In today’s fast-paced business environment, executives must make swift and informed decisions to remain competitive. Traditional decision-making methods, which rely on slow-moving analyses and intuition, lead to delays and poor outcomes. However, the emergence of AI technology and advanced data analytics can empower organizations with high-velocity decision systems. Indeed, these types of systems allow senior executives to access rapid insights on their own schedule at the optimal point of decision. Thus, this eliminates the need to wait for analysts’ reports or make decisions without the best available information. By leveraging high-velocity decision systems, executives can make timely, informed decisions that drive business success.
In this article, I’ll share with you three elements needed to exploit AI tech and traditional data analytics to build high-velocity Decision Systems. Specifically, this includes on-demand analytics, AI-driven analytics, and continuous feedback loops that enable agile decision-making.. See below for a detailed breakout of these three components to enable a high-velocity Decision System that exploits AI tech and advanced data analytics.
1. Decision Systems with On-Demand, Real-Time Analytics for Rapid Executive Insights.
First, real-time, on-demand analytics is a critical capability of a high-velocity Decision System. At the same time, this type of analytics still uses a combination of more traditional analytics. The key need for this type of analytics is that it provides immediate results based on the decision-maker’s operational tempo and decision requirements. Below is a more formal definition.
“[On-Demand], Real-time analytics is the discipline that applies logic and mathematics to data to provide insights for making better decisions quickly. For some use cases, real time simply means the analytics is completed within a few seconds or minutes after the arrival of new data. On-demand real-time analytics waits for users or systems to request a query and then delivers the analytic results. Continuous real-time analytics is more proactive and alerts users or triggers responses as events happen.”
Gartner
Indeed, real-time, on-demand analytics is a necessity for agile decision-making. In the past, executives had to wait for monthly reports or lengthy analysis from support teams before making decisions. Not anymore. Thanks to IoT sensors, instant digital communications, 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. For an example of a rapid decision-making framework for businesses, see my article, OODA – Enabling Business Agility: The Best Way To Disrupt Competitors, Seize Opportunities, And Overcome Obstacles.
“The entire effort of artificial intelligence is essentially a fight against computers’ rigidity.”
Douglas Hofstadter
2. AI-Powered Analytics – Data Analysis Supercharged and Agent-Based.
Without a doubt, recent AI advancements are accelerating the effectiveness of traditional data analytics and knowledge-based tools. Specifically, 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. What’s more, AI and knowledge-based technologies can now work with a wide range of diverse types of structured data (like shipping records and inventory counts) and unstructured data (like emails, images, and pdf documents). Also, agent-based AI can increasingly act autonomously on data analytics tasks. For more information, see my article, How Data And AI Work Together To Better Empower Analytics.
“In a sense, artificial intelligence will be the ultimate tool because it will help us build all possible tools.”
K. Eric Drexler
3. The Continuous Feedback loop – Enabling an Agile Decision System that Learns and Adapts Based on Real-World Outcomes.
Lastly, any high-velocity Decision System for executives isn’t a “one-and-done” solution like traditional business intelligence reports or decision briefs. Instead, think of it as a living, agile Decision System that is continuously learning and improving. As decisions play out in the real world, the Decision System captures what worked and what didn’t. Then it uses this feedback to get smarter over time. In fact, now Decision Systems can use AI like Machine Learning (ML) to better learn and adapt.
In particular, a Decision System needs to know how effective its insights, projected outcomes, recommendations, and action plans were. The platform needs to know exactly where there are gaps between its analysis and actual outcomes. For more information on AI feedback loops, see IrisAgent’s article, The Power of AI Feedback Loop: Learning From Mistakes.
“I think it’s very important to have a feedback loop, where you’re constantly thinking about what you’ve done and how you could be doing it better.”
Elon Musk
More References.
- A Data Analytics Perspective To Better Empower Supply Chain Managers
- Business Agility: The Best Way For Leveraging Digital Tech To Disrupt Competitors, Seize Opportunities, And Overcome Obstacles
- Exploit The Business Analytics Continuum For Awesome Data-Driven Decision-Making Results
- An Agile Decision Platform to Empower Executives For Superior Supply Chain Performance: Here Are The Best Attributes
For more from SC Tech Insights, see the latest articles on Decision Science, AI, and Data Analytics.
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.
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 logistics leaders. My focus is to drive transformation within the logistics industry by leveraging emerging LogTech, applying data-centric solutions, and increasing interoperability within supply chains. 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.