating AI is aligning it with the company’s specific challenges and capabilities. Not every problem needs an AI-driven solution, and a strategic approach that combines technology with human expertise can unlock the true potential of AI in business.

By grounding AI initiatives in practical applications and thoughtful planning, companies can leverage AI to improve decision-making, streamline operations, and enhance supply chain resilience—without falling into the trap of overpromising or underdelivering.