10 Ways for Businesses To Use AI in 2025

Artificial intelligence (AI) continues to see widespread adoption across industries and business functions, and to reshape how companies operate. From marketing to human resources, AI is being used throughout organizations to automate tasks, improve data analytics, and make better informed decisions more quickly.
Some of the most impactful technologies in AI include generative AI and natural language processing (NLP) tools like ChatGPT, machine learning, and deep learning. According to research from Accenture, 40% of all working hours could be impacted by large language models such as ChatGPT.

Rather than replacing workers, AI is used to reduce repetitive work, drive efficiencies, and maximize human potential by helping employees and businesses across industries work more strategically. 

How are businesses using AI? 

AI has many use cases and benefits for businesses. According to research from McKinsey, as of early 2024, 72% of organizations adopted AI in at least one business function. Below, we’ve highlighted some benefits of using AI in business. 

AI automation

Every role has repetitive, manual tasks—such as checking email, inputting data, and generating reports—that take time from more impactful work. AI can help automate routine tasks, which maximizes productivity, reduces the risk of human error, and drives team member engagement by enabling them to focus their time on more meaningful work. 

A 2024 survey of 450 financial executives conducted by Duke University’s Fuqua School of Business and the Federal Reserve Banks of Richmond and Atlanta found that businesses are increasingly turning to AI automation to streamline business processes. According to the survey, 40% respondents said their businesses used AI tools to automate employee tasks in the 12 months leading up to the survey, while 54% plan to use AI to automate tasks in the next 12 months.

Depending on which tasks you’re looking to automate using AI, a wide range of tools are available for different business functions and use cases, such as chatbots for customer service, applicant tracking systems for recruiting, and payroll platforms for accounting. 

Enhanced data analytics

Artificial intelligence and machine learning tools can process and analyze vast amounts of data at speeds and scale far beyond human capabilities. As a result, AI can help identify patterns, behaviors, and trends that may not be immediately visible to human analysts, which helps predict future outcomes based on historical data. 

According to research from Wavestone, 62% of senior data leaders surveyed said generative AI is a top organizational priority, with nearly 90% increasing investment in 2024. Additionally, 64% believe AI is the most transformational technology in a generation. Some use cases for enhanced data analytics include predicting customer churn, identifying worker turnover trends, projecting revenue, and flagging fraud patterns or behaviors. 

Improved decision-making

Based on insights from enhanced data analytics, AI can also help support improved decision-making. 

AI can improve decision-making by helping to identify new business opportunities, flag operational roadblocks, determine effective ways to personalize offerings and outreach based on customer data, and prepare for potential challenges. 

One commonly cited example of how AI supports decision-making is that airlines typically use predictive AI analytics to determine pricing. Airlines optimize ticket prices using AI to analyze and understand demand patterns, consumer behavior, competition, and other factors in real time, leading to better informed pricing decisions.

These capabilities are just a few of the many ways AI is used in business. However, while AI can offer significant benefits, human expertise and intelligence are necessary to ensure accuracy and credibility of critical business processes. 

10 applications of AI in business

AI can help drive efficiencies for businesses across industries and in departments throughout an organization. Let’s explore how AI is used across various business functions.

AI applications include:

1. Content generation
2. Marketing
3. Sales
4. Customer service
5. Operations
6. Human resources
7. Cybersecurity
8. Legal departments‍
9. Accounting‍
10. Finance

1. Content generation

Content creators are increasingly turning to generative AI tools to save time and improve the content generation process. Tools like ChatGPT, Google Gemini, and Jasper enable users to input text prompts to quickly generate new drafts of written content such as outlines, emails, or blog posts. Tools like Midjourney, Stable Diffusion, and DALL-E generate images based on text prompts.

A survey of business-to-business (B2B) marketers conducted by Demand Spring found that as of 2024, 82% of organizations use AI for content creation. According to the survey, AI-powered tools help with tasks such as topic suggestions, headline optimization, and initial draft generation. 

Some of the benefits of leveraging AI for content creation include:

• Increased productivity and scalability because using AI tools to generate content requires less time and fewer resources than developing content from scratch 
• Creative inspiration by using AI to brainstorm and come up with new ideas before developing content
• AI-powered analytics track content metrics including page views, social media shares, and engagement rates to help optimize content generation strategies

While artificial intelligence can make content marketing efforts more efficient, be aware that AI content isn’t publish-ready. Content creators and marketers should see AI content as inspiration for their own content or an initial draft. Any content created by generative AI writing tools should be proofread by a team member, fact-checked, and edited to ensure it aligns with brand voice, style, and guidelines.

