The landscape of business decision-making is being reshaped by groundbreaking AI advancements, notably in Machine Learning (ML) and Large Language Models (LLMs). It is just recently that AI enables both smart robots and software agents to make autonomous decisions and execute complex tasks. Moreover, AI is demonstrating that it is a potent alternative to conventional automation and a powerful amplifier for human intelligence. This transformation compels us to ask: What exactly is the AI impact on business decisions? More importantly, what AI strategies can we employ to optimize our decision-making processes for peak business performance?

To answer these questions, I’ll share with you in this article how AI plays a unique and varied role in supporting decision-making. Without a doubt, in recent years AI has shown us it is unlike any other forms of information technology. Moreover, I’ll also provide you with valuable tips on how businesses can leverage AI to enable rapid, informed decision-making. This includes using AI for decision support, augmentation, autonomous, and to surcharge executive decision-making. Lastly, I’ll highlight the competitive advantages and benefits that AI brings to businesses decision-making.

1. AI Impact On Business Decisions.

Undoubtedly, AI is transforming the human decision-making process. Indeed, AI is more and more involved in every step of decision-making and decision support. To illustrate, see below to see how AI can help in every step of making a decision.

AI’s Role in Making Decisions

• Identify Data Sources. Humans work with AI to determine data sets needed
• Gather and Collate Data. AI with or without human help, trains on data and organizes it.
• Analyze Data. Prompted by humans, AI examines data using full range of analytics.
• Determine Options and Make Recommendations. Based on human guidance, AI provides answers, Insights, and recommendations.
• Makes Decisions. Humans make decisions, or with human permission, the AI agent makes decisions, and even acts autonomously.

“AI is transforming the human decision-making process”

2. The Machine Learning (ML) Development Cycle Impact on Decision-Making.

In particular, Machine Learning (ML) AI is starting to profoundly impact how we make business decisions. Indeed, it is diverging sharply from traditional information technology’s support role. The key difference lies in ML’s software development cycle, which “pre-loads” extensive data processing upfront during its build and training phases. This upfront training is a unique characteristic of AI models. As a result, in production AI can rapidly assist or act autonomously across all five stages of the decision-making process. Below, I’ll detail how AI, specifically Machine Learning (ML) and Large Language Models (LLM), can function within each of the five decision-making steps.

a. Phase One – Build and Train the AI Software Model by Pre-loading Data and Decision Tools.

Here the AI developer configures a set of tools for data collection, synchronization, transformation, and analysis. In many cases for ML applications, software developers will train a neural network per the stakeholders’ requirements. Once the development team trains the AI, they will complete the development of the software application to include creating a user interface. Subsequently, the development team releases the software into production. So in the context of decision support, the AI development team completes the first two steps of the decision support process listed below.

AI Build & Train Phase: Decision Support Steps

• Step 1: Identify Data-Collection Opportunities. In the case of ML and LLM AI, the software development team identifies the data sets that will be used to train the AI application.
• Step 2: Gather And Collate Data. Again, here the software development team working with AI gathers and collates the data as part of training the AI application.

b. Phase Two – Move the AI Software Model to Production, Execute, and Support Real-Time Decision-Making.

In the AI execution phase, the AI ML application is now in production. As an example, let’s use ChatGPT. In this case, the ChatGPT application is ready, on-demand, waiting for input from a user’s prompt. Once it has the input, it will process the data, and provide a rapid response. From there the human can make a decision on what to do next. However, remember this is a simple example. Indeed, there are increasingly many more examples where AI acts as an agent and is completely autonomous in the decision-making process such as a self-driving car. So in regard to decision-making, this AI production phase is where AI can either support, augment, or even act autonomously.

