The global agentic AI market is projected to reach $140 billion by 2032, and nearly $200 billion by 2034.1 That’s a massive demand for agentic AI, and there are no signs of slowing down. In fact, according to a recent survey, 51% of companies have already deployed AI agents, and with another 35% planning to realize their agentic AI use cases within the next two years, we’re in for a drastic shift in how companies do business.2
While companies tend to have a clearer idea of their goals—mainly cost savings and efficiency—they’re less clear on how to realize the return on investment (ROI). Even though 94% of companies expect agentic AI adoption to be more accelerated than generative AI, few have a clear idea of what it is they’re adopting.3
We’ve gathered real-world agentic AI use cases from tech and vertical leaders across our organization to explore how this technology is transforming industries, highlight its advantages, and clarify the realities of adoption.
What Problems Does Agentic AI Solve for Automotive Companies?
Email Automation
AI agents for email automation can help automotive companies read, classify, triage, and auto-route high-volume inbound emails for issues such as:
- Dealer inquiries >> Parts
- Service appointments >> Scheduling
- Warranty >> Appropriate regional centers
- Recall notifications >> Safety teams
As a low-effort use case that can be deployed with existing large language model (LLM)-powered agents, it can deliver up to a 50% reduction in manual triage effort, faster SLA resolution, and scalable customer service without adding headcount.
Product Visualization & Content Generation
Product visualization and content generation AI agents generate photorealistic vehicle configurator images from CAD models showing every color/trim combination—without the need for physical photography shoots.
This medium effort use case (i.e., usually requires some integration effort) requires some CAD file format standardization, brand guideline encoding, and training/fine-tuning diffusion models on product-specific outputs. The result is a major reduction in photography and vendor costs, faster time to market, and consistent branding across digital storefronts.
What Can Agentic AI Do for Banks and Insurance Companies?
Proactive Compliance Monitoring
Proactive compliance monitoring is a low-effort agentic AI application. AI agents scan operations, communications, and transactions to identify suspicious transactions that might indicate fraud or money laundering, flagging them for auditors to review.
The impact? Aside from preventing losses associated with fraud, these AI agents can detect such risks and anomalies in real time—before they become regulatory penalties.
End-to-End Claims Processing
AI agents for end-to-end claims processing are one of our medium effort agentic AI use cases, requiring integration with policy databases, claims management systems, and document intake pipelines. A claims processing agent can orchestrate claims from intake to adjudication, extracting data, validating evidence, checking against policy terms, and preparing recommendations for adjusters.
By automating routine processing while still maintaining human oversight for complex exceptions, these AI agents can compress the claims lifecycle from weeks to days, saving time and effort for the company, and drive greater satisfaction for the customer.
How Does Agentic AI Benefit the Energy and Utilities Sector?
Continuous Market & Competitor Intelligence
Tracking market and competitor intelligence is an agentic AI use case that has strong value across industries by scanning public sources, news, filings, and social chatter, and summarizing risks or opportunities. It holds specific value to the energy and utilities industry in spotting policy changes or competitor investments in renewables. By delivering real-time competitive and market insights, this AI agent can enable proactive strategy adjustments and risk mitigation.
Proactive Risk & Incident Response
A more unique (medium effort) use case for the industry is a proactive risk and incident response agent, which monitors telemetry (logs, IoT, transactions, customer behavior), detects anomalies, and initiates corrective workflows. With integration into monitoring systems and authority to execute pre-defined actions safely, it can automatically detect grid fluctuations before incidents can escalate, and trigger corrective actions—such as re-routing power to avoid outages—in real time across critical systems.
Why Should Government Agencies Consider Agentic AI?
Continuous Market & Competitor Intelligence
Much like the energy and utilities industry, the government and public service agencies can benefit from a continuous market and competitor intelligence agent that scans public sources, news, filings, and social chatter to summarize risks or opportunities. It’s specifically valuable in tracking citizen sentiment on social platforms, emerging policy debates, inter-governmental initiatives, and potential crisis signals to enable proactive strategy adjustments and risk mitigation.
Transaction Scrutiny & Document Validation
AI agents for transaction scrutiny and document validation are able to extract structured data from scanned forms, validate against policies, auto-populate backend systems, and escalate cases. This could encompass:
- Permit applications
- License renewals
- Benefit eligibility forms
Incomplete or suspicious submissions are escalated for manual review, resulting in up to a 95% reduction in review time, 90%+ automation of manual validation, and faster compliance and fewer errors. It’s a medium effort agentic AI use case requiring policy rule codification, document schema definition, and validation logic mapping to backend systems, but the payoff is clear.
What Can Agentic AI Do for Healthcare Organizations?
Automated Knowledge Retrieval & Summarization
With automating knowledge retrieval and summarization, the AI agent autonomously retrieves relevant internal and external documents, then distills them into contextual answers or briefs.
Doctors, nurses, and clinicians can get instant summaries of patient history—including the details patients tend to forget—and the latest research guidelines before consultations, eliminating hours of research and preparation and accelerating decision-making.