2. Marketing

Artificial intelligence tools and machine learning algorithms are used by marketing teams to analyze data, identify customer trends and patterns, optimize marketing campaigns and strategies, and enhance the customer experience.

In addition to improving marketing strategy results, AI can also help team members save time by automating manual tasks. According to a 2024 survey of 1,800 marketers distributed by Marketing Artificial Intelligence Institute and Drift, 78% of respondents believe they will be intelligently automating more than a quarter of their tasks in the next three years. Additionally, 45% believe more than half of their marketing tasks will be intelligently automated to some degree by AI three years from now.

Here are a few examples of how AI is used in marketing:

• Improved audience segmentation and personalization. AI can simplify how marketers track, understand, and predict customer behavior. Using AI tools, businesses can also create customer profiles by segmenting customers into categories depending on their behavior, preferences, and demographic indicators. Based on customer profiles, marketing teams can personalize messaging and communications to highlight products, services, or promotions that are most relevant to specific customer segments. 
• Predictive marketing analytics. Marketers can use predictive analytics to identify patterns and trends from historical and current customer data, enabling teams to more effectively predict which strategies or campaigns may be most effective in the future. Predictive analytics help anticipate customer needs, optimize targeting, and identify upselling and cross-selling opportunities.

Enhanced market research and competitor analysis. Marketing teams and professionals spend a significant amount of time conducting marketing and competitor research. AI systems and tools like Crayon and AlphaSense have advanced capabilities to find, organize, and analyze relevant market data and competitive differentiators, saving time that would have been spent on manual research. Because AI can process large amounts of data automatically, important market or competitor insights may be uncovered that marketing team members may have missed.

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3. Sales

Predictive analytics and AI-powered algorithms enable sales teams to better understand customer behavior and preferences and automate and improve sales operations. 

Salesforce released insights featuring 5,500 sales professionals across 27 countries and found that sales teams are reaping the benefits of AI. The latest edition of the annual State of Sales report found that 41% of sales organizations have the technology fully implemented in their operations, while another 40% are experimenting with AI. The report also found that 83% of sales teams with AI tools in place saw revenue growth in the past year, compared to 66% without AI.

Sales teams can use AI to help with the following aspects of the sales process and strategy:

• Automating manual tasks. Much of sales professionals’ time is spent on manual tasks such as data entry and sharing updates on their deals in the pipeline—rather than actual selling. AI tools can help automate time-consuming tasks so sales representatives can spend more time on core functions. 
• Demand forecasting. AI can help analyze past performance and current economic indicators to help sales teams forecast future demand. Analyzing customer data, interactions, and historical sales patterns using AI algorithms can help identify leads, prioritize prospects, identify next steps or actions to engage prospects, and optimize the sales process.
• Writing and personalizing outbound email campaigns. Generative AI tools like ChatGPT can be used to draft outbound emails to customers and leads. Leveraging data from AI-powered lead scoring tools, sales team members can also personalize outreach and share product recommendations based on an individual’s score, preferences, and behaviors.

Lead generation and scoring.AI-powered lead generation and scoring tools like LeadIQ, Seamless.AI, and Outreach include capabilities like machine learning algorithms to provide real-time updates to sales teams about prospects and leads. This technology helps generate leads and ensure lead scores are accurate and up to date. Each time a lead clicks on a link in an email, downloads a resource from the company website, or takes another action, the AI algorithm automatically updates the lead's score.

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4. Customer service

AI can help transform how businesses engage with customers, identify relevant insights, and improve the customer experience. Some benefits of leveraging AI for customer service include faster response times, 24/7 availability and support, and opportunities to offer tailored solutions based on customers’ specific needs. 

A 2024 survey of 4,500 customer experience (CX) executives conducted by Zendesk found that, over the next 24 months, 70% of respondents planned to integrate generative AI into many of their customer touchpoints.

Common applications of AI in customer service include:

• Self-service and knowledge-base systems. Similar to chatbots, self-service and knowledge-base systems help customers find answers to their questions and solutions to common problems on their own. These systems feature advanced search functionality and include resources such as frequently asked questions (FAQs), tutorials, troubleshooting guides, and interactive tools. Accessing resources on self-service and knowledge base systems helps customers save time and frees up customer service representatives from answering the same questions or walking through repetitive tutorials.
• Enhanced analytics and customer insights. AI-powered data analytics can provide detailed insights about sentiment and satisfaction based on data from customer interactions, including chatbot logs, emails, social media posts, reviews, and surveys. Using this data, businesses can identify customer preferences, pain points, and opportunities for improvement, which can help enhance and personalize the overall customer experience.

