How to use AI judiciously in your work

Important points

Using AI judiciously requires assessing your needs and goals, choosing reliable AI tools, and monitoring AI performance.

• AI can help organizations with customer service, marketing, risk management, and supply chain management.

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How to use AI to your advantage: Understanding AI in the workplace

Exploring the role of AI in the workplace means understanding its potential to increase productivity, transform decision-making, and reimagine collaboration.

Businesses love AI because it appears to promise increased efficiency, innovation, and revenue. AI benefits businesses in a variety of use cases, including:

Employees will use AI for a variety of purposes in the workplace. For example, recruiters and interviewers use AI screening technology to identify and select qualified candidates from a pool of applicants. Managers in all sectors can use AI applications to measure employee performance indicators such as keystrokes and observe employee activity in a variety of ways.

The sensitivity of AI use cases in the workplace largely depends on how employees use the technology, including why they choose to use it and what they deem inappropriate.

How to use AI in your work wisely

AI should be used with caution in professional settings because of practical and ethical concerns. Below are some ways you can reduce unnecessary risks.

Assess your needs and goals.

While AI offers great capabilities, it is important to recognize its limitations. Rather than deploying AI without a clear strategy, consider identifying where AI can provide real value to your company. Consider which tasks and processes can benefit from AI without compromising quality, and which tasks are best suited to human expertise. Knowing where AI will work in your business is a matter of great practical importance, especially given that in a 2026 Deloitte report, business leaders reported that a lack of employee skills was a major barrier to implementing AI in the workplace. [1].

When used carefully and appropriately, AI can improve employee productivity. According to a Gallup poll, 38% of employees reported that their organization used AI to improve productivity, efficiency, and quality in the fourth quarter of 2025. [2].

Examples of effective use of AI include:

• Improve customer experience by deploying chatbots and virtual assistants and leveraging AI for content moderation purposes.

• Optimize processes by automatically summarizing and analyzing data in dense and long documents, including multimodal text, visual, and audio input.

Conversely, some companies use AI without strategic intent. This is not a prudent or productive use of technology and can lead to a variety of problems, including increased carbon emissions. For example, training an AI model with billions of parameters requires large amounts of electricity, which can emit carbon dioxide and strain the power grid. [3]. Considering that billions of people use AI every day, it highlights the importance of adopting sustainable practices to minimize environmental impact.

Choose an AI tool you can trust.

When choosing an AI tool, there are several things to consider, including price and features. After all, the purpose of AI onboarding is to improve workplace productivity, efficiency, and culture.

You also need to choose AI tools that prioritize security, data privacy, and transparency. AI tools with robust cybersecurity capabilities play a critical role in protecting sensitive employee information and preventing unauthorized access by potentially malicious third parties.

With this in mind, consider choosing a tool that focuses on AI governance. AI governance refers to efforts by AI developers to create guidelines for ethical and compliant use of AI. AI governance can help solidify trust in AI models and reduce potentially large financial penalties.

Monitor AI performance.

AI needs to be carefully managed in the workplace. As consumer information and data sources evolve, AI models can become outdated and lose their effectiveness. We need to continue this. Hard data is essential for informed, data-driven decision-making.

Automated AI assessment tools alert you to potential compliance violations while ensuring you’re meeting key metrics. These tools also help workplaces adhere to responsible AI guidelines, such as:

• Consistency: How human-like is the model output?

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

• Grounding: How well does the AI ​​tool’s output match the training input?

• Relevance: How relevant is the model’s output to the user’s prompts?

• Similarity: How similar is the model output word for word to the input text?

Keep in mind that AI is a tool that augments the workplace, not an autonomous and automated workflow process that can be trusted to run reliably and independently at all times. You should monitor performance and make changes as necessary.

Thorough human monitoring will be carried out.

No AI tool can be trusted in all situations. Therefore, AI will require some level of human oversight. Evaluating and improving output by training AI with more diverse sources requires people with critical thinking skills that machines lack.

AI models are completely dependent on the quality of the data used for training. Developers can train them using vast, diverse, and sometimes unstructured datasets. If your training input contains inaccurate or biased data, your output is likely to reflect discrepancies. This can put your business at risk of Title VII violations.

Generative AI models are complex autocomplete tools that rely on predictive analysis rather than conscious reflection to generate answers to queries. Unlike humans, these models do not think, but rather generate inferences based on the statistical probability that one word follows another. Without decision-making ability, AI cannot identify its own errors or recognize when its output seems pointless. Only humans can make that call.

read more: Understanding AI bias

Address ethical considerations.

The various ethical difficulties raised by AI need to be carefully addressed.

In October 2024, the US Department of Labor emphasized that AI should be used in the workplace to “expand equality, promote equity, develop opportunity, and improve the quality of work.” [4]. It is important to use AI in a way that minimizes the risk that workers may lose their jobs or experience other negative consequences as a result of its implementation. Deliberately introducing AI into the workplace is therefore essential to promoting equity and human well-being.

Transparency in AI is also a persistent ethical issue. It can be difficult to determine exactly what data programmers trained their AI models on. As a result, if a model consistently produces “hallucinations”, it is impossible to verify its accuracy. Additionally, there needs to be a clear liability framework for serious mistakes that AI may make, such as providing incorrect medical or legal advice. Without accountability, motivation to improve can be diminished.

Additionally, responsibility for AI work is not always clearly attributed, making it unclear who should take credit or ownership. For example, does the employee who creates the AI ​​model get credit, or does the developer of the AI ​​interface that the employee uses get credit? This is more than just speculation. According to a 2025 OC Tanner study, nearly two-thirds of employees expressed concern that AI would make employee recognition less personal. [5]. Therefore, questions about AI attribution can impact employee retention and turnover rates.

We prioritize data security and privacy.

The use of AI can pose challenges in terms of data privacy and security. Employee information stored in AI systems can be vulnerable to acquisition by third parties. Storing information in this manner may violate certain state laws.

This is a growing concern not only for businesses but also for lawmakers at all levels of government. U.S. regulatory agencies that oversee data privacy and copyright laws regarding AI may include:

• Federal Trade Commission

• U.S. Equal Employment Opportunity Commission

• Consumer Financial Protection Bureau

• Ministry of Justice

• Department of Homeland Security

Workplaces need to be transparent about how they collect data, what kind of data they collect, how administrators use that data, and how they protect it from potential theft. Prioritizing data privacy and security can help you avoid potential legal issues.

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