Agentic AI use cases: examples of AI improving business operations

1. Customer service automation

Agentic AI is reshaping customer service, delivering faster, more accurate, and highly personalized support without human intervention.

One key advantage of agentic AI in customer support is reducing wait times and avoiding unnecessary escalations. It can easily solve not only standard tasks, such as billing questions or password resets, but also more complex ones, like troubleshooting technical issues or approving refunds.

AI agents also provide customer support teams with real-time information. Instead of manually searching through databases or documentation, employees can rely on AI assistants to provide necessary data, suggest responses, and even predict customer needs based on historical interactions. This allows faster resolution, ensures consistency, and provides accuracy in customer communications.

Real-world use case: In October 2024, Salesforce added Agentforce, its agentic AI, to its Help site. After six months, Agentforce was answering over 80% of inquiries without human intervention, leaving more time for the employees to tackle more important tasks. What’s more, clients are no longer hesitant to ask “uncomfortable” questions because chatting with the AI feels less intimidating.

2. IT and software engineering automation

The flexibility of agentic AI enables organizations to resolve many IT incidents before they escalate, significantly reducing the workload on IT staff and ensuring that minor problems don’t become major disruptions.

For routine tasks such as password resets, access requests, and troubleshooting, agentic AI provides more than simple automation. It personalizes responses based on user behaviors and historical patterns, leading to faster, more relevant solutions. This approach also reduces wait times, minimizes support tickets, and allows your IT department to focus on more strategic work.

Agentic AI also brings powerful capabilities to software engineering teams. It can automate repetitive tasks, optimize resource allocation based on real-time demands, and continuously learn from project data to find bottlenecks and recommend improvements. For example, agentic AI can monitor code repositories for potential outages, streamline cloud resource provisioning, and even automate large parts of the software development cycle, including code generation, deployment, and troubleshooting.

Real-world use case: Morgan Stanley developed DevGen.AI, a tool that rewrites legacy code into modern programming languages. It helped save almost 300,000 developer hours and also successfully patented the solution.

3. Talent acquisition and HR automation

When used for talent acquisition and HR automation, AI agents enable organizations to streamline workflows, reduce administrative workload, and improve overall employee satisfaction.

They can efficiently screen resumes, assess candidates, and even engage with the shortlisted ones, scheduling interviews and answering common questions. This accelerates the hiring process and helps to evaluate the applicants in a more consistent and unbiased way.

Agentic AI can also enhance the onboarding process. It can automate the completion of paperwork, provide individual training recommendations, and offer 24/7 support to new hires, helping new employees integrate into the company from day one.

Additionally, agentic AI can provide self-service, such as updating personal data, checking the number of vacation days, and learning about benefits. This not only reduces the load on HR teams but also encourages autonomy among employees. Additionally, AI agents can monitor employee engagement, providing insights that address potential issues and help improve retention strategies.

Real-world use case: Unilever leveraged AI-driven recruitment tools to screen 250,000 resumes and hire 800 employees. This helped save more than £1 million per year and 50,000 hours of candidate and recruiter time. Also, the recruitment cycle was shortened by 75% or even more, leaving much less time for competitors.

4. Cybersecurity threat response

Proactive cybersecurity threat response benefits from agentic AI in many ways. Contrary to traditional security systems that rely on predefined rules and human oversight, AI agent systems independently monitor network traffic, analyze user behavior, and identify anomalies that could indicate malicious activity. Such a proactive approach allows for faster threat containment by isolating infected devices or blocking malicious IP addresses, helping minimize the damage.

Cyber threats are becoming more sophisticated, and the need for adaptive and autonomous defense grows. Agentic AI provides just that: it can learn from new data and adjust its behavior in real-time to counter emerging threats. With less need for human intervention, companies can react to security incidents faster while maintaining compliance and protecting sensitive information from potential breaches.

Real-world use case: In 2025, Microsoft launched six new AI agents that help cybersecurity teams deal with the ever-growing number of threats. Their capabilities range from triaging and processing phishing and data loss alerts to prioritizing critical incidents and monitoring for vulnerabilities.

5. Legal and compliance operations

Agentic AI is revolutionizing legal and compliance operations by automating complex tasks such as legal research, contract analysis, and compliance monitoring. 

Agentic AI can already search and analyze legal databases, case law, and regulations, providing comprehensive and accurate results way faster than any human could. Moreover, AI agents can quickly review and extract key data from legal documents and identify potential issues. In addition to speeding up the review, it also reduces the chance of human error.