AI-powered chatbots. Chatbots use natural language processing and machine learning to communicate with customers in real time. Many consumer-facing and business-to-business (B2B) websites and apps across industries incorporate AI-powered chatbots like CoSupport.AI, Zendesk, and DeepConverse to quickly answer customer questions, offer personalized recommendations, and provide support. Chatbots offer customers assistance without having to wait for an available representative, and can also route customers to the right agent or channel when necessary.

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5. Operations

In recent years, artificial intelligence has become so prevalent in IT operations that Gartner coined the term “AIOps” to describe the combination of big data, analytics, natural language processing, and machine learning to automate IT operations processes. 

AIOps enables IT operations teams to integrate multiple, separate IT operations tools using a centralized platform, which helps businesses more effectively manage an ever-expanding IT landscape. As a result, IT teams can quickly respond to—and even proactively identify—slowdowns and outages, which minimizes disruptions to day-to-day business operations. Examples of AIOps platforms include BigPanda, DataDog, and LogicMonitor. 

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Key benefits of AIOps include:

• Faster response and resolution time for IT tickets, slowdowns, and outages
• Lower operational costs by combining and integrating IT systems and tools 
• Enhanced collaboration and monitoring between DevOps, ITOps, governance, and security functions through integrated tools 
• Improved resource allocation by automating manual tasks and enabling IT operations team members to spend time on more complex work 
• Capabilities to shift from reactive to predictive IT operations management using predictive analytics

6. Human resources

Leveraging AI throughout the employee lifecycle—including for sourcing and recruiting candidates, onboarding, managing employee records, and developing existing team members—can help human resource teams drive efficiencies and keep talent engaged. 

While human resources has been slower than some other business functions to incorporate AI into day-to-day operations, research shows AI adoption among HR teams is on the rise. A survey conducted by Gartner of 179 HR leaders found that as of January 2024, 38% of HR leaders were piloting, planning implementation, or had already implemented generative AI, an increase from 19% in June 2023.

Some of the many use cases for AI in human resources include:

• Writing content for recruitment materials. Recruitment and talent acquisition teams can use generative AI tools such as ChatGPT or Google Gemini to help draft copy for recruitment and hiring purposes, including job descriptions, interview questions, candidate outreach emails, and job offer letters. By providing the tool with a prompt featuring relevant details about the job and type of content, the tool will generate suggested copy. While additional details may need to be shared and edits will likely be required to finalize recruitment materials, AI tools can save time compared to writing copy from scratch. 
• Automating candidate screening. Many applicant tracking systems (ATS) and recruiting tools like Workable, EVA, and Paradox include AI technology to automatically screen candidates based on specific criteria and qualifications. These systems often include capabilities to send automated emails to candidates letting them know how to move forward in the process. This enables recruitment and talent acquisition teams to focus their time on core functions. 
• Driving candidate engagement. Similar to customer service chatbots, some employers are embracing chatbots as an innovative solution to drive candidate engagement. Companies include AI chatbots and virtual assistants on their careers pages to direct individuals to jobs that align with their skills and experience, guide them through an interactive application process, and answer common questions along the way.

Simplifying workforce planning. Talent management systems and human resources information systems (HRIS) such as Leena AI, Beamery, and Gloat centralize employee data, making it easier for companies to manage and automate HR processes. Through these systems, organizations can manage payroll, benefits, time and attendance, learning and talent development, and other HR functions. Many of these systems also include data related to talent reviews, performance, engagement, retention, and skills, helping organizations better understand worker competencies, skills gaps, and future workforce needs.

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7. Cybersecurity

As cyberattacks become more sophisticated, security teams need the latest technology to detect and reduce the risk of emerging threats. According to a Ponemon Institute and MixMode survey of 641 IT and security professionals, 70% of respondents believe AI is highly effective at detecting previously undetectable threats. Additionally, the survey found that 66% of respondents believe the deployment of AI-based security technologies will increase the productivity of IT security teams.

Artificial intelligence and machine learning can benefit cybersecurity teams by helping them stay ahead of cybercriminals, automate threat detection, and quickly respond to the latest risks. 

