The impact of artificial intelligence on businesses is undeniable. As the technology continues to evolve, companies are adopting AI models to automate tedious processes, offer 24/7 customer support, and gain a competitive edge. According to a Next Move Strategy Consulting report, the AI market is expected to surge to $2 trillion by 2030, marking a 20-fold increase in under a decade.
Reshaping Enterprises
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.
Common AI Features in Businesses
A report by McKinsey & Company analyzed information from nearly 1,500 firms from various regions, industries, company sizes, and functional specialties. The report found that:
- Firms used deep learning for customer service, predictive analytics, speech recognition, fraud detection, and supply chain management.
- Firms used natural language text understanding for customer service automation, documentation management, and language translation.
- Firms used virtual agents and conversational interfaces for customer support and lead generation.
- Firms used computer vision for quality control, surveillance, virtual try-on software, and autonomous vehicles.
- Firms used robotic process automation for tedious business processes.
Deep Learning: A Key AI Technology
Deep learning is a subfield of machine learning that 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, 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. 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.
Conclusion
The future of business is being reshaped by artificial intelligence. As AI continues to evolve, companies are adopting AI models to automate tedious processes, offer 24/7 customer support, and gain a competitive edge. With innovation comes change—for both workers and organizations. While AI has reduced some roles, it has also allowed workers to spend more time on more complex tasks that can’t be automated. As the AI market continues to grow, it’s essential for companies to keep up with the latest innovations and understand their workplace relevance and function in today’s job market.