AI Production Phase: Decision Support Steps

• Step 3: AI Analyzes Data And Multiple Decision Alternatives. For instance, a LLM-based software application takes the input from the user’s prompt asking for decision alternatives. Then the AI will evaluate the prompt and narrow down feasible options based on the criteria provided or inferred from the user input.
• Step 4: AI Suggests Options And Provides Recommendations. If programmed or requested in the user prompt, the AI application will come back with one or multiple answers. Or the user could ask the AI to regenerate a response that would more than likely provide other feasible alternatives.
• Step 5: AI / Human Makes Decision To Proceed. In the case of ChatGPT, the user can make the decision based on the ChatGPT response and other decision factors. For more autonomous AI applications such as an autonomous vehicle or an AI agent, the AI would make the decision based on previous or pre-programmed guidance. Also, if it is autonomous, it would then execute the decision.

For more on AI’s impact on decision-making, see Comidor’s 5 Applications of Artificial Intelligence in Decision Making, Haley Massa’s article, What Does the ML Lifecycle Look Like for LLMs in Practice? and IBM’s article, What is a machine learning pipeline?. Also, for more on AI decision-making models, see Eric Colson‘s What AI-Driven Decision Making Looks Like.

“… AI can assist or act autonomously across all five stages of the decision-making process.”

3. Using AI In Decision-Making to Augment, to Support, be Autonomous, and Supercharge Executive Decision-Making.

When considering the use of AI to enhance business decision-making, it is always important to weigh key factors such as costs, implementation time, and business priorities. In addition to these considerations, there are some general guidelines you can follow to determine whether to employ AI and for which purposes. Below, I’ll share with you the opportunities where businesses can leverage AI to fully automate decision-making (autonomous), augment decision-making, leverage intelligent tools for decision support, and supercharge executive decision-making.

a. AI Decision Support Opportunities.

With AI decision support, humans make the decision, supported by analytical and AI tools. So in this case, AI is only a tool to support human decision-making. At the same time, AI as a decision tool offers countless opportunities to support business decisions, and it is only getting better. For example, AI keeps improving in providing decision support for X-ray diagnostics. So in the case of decision support, AI is not going to replace humans in decision-making. However, as a tool it provides unlimited opportunities to improve our decision-making. Hence, for decision support, AI tools are available to help, but humans take the lead in all stages of the decision-making process.

b. AI Decision Augmentation Opportunities.

In the case of decision augmentation, AI is more than a decision tool. It can also help streamline and augment decision processes, to include offering analyses and multiple decision alternatives. For instance, AI can use prescriptive or predictive analytics to augment the decision process. With decision augmentation, AI does all the heavy lifting, crunches the data, and comes up with one or more options to a business problem. Then the human can choose the option, ask follow-up questions, or develop a new course of action.

For several decades, many industries, such as medical diagnostics, have used AI expert systems to augment decision-making. As newer AI capabilities emerge, such as ML and LLMs, the range of decisions AI can augment expands. For instance, with LLMs, users can input a problem statement on any topic and receive instant, relevant suggestions. At the same time, AI, in particular LLMs, do have many weaknesses to include hallucinations. So, just as with any emerging technology, businesses must weigh the advantages over the risks. To conclude, AI augmentation offers limitless opportunities for decision-makers to leverage AI. Further, AI is able to augment decision-making regardless of whether the business environment or problem is simple, complex, or chaotic.

c. AI Decision Automation Opportunities. 

Moreover, AI can also automate decision processes, and even act as an AI agent or a smart robot where it is completely autonomous. Additionally, AI is maturing quickly where more and more it is able to take on a full range of tasks from simple, complex, and even make autonomous decisions in chaotic situations. At the same time, the quick win for AI decision automation is to automate simple decision-making tasks. When determining whether to employ AI automation or, even AI agents, the question to ask is “can the AI solution do better than a human or other automation?

The New AI Agent Business Specialist: You Need to Know Their Skill, Expertise, and Comparative Performance

For example, autonomous cars are powered by AI and the question to implement is a matter of risk. Today, car manufacturers are implementing autonomous features in their cars such as self-parking, but the risk is too great for fully autonomous operation in many cases. On the other hand, there are many autonomous pilot projects as well as on-going enhancements to AI that will eventually mitigate most risks involved with autonomous vehicles. Additionally AI software developers as well as software services are creating AI agents with autonomous decision-making capabilities. For more on autonomous AI agents, see my article, The New AI Agent Business Specialist: You Need to Know Their Skill, Expertise, and Comparative Performance.

