Artificial intelligence, or the development of computer systems and machine learning to mimic the problem-solving and decision-making capabilities of human intelligence, impacts an array of business processes. Organizations use artificial intelligence (AI) to strengthen data analysis and decision-making, improve customer experiences, generate content, optimize IT operations, sales, marketing and cybersecurity practices and more. As AI technologies improve and evolve, new business applications emerge.

Artificial intelligence is used as a tool to support a human workforce in optimizing workflows and making business operations more efficient. These gains are made in various ways, including using AI to automate repetitive tasks, generate information based on machine learning algorithms, quickly process vast amounts of data sets and extract meaningful insights, and predict future outcomes based on data analysis. AI systems power several types of business automation, including enterprise automation and process automation, helping to reduce human error and free up human workforces for higher-level work.

According to McKinsey & Company, the use of artificial intelligence in business operations has doubled since 2017.¹ This is largely because AI technology can be customized to meet an organization’s unique needs. 63% of McKinsey’s respondents expect their investment in AI technologies to increase over the next three years.² To use AI in an effective business strategy, an organization must have a clear understanding of its business functions, how AI works and what aspects of the business can be improved through AI implementation.

While the use of AI tools to automate repetitive tasks and increase employee productivity remains popular, businesses are also moving beyond these use cases and using AI to assist in higher-level, strategic initiatives that help drive broader business value.

Artificial intelligence, “the science and engineering of making intelligent machines, especially intelligent computer programs,”³ uses large amounts of data and human knowledge to power computer systems with the ability to categorize data, make predictions, identify errors, have conversations, and analyze information in a similar way to humans.

One of the goals of artificial intelligence is to create computer systems that can mimic the critical thinking skills of humans. These systems rely on business data and use technologies like natural language processing (NLP), machine learning (ML), and deep learning to facilitate business operations. Integrating AI into business functions requires a baseline understanding of the following components: 

These algorithms are a subset of artificial intelligence and are used to make predictions or classifications based on input data. Through training data sets, these algorithms can learn to identify patterns, discover anomalies, or make projections such as future sales revenue. Machine learning algorithms help mine large datasets for key insights that can offer real-world benefits for improved business decisions. Machine learning algorithms benefit from labeled data, which is data that a human expert categorizes before it is processed.

Deep learning is a subset of machine learning that allows for the automation of tasks without human intervention. Virtual assistants, chatbots, facial recognition and fraud prevention technology all rely on deep learning. By examining data that is related to user behavior, deep learning models can make predictions about future behavior. Compared to general machine learning, deep learning models can more accurately extract information from unstructured data such as text and images and do not require as much human intervention.

Natural language processing is a branch of AI that “enables computers and digital devices to recognize, understand, and generate text and speech.”⁴ Customer support chatbots, digital assistants, and voice-operated technologies such as GPS systems are all powered by NLP. Used with machine learning algorithms and deep learning models, NLP allows systems to extract insights from unstructured data that are text- or voice-driven.

Computer vision is a subset of AI that allows computer systems to extract information from digital images, videos and other visual inputs.⁵ Computer vision uses both deep learning and machine learning algorithms to learn and identify specific elements of digital imagery. Computer vision is currently applied in several ways, and applications are expanding as the technology progresses. For example, computer vision can be implemented in production lines to detect minor defects during the manufacturing process.

Integrating enterprise-grade AI can help free human workforces from repetitive manual tasks, improve data analysis, business strategy and decision-making, and optimize processes organization-wide. To do so, enterprises must have an infrastructure that properly manages data and supports AI technology. Having a strong data governance framework helps keep data available to all relevant stakeholders and secure from data breaches.

It also helps promote the use of advanced data analytics. Part of this framework involves a digital transformation and the integration of hybrid cloud and multicloud environments to help manage large volumes of data. Once these systems are in place, an organization can begin mining data for insights and building training models to instruct AI technologies.

As new technologies enter the market, and existing ones improve, the possible applications of artificial intelligence in business grow more numerous. The benefits of AI vary and require the integration of technologies and human workforces to improve operational efficiency and drive business value.

Some examples that demonstrate the use of artificial intelligence in business include:

AIOps—artificial intelligence for IT operations—consists of the practice of using AI, machine learning and natural language processing models to streamline IT operations and service management. AIOps allows IT teams to quickly sift through large amounts of data and reduce the amount of time it takes to detect anomalies, troubleshoot errors, and monitor the performance of IT systems. Artificial intelligence helps IT teams achieve greater observability and provides real-time insights into operations.

Customer data helps marketing teams develop marketing strategies by identify trends and spending patterns. Artificial intelligence tools help process these big data sets to forecast future spending trends and conduct competitor analysis. This helps an organization gain a deeper understanding of its place in the market.

AI tools allow for marketing segmentation, a strategy that uses data to tailor marketing campaigns to specific customers based on their interests. Sales teams can use this same data to make product recommendations based on customer analytics.

AI enables businesses to provide 24/7 customer service and faster response times, which help improve the customer experience. AI-powered chatbots can help customers resolve simple queries without requiring a human agent. This ability allows the human customer service workforce to address more complex issues.

McKinsey reported savings of USD 80 million for a South American telecommunications company that used conversational AI to prioritize higher-value clients.⁶ Powerful conversational AI tools such as IBM watsonx™ Assistant help chatbots overcome some of the pain points of earlier models, which were unable to handle many customer questions. 

Generative AI (GenAI) is a growing field that helps organizations optimize content creation. Tools such as ChatGPT provide content teams with powerful tools to create original content. These tools can generate images or text based on input prompts, and designers, writers, and content leads can use these generative AI outputs to help with brainstorming, outlining, and other project tasks. Gartner estimates that by 2025 generative AI will be used to create 30% of outbound marketing content, up from 2% in 2022.⁷ Generative tools such as IBM watsonx™ Code Assistant can help developers by generating code.

While AI content generation is still largely unregulated, human employees should monitor the use of AI in generating content to prevent copyright infringement, the publication of misinformation, or other unethical business practices.

Artificial intelligence tools can be used to improve network security, anomaly detection, fraud detection, and help prevent data breaches. The increased use of technology in the workplace creates greater opportunities for security breaches; to thwart threats and protect organizational and customer data, organizations must be proactive in detecting anomalies. For example, deep learning models can be used to examine large sets of network traffic data and identify behavior that might signal an attempted attack on the network.

Data breaches can be costly and erode customer trust. The IBM Cost of a Data Breach Report 2023 indicates that the average savings for organizations that “use security AI and automation extensively is USD 1.76 million compared to organizations that don’t.”

The application of AI in supply chain management comes in the form of predictive analytics, which helps forecast future pricing of shipping and material costs. Predictive analytics also helps organizations maintain appropriate levels of inventory. This reduces bottlenecks, or the overstocking of products.

AI technologies are rapidly evolving, and their use is expanding to meet a wider variety of business needs and strategies. New technologies and the innovation of business leaders will dictate the future of AI—understanding how AI fits into your business model is key to maintaining a competitive edge.