AI in the workplace: Digital labor and the future 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.

• Agentic AI: A series of autonomous AI systems capable of operating independently with minimal human intervention. 

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.

Other 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, chatbots and AI agents.

But increasingly, organizations see the most long-term value-creation when AI is deeply embedded into core business processes across departments. Today, only 41% of enterprise data on average is usable by AI, limiting the technology’s potential to drive business transformation.

As Manish Goyal, senior partner at IBM Consulting recently told the Harvard Business Review: “AI can raise the floor for everyone, but leadership advantage comes from using AI to break the ceiling, redefining how the business competes, not just how it operates.”

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 the IBM Institute for Business Value recently found, 79% of surveyed executives expect AI to significantly contribute to their revenue by 2030. 

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 employees by automating routine tasks, improving productivity and encouraging the development of new skills and more creative workflows. Internal AI tools such as IBM’s AskHR, an agentic self-service platform, facilitate more seamless employee experiences. 

AI allows business leaders to craft more powerful data-driven strategies and gain a competitive edge through improved efficiency and agility. Today, the majority of business leaders believe that their competitive advantage will primarily stem from the sophistication of their AI models. 

AI fosters innovation by unlocking new possibilities, accelerating the research and development process. It also mines 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 use AI include:

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

IT processes are 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. And with the sophistication of agentic AI, agents handle a number of day-to-day IT tasks including running diagnostics, triaging IT tickets, running diagnostics and categorizing issues. 

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, thus 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. Today’s proactive AI also handles routine customer service tasks autonomously, such as processing returns or guiding users through troubleshooting processes.

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 agentic systems to provide conversational HR self-service at any time of day. IBM’s internal virtual agent, AskHR, currently automates over 80 tasks and handles over 2.1 million employee conversations every year, streamlining complex HR processes. 

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, using recommendation engines to surface products or using generative AI to create hyperpersonalized websites and bespoke communications.

Another common use for AI in marketing is the analysis of digital advertising campaigns in real-time to maximize revenue from a campaign. Increasingly, sales teams use AI agents to interact with customers autonomously, guidinging prospects toward a sale—and handing off high-value or difficult interactions to a human agent. 

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.

During this time, some organizations map the existing skills and job descriptions present in their enterprise. As with any transformation, an AI transformation will reduce the need for some skills while demanding new ones. To set up employees for success, reskilling initiatives should be top-of-mind before AI deployment. “It’s hard to develop a strategy to take your best employees off their work and put them into roles that don’t exist yet,” Anthony Marshall, Global Leader of the IBM Institute for Business Value, recently told the Harvard Business Review. “But bold visionary leadership will be rewarded.” 

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 typically includes evaluating both the availability of data and tech infrastructure. During this stage, an organization also identifies the most appropriate datasets, models and architectures for its enterprise use case. 

A strong data strategy and strong data governance policies are 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.

Engineering data-sharing practices across an enterprise is also critical. According to recent research from IBM, only 41% of enterprise data is usable by AI, preventing many businesses with expensive deployments from properly seeing returns on investment. Building seamless data-sharing practices early helps create ecosystems that don’t just work, but can continue to scale. 

After a data strategy is in place, 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.

“Leaders need to clearly demonstrate how AI will improve employees’ experience as the path to a better career,” Marshall said. “They need to reward people who learn new AI skills. And leaders need to use it, too, even if it’s uncomfortable for them or if there’s a learning curve. They need to lead the shift be demonstrating it.” 

Enterprises that treat AI as an ongoing system rateht than a one-time implementation have the chance to capture longer-term ROI. They also stand to benefit from more innovated uses of the technology. Data patterns and use cases change frequently, requiring regular feedback loops and express audit trails. With large network systems operating more or less autonomously, a single error can cascade into a serious problem. Tools like AI orchestration layers and control planes help enterprises track, test and tweak AI tools continuously. 

AI in 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 lead to a broad shift in what kinds of jobs workers do and how they’re trained. In short, the most successful implementations will be the most disruptive, as IBM CEO Arvind Krishna recently said at IBM’s Think 2026 event. 

“The gap between who’s winning and who’s falling behind is increasing. So what I want you to start thinking about is, why is that happening? Why is it that there are winners and there are people who are not yet trying to embrace AI?” Krishna asked. “And that comes down to: Are you using AI to fundamentally rethink your business? Are you using AI to what we are now calling a new operating model for your business? Or are you trying to confine it to little pilots and little projects?” 

“So it’s no longer how much is your budget or how big your team is,” he added. “The question comes down to how deeply is AI embedded in your business processes?”

As part of its own initiative to transform the workplace, IBM recently tripled its entry-level headcount, focusing on analysis and problem-solving over easily automated skills. The reality, said IBM’s chief human resources officer Nickle LaMoreaux, is that companies are required to “rewrite every job” to stay competitive today. 

The widespread adoption of AI technologies will 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. And the most successful will seek not just to automate select processes, but to use these technologies to fundamentally reimagine and elevate the nature of their work.