AI in the Workplace: What It Means for Leaders, Employees, and Customers

It’s hard to imagine a time before computers and the internet. And soon, employees may feel the same about AI in the workplace. Automation — first used to power basic, repetitive tasks — is now embedded in business processes, helping teams find and share relevant information faster and more easily.

As AI helps drive workforce productivity and decision-making, leaders are exploring new use cases to improve how they engage employees and deliver customer experiences. They’re tapping into AI’s benefits while also addressing the challenge of cybersecurity and staff concerns.

Read on to learn how various industries are using AI to drive operational efficiency.

Evolution of AI in modern workplaces

AI adoption is rising quickly. But that wasn’t always the case. In 2017, around 20% of organizations reported using AI in the workplace, and rates hovered around 50% for six years, according to McKinsey’s global AI report. In 2024, the survey found that adoption has jumped to 72%. 

The first AI use cases included simple, rule-based, customer service chatbots and single-step automation for repetitive tasks like scheduling meetings or filtering spam emails. Then generative AI debuted, and workforces started to pay more attention to its potential productivity benefits, including content creation and ideation, summarization, and personalization.

This wave of AI is moving rapidly. Nearly half of Deloitte Generative AI Survey respondents said they’re scaling fast and increasing workforce access to gen AI tools.

As leaders reimagine business processes and explore use cases for how to use AI in the workplace, natural language processing and machine learning advancements will make collaborative intelligence — AI and humans working together — possible, helping teams work smarter and faster.

How AI is reshaping the manufacturing industry

With 85% of manufacturers telling Salesforce they must revamp daily operations to remain competitive, integrating AI in the workplace could be the solution. Both natural language processing and machine learning systems support use cases for increasing supply chain and production visibility.

Manufacturers can use AI to:

• Identify production issues. ABI research found that 78% of manufacturers believe gen AI’s most common use case is to identify the root cause of an issue more quickly. An AI-enabled machine learning system is like a smart assistant for production lines. It collects data, finds patterns, and alerts workers when problems arise.
• Reduce changeover time. AI helps collect and analyze changeover data, making it easier to spot patterns and improvements. Machine learning keeps an eye on the production lines, flagging anything unusual for the team to address. Think of machine intelligence as a conductor, keeping everything in sync by managing the timing and sequencing of swaps seamlessly.
• Fix software code. Factories that manage machines powered by software have downtime when platforms freeze or give incorrect information — an issue 52% of manufacturers think AI could help solve, according to ABI research. AI searches for errors so IT teams can fix bugs quickly.
• Provide critical alerts. Similar to certain smart devices that consumers may use at home, AI devices and sensors in a manufacturing setting can monitor things like air quality, machine performance, and vital signs. With real-time alerts, plant managers and frontline workers can respond quickly to threats.
• Centralize operations. To increase visibility without sacrificing efficiency, one electric vehicle company implemented Slack, the AI-powered work operating system, to coordinate plant workers and back-office teams. It saves millions of dollars yearly, thanks to hundreds of app integrations and thousands of workflow automations.

 

AI-enhanced customer service experiences

When companies bring AI into the mix, both customers and service teams see the benefits. According to the National Bureau of Economic Research data, AI assistance not only boosts productivity but helps employees learn faster and improves how customers feel about their interactions. For leaders looking to enhance the customer experience, adding generative AI to their service teams could be a game-changer.

AI can improve customer service operations by:

