The 5 most common AI capabilities adopted by businesses, and how they may impact employees
Discover the five most commonly adopted AI capabilities in business—like deep learning and RPA—and learn how they’re transforming workflows and reshaping employee roles in 2025.
By Amber ChenMay 9 2025, Published 12:44 p.m. ET
Whether it's automating tedious processes or offering round-the-clock customer support via chatbots, artificial intelligence models are transforming how businesses operate. As the potential of AI continues to unfold, companies are taking note: The AI market is projected to surge to $2 trillion by 2030, marking a 20-fold increase in under a decade, according to a Next Move Strategy Consulting report.
In 2025, the technology's evolution shows no signs of slowing down. Large language models capable of reasoning on par with humans, adaptive and independent agentic AI, and new frontier models that propel natural-language processing forward are just some of the ways AI is reshaping enterprises this year, according to Morgan Stanley's 2025 report on trends shaping AI innovation and return on investment.
With innovation comes change—for both workers and organizations. McKinsey & Company's 2024 Global Survey on AI found that companies are reskilling current employees and hiring for an array of AI-related roles, including data scientists, machine learning engineers, and data engineers. While AI has reduced some roles, the report found that it has also allowed workers to spend more time on more complex tasks that can't be automated.
With change, however, comes enhanced risk, and companies have also seen a rise in risk-related AI roles. AI compliance specialists and AI ethics specialists are among the new positions organizations report their companies hiring for, at 13% and 6%, respectively.
AI is not merely shifting specific roles; it's upending how business is done. Companies are redesigning workflows, restructuring organizations, and changing governance. For employees, keeping up with the latest innovations and understanding their workplace relevance and function is crucial in today's job market.
WorkTango analyzed some of the most commonly used AI features within businesses based on reports and surveys from Morgan Stanley and McKinsey. The McKinsey survey gathered information from nearly 1,500 firms from various regions, industries, company sizes, and functional specialties. Percentages of firms using each AI capability were calculated only out of those that said they had adopted at least one AI function.
Deep learning
Deep learning, a subfield of machine learning, processes large volumes of data through an interconnected network that resembles the human brain. It identifies patterns and specific features within the data to guide decision-making, and its performance improves as it "learns" and iterates over time. Common applications of deep learning in business include customer service, predictive analytics to guide decision-making, speech recognition, fraud detection, and supply chain management.
While deep learning may potentially revolutionize business operations and processes, employees can probably expect widespread adoption to take time as its use is also associated with ethical concerns about discrimination and data privacy.
Natural language text understanding
Natural language text understanding is a type of AI that emphasizes the interpretation of human language and communication in a contextually appropriate manner. The technology's ability to correctly engage in a conversation makes it ideal for customer service automation, though it can also be successfully used in other ways, such as documentation management and language translation. Amid increased globalization and the rise in international remote workers, revenue from the global natural language processing market is estimated to reach $240 billion by 2032, according to Allied Market Research.
By automating repetitive tasks and managing customer service, natural language models can improve efficiency, freeing up workers to take on more creative or complex tasks.
Virtual agents and conversational interfaces
Virtual agents, or chatbots, leverage natural language processing to simulate human conversation with users, providing real-time assistance. These interfaces can be supplemented with other AI technologies to capture customer information for leads or marketing. Since the first chatbot was created in 1966, they have gained mass adoption across all sectors thanks to their 24/7 availability. However, more advanced chatbots like ChatGPT, Gemini, or Microsoft Copilot are still less common.
A 2025 Pew Research Center survey found that among workers who use chatbots, their primary functions include research, drafting reports, and editing documents. Four in 10 workers have found AI chatbots useful for summarizing information, and 2 in 5 workers who have experience with AI chatbots found them helpful in making their work more efficient.
Computer vision
Computer vision examines digital images and videos to extract relevant information. This may include detecting specific objects, recognizing faces, and analyzing video. Those features can improve quality control, surveillance, virtual try-on software, and autonomous vehicles. The field has undergone rapid improvements and transformations in the past decade thanks to the integration of deep-learning technologies. In the most comprehensive analysis, researchers from a 2021 study published on arXiv estimated that the average error rate for computer vision tasks across all datasets is at least 3.3%.
By identifying equipment defects, environmental conditions, and workplace patterns that lead to injuries, computer vision tools have the capacity to improve workplace safety.
Robotic process automation
Robotic process automation is the ideal tool to expedite tedious business processes. The technology automates repetitive, rules-based tasks, facilitating people to do other tasks, as well as reducing errors and increasing efficiency. As the technology expands into new sectors, McKinsey estimates the automation market will have the second largest economic impact of 12 emerging disruptive technologies, second only to mobile internet service. According to Grand View Research, the global RPA industry will reach about $31 billion by 2030.
RPA has various applications, from helping to schedule appointments and process health insurance claims to processing credit cards and order fulfillment for online businesses. This means greater accuracy for transactional processes while freeing up workers to focus on higher-value tasks.
Story editing by Jeff Inglis and Alizah Salario. Copy editing by Paris Close. Photo selection by Clarese Moller.