Compliance monitoring is another area where agentic AI excels. It can monitor changes in laws and regulations, cross-reference them with organizational practices, and alert your legal team when there’s a need to intervene.

All these implementations lead to increased efficiency, reduced costs, and improved accuracy in legal and compliance operations.

Real-world use case: In May 2025, Garfield.Law became the first law company to offer approved and regulated legal services primarily based on agentic AI rather than human work. The agent helps their England and Wales clients, who are mainly people and SMBs, recover unpaid debts by generating “chaser” and “before-action” letters. According to one of the co-founders, over £50B of smaller unpaid debts are never recovered every year in the UK alone, and Garfield.Law aims to change that.

6. Financial analysis and fraud detection

Agentic AI revolutionizes financial analysis and fraud detection by autonomously identifying anomalies, modeling risk, and making real-time portfolio decisions. Contrary to traditional systems that rely on static rules, AI agents constantly learn from new data, which allows them to detect patterns and take action without human intervention.

Regarding fraud detection, agentic AI systems can analyze huge amounts of transaction information and find irregularities that might show illegal activity. Moreover, AI agents enhance financial analysis by automating tasks, such as expense reporting or financial forecasting.

AI agents can also manage your portfolio by analyzing market trends, assessing risk, and adjusting investment strategies in real time. This approach allows more responsive and better-informed investments that are aligned with current market conditions and individual financial goals.

Real-world use case: Riskified's Adaptive Checkout tool uses AI to assess the risk of each transaction in real time, based on customer data, such as shopping history and billing information. When there’s doubt about the order's legitimacy, it may ask for a card verification value or set up a one-time password. This approach allowed TickPick, an online ticket marketplace, to recover $3 million in revenue within three months by reducing false declines of legitimate transactions.

7. Hyper-personalized marketing and advertising

AI for marketing and advertising is transforming these areas by enabling real-time, autonomous campaign optimization. Unlike traditional automation, AI agents can independently analyze customer behavior, generate tailored content, and adjust strategies on the go to maximize engagement and AI ROI.

These systems leverage vast datasets to personalize every aspect of a campaign, from message timing and delivery channels to content format and offers. For example, agentic AI can determine the best time to send messages based on subscribers’ browsing habits or pick the most effective social platform, such as LinkedIn, for certain user segments. Such personalization guarantees that marketing and advertising are relevant and effective.

AI agents can also quickly find materials for presentations and campaigns, especially when combining web search and the company’s internal data. They can even prepare content drafts for different target audiences across multiple channels. Moreover, you can use AI in marketing to gain insights about your competitors’ campaigns and tips on countering them.

Real-world use case: Together with IBM Watson, Unilever created an AI-powered platform that was used for context tagging and analysis (marking themes, sentiment, style, and audience reaction in previous content), creative optimization (tips on color schemes, brand tone, and CTA placement), cultural context modeling (detecting sentiments across regions and demographics), and predictive performance (estimating how the content will perform). This resulted in reduced production costs by 30%, cut campaign planning time by 50% in some cases, and 35% higher engagement rate in emerging markets.

8. Sales optimization and automation

Agentic AI transforms sales operations by automating tasks, enhancing decision-making, and enabling sales teams to focus on building relationships. By integrating with Customer Relationship Management (CRM) systems and communication platforms, agentic AI provides real-time insights, automates lead management, and personalizes customer interactions.

One of the key advantages of agentic AI is its ability to deliver actionable insights into customer behavior and opportunity management. By analyzing purchasing habits and sales performance across channels, AI can guide your sales team to make better decisions.

Additionally, agentic AI makes sales support and account management less complicated. It can automate support ticket assignment, draft personalized follow-up emails, and provide relevant product information for each customer, allowing your sales department to meet your clients’ needs.

Real-world use case: Salesforce's Agentforce AI is used to gather client data, receive advice in real time, and simulate negotiation techniques. For example, one Salesforce seller used Agentforce AI to obtain a comprehensive dossier on a client's industry, previous interactions, and key stakeholders, which resulted in a much more effective meeting.

9. Supply chain and logistics optimization

Unlike traditional automation, which often requires human oversight, agentic AI can independently manage complex workflows such as demand forecasting, procurement, and routing. For example, when a supply disruption occurs, agentic AI can automatically find alternative suppliers, redraw distribution routes, and adjust inventory levels to mitigate the impact without human intervention.

