AI in the workplace: Digital labor and the future of work

Artificial intelligence (AI) is transforming the workplace, impacting how businesses operate and how employees do their jobs. The technology is expected to significantly impact the global economy by transforming the labor market and changing the nature of work.

Organizations use AI in the workplace by deploying a wide range of technologies, including machine learning and natural language processing, that can mimic human intelligence to solve problems, make decisions and perform tasks traditionally handled by humans. AI can analyze data, recognize patterns, learn from experience and adapt over time. It is often used to streamline operations, enhance productivity, automate repetitive tasks and support decision-making.

Generally, deploying AI in the workplace involves a wide ecosystem of technologies, the most common of which are:

• Machine learning: A branch of computer science that focuses on using algorithms to allow AI to imitate the way humans learn, gradually improving its functionality over time.

• Natural language processing (NLP): A form of AI that uses machine learning to understand and communicate in human language.

• Generative AI: A form of AI recently popularized by ChatGPT that can create original content in response to a user’s request.

• Robotic Process Automation (RPA): A process-driven intelligent automation technology often used to perform repetitive office tasks.

Using a combination of these technologies, deploying AI in the workplace might be as simple as automatically digitizing and filing employee records, or translating Spanish into English. It might be as complex as providing decision-makers with guidance on how to improve a company’s business processes enterprise-wide.

In the healthcare, insurance and banking industries, AI has become increasingly common. Examples include helping researchers identify new drug compounds and predict their effectiveness, or assisting cybersecurity professionals identify and mitigate fraud. AI is also routinely used to enhance employee and customer experiences through AI assistants, such as chatbots and AI agents.

Organizations that embrace the use of AI have the potential to enhance efficiency, improve decision-making and drive innovation. Some of the key advantages related to the use of AI include:

• Increased revenue
• Leveraged data
• Improved customer experience 
• Enhanced employee well-being 
• Competitive advantage
• Increased innovation

AI helps businesses boost revenue and gain increased cost savings by optimizing operations, enhancing decision-making and identifying new opportunities for growth. By augmenting a human workforce with AI tools, businesses can spend fewer resources on routine tasks and encourage employees to engage in more creative and valuable tasks.

As AI can analyze more information than a human can at one time, the technology enables businesses to unlock the full potential of their data, turning raw information into actionable insights.

AI enhances the customer experience by delivering personalized interactions, faster service and more accurate responses. It’s adept at analyzing customer behavior to offer highly personalized communications and recommendations, promoting long-term customer loyalty.

AI supports employee well-being by automating routine tasks, improving productivity and encouraging the development of new skills and more creative workflows.

AI allows business leaders to craft more powerful data-driven strategies and gain a competitive edge through improved efficiency and agility.

AI fosters innovation by unlocking new possibilities, accelerating the research and development process and mining data like customer feedback or market trends to explore new product solutions. 

AI is used for various business functions across industries to increase efficiency and provide data-driven insights. Some key areas in which organizations deploy AI include:

• IT processes
• Customer service workflows
• Supply chain
• Human resources and talent management
• Sales and marketing
• Operations
• Finance

IT processes are particularly well suited to AI integration, with one survey suggesting over half of executive respondents are already embracing generative AI to streamline those processes. Traditional AI can automate routine tasks, improve security and enhance systems management, for instance by optimizing network performance and monitoring IT infrastructure.

Increasingly, IT departments use generative AI for application modernization and platform engineering, increasing productivity. AI has also become a crucial tool to improve cybersecurity, monitoring vast amounts of network data to identify suspicious behavior or breaches. 

AI is used to provide instant response times, personalized interactions and optimized support processes in customer service. Using NLP, AI tools can understand and respond to customer inquiries in real-time, enhancing customer experience, or perform sentiment analysis to gauge consumer reactions.

Chatbots and virtual assistants powered by AI handle customer queries and resolve common issues, providing customer self-service and freeing up human employees for more valuable tasks. AI-fueled tools also summarize and analyze complaints from reviews, social media or other data to offer insights on performance or uncertainties. 

AI streamlines supply chain operations by improving forecasting, optimizing inventory and enhancing logistics. This might include demand forecasting, in which AI models analyze historical sales data along with external factors to predict future ordering trends, optimizing inventory levels. AI can also evaluate supplier performance, automate inventory replenishment and optimize transportation routes to minimize delivery times and reduce costs.