A few examples of AI cybersecurity include: 

• Security monitoring. Cybersecurity threats continue to evolve and AI-powered security monitoring solutions use data analytics to continually learn about and adapt to evolving threats and environments. Using this data, detection models can be adjusted over time, helping to enhance security capabilities and more proactively detect and prevent threats. 
• Bot prevention. Bots pose many threats to businesses, including delivering spam emails, sending a high volume of illegitimate traffic to websites, and initiating account takeovers using stolen credentials. AI-powered machine learning algorithms can be used to automatically scan incoming email for red flags such as malicious IP addresses and links, suspicious keywords, and large attachments and filter email to spam folders. Similar capabilities can also be used to differentiate between authentic website traffic, good bots (such as search engine crawlers), and bad bots. 

Threat detection and response. AI can analyze large amounts of data to identify patterns in user behavior and automatically flag anomalies that may indicate a fraud or another cyber threat. This can help cybersecurity teams detect threats in real time. AI-enabled tools and platforms like Vectra AI, Cyware, and Darktrace can also automate tasks related to threat detection, incident response, and remediation, helping to address threats before they cause significant damage.

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While AI can drive efficiencies and improvements from a cybersecurity perspective, AI tools have the potential to pose security and privacy risks. Security teams should thoroughly vet any AI solutions or engage an outside information security professional to do so before selecting tools. Implementing company-wide policies to ensure AI tools are used securely can also be helpful.

8. Legal departments

Corporate legal departments process and analyze large volumes of data and documents. Rather than reviewing documents and completing other administrative processes manually, AI can simplify many legal tasks, enabling lawyers to spend more time providing clients with expert guidance. 

Litify research featuring insights from legal professionals found that AI adoption among respondents doubled from 2023 to 2024, reaching 47%. The research also found that 92% of respondents using AI are saving time on legal work—with 33% now saving up to 10 hours per week

Here are some specific ways that AI can be used in legal departments: 

• Research and analysis. Legal research involves spending a significant amount of time reviewing legal cases, laws, regulations, and precedents, among other information. AI-powered tools and platforms like CoCounsel, Paxton, and Callidus can automatically retrieve, organize, and analyze relevant legal documents. This simplifies the process of legal professionals finding critical information, helping them to more quickly extract key insights and make informed decisions.
• Contract review and due diligence. Manually reviewing legal contracts and documents can be time-consuming and is prone to human error. In addition to being reviewed by human legal experts, AI tools with machine learning capabilities can review and analyze contract language to flag potential issues or unfavorable terms that may otherwise be overlooked. 
• Compliance. Different industries and businesses have to maintain compliance with specific regulations, such as HIPAA for U.S. healthcare data and GDPR for businesses that collect data from European Union Citizens. Compliance standards are complex and failing to maintain compliance can lead to breaches or penalties. Companies can leverage and train AI algorithms to understand specific laws and regulations and identify discrepancies more efficiently than manual methods.

Document automation. AI tools such as Gavel, Clio Draft, and Briefpoint can help generate initial drafts of standard legal documents, such as contracts, nondisclosure agreements (NDAs), wills, and leases, saving legal teams time and reducing human error. Keep in mind, while AI can create drafted legal documents, thorough review by a legal professional is critical before using any AI-generated documents.

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9. Accounting

Accounting teams often spend a great deal of time on manual, repetitive tasks, such as data entry, managing payroll, and approving expenses. By adopting artificial intelligence on accounting teams, many of these tasks can be automated, saving time and resources. 

According to a report distributed by Intuit Quickbooks in June 2024 featuring insights from 707 U.S.-based accountants and bookkeepers, 98% of respondents used AI to help clients and their businesses in the 12 months leading up to the survey. Some of the top tasks and processes respondents use AI for include data entry and processing, managing client portfolios, client communication, and invoicing and payments. 

Benefits of using AI tools in accounting include: 

• Automated tasks. Artificial intelligence accounting tools like FloQast, ClickUp, and Zeni can automate many manual accounting tasks, including data collection, data entry, categorization, reconciliation, and invoicing, freeing up accountants’ time to work on more strategic projects and interact with clients. 
• Streamlined payroll management. The American Payroll Association estimates up to an 8% human error when manually processing payroll. AI and machine learning can automatically process payroll, saving time, eliminating human error, and ensuring payroll is accurate. 
• Tax audit support. AI-powered tax audit support tools such as Hyperproof, Trullion, and Mindbridge can help auditors and accountants effectively prepare financial statements and records to ensure they’re accurate, up to date, relevant, and align with compliance regulations. AI also simplifies document management, enabling tax auditors to easily identify and access relevant financial data and reducing the time and resources spent to complete audits. 

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10. Finance

AI in finance can help teams and organizations analyze patterns from large data sets, streamline processes, improve decision-making, prevent fraud, and maintain compliance, among other benefits.

