Welcome to the fourth part of my 5-part series about the AI impact on business decisions! In this article, we’re going to explore how AI plays a unique role in supporting decision-making, unlike other forms of automation. I’ll also share valuable tips on when you should consider using AI for different decision-making scenarios and discuss the amazing benefits that AI brings to the table.
Thanks to recent advancements in AI, particularly Large Language Models (LLM) and more generalized AI, AI can now assist in a wider range of business decision-making processes compared to traditional automation. In fact, AI combined with robotics can now autonomously handle both decision-making and physical tasks in many cases. This means that AI can increasingly replace or enhance both human decision-making and conventional forms of automation, whether the scenarios are simple, complex, or even chaotic. So, let’s dive into how you can apply AI to improve business decision-making and, ultimately, enhance your overall business execution.
“Decision is the spark that ignites action. Until a decision is made nothing happens.”Wilferd Peterson
AI Impact On Business Decisions: The Machine Learning (ML) Effect.
Indeed, AI is starting to transform decision-making. In particular, AI versus traditional automation can participate in all aspects of the decision-making process. Now, AI and particularly Machine Learning (ML) has some specific peculiarities within the decision-making process. Specifically, an AI ML application will go through a training phase in preparation to support decision-making. Then AI in its execution phase, will continue to support the decision-making process. As an example, see below where a given decision-making process will go through the 2-step life cycle of an AL Large Language Model (LLM) software application like ChatGPT.
“Life is about choices. Some we regret, some we’re proud of. Some will haunt us forever. The message: we are what we chose to be.”Graham Brown
First Phase, Train: Build And Train The AI Machine Learning (ML) Application.
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, the 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-making, the AI development team completed the first two steps of the decision-making process listed below.
- Decision-Making Step 1: Identify Data-Collection Opportunities. In the case of ML AI, the software development team identifies the data set that will be used to train the AI application.
- Decision-Making Step 2: Gather And Collate Data. Again, here the software development team gathers and collates the data as part of training the AI application.
“An expert is someone who has succeeded in making decisions and judgements simpler through knowing what to pay attention to and what to ignore.”Edward de Bono
Second Phase, Execute: AI Receives New Input, Processes, And Decisions Are Made.
In this execution phase, the AI ML application is in production. As an example, let’s use ChatGPT as the production AI application. In this case, the ChatGPT application is looking for an input prompt from the user. Once it has the input, it will process the data, and provide a response. From there the human can make a decision on what to do next. However, remember this is a simple example where there are many other examples where the AI is completely autonomous where no human is involved such as a self-driving car. So in regard to decision-making, this AI execution step is where the rest of the decision-making steps occur listed below.
- Decision-Making Step 3: AI Analyzes Data And Multiple Decision Alternatives. Here the AI application takes the input from the user’s prompt. Then the AI will evaluate multiple decision alternations and narrow down feasible options based on the criteria provided or inferred in the user prompt.
- Decision-Making 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 user could ask the AI to regenerate a response that would more than likely provide other feasible alternatives.
- Decision-Making Step 5: AI / Human Makes Decision To Proceed. In the case of ChatGPT, the user will make the decision on what to do with the ChatGPT output. For more autonomous AI applications such as an autonomous vehicle or an AI agent, the AI would make the decision. Also, if it is autonomous, it would then execute the decision.
“A good decision is based on knowledge and not on numbers.”Plato
For more on AI’s impact on decision-making, see Comidor’s 5 Applications of Artificial Intelligence in Decision Making.
Deciding When To Use AI In 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, here are some general guidelines for determining whether to employ AI and for which purposes.
“Whenever you see a successful business, someone once made a courageous decision.”Peter F Drucker
1. AI Decision Automation Opportunities.
Decision automation is usually used to automate simple business processes or tasks that can be completely autonomous. So the question here is can the AI solution do better than a human or other automation. Also as AI is advancing so quickly, it does not matter if the task is simple, complex, or even involves decision-making in a chaotic situation. It is really a risk or risk mitigation implementation decision for businesses to use AI or not.
