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Labour Productivity & Generative AI in Professional Services

Improved productivity is the key to increased revenue for professional services. Taking advantage of the latest technology – efficiently – is a must for improved productivity. One such resource that should be on your radar, if it isn’t already, is generative AI. This technology is showing its capabilities in a number of industries, and many businesses are already using it to see results in their labour productivity levels. If you’re still on the fence about generative AI or you’re not sure how you can take advantage, we’ve outlined some must-know information.

Generative AI Is Set To Have a Growing Impact for Professional Services

Generative artificial intelligence (AI) works by first learning patterns from input data. By utilizing this data, which can be written content, images, and other types of media, the technology can generate information and content at the user's command. A well-known example of generative AI is ChatGPT, but there are other resources that exist, such as DALL-E and Microsoft’s Copilot.

Many departments, organizations, and industries are already benefiting from the technology, including professional services, retail, marketing, sales, HR, and more, unlocking the potential for further growth. Research has even found that generative AI and its impact on productivity could add up to $4.4 trillion to the global economy.

How Can Labour Productivity Benefit From Generative AI?

By leaning into generative AI, professional services stand to see labour productivity improve as long as they use the technology correctly. Specifically, it can automate routine tasks and processes, enhancing efficiency. Companies using generative AI can find it easier to craft emails, organize (and understand) data, create content for their client base, and more.

This in turn can:

● Streamline processes

● Reduce manual workload

● Eliminate redundancies

● Improve the employee experience

● Increase employee retention

● Improve the client experience

● Increase client satisfaction and retention

● Allow for more effective allocation and management of time

● Reduce costs

● Maintain a competitive edge in the industry

● Offer personalized experiences to clients without an extra workload

● Give insight into processes and practices

The Technology Is Not Without Its Challenges

One of the biggest challenges that professional services face when it comes to generative AI (and other new technologies) is using the resources efficiently and intelligently. If missing the mark, companies could find the technology actually hampers their efforts rather than improves them. A few challenges organizations face include:

Data Privacy Concerns

Generative AI works by pulling from other resources, and this can include individual and sensitive data. This opens the door to privacy concerns, as malicious actors could gain access to that information in the event of a breach. With AI regulations increasing worldwide to help stop this concern, companies need to overcome this hurdle, and quickly.

Solution: Professional services can work to offset this challenge by being diligent with the data they supply to the AI tool. No personal information or sensitive data should be supplied, especially during training of the model.

Job Displacement Fears

A major concern when it comes to Generative AI is the impact it will have on the workforce. With its ability to reduce redundancies and streamline entire processes and jobs, employees are worried that they will soon lose their position to the technology. This can directly impact employee satisfaction and retention.

Solution: Companies need to first reassure their employees that they will not lose their positions and assist them in pivoting. Organizations should offer training to help their team members learn the new technology and understand how it can improve their efforts.

AI Literacy

Using generative AI efficiently is central to making use of the technology. AI literacy is a must-have for professional services leaders and their team members. Without this understanding, employees could push back against the change and the improved productivity could be lost. It should never be implemented on a whim.

Solution: Organizations should work to train their leaders first on generative AI, its uses, and its capabilities. Through communication, training modules, and other tactics, these leaders can then educate their team members and assist with change management.

Data Quality

The output data from generative AI is only as good as the input data. If the content supplied to the model is lacking or incorrect, companies could be facing an uphill battle when it comes to AI. Companies are sitting on stockpiles of data, yet not all that information will help with productivity. If an organization uploads the wrong data sets, the data output will be inefficient.

Solution: Data governance and quality strategies are key to ensuring generative AI has access to the more important information that will enhance labour productivity. Assessments should also be conducted regularly to measure goals, metrics, and success.

Generative AI is already showing its capabilities in a number of industries, and the implications for professional services’ productivity can’t be understated. Organizations in this industry should be carefully considering their approach to AI and adjusting their strategies to make use of this valuable resource.


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