In logistics, AI agents can improve efficiency with dynamic route planning and real-time decision-making. They continuously analyze data from multiple sources, including traffic patterns, weather conditions, and delivery schedules, and optimize transportation routes accordingly. Such adaptability leads to timely deliveries and reduces operational costs. 

Additionally, agentic AI can manage warehouse operations by monitoring inventory levels, predicting demand fluctuations, and automating restocking processes. It can also help with vendor coordination by handling procurement tasks. An AI agent can evaluate supplier performance, negotiate contracts, and manage purchase orders based on real-time data and business goals.

Real-world use case: Amazon’s Lab126 device unit is creating warehouse robots that, thanks to agentic AI, can perform multiple tasks compared to regular single-task versions. These robots will be able to retrieve parts for repairs, unload trailers, and lift heavy objects in confined spaces. For customers, this means faster delivery, and for the company, it means minimizing costs, waste, and carbon emissions.

10. Product management and development automation

Agentic AI is transforming product management by automating complex tasks such as roadmap planning, backlog triage, and user feedback analysis. These AI systems process huge volumes of data, identify patterns, and make informed decisions, enhancing efficiency and reducing manual workload.

In roadmap planning, AI agents analyze market trends, customer feedback, and competitor strategies to suggest optimal product features and timelines. By monitoring these variables in real time, they can adjust the roadmap whenever needed, thus ensuring that the product remains aligned with market demands and organizational goals. 

For backlog triage, agentic AI can prioritize tasks based on customer impact, development effort, and strategic importance. Additionally, by analyzing user feedback from multiple channels, the AI can identify common pain points and suggest improvements, facilitating a more user-centric development approach.

Real-world use case: ThriveAI, a startup founded by former employees of Google and Palantir, has developed AI agents that function as junior product managers. These agents can analyse user feedback, monitor competitor activity, and assist with roadmap planning. These agents can also integrate with Slack and other tools, providing seamless support to product teams and enabling them to focus on strategic decision-making.

11. Scientific research and discovery

In science, agentic AI automates tasks such as literature reviews, hypothesis generation, experiment design, and data synthesis. It explores vast scientific databases, identifies relevant studies, and extracts key insights, saving time for human researchers. With the help of advanced natural language processing (NLP) and machine learning, AI agents can generate comprehensive summaries and highlight emerging trends, enabling scientists to stay up to date with the latest developments in their areas of expertise.

Agentic AI systems provide a level of automation that allows for rapid iteration and optimization of experimental conditions. For instance, agentic AI can identify potential drug candidates, predict their interactions, and suggest optimal synthesis pathways, thereby speeding up the development of new therapeutics.

Furthermore, AI agents enhance data synthesis by integrating and analyzing diverse datasets to uncover novel correlations and insights. They can generate research reports, complete with data visualizations and interpretations, allowing for more informed decision-making. In fields like materials science, agentic AI has been instrumental in discovering new materials by predicting their properties and guiding experimental validation.

Real-world use case: A notable real-world application of agentic AI is Google's "co-scientist" tool, developed to accelerate biomedical research. This AI assistant helps scientists identify knowledge gaps and propose new research ideas. It matched the findings of Imperial College researchers on a new gene transfer mechanism for antimicrobial resistance, reaching the same conclusion in days while the human team took years. It also aided Stanford scientists by identifying two existing drug types that could be repurposed to treat liver fibrosis, which researchers then validated as effective against the disease.

12. Business process management (BPM) and business intelligence

Agentic AI is transforming Business Process Management (BPM) by autonomously monitoring, optimizing, and executing workflows. For example, AI agents can convert meeting notes into project tickets or trigger supplier orders based on predictive analytics, streamlining operations and reducing manual intervention.

In business intelligence (BI), agentic AI improves data accessibility and insight generation. Whereas traditional BI tools often require manual data extraction and analysis, AI agents can independently interpret ambiguous queries and provide actionable insights. This democratizes data analysis, allowing non-technical users to gain meaningful insights without relying on data specialists.

Real-world use case: Quantexa, a UK-based AI firm, assists organizations in connecting diverse data sources to identify fraud and improve decision-making. With agentic AI, Quantexa allows companies to analyze complex data sets, uncover hidden relationships, and make informed decisions, enhancing operational efficiency and reducing risk.