13 AI Automation Examples and Use Cases for Businesses to Improve Productivity

Think about how you use AI in everyday life. You can ask Alexa to add groceries to your list, discover new music through Spotify playlists tailored to your taste, or get real-time directions without thinking twice. 

Behind the scenes, AI helps automate decisions and remove friction from routine tasks. In fact, as many as 99% of Americans use AI-powered products each week, often without realizing it.

In the enterprise, automation has historically been far less seamless. 

Many organizations still rely on fragmented systems, manual handoffs, and rule-based workflows that slow teams down. Employees spend too much time chasing approvals, resolving repetitive requests, and navigating disconnected applications, limiting productivity instead of improving it.

AI automation is beginning to change this. Modern platforms can: 

• Interpret intent 
• Learn from context
• Coordinate actions across business systems

The latest evolution, agentic AI, takes this further by reasoning through requests and helping orchestrate end-to-end workflows, allowing work to get done with minimal human involvement.

In this guide, you’ll explore 13 practical AI automation examples across HR, IT, finance, marketing, and operations. Each use case highlights how organizations are reducing manual effort and streamlining workflows to achieve measurable productivity gains.

What is AI automation?

AI automation combines machine learning, natural language understanding, and reasoning to help make decisions and execute actions with minimal human oversight. Instead of relying on fixed rules or predefined scripts, AI automation systems can interpret intent, adapt to new inputs, and coordinate work across multiple business tools.

Traditional automation is rule-based. It follows explicit instructions and can only complete the steps it has been programmed to perform. While this works well for repetitive, predictable tasks, it breaks down when workflows become complex, change frequently, or span multiple systems.

More recent advances have shifted AI automation toward agentic automation, where reasoning plays a central role. In this model, automation does not simply carry out tasks faster. When it becomes agentic, it can: 

• Understand what needs to be done
• Evaluate context such as user role or policy constraints
• Determine the right sequence of actions to reach an outcome

This evolution represents a shift toward systems that can help move work forward with greater autonomy by reasoning through requests and coordinating next steps. With agentic AI, automation moves beyond isolated processes and begins to orchestrate end to end workflows across HR, IT, finance, and operations, enabling work to get done with far less manual coordination.

The benefits of AI-powered automation

AI-powered automation delivers more than incremental efficiency gains. At the enterprise level, its value comes from coordinating work across systems, teams, and workflows that were previously managed in silos.

Key enterprise-level benefits include:

• Coordinated workflows across departments
  
  AI automation can reason across HR, IT, and finance systems to move work forward without constant handoffs or manual coordination.

• Reduced mean time to resolution (MTTR)
  
  By analyzing issues, identifying likely next steps, and completing many tasks automatically, AI automation can help shorten resolution cycles for employee requests.

• Stronger governance and compliance controls
  
  Automation enables organizations to apply policies more consistently at scale by helping validate access, route approvals, and document actions across systems.

• Improved employee experience
  
  Conversational interfaces allow employees to get help or complete tasks in natural language, reducing friction and eliminating the need to navigate multiple tools.

The 13 examples below show how these benefits come to life across real business workflows, highlighting practical ways organizations use AI automation to drive productivity at scale.

1. Streamline talent acquisition and new employee onboarding

Talent acquisition and onboarding span multiple recruiting, HRIS, and IT systems. Recruiters manage resume screening, interview scheduling, approvals, and compliance, while new hires wait for access, equipment, and answers to basic questions. 

When these steps rely on manual coordination, delays add up. The average time to hire is around 44 days, creating friction for both candidates and internal teams.

AI automation helps scale recruiting and onboarding by reasoning across systems like Workday and Greenhouse. Instead of handling tasks in isolation, automation can evaluate candidate data, schedule interviews, track approvals, and initiate onboarding steps automatically once an offer is accepted.

