AI Has Taken Over Every Industry — Here’s How Your Business Can Actually Use It

The architects of artificial intelligence (AI) have been named Time Magazine’s 2025 “Person of the Year,” reflecting the far-reaching impact AI has on every industry, company and nation. AI is reshaping business strategies to build a smarter future, significantly improving efficiencies, value and the customer experience. Those who fail to incorporate AI into their business models will ultimately lose relevance and competitiveness.

Generally speaking, AI performs human tasks, enabling machines to learn from data and experience to help businesses make better decisions, offer personalized experiences, streamline operations, enhance productivity, create new capabilities and generate more growth.

Generative AI

Generative AI, such as ChatGPT, Google Gemini, Microsoft Copilot, Meta AI and others, learn from billions of data points and generate content based on human input. It helps businesses create marketing strategies, full-scale marketing campaigns and content for blogs, websites, social media posts and email campaigns. Tools such as Perplexity AI are used for research, and other AI tools are used for coding and software development, product design and prototyping.

These AI tools save you time and money by helping you develop a marketing plan, research the competition, launch new products, create collateral material and boost customer communication and engagement. What once took hours of mining consumer data, conducting research, writing and creating visuals can be done in a fraction of the time with AI and human input.

Generative AI is also being used by businesses to automate customer service with chatbots and virtual assistants that handle complex queries and provide personalized support, offering tremendous operational efficiency and helping ensure requests, complaints and other service-related issues don’t fall through the cracks. At the same time, the service team has more time to focus on growing the business through upselling and other strategies.

Machine learning and prescriptive AI

Machine learning (ML) is a subset of AI that uses algorithms and statistical models to analyze raw data, identify patterns and generate insights. It processes searchable data, like names, purchase histories and website behavior, and analyzes unstructured content, including images, videos and social media posts. Prescriptive AI builds on those insights so you can take action.

On the retail side, for example, businesses gain insight into customer preferences and motivations from their behavior, enabling them to personalize marketing messages that will resonate with individual needs and create data-driven journeys and experiences that make customers feel seen and valued. Retailers know who will open an email, click through and make a purchase based on known behaviors and shopping trends.

In real estate, ML and prescriptive AI are helping developers leverage insights from datasets that include historical sales, demographic shifts, economic indicators and foot traffic to identify the next high-growth neighborhood or determine where properties are undervalued for purchase. Property owners use AI to assess potential development sites, with zoning laws, environmental data and local damage data at their fingertips to choose strategic locations and avoid delays or fines. Additionally, ML powers automated valuation models (AVMs), which provide instant data-backed property valuations by assessing various property attributes and market conditions. This allows for faster, more consistent appraisals and minimizes the impact of human bias.

Manufacturers use ML data and prescriptive AI to predict maintenance schedules, forecast equipment failures and optimize supply chains and production processes. Manufacturing operations can use real-time data to make quick, informed decisions to minimize errors, reduce waste and enhance productivity.

The insurance industry uses historical data to help determine underwriting risk patterns, whether an account is likely to have a loss, and if it financially makes sense to insure it. For example, one company used machine learning to develop a crash-prediction model for the trucking industry.

By correlating insured data, claims and millions of records from government and other proprietary data sets, the company can predict whether a trucking account will have a crash. Using machine learning, it can accurately predict the highest-risk accounts. It can ultimately avoid writing business with customers who have high crash scores or can provide credits or discounts to trucking clients with low scores, ultimately resulting in better loss ratios.

Agentic AI and autonomous decision-making

Agentic AI analyzes unstructured data to enable real-time decision-making without ongoing human prompting, understands complex workflows to improve execution and results and interacts with other systems to adapt to evolving conditions.

For example, agentic AI can automate payroll logistics, adjust supply chains based on real-time data to improve efficiency and margins, help healthcare organizations interpret physician notes and medical images to adjust treatments in real time and reduce misdiagnosis rates across healthcare, auto care and other service organizations. Law firms are using agentic AI to autonomously review unstructured legal documents and contracts to support preparation and risk identification.

AI is helping businesses build a better future, but it’s important to use it responsibly and with intention. Prioritize security, transparency, fairness and accountability.

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