“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. Conscious choice making is a critical step in making your dreams a reality.”Jillian Michaels
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. 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. Also, with AI software developers can create AI agents that will work with AI software like ChatGPT to automate the decision-making process.
“A real decision is measured by the fact that you’ve taken a new action. If there’s no action, you haven’t truly decided.”Anthony Robbins
2. AI Decision Augmentation Opportunities.
Decision augmentation is when a system recommends a decision, or multiple decision alternatives, to humans using prescriptive or predictive analytics. So with decision augmentation, AI coupled with data science offers unlimited opportunities. Here the 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 or develop a new course of action and implement it.
To explain, AI has done this for years with expert systems. Now as a more artificial general intelligence (AGI) has advanced to its current state where AI can augment most decisions. For example, with ChatGPT a human can type in a problem-statement on any subject, and real-time ChatGPT will provide one or more options. So now there are endless opportunities for business leaders to have AI augment business decision-making. And it does not matter how simple, complex, or chaotic the business environment or problem is.
3. AI Decision Support Opportunities.
“We may think that our decisions are guided purely by logic and rationality, but our emotions always play a role in our good decision making process.”Salma Stockdale
With AI decision support, the human employees make the decision, supported by descriptive, diagnostic or predictive analytics. Here humans use AI as a tool to assist with decision-making. So it is not embedded in the decision process like with decision augmentation. Indeed, AI offers countless opportunities to support business decisions. For example, AI can provide decision support in medical diagnostics and it just keeps getting better as with x ray diagnostics
So AI is not going to replace humans in decision-making, but it provides unlimited opportunities to improve it. Humans are still needed in business decision making. For example, this quote from Eric Colson sums it up how critical humans are for business decision-making.
“…There are many business decisions that depend on more than just structured data. Vision statements, company strategies, corporate values, market dynamics all are examples of information that is only available in our minds and transmitted through culture and other forms of non-digital communication. This information is inaccessible to AI and extremely relevant to business decisions.
For example, AI may objectively determine the right inventory levels in order to maximize profits. However, in a competitive environment a company may opt for higher inventory levels in order to provide a better customer experience, even at the expense of profits.”Eric Colson, What AI-Driven Decision Making Looks Like
AI Impact On Business Decisions: Benefits.
So to recap, below are the major benefits that AI brings to Business Decision-Making.
1. Increases Efficiencies.
As with all automation, AI can save time and money by automating and optimizing routine processes and tasks. With AI, it has the potential to outperform traditional process automation plus take on more complex tasks to include autonomous, robotics tasks.
2. Enables Faster Decision-Making.
AI coupled with a cloud-based, high-performance IT infrastructure, enable business-decisions to happen much quicker in real-time versus traditional batch or ad hoc processing.
3. Avoids Human Error.
AI once trained for the task will avoid mistakes and ‘human error’.
“Poor decision making I think, is the number one cause for most of our mistakes. So to make fewer mistakes means to make better decisions, and to make better decisions you must train yourself to think more clearly.”Rashard Royster
4. 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.
“Never make a decision when you are upset, sad, jealous or in love.”Mario Teguh
5. It Continues To Learn And Get Better.
Advanced AI is able to continue to grow its expertise.
6. Easy To Use.
You do not need deep technical experience with the latest iterations of AI. In fact, for assisting with business decisions it is much better for the business user to interface directly with the AI.
7. A Great Equalizer For Knowledge-based Work.
As the AGI such as ChatGPT is easy to use, it can greatly improve the knowledge-based deliverables for less skilled or experienced workers. Another way to put this is that less skilled workers are able to use the power of AI to greatly increase the quality and timeliness of their knowledge-based deliverables. On the other hand, a skilled worker will benefit using GAI, but not as much.
For more on the benefits of AI in decision-making, see NIBusinessInfo’s Artificial intelligence in business.
For more on our 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
Greetings! As an independent supply chain tech expert with 30+ years of hands-on experience, I take great pleasure in providing actionable insights to logistics leaders. My background includes implementing 100s of innovative solutions using emerging technologies and a data-centric development approach. I have also provided business intelligence (BI) solutions for 1,000s of shippers. For more about me, click here.