In today’s fast-paced business landscape, executives face increasingly complex decisions that can make or break their organizations. The need for robust, intelligent Decision Systems that directly support senior executives has never been more pressing. Traditional tools and methods are no longer sufficient to keep pace with the demands of modern business. However, there are emerging AI and advanced data analytics capabilities that can significantly enhance executive-level decision-making. Namely, Decision Systems that directly support executives that are on-demand, more insightful, and adaptable in today’s volatile business landscape.

This article looks at the limitations of traditional analytical tools like Business Intelligence and MS Excel. Moreover, I’ll examine the shortcomings of “bolting on” decision tools to existing software and the growing field of Decision Intelligence, which primarily supports lower-level decision flows and specific functional users. Also, I’ll introduce you to several emerging AI analytics capabilities that are on-demand, insightful, and adaptable. Without a doubt, these analytical capabilities offer a breakthrough in supporting high-velocity executive-level decision-making. By examining these topics, you’ll gain a deeper understanding of how to leverage AI analytics to drive business success.

1. Excel and Traditional Analytics Fall Short as Executive-Level Decision Systems

In today’s fast-paced business environment, executives require timely and reliable insights to make informed decisions. However, traditional analytics tools, including MS Excel and Business Intelligence (BI) reports, often fail to deliver the necessary level of Insights, accuracy, and timeliness. This results in too many human errors, missed opportunities, and sub-optimal outcomes. So, let’s examine why executives need better decision tools and why current information technology solutions are failing them.

a. Humans Make Mistakes And Need Tech Tools To Make Better Decisions.

Without a doubt, humans make mistakes when it comes to decision-making. To detail, below are the top 5 stumbling blocks humans face in decision-making.

The Problem-Solving Process: Humans Make Mistakes And Need Tech To Make Better Decisions

• Biases. Our biases affect the way we perceive information, interpret data, and make decisions.  
• Emotions. Without a doubt, this can cloud judgment, leading to impulsive or irrational decisions.
• Human Errors. Also, we take action and make mistakes due to lack of knowledge, miscommunication, or other cognitive weaknesses. 
• Get Tired and Distracted. Additionally, fatigue, boredom, and distraction can all impair our cognitive functions, resulting in poor judgment, decreased productivity, and increased errors.
• Information Processing Is Limited. Lastly, our brain can only process a certain amount of information at a time before we have cognitive overload. This leads us to misinterpret or ignore important information.

For more on human decision-making and the errors we make, see my article, The Problem-Solving Process: Humans Make Mistakes And Need Tech To Make Better Decisions.

b. The Mismatch Between Enterprise Software and the Insights Executive Needs.

Agile Supply Chain Decision-Making: First You Need to Know The Truth About Enterprise Software

Without a doubt, modern supply chains are characterized by volatility. However, many of our investments in information technologies, including enterprise software, fail to provide the agility executives need in these types of dynamic environments. While these systems excel at operational efficiency and being a system of record, they struggle to keep pace with rapid changes and disruptions. As a result, organizations have a wealth of data but lack timely insights and actionable recommendations to support agile supply chain management. For a more detailed discussion on this topic, see my article, Agile Supply Chain Decision-Making: First You Need to Know The Truth About Enterprise Software.

c. The Limitations of Traditional Analytics to Deliver Executive Insights.

Businesses currently rely on tools like MS Excel and Business Intelligence (BI) reports to support executive decision-making. However, these decision tools have significant limitations in fully leveraging data analytics and Artificial Intelligence (AI). It is a fact that organizations do use various analytical tools – descriptive, diagnostic, predictive, and prescriptive – but they are often used in isolation across different departments. Despite advances in computing power, storage, and AI, most organizations fail to integrate these tools into a cohesive Business Analytics continuum. Without a doubt, this is what is needed to directly support executives’ rapid decision-making cycles. Moreover, our fragmented approach to analytics supporting decision-making is largely driven by outdated habits and outdated technological constraints.

2. Rethinking Decision Intelligence: Beyond Planning, Processing, and Post-Analysis.

It is a fact that current decision intelligence solutions have made significant strides in supporting business decision-making, particularly in areas such as planning, functional-level processing, and post-analysis. Hence, these solutions do provide valuable insights and help organizations optimize their operations. However, they also fall short in fully leveraging the latest capabilities of both data analytics and emerging tech such as Artificial Intelligence (AI). So, let’s look at why most Decision Intelligence solutions are not enabling executives to make swift and informed decisions in today’s rapidly changing business environment. 

a. Compartmentalized Analytical Solutions: A Narrow View of the Past or Future

Traditionally, businesses have used different analytics types – descriptive, diagnostic, predictive, and prescriptive – in isolation, primarily catering to specialists rather than decision-makers. This fragmented approach, despite advances in computing power and storage, results in disjointed tools that are out of sync with senior executives’ decision cycles. For instance, companies often analyze past sales data and predict future demand using separate tools, failing to integrate these insights for timely, informed decisions. This highlights the need for a more integrated analytics strategy to support effective decision-making. To break it out, let’s look at how different forms of data analytics work in isolation today. 