AI-assisted software and apps can transform the HR process by streamlining recruitment, improving employee engagement and enhancing workforce management. This might include automating critical and repetitive processes like job requisition requests, resume screening or employment verification. It also includes using an AI system to create personalized onboarding trainings.

Some organizations use AI to analyze employee performance data such as productivity metrics to surface strong candidates for internal promotion or identify promising job seekers. Others might deploy chatbots to provide conversational HR self-service at any time of day.

AI enhances sales and marketing by providing personalized customer experiences, improving lead generation and optimizing marketing campaigns. This might include using predictive analytics to analyze customer data and sales trends, surfacing which leads are most likely to convert into valuable customers.

AI also helps marketing departments segment their customers more effectively and personalize their customer experience—for example, by using recommendation engines to surface products or, using generative AI, by creating hyperpersonalized websites and bespoke communications. Also, a common use for AI in marketing is the analysis of digital advertising campaigns in real-time to maximize revenue from a campaign.

AI is increasingly used to improve operational efficiency by automating workflows, optimizing resource allocation and enhancing productivity. AI-powered RPA tools automate repetitive tasks such as data entry, document processing and invoicing, reducing human error and allowing employees to focus on more strategic activities.

AI also helps businesses identify inefficiencies in their operations by analyzing performance data and suggesting process improvements, such as reallocating resources or adjusting production schedules. And in industries like manufacturing, AI tools can perform predictive maintenance, reducing downtime and repair costs. 

AI is commonly used for improving risk management, automating financial tasks and enhancing decision-making. AI systems can analyze transaction patterns to detect anomalies in real-time, preventing fraud. Some AI tools automate tasks such as expense tracking, invoice processing and financial reporting to reduce the time spent on manual data entry. AI-powered analytics tools also help companies predict financial trends, including revenue and cash flow. These forecasts enable businesses to make proactive decisions, identify potential issues and better manage their finances. 

Before introducing AI, it can be helpful to identify specific business objectives that AI can address—essentially, letting the business strategy guide AI strategy. This process might involve mapping how AI integrates into existing workflows and systems, identifying key processes best suited to augmentation and defining measurable goals for success.

AI tools are only as reliable as the data used to train them. An organization generally evaluates its current technological infrastructure for AI readiness following the planning stage. This stage typically includes evaluating the availability of data as well as the skill level of employees. During this stage, an organization also identifies the most appropriate data sets, models and architectures for its enterprise use case. 

A strong data strategy, and strong data governance policies, can be essential for ethical AI. During this phase, an organization generally builds in processes to improve transparency and security, as well as establishing company-wide guidance for the use of data and AI.

After a data strategy is instituted and data is collected and cleaned, a business typically helps ensure it has the correct skills and stakeholders for the implementation. This process might involve significant collaboration between business, operations and technical teams that are able to prioritize AI use cases balancing risk and reward. If a business finds it doesn’t have access to the correct experts, or needs more skills to implement an AI project, it might partner with a third party to help ensure success.

Rather than immediately infusing AI across a business, successful organizations often apply AI to a specific task or workflow in a less risky environment. These pilots can then be tested and refined before they’re scaled across the business.

The impact of AI on the workplace has broad implications for the labor market and the future of work. While the use of AI is generally associated with productivity gains for businesses, many expect the technology to require a broad shift in what kinds of jobs workers do, and how they’re trained.

According to the consultancy McKinsey, up to 30% of hours worked across the US economy could be automated by 2030, with 12 million occupational transitions required by the same year.¹ Concurrently, recent research completed by the IBM Institute for Business Value found that organizations deploying AI at an operational level, rather than a skills-based level, outperformed their peers by 44% when it came to metrics such as employee retention and revenue growth. These findings are compatible with World Economic Forum estimates, which forecast that while over the next few years there might be 85 million job losses globally, new technologies might create 97 million new jobs.²

Taken together, these statistics suggest that widespread adoption of AI technologies might require significant upskilling initiatives to retrain the global workforce. As AI tools are used with increasing frequency, and AI-augmented work becomes more common, organizations will likely focus more heavily on maximizing the efficiency of these human-machine interactions.