I’ve sat in enough boardrooms to know the sheer terror of an executive staring down a multi-million-dollar decision armed with nothing but a gut feeling and a sixty-tab Excel spreadsheet. In today’s unforgiving, high-velocity market, the strategic calls you make can break your organization overnight, yet the Decision Systems we give senior leaders are stuck in the dial-up era. Let’s be brutally honest: traditional Business Intelligence dashboards only tell you what happened last month. Moreover, awkwardly “bolting on” new decision tools to legacy software just creates a Frankenstein’s monster of data. Even the buzzy field of Decision Intelligence is currently bogged down optimizing lower-level workflows, leaving the C-suite flying blind when it matters most.
But there is a better way for executive decision-making. We are standing at the edge of a massive breakthrough in AI and advanced data analytics engineered specifically for the executive tier. These are Decision Systems delivering on-demand, highly adaptable insights that actually match the speed of your market. In this article, I’ll break down exactly why our old tools are failing senior decision-makers and introduce you to the emerging AI capabilities purpose-built for high-velocity executive decision-making. If you’re ready to stop guessing and start leveraging AI to drive undeniable business success, keep reading. It’s time to upgrade your executive intuition.
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 senior executives by not delivering 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.
First, humans make mistakes when it comes to decision-making. We need help. Below are the top 5 stumbling blocks humans face in decision-making.
- 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.
Without a doubt, modern businesses 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 from a supply chain perspective, 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.
While most businesses still rely on MS Excel and traditional BI reports for executive decisions, these legacy tools severely limit our ability to leverage modern AI and data analytics. Some organizations use other analytical methods—such as diagnostic, predictive, and prescriptive analytics—but they use them in isolation across many departments. Today, with major advances in computing power and AI, we can now integrate these analytical methods into a seamless continuum that delivers on-demand insights to decision-makers. Ultimately, our fragmented approach to analytics is no longer driven by technological constraints, but by outdated habits we can no longer afford.
“… traditional analytics tools, including MS Excel and Business Intelligence (BI) reports, often fail senior executives by not delivering the necessary level of Insights, accuracy, and timeliness.”
2. Rethinking Decision Intelligence: Beyond Planning, Processing, and Post-Analysis.
At the same time, there are emerging decision intelligence solutions that are making significant strides in supporting business decision-making. This is particularly true in areas of planning, functional-level processing, and post-analysis. As a result, Decision Intelligence solutions are starting to provide valuable insights and help organizations optimize their operations. However, they also still fall short of supporting senior executives despite the availability of both advanced 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
- 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, diagnostic 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 predictive 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, prescriptive 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.
Also, especially for these forward-thinking analytics types, it is critical for them to work seamlessly in unison to support rapid, informed decision-making. For more on this topic, see my article, see my article, Beyond the Forecast: Forward-Thinking Analytics for Supply Chain Leaders.
b. Decision Intelligence Solutions: Improving Processes, Not Executive Decisions
Without a doubt, 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) … advances decision making by … engineering how decisions are made and how outcomes are evaluated, managed and improved via feedback.”
Gartner
While extremely helpful to businesses, today’s Decision Intelligence tools cater more for analysts, functional leads, and operators looking to automate low-level decision processes, not senior decision-makers. Amazingly, despite the incredible advances in this type of information technology, most senior executives’ still use Excel, regular staff briefings, and the “stubby pencil” as their most trusted decision aid. For more specific examples of what Decision Intelligence can do for businesses as a whole, see my article, Decision Intelligence Tech To Empower Logistics: Ten Ways These New Analytic Practices Are Better.
… Decision Intelligence solutions … still fall short of supporting senior executives despite the availability of both advanced analytics and emerging tech such as Artificial Intelligence (AI).”
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 executive-level 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
- 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 to work with massive data sets, uncovering unprecedented 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.
“… new capabilities … characterized by being on-demand, insightful, and adaptive, are powered by AI analytics and represent a significant departure from the limitations of current executive-level Decision Systems.”
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 Working Together: How Data And AI Work Together To Better Empower Analytics
- Business Agility Powered by 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
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.
Greetings! As a supply chain tech advisor with 30+ years of hands-on experience, I take great pleasure in providing actionable insights and solutions to industry leaders. My focus is on supply chains leveraging emerging LogTech. I zero in on tech opportunities and those critical issues that are solvable, but not well addressed, offering industry executives clear paths to resolution. I have a wide range of experience to include successfully leading the development of 100s of innovative software solutions across supply chains and delivering business intelligence (BI) solutions to 1,000s of shippers. Click here for more info.