Different Forms of Data Analytics – Their Focus

Exploit The Business Analytics Continuum For Awesome Data-Driven Decision-Making Results

• Reflective Analytics Focused on Past Events and Anomalies.
  • Descriptive. Here, this type of analytics will answer the question, “What Happened?” For instance, this could include Business Intelligence (BI) reports, but in many cases actual decision-makers seldom look at these reports, nor are they integrated into their decision cycle. 
  • Diagnostic. In this case, this type of analytics answers the question, “Why Did This Happen?” For example, a diagnostic report may point out a recurring problem, but the report does not trigger a solution or options to solve the problem.
• Forward Thinking Analytics Focused on Future Trends, Events, and Actions.
  • Predictive. This type of analytics answers the question, “What Is Most Likely To Happen?” In many cases, this type of analytics is used by planners. Rarely do decision-makers directly use this type of analytics for fast-moving situations. At best, they wait for the “next”  meeting to get these types of insights based on out-of-date historical data.
  • Prescriptive. Here, this type of analytics answers the question, “What Action Should We Take?” In this case, this type of analytics may provide decision-makers options to act on, but many times this analytics is neither timely, nor fully integrated with other analytics to provide the best recommendations.

For more discussion on these types of analytics and the challenges of them working in unison, see my article, Exploit The Business Analytics Continuum For Awesome Data-Driven Decision-Making Results.

b. Decision Intelligence Solutions: Improving Processes, Not Executive Decisions

Decision Intelligence Tech To Empower Logistics: Ten Ways These New Analytic Practices Are Better

Indeed, decision Intelligence vendors are helping to shape analytics for better decision-making. However, in most cases, these software tools do not directly support the rapid decision cycles of senior executives. This is because Decision Intelligence, being a broad discipline, supports a wide range of business functions to include automating specific back-end and operational decision flows. For a definition of this analytical discipline, see below.

“Decision intelligence (DI) is a practical discipline that advances decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed and improved via feedback.”

Gartner

Indeed, it is very helpful that these Decision Intelligence tools do go beyond descriptive BI reports and predictive planning tools. However, these Decision Intelligence tools cater more for analysts, functional leads, and operators looking to automate low-level decision processes, not senior decision-makers. Thus, the primary decision aids for senior executives continue to be MS Excel, staff briefings, and “stubby pencil”. For more specific examples of what Decision Intelligence can do at the functional level, see my article, Decision Intelligence Tech To Empower Logistics: Ten Ways These New Analytic Practices Are Better.

3. New Type of Decision Systems Powered by AI Analytics: On-Demand, Insightful, Adaptive.

As discussed, traditional business systems and Decision Intelligence tools often fall short in supporting the rapid decision cycles of senior executives. However, recent advancements in AI and data analytics have introduced new capabilities that can significantly enhance executive-level decision-making. These capabilities, characterized by being on-demand, insightful, and adaptive, are powered by AI analytics and represent a significant departure from the limitations of current Decision Systems. By leveraging these agile capabilities, organizations can unlock new opportunities for high-velocity, executive-level decision-making. See below for a breakout of these capabilities.

Elements Needed for a High-Velocity Executive Decision System

High-Velocity Decision Systems for Executives: The Three Ways To Best Exploit AI Tech And Data Analytics

• On-Demand, Real-Time Analytics. First, because of the availability of Internet of Things (IoT) sensors, instant digital communications, AI, and cloud computing, decision-makers can now access insights exactly when they need them. This means no more delayed decisions or missed opportunities – leaders can act quickly with data-backed confidence.
• AI-Driven Analytics. In this case, AI enables analytics that work with massive data sets to uncover more insights. As a result, AI can analyze data in ways that humans can’t, revealing new insights and answering unforeseen questions.
• A Continuous Feedback Loop. Lastly, executives need a high-velocity Decision System that continuously learns and improves based on real-world outcomes, leveraging emerging tech such as Machine Learning (ML). Moreover, this feedback loop capability captures the results of what works and what doesn’t. Thus, it is able to continually refine its insights and recommendations to support better decision-making.

For more on making Decision Systems more agile, see my article, High-Velocity Decision Systems for Executives: The Three Ways To Best Exploit AI Tech And Data Analytics.

More References.

See below for more references on Decision Systems and ways Decision Intelligence, data analytics, and AI can support the rapid decision cycles of senior executives.  

• High-Velocity Analytics: Agile Decision Intelligence: High-Velocity Analytics To Best Empower Executives In A Quickly Changing World
• Data and AI: How Data And AI Work Together To Better Empower Analytics
• Digital Technology: Business Agility and Its Many Ways to Exploit Digital Tech
• Decision System Characteristics: An Agile Decision Platform to Empower Executives For Superior Supply Chain Performance: Here Are The Best Attributes

An Agile Decision Platform to Empower Executives For Superior Supply Chain Performance: Here Are The Best Attributes

Lastly, if you are in the supply chain industry and have a need to supercharge your decision-making cycles, please contact me to discuss next steps. I’m Randy McClure, and I’ve spent many years solving data analytics and decision support problems. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers.  I specialize in launching new analytics-based strategies, proof-of-concepts and operational pilot projects using emerging technologies and methodologies. To reach me, click here to access my contact form or you can find me on LinkedIn.

For more from SC Tech Insights, see the latest articles on Decision Science, AI, and Data Analytics.