Written by Ngoc (Lee) Duong, Psychometrics & Data Analysis Consultant
The development of artificial intelligence has quickly become part of our everyday lives as it can be used for almost anything from facial recognition to unlock our phones to automated prompts we answer to solve a problem. More recently it’s become even more valuable for more personal tasks such as generating a workout plan or establishing a monthly budget and spreadsheet to track expenses within seconds.
Given its growing utility, it’s no surprise that using AI in the workplace has quickly become the norm and companies are left determining how to best integrate it into their business practices. For example, McKinsey has their own proprietary AI assistant for their employees, while other organizations have been providing their staff with AI tools to speed up deliverables like presentations or technical reports. As AI in the workplace continues to grow, it is essential that employees and organizations understand its benefits and how it can be used effectively and responsibly.
What is generative AI?
Whether you realize it or not, generative AI – or GenAI – has infiltrated much of our lives. Broadly, generative AI is a branch of computer science focused on creating systems that can perform tasks that have traditionally required human intelligence. It is about teaching machines to learn from data, recognize patterns, make decisions, and then improve over time. Some applications of AI, such as machine learning (ML), have seen some major gains when it comes to using AI in the workplace. For instance, ML has been applied to improve fraud detection in finance, develop customer support chatbots, provide predictive maintenance in manufacturing, and develop talent analytics in human resources.
The GenAI tools that you’re likely most familiar with are platforms like ChatGPT or Microsoft CoPilot whose functionality can do everything from helping to create a grocery list with corresponding dinner recipes to finding the cheapest travel arrangements and itinerary for your dream vacation. This of course also opens up major possibilities for how AI in the workplace can be a game-changer. For example, an employee can prompt an AI tool to generate a first draft of a technical report within seconds, dramatically reducing the time spent on routine drafting.
Benefits of using AI in the workplace
The ways in which organizations and employees choose to use AI will naturally differ from one company to another. Despite those differences, there are some universal opportunities that are worth consideration regardless of industry, organization size, or specific role. A few ideas include:
- Use AI in training and development: More companies are learning how to use generative AI in talent assessment and development practices as a way to help close the gap between low- and high-skilled workers. AI can serve as a great tool for coach/mentor relationships that help new or less experienced employees get up to speed in their role. For instance, AI tools can be used to simulate customer interactions for sales training or provide instant feedback on writing or coding exercises. This can accelerate learning and promote consistent skill development across teams.
- Leverage AI for repetitive and time-consuming tasks: Employees can use AI for simple, standardized, and rule-based tasks such as drafting a standard technical report, summarizing meeting notes, and other routine work where it can save time and increase efficiency. For instance, in HR, AI can be used to help process job applications by screening resumes for key qualifications. In operations management, AI can support scheduling, documentation, or workflow optimization. By leveraging AI for these types of tasks, organizations can reduce administrative burdens and open more room for employees to focus on problem solving, innovation and strategy, and relationship-building.
- Apply AI to enhance knowledge management and decision support: AI can help employees efficiently stay up to date with organizational knowledge by summarizing internal documents. More importantly, AI can be used as an advanced search engine that helps employees maintain active engagement with the latest research evidence and support data-driven decision making. For instance, using a combination of ChatGPT to search for research papers and Adobe AI to help with summarization has helped reduce the amount of time it takes for me to perform literature reviews and hone my skills as an evidence-based practitioner. This use of AI opens an opportunity for AI to be a bridge that helps with closing the scientist-practitioner gap.
Can GenAI close the gap between inexperienced and experienced employees?
While generative AI can help with specific tasks, it can also make a difference in overall employee performance, particularly for early career staff. Research shows there are some notable differences in how AI can improve workers’ performance depending on their level of skill and experience.
Less experienced workers are the ones that stand to gain the most from using AI in their roles. For instance, one study found that customer support agents using AI improved productivity by up to 30%, largely because AI exposed them to expert-level best practices. Similar benefits of using AI in the workplace have been found in marketing, consulting, writing, programming, and even legal education.
For experts, however, the impact is mixed. Some studies find a slight decrease in quality when AI limits creativity or causes over-reliance on AI-generated outputs. Others find modest gains or no effect at all. In many cases, this is due to a ‘ceiling effect’ that occurs when experienced workers already operate at a high level, leaving less room for AI to add value.
Plan ahead before using AI in the workplace
When determining the best uses of AI for your employees, there are a couple of considerations for organizations to keep in mind before getting to the point of no return.
- AI adoption is sticky: Once employees start using AI, reverting back may be difficult. Often when people use AI, they are more likely to continue using it again in the future. One study reported that when workers use AI and transition back to working without it, while they feel a greater sense of autonomy with their job, they also experience lower intrinsic motivation and increased boredom. These findings suggest that once AI is implemented, it may fundamentally reshape job characteristics and design.
- AI should augment – not replace – humans: Research has shown that AI performs better on simple, routine, and standardized tasks than on complex ones. Thus, because AI struggles with complicated, real-world tasks, it should support and not replace human judgment. Experts caution that replacing employees with AI not only risks lower work quality but could also raise ethical and legal challenges.
Striking a balance between talent and tech
Using AI in the workplace is no longer just a futuristic concept, it is rapidly becoming a practical tool. While the less experienced employees typically stand to gain the greatest benefits, organizations that adopt AI thoughtfully can create opportunities for all workers to grow and enhance their skills. The key is to use AI as a partner: a tool that speeds up standardized work, supports training, and frees employees to focus on higher-value, human-centered tasks. Organizations that strike this balance will not only boost productivity but also create workplaces where people and technology complement each other, rather than compete.
About the author: Ngoc (Lee) Duong, MS, is a Psychometric and Data Analysis Consultant at Talogy, specializing in performing various types of psychometric and data analysis as well as test scoring for various clients in public safety and other industries. He is also a core member of the AI Taskforce in Consulting at Talogy. He is currently finishing up his PhD in IO Psychology at the Florida Institute of Technology. Lee has had three book chapters and three research articles published in Equality, Diversity, and Inclusion: An International Journal, Organizational Psychology Review, and Small Group Research.
