Despite artificial intelligence’s astonishing capabilities, there are many serious pitfalls to watch out for – 11 to be exact. 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. Moreover, I’ll offer some suggestions to mitigate these AI weaknesses. Without a doubt 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. In particular, 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.

While AI is revolutionary in supporting decision-making, it has significant limitations that need to be addressed. Consequently to mitigate these risks, businesses must be aware of the limitations of AI and take steps to address them. Below, I have identified 11 key AI limitations that businesses need to consider when implementing AI to support decision-making. Moreover for each weakness, I offer suggestions on approaches that businesses can take to mitigate these AI weakness.

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. Without a doubt, making AI explainable makes it more transparent. 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. Hence, AI developers must be cognizant that biased data leads to biased algorithms. Moreover, developers will 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 huge data center costs for training the AI as well as supporting users in production. On the other hand, the AI industry is making in-roads with reducing AI power requirements through hardware innovation, software optimization, and sustainable data center practices.

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. This is where we humans need to do better to communicate our intent to AI, provide oversight, and give AI strategic context to help it prioritize.

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. Indeed, the AI industry is actively working to overcome this AI weakness using strategies such as knowledge representation, learning / reasoning mechanisms, and human-AI collaboration.

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.

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. To mitigate AI being too singularly task focused, we need to use specific prompts with context, incorporate human oversight, provide up-to-date data, and create feedback loops for AI learning.

8. Increases Privacy and Security Concerns.

Data Sensitivity: What You Need to Know For Your Business

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. Consequently, the AI and business community will need to mitigate AI privacy and security concerns with a multi-faceted approach involving a combination of technical safeguards, robust governance policies, and user education. In regard to business leadership education, they need to know what Data their business owns is sensitive and what is not. For more details, see my article.Data Sensitivity: What You Need to Know For Your Business

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 original, innovative solutions. Thus, if a particular AI is not providing the creativity that is needed, humans need to step in. This includes providing AI detailed prompts, keeping a human-in-the-loop during the creative process, and making refinements as necessary using your own judgment. For more on the creative process, see this article, Improve Creativity: Here Is The 5-Step Process And How To Techniques

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. Without a doubt, the AI industry is attempting to address AI weaknesses that are at the heart of being human. Mitigation approaches include technical design development, strategic human integration, and implementing strong ethical governance. However, the core challenge is that AI simulates empathy based on data patterns rather than genuinely understanding or feeling human emotions.

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. To mitigate this AI weakness, a multifaceted approach is needed that focuses on human accountability (not AI), robust governance frameworks, and the implementation of procedural safeguards.

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

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

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.