How AI is Transforming Decision-Making and Business

By Fred Laluyaux, CEO, Aera Technology

An executive at a global pharmaceutical company shared with me that decision-making needs across her supply chain operations have grown past her team’s ability to keep pace.

The digitization of the economy has driven an acceleration of business cycles and an explosion in the decisions that companies have to manage in order to remain competitive. In fact, IDC recently proposed that organizations must now move from asking “What data do we need?” to “What decisions do we need to make?”* 

There are also two types of decisions emerging — the decisions that companies have historically been making, and also new types of decisions that have been “born in digital” and that rely on complex global variables that must be executed before the decision opportunity passes.

Unmade decisions are costly. They impact your company’s efficiency, bottom line, and sustainability metrics. This is where AI adds massive value: providing decision-making support, augmenting and automating decisions, and enabling companies to address the volume and complexity of decisions they must manage today.

Leaders vs. others: Why AI-powered decision making makes a difference

While companies in various stages of digital maturity have their eyes on predicting and acting at speed to generate value, what is preventing more companies from achieving faster, better decision making? What capabilities separate leaders from others?

In my last blog, I shared a few findings from a recent IDC study contrasting those actively using AI for better decisions (“leaders”) and others that have not yet made the investment in decision intelligence technology and processes (“followers”).

“Leaders,” as defined by IDC, are those making “clear choices in how they allocate their AI investments and are tightly connecting IDC’s six steps of a decision-making process.”

IDC found that 70% of leaders — but only 35% of followers — have current initiatives to transform operational or tactical decision-making processes.

There are also differences in the stage at which they are changing the ways decisions are made in their organizations. For example, followers face roadblocks at these two points:

• Right after initial consideration of decision transformation:
  • The percentage that moves forward to develop a roadmap for improving decisions starts to drop. As IDC explains, here is where enterprises may evaluate the commitment required or even abandon the initiative per lack of leadership and funding. In contrast, leaders commit, invest, and proceed. 
• The stage when decisioning projects need to be scaled:
  • Nearly double the number of leaders vs followers (33% vs 17%) indicated that they have a program for ongoing monitoring, review, and transformation of decision-making processes.

Yet, is this investment generating value? The answer is yes.

IDC found an estimated 11%-30% of leaders experienced improvements over the previous fiscal year across business metrics that include:

• Product or service innovation: 86% of leaders vs 53% of followers
• Employee retention: 65% of leaders vs 34% of followers
• Customer retention: 73% of leaders vs 59% of followers
• Risk management: 70% of leaders vs 57% of followers

Taking the first step

As more enterprises adopt AI for decision making, the number of use cases for automating decisions continues to grow. 

Consider a multinational biopharma company that leveraged AI-powered decision making to improve inventory management, logistics, and planning processes across its global supply chain. The company is using AI to determine the best delivery options in real time, generating data-driven recommendations that enable the team to consolidate orders, improve container optimization, and find the best shipping options. Results include improved shipping efficiencies and reduced CO2 emissions.

Another global leader in spirits production turned to AI to automate forecasting decisions across its U.S. territory – where regulatory differences across states can turn supply and demand decisions into a massively complex job. The company now uses AI decision automation as the single tool for its consensus demand plan, spanning 70+ markets, 100+ distributors, and 1,000 SKUs, generating nearly 10 million forecast numbers every month.

A pivotal time for businesses and AI

While AI-powered decisions have created new value, it requires thoughtful change management to ensure successful adoption and outcomes. 

One supply chain executive at a global consumer packaged goods leader — an early adopter of AI for decision automation that has continued to scale the use of this technology widely — shared with me that transforming decision making requires the ability to learn, unlearn, and relearn. She emphasized that because this is a shift in current processes and a learning journey, it’s vital to create an organizational culture where being able to fail is ok — to take the time to build trust in the technology, continually ask questions, try new approaches, and apply learnings to advance adoption.

Companies that take the time for these steps, and (more importantly) involve their teams in this collaborative journey, will be the next wave of “leaders.” They will achieve the full value and potential of self-driving enterprises — improving work and bottom lines, elevating customer service, and operating more sustainably.

*Source: IDC White Paper, commissioned by Aera Technology, What Every Executive Needs to Know About AI-Powered Decision Intelligence, IDC #US51338623, November 2023

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.