Agentic AI extends this by orchestrating more of the onboarding workflow. That’s because its planning and reasoning let it help coordinate background checks, support access provisioning, assist in assigning equipment, and surface compliance requirements, helping to reduce delays caused by missed steps or disconnected tools.

Enterprises are already seeing results. Unilever saved 70,000 hours by automating candidate screening across the 1.8 million applications it processes each year, allowing the company to scale hiring while improving the candidate experience.

Within Moveworks, onboarding agents built in Agent Studio can be deployed to the Moveworks AI Assistant. New hires can complete onboarding tasks and get answers in natural language, while the AI reasons across HR and IT systems to move work forward.

The result is faster hiring, smoother onboarding, and less manual effort for HR and IT teams.

2. Answer employee questions about company policy and more

Employees ask constant questions about company policies, benefits, access, and procedures. When answers are scattered across intranets, HR portals, and ticketing systems, even simple requests become slow and frustrating. Traditional chatbots can return responses, but they often lack context and cannot take action.

AI automation improves this by interpreting natural language questions and retrieving information from internal systems. Agentic AI can extend this capability by reasoning about intent, comparing responses against trusted sources, and helping initiate follow-up actions such as submitting a request or suggesting updates to a record.

Amadeus illustrates this shift at scale. With more than 16,000 employees globally, the company needed a consistent way to handle HR and IT questions across regions and tools. Employees previously navigated multiple systems to reset passwords, request access, or find policies, while support teams managed high volumes of repetitive requests.

By introducing a single AI-powered entry point for employee support, Amadeus has unified answers and workflows across systems like ServiceNow, Microsoft 365, and Workday. 

Within months, support calls have dropped approximately 30 to 40 percent, and employees saved over 16,000 hours each month. Many common requests that once took days were resolved in minutes.

This example shows how reasoning-based automation turns employee support into a faster, more accurate, and more productive experience.

3. Automate monthly direct deposits and manage payroll

Payroll is a recurring, high-stakes process that depends on accurate data, timely approvals, and strict compliance. Timesheets, wage calculations, tax deductions, and direct deposits must align every pay cycle, often across multiple systems like Workday, ADP, or SAP. When handled manually, even small errors can create delays, disputes, and rework for finance teams.

AI automation can streamline payroll by helping monitor inputs from time-tracking and HR systems, surfacing potential anomalies, and supporting data checks throughout the pay cycle. Instead of relying solely on manual review at the end of each period, automation may help improve accuracy by providing earlier visibility into issues.

Agentic AI can extend this further by assisting with payroll-related workflows. It can help analyze discrepancies, suggest appropriate approval paths, and support coordination across finance and HR systems, reducing the amount of manual effort required for routine steps while giving teams more time to focus on oversight and planning.

Organizations using payroll automation consistently report fewer errors, faster processing cycles, and reduced operational risk. By embedding reasoning and orchestration into payroll workflows, finance teams can shift focus away from repetitive processing and toward oversight, planning, and compliance.

4. Streamline reviews, approvals, and tracking employee expenses

Expense management is a core finance workflow that often involves manual reviews, policy checks, and back-and-forth approvals. Employees submit receipts, managers review claims, and finance teams verify compliance across multiple systems. When handled manually, this process creates delays, inconsistencies, and unnecessary rework.

AI automation can streamline expense workflows by helping review receipts, assisting with spend categorization, and checking submitted details against company policy. Instead of routing every submission through manual review, automation can flag potential exceptions, surface missing information, and suggest when an expense may be compliant based on defined rules.

Agentic AI can take this further by reasoning across documents, policies, and approval guidelines. It can help assess whether an expense appears to align with policy, recommend the appropriate approval path, and support initiating reimbursement steps — reducing the amount of manual effort required for routine reviews while giving finance teams clearer visibility into potential issues.

Organizations are already seeing the impact. Uber integrated automated expense tracking into its business operations, saving an estimated $287,000 in employee hours while significantly reducing errors. For employees, expense reporting becomes faster and simpler. For finance teams, automation reduces manual verification and creates a more consistent, scalable process.

