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AI models can potentially revolutionize businesses, but they often face resistance from the very people they’re designed to help. In this article, we’ll explore the seven most common reasons why people resist AI models and provide practical solutions to overcome these challenges.

7 Reasons Why People Resist AI Models

In this section, we’ll explain the seven primary reasons people are hesitant to embrace AI models.

1. Fear of Job Loss

Today, the most common fear is that AI will replace human jobs. This fear is especially prevalent when AI is used to automate mental tasks. However, it’s important to understand that AI is designed to empower individuals to improve their results, not to replace them. 

2. AI is a “Black Box”

Another common reason why people resist AI models is because they perceive it as a ‘black box’ that they can’t understand or trust. This is particularly common in high-stakes domains like healthcare. However, this is often more of an excuse than a reason. The real issue is usually about accountability, which brings us to the next point.

3. Accountability

People want to know who will be responsible for the consequences of AI decisions. Resistance is likely when a human is held accountable for an AI’s decision. The solution here is to be transparent about who will be accountable for the AI’s decisions and to consider the ethical and regulatory implications.

4. Lack of Alignment

Sometimes, the goals of the AI model and the human user are not aligned. For example, the model might be optimizing for long-term customer engagement, while the human user is focused on next month’s commissions. The solution is to align the goals of the AI and the human user.

5. Different Perception of Risk

Humans are not always good at estimating risks. This can lead to resistance if the AI model suggests a course of action that the human user perceives as risky. Tools that help people understand and manage risk can be helpful in these situations.

6. Misconceptions and Biases

Humans have biases and misconceptions that can lead to resistance to AI models. Resistance is likely if an AI model contradicts a human’s biases or misconceptions. The solution is to help the human user understand their biases and misconceptions rather than trying to prove them wrong.

7. Bad Habits

Humans are creatures of habit, and changing habits can be difficult. If using an AI model requires changing established habits, resistance is likely. The solution is to make the transition to using the AI model as seamless as possible.

How Can We Overcome These Challenges?

To answer the question “Why people resist AI models,” we must address the root causes of resistance. Two books that can help with this are “Flawless Consulting” by Peter Block and “Atomic Habits” by James Clear.

“Flawless Consulting” provides strategies for overcoming resistance, such as labeling resistance and discussing it openly. It also emphasizes the importance of maintaining a balance of control and engaging stakeholders throughout the process.

“Atomic Habits” provides a framework for creating good habits and breaking bad ones. It suggests making good habits obvious, attractive, easy, and satisfying and making bad habits invisible, unattractive, difficult, and unsatisfying.

One practical way to encourage the use of AI models is to align compensation plans with the use of AI. For example, if following the AI model’s advice leads to increased profits, part of that increase could be a bonus to the person using the AI model.

Conclusion

Resistance to AI models is common, but it can be overcome by addressing the root causes of resistance and using strategies to encourage the adoption of AI. By doing so, businesses can fully leverage the power of AI to improve their operations and results.

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