• Boosting workforce confidence. AI systems like Dialpad are stepping up to help agents in real time, offering on-screen tips if they’re speaking too quickly or dominating the conversation. For multichannel support, take a page from Intuit QuickBooks’s playbook: They boosted agent confidence by equipping their teams with an AI-powered customer service bot and a searchable knowledge base.
• Personalizing conversations. Connecting your AI-powered work operating system to your customer service software can provide historical data and customer sentiment to human or AI agents. No matter where teams work or what channel clients use, your workforce can use AI to get relevant information fast and personalize the customer experience.
• Reducing time to close. Imagine changing business processes in customer service and saving thousands of agent hours yearly. With AI tools, Salesforce improved their days-to-close case rates by 26%. By combining automation, AI search, and collaboration tools, the team saw increased productivity and improved workflows.
• Improving recordkeeping. High-touch support cases require lots of back-and-forth. As a result, customer service agents spend significant time logging and uploading customer summaries into CRMs. A consulting company implemented gen AI to automate summarization, reducing time spent on this task by 80%.
• Enhancing customer experience. Salesforce research found that 61% of customers would rather use self-service for simple issues. By adding self-service options, such as conversational chatbots and searchable knowledge bases, you empower customers to find answers on their own. This not only frees up your customer support team for more complex issues, but it also can improve the customer experience.

 

AI’s influence in healthcare

With healthcare workforce shortages and burnout plaguing the industry, AI tools and technology may help improve staff and patient experiences. Notably, 69% of life sciences and healthcare employees told Salesforce that AI is important to their company’s future.

In healthcare, AI can:

• Summarize patient records. Imagine having an AI-generated snapshot of key data from a patient’s comprehensive medical history before their first visit. When new forms come in, you can click to condense them without losing valuable context.
• Surface information quickly. With AI-powered search and machine learning tools, providers can answer questions during visits or use data to identify health or behavior patterns to develop an effective patient care strategy.
• Automate clinical documentation. According to the American Medical Informatics Association, almost 75% of healthcare professionals feel clinical documentation hampers patient care. Using AI to turn transcriptions into clinical notes can reduce manual time and effort spent on documentation.
• Transcribe visits. Picture spending less time taking notes while caring for a patient. AI, machine learning, and natural language processing make this possible. A study published in NEJM Catalyst found an AI-approach that reduced in-visit note-taking and after-hours work.
• Optimize medical billing. Hundreds of U.S.-based health systems use AI technologies to analyze operative and clinical notes. Within seconds, these tools provide billing codes, allowing medical coders to dedicate time to complex cases.
• Reduce repetitive tasks. AI can turn daily patient, provider, or supplier requests into automated workflows through business process standardization. For example, clients can submit refill requests online or via voicemail. In both cases, the form or message transcription generates a ticket, beginning a custom workflow automation.

 

AI and workplace efficiency

With AI handling repetitive tasks and predictive maintenance, a company’s workforce can focus on essential responsibilities. And because AI can collect and analyze vast datasets much faster than humans, it becomes easier to make informed decisions. As a result, over three-quarters of leaders told Ernst & Young they’re seeing a good return on their AI investment, especially in increasing operational efficiency.

Automating routine tasks

Think about your daily responsibilities. You might request status updates, summarize customer service cases, or track onboarding progress. AI tools can help you manage repetitive tasks and save time.

For example, when a customer needs assistance, service reps can use AI to get up to speed. It can search and summarize case histories, so when you help a customer, they don’t respond with, “I already told that to the last agent.”

Improving knowledge sharing and decision-making

Automation and generative AI empower decision makers by tapping into company knowledge. Instead of sifting through knowledge bases or messaging co-workers for answers, teams can use AI-powered search to find resources and connect with subject matter experts.

Data analytics and machine learning tools make it easier for managers to spot patterns and trends. For example, a manufacturing IT team might use AI to generate root-cause analysis reports, while customer service leaders use it to summarize incidents and identify agents who could benefit from extra coaching. These tools are all about helping companies make the most of their data.

Maintaining systems and equipment

Predictive maintenance keeps expensive machines and systems in good repair. Factories, for instance, use AI to monitor production lines and equipment, but the benefits go beyond the factory floor: Organizations can also boost efficiency by applying AI to building maintenance. Imagine a tool that reminds teams to change air filters or flags unusual usage patterns. 

Advanced systems can even analyze equipment data and use machine learning to predict problems before they arise. Even basic facility maintenance teams can streamline their work with AI-powered operating systems that automate incident reporting and send timely reminders.