5. Enhancing troubleshooting and resolution of common IT issues

IT teams handle a high volume of routine issues such as password resets, account access, and device troubleshooting. Traditional automation can resolve simple requests, but many issues still require manual diagnosis and searching across disconnected systems, which slows resolution and increases ticket volume.

AI automation can improve IT support by allowing employees to describe problems in natural language while the system helps identify the likely cause and surfaces potential solutions. Instead of relying on static scripts, automation can reference permission information, retrieve relevant knowledge, and assist with completing certain actions when appropriate.

Agentic AI can extend this further by reasoning through symptoms and suggesting likely fixes. It can assist with applying recommended steps across IT systems and help move a request toward closure, which may reduce the amount of manual work required from support teams.

Broadcom shows how this works at scale. By integrating multiple knowledge bases into a single AI-driven support experience, Broadcom was able to support 88 percent of IT issues in under a minute, allowing employees to solve problems quickly without waiting on the help desk.

This example highlights how agentic IT automation shifts troubleshooting from manual intervention to fast, autonomous resolution, improving employee experience while reducing operational load for IT teams.

6. Provide personalized tech support

As organizations scale, employees expect support that reflects their role, location, tools, and access level. Generic help articles or scripted chatbots often fall short, forcing employees to repeat information or escalate requests that could be resolved faster with context.

AI automation enables personalized tech support by using signals from enterprise systems to tailor responses. Instead of offering the same answer to everyone, automation can factor in who the employee is, what they are trying to do, and which systems are involved, then guide them to the right resolution.

Agentic AI adds a deeper layer of reasoning by working across IT and HR systems to identify what a request may require, whether that’s information, an approval, or an action. It can guide the request through the appropriate workflow, reducing friction for employees and freeing support teams from many of the repetitive steps in the process.

Databricks shows how personalized support scales in practice. As the company grew rapidly, it introduced an AI-driven support experience that delivered context-aware assistance across IT and HR. This approach helped deflect as many as 50 percent of support tickets and drove a major improvement in employee experience, with net promoter score increasing from 30 to 70 within months.

Personalized, reasoning-based automation helps employees get the support they need faster while enabling IT teams to scale without increasing headcount.

7. Strengthen security with continuous monitoring capabilities

Cybersecurity teams have their work cut out for them, from data leaks to new tools that allow hackers to launch attacks at scale. Amazon alone sees more than 1 billion cyber threats per day, far too many for humans to monitor and prevent.

AI automation can strengthen security efforts by helping monitor activity across networks, endpoints, and identity systems. It can flag unusual patterns, surface potential indicators of risk, and support faster response by giving teams earlier visibility into suspicious behavior.

Agentic AI can expand on this by helping connect detection signals with response workflows. Instead of simply alerting teams, it can analyze contextual factors to suggest appropriate next steps and support initiating mitigation actions, such as notifying security teams or guiding access-related workflows, which may help reduce response time while preserving governance and control.

Darktrace, a cybersecurity firm, uses AI to continuously analyze network activity. They enable organizations to identify and respond to threats in real time, even as attack patterns evolve.

This example shows how agentic security automation can help organizations move from reactive alerting toward a more proactive posture, which may help improve resilience while reducing the burden on security teams.

8. Generate unique copy for marketing campaigns with a few clicks

Marketing teams produce a constant stream of content across emails, ads, landing pages, and social channels. Creating and iterating on copy can quickly become a bottleneck, especially when teams are supporting multiple campaigns, audiences, and timelines.

AI automation helps reduce this friction by generating draft copy, headlines, and variations in seconds. Marketers can use AI to explore messaging options quickly, adapt tone for different channels, and accelerate campaign launches without starting from scratch.

This type of automation focuses on content generation rather than reasoning or orchestration. It does not manage workflows or trigger downstream actions. Instead, it removes repetitive writing tasks so teams can move faster.

