Top pitfalls to avoid when implementing AI in the enterprise

If you’re a leader in the business world, there’s a good chance your company has already implemented some form of artificial intelligence (AI) or is planning to in the next 12 months. In fact, according to a recent study, that’s the case for about 81% of very large organizations.¹

The increased efficiency, innovation and productivity that comes with AI is exciting, and it’s clear the business world is all-in on an AI future. At the same time, AI presents a host of challenges and uncertainties that we don’t yet fully understand. The best way to prepare for this uncertain future is to identify and understand the challenges and issues that come with this new world, then develop and implement strategies to mitigate potential risks to your business.

Let’s dive into some of the most common uncertainties and challenges associated with AI technologies, then look at strategies you can implement to minimize risk.

6 common AI implementation challenges

Implementing AI in your business can be beneficial but also challenging. Here are six common pitfalls to be aware of so you can better leverage AI technology and harness its potential.

Impact of data quality on AI outputs

A recent study² examining the rising use of generative (Gen) AI found that inaccuracy was the top concern among participants. This is because AI models often rely on vast amounts of data for training, and the quality of this data directly impacts the accuracy of the AI outputs. As AI systems are increasingly integrated into critical decision-making processes, inaccuracies can have serious consequences such as misdiagnoses in healthcare, erroneous financial predictions, or biased hiring practices.

Risk of intellectual property infringement

According to business and intellectual property (IP) law firm Stafford Rosenbaum LLP, IP infringement is one of the greatest legal risks associated with AI because Gen AI often uses copyrighted materials without proper authorization. To complicate the issue, current laws do not adequately address the complexities of AI-generated content, leaving gaps in regulation and enforcement.³

Bias in AI models

This might be tough to hear, but you are biased. So am I. In fact, bias is an inherently human characteristic that prepares us for evaluating information we will encounter in the future.

Bias can be a problem because we use data—historical information—to teach machines how to learn and recognize different kinds of patterns. When that data is incomplete or not wholly representative, the results become skewed and biased.⁴

AI systems, therefore, are only as good as the data on which they are trained. If the training data contains biases, the AI system can perpetuate and even amplify that bias.

Privacy and security concerns

The use of AI makes you a steward of your customers’ data, and they expect you to uphold the highest standards of security, privacy and ethics. Governments are responding with regulations aimed at balancing technological innovation with the need for ethical practices and accountability for misuse.

In addition, AI systems can be vulnerable to cyberattacks, which can compromise sensitive data and disrupt business operations. As AI becomes more integrated into critical business processes, the potential impact of such attacks increases.

High operational costs

Companies looking to expand their AI capabilities typically must do so in the face of budgetary pressure to optimize costs. A recent article^₅ in Forbes Magazine outlined the costs companies can expect to incur with AI implementation, including upfront and ongoing costs; unforeseen expenses related to data acquisition, compliance with regulations and addressing ethical concerns; and the potential opportunity cost if funds are diverted from other projects to AI initiatives.

Insufficient network capacity

As the use of AI continues its exponential growth, the underlying network infrastructure becomes the backbone of their success. And yet, 86% of CIOs don’t think their enterprise networks are prepared for the AI revolution⁶, underscoring the urgent need for a new approach to business connectivity.

By strategically addressing these critical areas, you can build transformative AI applications that drive ongoing value and success for your organization.

Effective strategies to help mitigate AI risks

Despite the varying challenges and uncertainties of AI tools, there are a handful of best practices that can help mitigate many of these issues, and a partner like Lumen can help you navigate through many of them.

• Continuously validate and test AI models using diverse and representative datasets to train and test models and implement robust validation techniques to detect and correct errors.
• Establish feedback loops where users can report inaccuracies or unexpected behaviors. Use this feedback to refine and improve AI models over time.
• Conduct regular IP audits to identify and address potential infringement risks and train employees about the importance of compliance.
• Implement data anonymization techniques to protect the privacy of individuals whose data is used in AI systems and remove or obfuscate personally identifiable information from datasets.
• Conduct privacy impact assessments to evaluate the potential privacy risks associated with AI projects, then implement appropriate safeguards and technologies.
• Ensure compliance with data protection regulations such as HIPAA and GDPR.
• Implement the latest cybersecurity measures to protect AI systems from cyberthreats and consider security products that are based on and informed by vigorous threat intelligence. Conduct regular security audits to identify and address vulnerabilities in AI systems and maintain incident response plans to quickly and effectively respond to breaches.
• Use diverse and representative training data to minimize biases in AI models and conduct regular audits to address it.
• An AI strategy is more than cloud and data—the infrastructure and network strategy are vital to succeed. Develop scalable AI solutions that can grow with the company’s needs to help manage costs and optimize resources by leveraging cloud-based AI services and platforms.

While AI has the potential to revolutionize the workplace, it is essential for organizations to be aware of its challenges and potential downsides. Ultimately, the successful integration of AI into the workplace will depend on a balanced approach that considers both technological advancements and human factors.

The key to maximizing AI’s potential is flexible, scalable and secure infrastructure built for today’s demands and tomorrow’s innovations. See why Lumen is the backbone of the AI economy.

¹_{IDC, InfoBrief:Empowering Digital Transformation, July 2024.}

²_{McKinsey, The state of AI in early 2024: Gen AI adoption spikes and starts to generate value, May 2024.}

³_{Stafford Rosenbaum LLP, Generative Artificial Intelligence 101: The High Risk of Intellectual Property Infringement with Use of Generative AI, October 2024.}

⁴_{Forbes, How Businesses Can Help Reduce Bias in AI, April 2023.}

₅_{Forbes, The Hidden Costs of Implementing AI In Enterprise, August 2023.}

_{⁶IDC, Enterprise Horizons 2024, June 2024.}

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