How to Cautiously Use AI for Work

A great many businesses have adopted AI in the last few years. People utilize AI in various ways in industries and sectors as diverse as health care, transportation, and agriculture. Despite the widespread acceptance of AI into the workplace, it still comes with its share of challenges and limitations.

Learning how to cautiously use AI for work is becoming increasingly valuable and essential. In the absence of undue caution, AI use in the workplace can result in risk management issues and ethical quandaries. Discover the steps you can take to effectively and ethically use AI at work.

Understanding AI in the workplace

Exploring AI's role in the workplace means understanding its potential to heighten productivity, transform decision-making, and reshape collaboration.

Companies favor AI because it seems to promise an increase in efficiency, innovation, and revenue. AI benefits companies in a variety of use cases, such as: 

• Customer service

• Marketing

• Risk management

• Supply chain management

• Operations of various kinds

Workers use AI for a variety of workplace applications. For example, recruiters and interviewers use AI screening technology to identify and select qualified candidates from large pools of applicants. Managers in all fields can use AI applications to measure employee performance metrics, such as keystrokes, and observe employee activities through various methods.

The cautiousness of workplace AI use cases largely depends on how workers utilize the technology—what they choose to use it for and what they deem inappropriate.

How to cautiously use AI for work

Due to practical and ethical concerns, you’ll want to use AI cautiously in professional settings. Below are some ways you can mitigate unnecessary risk. 

Assess your needs and goals

While AI offers impressive capabilities, it’s important to recognize its limitations. Consider identifying where AI can provide real value to your company rather than adopting it without a clear strategy. Consider which tasks and processes can benefit from AI without compromising quality and which tasks best suit human expertise. Knowing where AI will work for your business is a matter of significant practical importance, especially considering 76 percent of C-suite executives say they have difficulty figuring out how to implement AI productively [1]. 

When you use AI cautiously and appropriately, it can help boost worker productivity by as much as 40 percent [2]. However, when you utilize it in use cases for which it’s irrelevant, worker performance declines by an average of 19 percent [2]. 

Effective use cases for AI include: 

• Improving customer experiences by deploying chatbots and virtual assistants and by utilizing AI for content moderation purposes. 

• Increasing employee productivity through user-friendly research databases and generative AI capabilities, as well as automating report generation and code writing. 

• Optimizing processes by automatically summarizing and analyzing data from reams of dense, lengthy documents, including multimodal textual, visual, and audio inputs. 

Conversely, some businesses use AI without strategic intent. This isn’t a cautious or productive use of the technology, and it can result in various issues, including a high carbon footprint. For instance, using AI to generate one image requires as much energy as it takes to charge a smartphone fully [3]. Considering billions of people use AI daily, this highlights the importance of adopting sustainable practices to minimize its environmental impact. 

Choose reliable AI tools

When choosing an AI tool, you’ll want to consider several things, such as price and functionality. After all, the purpose of onboarding AI is to improve workplace productivity, efficiency, and culture. 

You’ll also want to make sure you choose an AI tool that prioritizes security, data privacy, and transparency. An AI tool equipped with robust cybersecurity features can play a crucial role in safeguarding sensitive worker information and preventing unauthorized access by potentially malicious third-party actors.

With that in mind, consider choosing a tool that focuses on AI governance. AI governance refers to an AI developer’s commitment to creating guidelines for ethical and regulation-compliant AI use. AI governance helps solidify trust in an AI model and mitigate potentially substantial financial penalties.

Monitor AI performance

AI should be carefully managed in the workplace. As consumer information and data sources evolve, your AI model may become outdated and lose its effectiveness. You should keep up with this; solid data is crucial for informed, data-driven decision-making. 

Automated AI evaluation tools can help ensure you’re meeting vital metrics while alerting you to potential compliance violations. These tools can also help your workplace adhere to responsible AI guidelines such as: 

• Coherence: How human-like a model’s output is

• Fluency: How linguistically and grammatically correct a model’s output is

• Groundedness: The extent to which an AI tool’s output aligns with its training input

• Relevance: How relevant a model’s output is to a user’s prompt

• Similarity: How word-for-word similar a model’s output is to the input texts

Remember that AI is a workplace augmentation tool, not an autonomous, automatic workflow process you can trust to perform reliably and independently at all times. You will have to monitor its performance and make changes where necessary. 

Ensure human oversight

No AI tool is reliable in every situation. AI will, therefore, require some degree of human oversight. It takes people with critical thinking skills, which machines lack, to evaluate and improve output by training AI on a greater variety of sources. 

AI models depend entirely on the quality of the data used for training. Developers can train them on vast, diverse, and sometimes unstructured data sets. If their training inputs include inaccurate or biased data, their output is more likely to reflect discrepancies. This has the potential to put a business at risk of Title VII violations. 

Generative AI models are complex autocomplete tools relying on predictive analytics, not mindful reflection, to generate answers to queries. Unlike humans, these models do not engage in thinking but instead generate guesses based on the statistical probability of one word following another. Without decision-making abilities, AI cannot identify its own errors or recognize when its outputs seem nonsensical. Only a human can make that call. 

Address ethical considerations

You’ll want to thoughtfully address the variety of ethical quandaries AI presents. 

In October 2024, the US Department of Labor stressed that you should use AI in the workplace to “expand equality, advance equity, develop opportunity, and improve job quality” [4]. It's important to use AI in ways that minimize the risk of workers potentially losing their jobs or experiencing other negative impacts from its adoption. Therefore, implementing AI thoughtfully in the workplace is essential for fairness and promoting human well-being.

AI transparency is another persistent ethical issue. It can be challenging to determine exactly what data programmers trained an AI model on. As a result, it's impossible to verify its accuracy when the model consistently generates “hallucinations”. Furthermore, clear accountability frameworks are required for the potentially serious mistakes AI can make, such as offering incorrect medical or legal advice. In the absence of accountability, the motivation for improvement may be diminished.

Moreover, the responsibility for AI's work isn't always clearly attributed, making it unclear who should take credit or ownership. For example, does the employee who prompted the AI model get the credit, or does the credit go to the developers of the AI interface the employee used? This isn’t merely conjecture; employees whose work goes unrecognized are twice as likely to consider moving jobs within a year [5]. As such, questions surrounding AI attribution can impact employee retention and attrition rates. 

Prioritize data security and privacy

AI use can present challenges in terms of data privacy and security. Employee information stored in an AI system may be vulnerable to retrieval by third parties. Storing information in such a way may also violate certain state laws. 

This is a growing concern not only for businesses but for legislators at all levels of government. American regulatory bodies that monitor data privacy and copyright law when it comes to AI could include: 

• Federal Trade Commission

• US Equal Opportunity Employment Commission

• Consumer Financial Protection Bureau

• Department of Justice

• Department of Homeland Security

Your workplace should be transparent about how it collects data, what sort of data it collects, how management uses that data, and how it protects it against possible theft. Prioritizing data privacy and security helps avoid potential legal issues. 

Learn how to cautiously use AI for work with Coursera

Workplace applications for AI are extensive. Learning to use AI responsibly in the workplace is pivotal to maximizing its benefits while minimizing potential challenges. Discover more with Coursera. When it comes to implementing AI in a cautious, ethical manner in your place of work, consider exploring IBM's AI Foundations for Everyone Specialization.