The impact can be significant. Sage Publishing reduced time spent on content drafting by 99 percent by using AI to generate book descriptions and marketing copy at scale. This allowed their team to shift focus from manual drafting to improving content quality and campaign effectiveness.

By handling early drafts and routine variations, AI automation frees marketing teams to focus on strategy, testing, and higher-impact creative work.

9. Simplify and improve supply chain management

Supply chains are complex systems that depend on accurate forecasting, inventory visibility, and timely coordination across suppliers, warehouses, and logistics partners. Manual planning and static automation often struggle to keep up with demand volatility, disruptions, and changing market conditions.

AI automation can improve supply chain management by analyzing large volumes of data, such as sales trends, inventory levels, and external signals, to help anticipate demand and inform better decisions. This may allow organizations to adjust inventory, routing, and replenishment plans more proactively rather than reacting after issues arise.

More advanced automation can also support coordination across supply chain systems. By monitoring relevant conditions, AI can recommend adjustments, surface potential risks earlier, and assist with initiating corrective actions that may help operations run more smoothly.

This approach has proven impact at scale. Walmart uses AI-driven automation to optimize its supply chain, helping reduce unnecessary transportation and improve product availability. As a result, the company eliminated 30 million miles from its supply chain routes, avoiding up to 94 million pounds of CO₂ emissions while improving operational efficiency.

By combining predictive insights with automated decision support, AI automation can help organizations work toward more resilient and efficient supply chains that respond more effectively to changing conditions.

10. Summarize lengthy PDF documents with ease

Enterprises rely on long-form documents such as contracts, policies, research reports, and technical manuals. Reviewing these materials manually takes time and often slows decision-making across HR, Legal, Finance, and operations teams.

AI automation can reduce this friction by helping generate concise overviews of lengthy documents. Instead of reading hundreds of pages, employees can gain quicker visibility into key points, identify areas that may require deeper review, and focus their attention more efficiently. This may help speed up understanding and support productivity across knowledge-heavy roles.

More advanced approaches can integrate summarization into enterprise knowledge experiences. Rather than acting as standalone tools, AI can offer summaries in context, respond to follow-up queries based on available information, and guide employees toward relevant sections of source material when more detail is needed.

This approach is already in use at scale. One well-known enterprise financial company deployed AI tools to help tens of thousands of employees summarize and analyze complex financial documents, accelerating research workflows and improving access to critical information across the organization.

By automating parts of the document‐review process, organizations may help employees understand information more quickly, reduce cognitive load, and support more informed decision-making without requiring teams to navigate large volumes of text manually.

11. Qualify and nurture business leads

Sales and marketing teams handle large volumes of leads across websites, emails, events, and campaigns. Manually reviewing engagement data and updating CRM records slows response times and makes it difficult to prioritize the right opportunities.

AI automation can help streamline lead qualification by analyzing signals such as page visits, email interactions, content downloads, and buying behavior. This analysis can suggest which prospects appear more engaged, helping teams focus outreach where it may have greater impact.

More advanced automation can interpret context and suggest appropriate next steps. Beyond basic scoring, AI can assist with CRM updates, recommend follow-up actions, support routing leads to the right teams, and help tailor nurture paths based on stage and intent.

This approach has a proven impact in practice. Commercial Real Estate Exchange uses AI automation to support its sales teams, saving each sales representative several hours per day, allowing sellers to spend more time engaging prospects.

By automating portions of qualification and nurturing workflows, organizations can respond faster and may improve how efficiently revenue teams focus on relationship-building rather than administrative work.

12. Identify patterns in customer behavior

Understanding how customers behave across channels is critical for improving engagement, retention, and revenue. In B2B environments, this data is often complex and fragmented, making it difficult to spot meaningful trends or act on them quickly.

AI automation can help organizations analyze large volumes of behavioral data such as campaign responses, purchase history, and engagement signals. Instead of relying on manual analysis, teams can spot potential shifts in customer behavior, identify emerging opportunities, and surface areas of friction more efficiently.

