Despite artificial intelligence’s astonishing capabilities, there are many serious pitfalls to watch out for. As AI becomes more integral in augmenting business decision-making or even making autonomous decisions, it’s crucial to be aware of its flaws. Recognizing its shortcomings may be even more vital than understanding its strengths. This article will look at the AI impact on business decisions and the challenges that need to be addressed. To effectively leverage AI, we must fully comprehend its impact on business decisions – both the good and the bad. By identifying where AI falls short, we can mitigate its limitations and harness its true power.

“It’s more important to know your weaknesses than your strengths.”

Ray Lee Hunt

How AI Can Be Used In Decision-Making And Its Benefits.

AI Impact On Business Decisions – Know How To Best Apply To Get The Most Benefits

Before discussing AI’s weaknesses, let’s review the significant opportunities AI offers in enhancing business decision-making. Recent advancements, particularly in Large Language Models (LLMs), Machine Learning (ML), and generative AI, now enable AI to augment business decision-making processes beyond traditional automation. In many cases, AI combined with robotics has become fully autonomous in both decision-making and physical tasks.

So AI increasingly can replace or augment both human and older forms of automation. Specifically, AI can automate, augment, and support decision-making. Additionally, it can do this in use cases that are simple, complex, and even chaotic. For a detail rundown of the opportunities and benefits in AI supporting decision-making, see my article, AI Impact On Business Decisions – Know How To Best Apply To Get The Most Benefits.

“Once we know our weaknesses they cease to do us any harm.”

Georg C. Lichtenberg

AI Impact on Business Decisions – Here Are 11 Limitations.

AI is truly revolutionary when it comes to supporting decision-making. However, AI is not without its limitations. In fact, it has a high number of weaknesses that decision-makers need to mitigate. For example, one major concern that users have is AI lacks transparency. This is because AI systems are complex and difficult to understand. Thus, AI users have difficulty interpreting AI decisions and recommendations. 

Another huge concern is that users have fears around the potential for AI to perpetuate existing biases and inequalities. Particularly, this is the case where developers use training data that is biased or incomplete. Finally, there is the risk that AI systems may make decisions that are ethically or morally questionable. Consequently, AI decisions left unchecked could have serious consequences for businesses and society as a whole. To detail, below are 11 AI limitations that businesses need to be aware of and mitigate when implementing AI to support business decision-making

1. Not Transparent.

First, a major drawback of AI is its lack of transparency, making it hard to understand and trust its decisions. To address this, AI developers are creating more explainable solutions. For example, AI outputs can include authoritative references and links to support their answers.

2. Can Be Bias.

Also, AI faces another limitation in that it can have inherent biases. A major factor that determines AI’s level of impartiality depends on the data used for training them. As a result, biased data leads to biased algorithms. Moreover, developers encounter difficulties when training Machine Learning (ML) applications involving vast amounts of data that require summarization. This summarization process sometimes results in concealed biases within the trained ML code. For examples of bias, see Unvarnished Facts’ article, Bias With Examples – Everything You Need To Know.

3. Consumes Enormous Computing and Power Resources.

AI requires massive amounts of computing power to function, which can make it expensive and environmentally unsustainable. For instance with Large Language Model (LLM) or Machine Learning (ML) powered applications, there are the huge data center costs for training the AI as well as supporting users in production.

4. Limited in Juggling Priorities.

In dynamic situations, AI’s limitations can hinder its ability to juggle priorities effectively. Consequently, it can have difficulty prioritizing tasks and making good decisions in the face of rapid changes.

5. Lacks Intuition and Common Sense.

Also, AI lacks intuition and “common sense”. As a result, it has difficulties adapting to new inputs or ambiguous criteria. In particular, it does not think abstractly and may struggle to make judgments that humans would consider to be common sense.

6. Susceptible to Hallucinations.

Moreover, AI can be susceptible to hallucinations, which can lead to inaccurate outputs. For instance, AI Large Language Models (LLM) in many cases act like a stochastic parrot. It repeats patterns in the data, returning results that appear plausible, but are erroneous. One way to combat hallucinations is to incorporate knowledge graphs into AI applications in order to generate output within context. For a more detailed discussion see my article, Knowledge Graph Tech: Enabling A More Discerning Perspective For AI.

7. Singularly Task Focused.

AI can exhibit a singular focus on tasks, which may pose challenges in understanding context, culture, or past events. In some instances, AI might not draw on prior experiences to make well-informed decisions and suggestions.

Data Sensitivity: What You Need to Know For Your Business

8. Increases Privacy and Security Concerns.

Additionally, the use of AI can increase privacy and security concerns. This is because as more data is collected and analyzed, there is a risk that sensitive information could be compromised.

9. Limited On Creativity and Originality.

AI is limited in its creativity and originality. While it can generate creative outputs based on existing patterns in the data, it may struggle to generate truly orginal, innovative solutions.

10. Lacks Empathy, Emotional Intelligence, and Conscience.

AI lacks empathy, emotional intelligence, and conscience, which can make it difficult for it to participate fully in collaborative situations. As a result, it may not be able to pick up on nonverbal cues or understand the emotional context of a conversation.

11. Not Morally Responsible and in the Wrong Hands Dangerous.

Finally, AI is not morally responsible, and in the wrong hands, it can be dangerous. The decisions made by AI can have real-world consequences, and there is a risk that these decisions could be used for harmful purposes.

More References.

AI Impact On Business Decisions – Know How To Best Apply To Get The Most Benefits

For more discussions on AI software when it comes to supporting decision-making, see PlatAI’s Can AI Overcome Its Limitations?, Adcock Solutions’ 6 Limitations of AI & Why it Won’t Quite Take Over In 2023!, and Nicole Hilbig’s How Far Can Artificial Intelligence Go? The 8 Limits of Machine Learning. 

For more on my 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 – Part 4
• AI Impact on Business Decisions – Challenges (This Article) – 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.