End-to-End Claims Processing
A claims processing AI agent orchestrates medical claims from submission through adjudication, validating codes, checking coverage, identifying duplicate claims, and flagging complex cases for review. This medium effort agentic AI use case requires integration with policy databases, claims management systems, and document intake pipelines.
The result is a significant compression of the claims lifecycle (from weeks to days), saving time and effort for healthcare payers and providers and driving greater patient satisfaction.
What Opportunities Does Agentic AI Create for Media Companies?
Customer Support Triage
As one of the industries with the highest volumes of customer support inquiries, the media and communications sector is well-primed for exploring agent-led customer support triage as a use case. The AI agent classifies, routes, and even resolves routine customer queries before they ever reach a human. A prime example of this would be automated troubleshooting of device or connectivity issues—routine queries that follow solution trees to resolve—freeing advisors to focus only on complex, high-value cases.
Transaction Scrutiny & Document Validation
Much like the public sector, media and communications companies generate a large volume of contracts, agreements, bills, and credits, making a transaction scrutiny and document validation agent almost essential. With the right policy rule codification, document schema definition, and validation logic, it can extract structured data from scanned forms, validate against policies, auto-populate backend systems, and escalate incomplete submissions for manual review, saving up to 95% of human review time.
How Does Agentic AI Benefit Retail and Ecommerce Businesses?
Customer Support Triage
Much like the media and communications industry, retail is a perfect candidate for exploring agent-led customer support triage. The AI agent classifies, routes, and resolves routine customer queries (like “Where’s my order?” or “How do I return my package?”) while escalating more complex cases to advisors.
Product Visualization & Content Generation
Using AI for product visualization and content generation holds strong value for retail. The AI agent auto-produces consistent product photography, size charts, and copy at scale for use across marketing and sales channels.
A medium effort use case, it requires CAD file format standardization, brand guideline encoding, and training/fine-tuning on product-specific outputs. The result is a major reduction in photography costs, faster time to market, and more consistent branding across digital storefronts.
How Can Agentic AI Advance the Tech Industry?
Automated Knowledge Retrieval & Summarization
In terms of low-effort agentic AI use cases for the technology and consumer electronics sector, automated knowledge retrieval and summarization is one of the strongest. The AI agent autonomously retrieves relevant internal and external documents, then distills them into contextual answers or briefs, enabling:
- Product manager access to synthesized competitive feature analyses
- Developer access to user feedback correlated by themes
- Teams to access consolidated plans, updates, and notes to accelerate roadmap planning
- Users to access technical documentation relevant to them
RFP Automation
RFP automation is a medium-effort, high-value use case that can enable automated proposal drafting using retrieval, semantic chunking, and self-verification. This AI agent requires curation of a knowledge base, chunking of past proposal content into a Q&A format, and compliance guardrail implementation, but the ROI is significant.
By drafting proposals that reuse IP safely, reducing manual copy-paste while enforcing compliance guardrails, the RFP automation agent can reduce four-day RFP cycles to just four hours, with a reduced workload on sales teams and improved knowledge reuse.
Why Should Travel Companies Embrace Agentic AI?
Email Automation
Like the automotive industry, travel, transportation, and tourism companies are equally suited to email automation agents as an AI use case. These AI agents read, classify, triage, and route shipment delay queries to operations, billing issues to finance, claims to insurance, and more. A low-effort, high-value use case, these agents can deliver up to a 50% reduction in manual triage effort, resulting in faster resolution times and a more scalable customer service response without adding headcount.
RFP Automation
Like the technology sector, the travel industry has a strong use case for RFP automation agents. These AI agents automate proposal drafting with tailored responses when responding to high-volume RFPs for transport contracts. Retrieval requires knowledge base curation and semantic chunking of past proposal content, along with implementation of compliance guardrails, but it can reduce four-day RFP cycles to just four hours.
Is It Really Worth Exploring Agentic AI Use Cases?
The answer is yes. Whether you’re looking for quick wins with low-effort implementations or are ready to tackle high-impact projects that require deeper integration, the frameworks presented here will give you the clarity to move from strategy to execution.
As AI shifts how humans operate and evolves our expectations, it’s important to get ahead of the game and start exploring—before your competition gets there first. Organizations across every industry are already deploying agentic AI to automate workflows, accelerate decision-making, and unlock new levels of efficiency, and new use cases are being discovered every day.
Wondering how agentic AI can deliver the most value to your organization? Take our Agentic AI Maturity Assessment to find out where you stand, and receive a personalized report with industry-specific agentic AI use cases.
1 “Agentic AI Market,” Market.US, October. 2025.
2 “51% of companies now use AI agents, finds new survey,” Aninda Chakraborty, Tech Monitor, April 2, 2025.
3 “Survey Sees Pace of AI Agent Adoption Accelerating,” Mike Vizard, Techstrong.ai, April 1, 2025.