Addressing challenges and ethical concerns of AI in the workplace

As AI reshapes the future of work, it brings new opportunities and challenges. And executives and IT leaders agree that AI is worth the investment. Salesforce reports that 79% of service organizations allocate funds to AI, and 84% of IT leaders say gen AI will help their company support customers. Yet organizations must understand and address concerns to realize the benefits of AI in the workplace.

Job displacement fears

Feelings of job instability and uncertainty may hurt productivity and workforce morale. This is concerning, considering that Salesforce’s digital skills survey found 42% of employees worry that gen AI will replace them.

Leaders can help teams understand how collaborative AI simplifies workflows and enhances operations so they can work smarter, not harder. To alleviate fears, consider these actions based on AI workplace use trends:

• Develop clear AI usage policies. Role- or task-based guidelines with examples can show teams how the organization will use AI.
• Embed AI into workflows. By automating weekly email reminders or case summarization, automatic actions will start to feel like second nature in the workplace.
• Build champion networks. AI advocates can share best practices and success stories with employees while listening to concerns and helping teams overcome challenges.
• Give your team the green light. Suggest tools that can support team members with specific tasks and encourage them to give them a try.  It’s a great way to show you trust your team to explore AI and use it effectively.
• Offer learning opportunities. Use informational channels and interactive huddles to boost employee confidence and give them the opportunity to ask questions or test new AI tools.

AI surveillance issues

As AI in the workplace evolves into digital assistant roles and handles more business processes, questions may arise about employee monitoring. In a recent study by Cornell Chronicle, participants reported decreased performance when they thought AI was judging their actions.

When researchers said AI tools would give developmental feedback, perceptions changed. Compared with previous results, participants didn’t feel AI threatened their autonomy, nor did they express a desire to quit. As organizations embed AI in the workplace, experts suggest leading with empathy and encouraging feedback.

Ethical concerns

AI systems use all available information when learning. If this data contains biases or inappropriate terms, AI remembers and may even boost those views or words in its outputs. For example, consider AI as a hiring tool. If a company trains its model on biased historical employment data, HR professionals may not receive a list of diverse candidates.

Another issue arises when AI sources information from the internet. A phenomenon known as “hallucinations” causes generative AI models to give false or baseless answers. Without a way to confirm sources, this can cause problems instead of solving them. Understanding AI trends in IT can help leaders:

• Establish ethical frameworks. A bias mitigation policy, for example, involves auditing AI systems like hiring platforms. With standard guidelines, companies can maintain transparency and fairness across AI-powered tools.
• Ensure accuracy. Your AI tools, especially AI search and summarization features, should cite sources. Citations help teams verify accuracy before making decisions that could have a legal or reputational impact.
• Create data management policies. Establish rules for reviewing and updating AI training data. This can prevent inaccurate or biased information from affecting outcomes when using AI tools.

Cybersecurity and privacy

A Slack Workforce Lab survey recently revealed that 44% of executives worry about data security and privacy when incorporating AI into business processes. IT leaders also voiced concern, with 71% of respondents telling Salesforce that generative AI could present new risks. To mitigate privacy and security risks, experts interviewed by Slack in a Harvard Business Review Analytic Services report recommend:

• Reviewing best practices. Companies should create security controls for integrating AI in the workplace to find shortcomings in their approach and address policies before implementation.
• Choosing a secure environment. Rather than using external applications, a self-hosted AI model gives companies control over their data and policies, which can limit data leakage and maintain compliance.
• Limiting use cases. Test and roll out AI tools in phases and review per use case. By assessing workflows and risks, IT can adjust future rollouts to plug security holes and reduce threats.
• Understanding data privacy. Learning how AI and machine learning systems use your data, and who actually owns it, is key. This helps protect proprietary information, wins over stakeholders for AI projects, and builds trust with your teams and customers.

Employees and AI working together

Leaders believe AI in the workplace can improve business processes, and recent data supports this. Companies that have already invested in AI are seeing its benefits, such as increased efficiency, better customer experiences, and higher employee productivity. With proper training and policies in place, employees are more likely to see how AI can support their work — not take it away — and companies can begin to experience its full potential.