More advanced automation can move from insight to action. By reasoning across datasets, AI can suggest potential next steps for marketing and sales teams — such as refining audience segments, adjusting messaging, or prioritizing outreach based on patterns in the data.

In practice, AI-driven analysis has been shown to improve response rates by 3 to 5 percent by identifying more relevant, high-intent audiences and filtering out low-quality prospects. This allows teams to focus effort where it is most likely to drive results.

By automating parts of the path from data analysis to decision-making, AI can help B2B organizations respond faster to customer behavior and support improvements across revenue-focused teams.

13. Improving business processes with predictive maintenance

Unplanned equipment failures can disrupt operations and drive up costs. Traditional maintenance approaches rely on fixed schedules or reactive repairs, which often result in unnecessary servicing or delayed response to issues.

AI automation can support predictive maintenance by helping monitor equipment data and surfacing patterns that may indicate emerging issues. Instead of reacting only after breakdowns occur, teams can gain earlier visibility into potential problems and plan maintenance more effectively.

More advanced automation can move from prediction to action. Agentic AI can assist with initiating maintenance workflows, alerting technicians, and coordinating parts or system updates, reducing the amount of manual effort required for routine steps.

Industrial companies use AI-driven predictive maintenance to monitor complex equipment across manufacturing and infrastructure environments, helping teams shift from reactive repairs to more proactive, data-driven operations.

By automating parts of the path from detection to resolution, predictive maintenance can help organizations lower the likelihood of unexpected downtime by surfacing patterns that may indicate emerging issues.

How AI assistants are redefining enterprise automation

Early automation focused on speeding up individual tasks. AI assistants represent a shift toward coordinated, end-to-end automation that works across systems, teams, and workflows.

Instead of following static rules, modern AI assistants can interpret intent, reason through context, and suggest appropriate next steps. A single request may involve locating information, referencing permission details, and assisting with actions across multiple enterprise applications.

This is what differentiates AI assistants from traditional automation tools.

AI assistants redefine automation by:

• Unifying search and action to help reduce the amount of tool-switching employees typically experience.
• Reasoning across systems such as IT, HR, finance, and operations to interpret context and inform potential next steps.
• Supporting aspects of multi-step workflows rather than limiting automation to isolated tasks.
• Adapting to different roles and situations instead of delivering one-size-fits-all responses
• Operating through familiar interfaces like chat tools or internal portals, reducing friction and training overhead

As automation becomes more agentic, AI assistants can serve as a central layer that helps connect enterprise knowledge with execution. They may help streamline fragmented processes and support organizations in moving from task-level automation toward more coordinated workflows.

This shift marks the evolution of automation from simply doing tasks faster to intelligently getting work done across the enterprise.

Rethink business productivity with Moveworks agentic automation

Enterprise productivity breaks down when employees have to search for information, navigate fragmented systems, and manually coordinate work across teams. Moveworks helps work move forward with less friction by connecting search, reasoning, and action in one place.

At the center of this approach is a single entry point where employees can ask questions, get answers, and complete common tasks in one flow. Instead of automating isolated steps, Moveworks helps coordinate work across IT, HR, finance, and operations.

Moveworks enables agentic automation through:

• Moveworks AI Assistant provides a conversational interface for employees to search for information and take action in natural language.
• Reasoning Engine that can interpret intent, identity, and context to suggest appropriate next steps.
• Agent Studio that enables teams to build and customize agents to automate workflows across enterprise systems
• Knowledge Studio that grounds answers in trusted sources and supports governance and reliability across knowledge assets

By connecting enterprise knowledge with execution, Moveworks can help decrease case volume, shorten resolution times, and enable employees to address everyday needs with less tool-switching.

The result is not just faster task completion, but more coordinated automation that can scale across departments and workflows.

Learn how Moveworks AI Assistant helps enterprises search, reason, and act from one place.