Is Rapid AI Adoption Posing Serious Risks for Corporations? Is Rapid AI Adoption Posing Serious Risks for Corporations?
Artificial intelligence adoption is booming across businesses of all industries. This is a promising shift for AI developers, and many organizations... Is Rapid AI Adoption Posing Serious Risks for Corporations?

Artificial intelligence adoption is booming across businesses of all industries. This is a promising shift for AI developers, and many organizations have realized impressive benefits from the technology, but it also comes with significant risks. AI’s rapid growth could lead more companies to implement it without fully understanding how to manage it safely and ethically.

As of mid-2022, 35% of companies worldwide reported using AI, with 53% saying they’re accelerating their rollout plans. However, 74% of adopters have not taken steps to reduce unintended bias, 60% haven’t formed ethical AI policies, and 52% don’t guard data privacy throughout the AI life cycle. Rapid AI adoption could leave corporations with substantial legal, security, and ethical risks if these trends continue.

What Risks Does AI Pose to Corporations?

Organizations must first understand AI’s risks to comprehend how it may endanger their business. Here are five of the most significant issues they’re at risk of without a careful implementation strategy.

Security and Compliance

Cybersecurity is one of the biggest risks of rising AI adoption. These models require substantial amounts of data, and many organizations use “black box” models where they’re unsure of how the system uses the information they give it. As a result, organizations can easily expose or misuse sensitive data without realizing it.

These privacy concerns also introduce legal risks as data regulations become more common. Violations of Europe’s General Data Protection Regulation (GDPR) have already cost some corporations more than $1 billion in fines, so companies must be increasingly careful of how they use their information.

Bias and Inequality

AI can also introduce societal issues like exaggerating bias if corporations aren’t careful. Amazon’s scrapped hiring AI model infamously penalized women’s resumes as the machine learning algorithm expanded on implicit biases within the training data. Similar issues could arise as companies apply self-learning models to more areas.

This trend is particularly concerning as nonprofits increase their AI implementation to capitalize on its efficiency. These organizations could face significant scandals and drops in public perception if these applications don’t account for how bias or similar societal issues can impact AI and vice versa.


The lack of transparency in many AI models can also cause issues. Users may not understand how these systems work and it can be difficult to figure out, especially with black-box AI.

This limited visibility makes it difficult to see where issues stem from if the model has a problem. Being unable to resolve things could lead businesses to experience significant losses from unreliable AI applications. A lack of transparency can also introduce regulatory issues, as companies may be unable to say how they’ve used customer data.

Job Displacement

As with any automated system, AI raises concerns about job losses. Experts predict that AI will create millions more jobs than it takes in the long term, but this shift may still mean considerable short-term displacement.

The jobs AI creates don’t arise immediately, and they’ll require different skills and experience than the ones it destroys. Consequently, rapid AI adoption without proper reskilling can leave many workers with declining employment opportunities and lower wages in the near term. That could leave corporations with a negative public image and affect their former employees’ well-being.


Unclear legal liabilities are another risk corporations may run into with rapid AI adoption. If an intelligent system causes injury or losses, who’s legally responsible for it? 

In the case of self-driving vehicles, automakers are legally responsible for crashes in the U.K., but drivers are liable in the U.S. This uneven regulation could become increasingly muddled as AI’s capabilities and uses expand. Consequently, organizations may face difficult legal scenarios when implementing the technology.

How Corporations Can Manage AI Risks

These risks are concerning, but they don’t necessarily mean AI is too risky to be worth the investment. Rather, they highlight the need for a more careful approach to the technology, with specific steps to address these concerns.

Mitigation starts with recognizing the specific risks an AI application could pose to an organization. Better understanding allows companies to form policies and standards around them. These rules should involve regular checks with diverse, experienced committees to ensure AI applications exceed regulatory and ethical guidelines.

Diversity in AI development is particularly important to catch implicit biases and prevent social consequences from careless deployment. Similarly, organizations should keep the AI process as transparent as possible. That will make it easier to use and audit for regulatory and security purposes.

Thankfully, the world is moving in this direction. More than half of organizations in 2020 took steps to mitigate AI concerns, a three-point increase over 2019. Mitigation steps also rose in AI regulatory compliance, explainability, labor displacement, and equity and fairness.

AI Risks Don’t Outweigh Benefits With the Right Approach

AI can pose significant risks to corporations if they don’t take care when developing and implementing it. However, as these issues become more widely known, relevant and helpful mitigation measures will likewise rise.

Organizations must recognize these risks to use AI safely. If they can consider these concerns throughout the entire AI life cycle, they can fully capitalize on its benefits while minimizing costs.

April Miller

April Miller

April Miller is a staff writer at ReHack Magazine who specializes in AI, machine learning while writing on topics across the technology sphere. You can find her work on ReHack.com and by following ReHack's Twitter page.