A survey of 150 finance executives distributed by Tipalti and CFO Dive found that 59% of respondents believe it’s extremely or very important for their finance teams to be trained in automation and AI tools and technology.

A few specific ways AI is being used in the finance sector include: 

• Fraud detection and anti-money laundering (AML). Many fraud detection and AML processes are manual, time-consuming, and risk human error. AI-powered data analytics platforms such as Comply Advantage and Napier AI can evaluate financial transactions and related activities in real time to identify normal versus abnormal behavior. Once suspicious activity is detected, AI tools can notify analysts or relevant parties to review further, which helps increase efficiency.
• Compliance. AI can help automate compliance checks and maintain real-time records of all financial transactions and activities, which can reduce the overall risk of regulatory breaches and penalties.
• Forecasting and budgeting. AI tools can analyze financial data to make predictions. AI can process large volumes of historical performance data, including market trends, economic indicators, and company-specific metrics, to generate predictions about future trends or outcomes. As a result, AI can help inform improved budgeting and resources allocation decisions, while minimizing potential financial risks. 

Real-time data analysis. Finance teams and financial services organizations manage a significant amount of data. AI tools like Datarails, Planful Predict, and Vena can automatically process high volumes of data and identify patterns and trends, saving time and uncovering insights that may have otherwise been overlooked. These insights help finance professionals make better data-driven decisions related to managing credit, calculating risk, vetting borrowers, and determining investments.

Challenges and ethical considerations with AI in business

While artificial intelligence offers many benefits, understanding and addressing challenges, potential legal issues, and ethical considerations is essential for any business that embraces AI. According to a survey of 1,800 professionals conducted by Deloitte, 54% of respondents believe cognitive technologies like AI and generative AI present the most severe ethical risks compared to other emerging technologies.

Challenges and ethical considerations to keep in mind include:

• Privacy concerns. Many AI tools and platforms collect and use sensitive personal information such as contact or demographic details for further training and refinement. This risks exposing sensitive data or violating data privacy laws and guidelines. To address privacy concerns, implement safeguards and policies related to AI use at your organization. Also thoroughly vet any AI tools to ensure providers are prioritizing privacy.  
• Bias and discrimination. AI tools are often used to reduce conscious and unconscious bias introduced by human decision-making. However, AI technology isn’t immune to bias and AI systems are often unintentionally trained using data that introduces existing societal biases. Whether you’re building internal AI capabilities or implementing AI tools from a vendor, identify ways to mitigate biases and avoid discrimination.
• Accuracy. In addition to potentially introducing biases, AI tools have also been shown to generate inaccurate or factually incorrect information, also known as “AI hallucinations.” When using AI tools at your business, keep in mind that they can’t fact check, and avoid trusting that information produced by AI is fully accurate. 
• Technical integration issues. Integrating AI solutions with existing technology and processes can be complex, costly, and challenging. Confirm any AI tools your business uses are designed with human needs and capabilities in mind and augment human decision-making, rather than replacing human intelligence. Also implement tools that support your strategic business goals and existing technology solutions. After implementing AI solutions, continually measure the effectiveness and return-on-investment (ROI) of tools to improve efficiencies over time. 
• Transparency and stakeholder buy-in. When you introduce new technology at your organization, maintaining transparency and alignment across key stakeholders is essential to successful implementation. For example, with AI technology, present your rationale for investing in new tools and ensure buy-in from stakeholders such as your leadership team and investors. Also be transparent with your customers by clearly stating when a customer is interacting with AI versus a member of your team and outlining how their data is protected by AI tools.
• Worker resistance and skills gaps. If your business doesn’t already have AI solutions in place, workers at your organization may be hesitant to learn and implement new technology. To address this concern, take a thoughtful, proactive approach to change management, such as communicating the benefits of AI technology, scheduling training sessions for workers, and setting an implementation timeline. 

Helpful AI resources 

Now that we’ve covered how businesses use AI, check out the following resources to help you learn more about how to integrate artificial intelligence into your business.

• AI for Businesses Guide
• The Best AI Platforms: Top 12 AI Tools for Business
• How To Use AI: A Guide for Businesses and Professionals
• How To Incorporate AI Into Your Business
• How To Implement AI for Businesses
• How Does AI Work? Step-by-Step Guide in Simple Terms
• Harness ChatGPT for Your Company Through AI Experts
• How To Use Generative AI To Scale Your Enterprise Business

Upwork can help you embrace AI across your business

By integrating AI across your business, your organization can save time, reduce costs, leverage advanced data for improved decision-making, and empower your team members to focus on more strategic priorities. 

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