“Whenever you’re making an important decision, first ask if it gets you closer to your goals or farther away. If the answer is closer, pull the trigger. If it’s farther away, make a different choice. ”

Jillian Michaels

To summarize, the diagram below depicts the differences between decision support, augmentation, and automation (autonomous).

d. Decision Systems: Powered by Decision Intelligence.

A Breakthrough In Decision Systems: The Need For AI Analytics To Best Empower Executive Insights

Lastly, with AI’s rapidly advancing capabilities, driven by powerful computing and advanced data analytics, there is an increasing opportunity for senior executives to directly engage with both AI and Decision Intelligence analytics. Without a doubt, senior leaders in this digital age must now navigate an increasingly complex and volatile business environment, where critical decisions demand immediate, informed action. Specifically, what senior executives require is a Decision System that is on-demand, more insightful, and adaptable, specifically designed to empower rapid, informed decision-making. To learn more about how Decision Intelligence analytics can power an executive-level Decision System, see my article, A Breakthrough In Decision Systems: The Need For AI Analytics To Best Empower Executive Insights

“… businesses can leverage AI to fully automate decision-making (autonomous), augment decision-making, leverage intelligent tools for decision support, and supercharge executive decision-making.”

4. AI Impact On Business Decisions: Benefits.

So to recap, below are the major benefits that AI brings to Business Decision-Making.

• Increases Efficiencies. As with all automation, AI can save time and money by automating and optimizing routine decision processes and tasks. Moreover, it can take on more complex tasks to include autonomous, robotics tasks.
• Enables Faster Decision-Making. AI coupled with a cloud-based, high-performance IT infrastructure, enable business-decisions to happen much quicker in real-time and on-demand versus traditional batch or ad hoc processing.
• Avoids Human Error. AI once trained for the task will avoid mistakes and “human error”.
• Real-Time Processing / 24-Hours A Day. AI does not get tired, does not need breaks, and can provide answers in seconds. This is not the case with traditional automation or humans.
• Learns and Adapts. Advanced AI is able to grow its expertise and adapt in changing situations.
• Easy for Business Users to Interact with Directly. With advances in AI such as LLMs, business users do not need deep technical experience to directly interact with AI. 
• A Great Equalizer For Knowledge-based Work. Less skilled workers are able to use the power of AI to greatly increase the quality and timeliness of their knowledge-based deliverables. This provides more bandwidth for skilled workers to work on more advanced task and spend less time training new employees.

Final Thoughts.

Without a doubt, groundbreaking AI advancements are reshaping the landscape of business decision-making. Moreover, AI is demonstrating that it is a potent alternative to conventional automation and a powerful amplifier for human intelligence. The big question is how do senior leaders leverage AI to make it a competitive advantage, optimizing decision-making processes for peak business performance? As a primer, this article tells you how AI is impacting business decision-making, how you can employ AI, and its benefits to optimize and enable rapid, informed decision-making.

Without a doubt, an AI strategy to supercharge your business decisions isn’t optional; it’s essential. Start building yours today. Please use the references and resources in this article, for more on how you can employ AI to enhance business decision-making in your organization.

“An AI strategy to supercharge your business decisions isn’t optional; it’s essential. Start building yours today”.

More References.

For more on the benefits of AI in decision-making, see NIBusinessInfo’s Artificial intelligence in business. Also, for more information on the growing technical domain of decision intelligence, see my article, This Is What Decision Intelligence Technology Is And Know What Its Not.

This Is What Decision Intelligence Technology Is And Know What Its Not

This article is the fourth in my 5-part series on AI Impact On Business Decisions, see links below:

• The Human Problem-Solving Process – Part 1
• Process Automation – Part 2
• Data-Driven Decision-Making – Part 3
• AI Impact on Business Decisions – Opportunities (This Article) – Part 4
• AI Impact on Business Decisions – Limitations – Part 5

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 AI and Decision Science.