Everyone’s talking about AI agents, but it can be hard to know where to begin. Whether you’re a leader trying to build an AI strategy or an individual looking for smarter ways to get your job done, chances are the best way to begin is by using the tools you’re already familiar with. For example, this may be your operational workflow platform, where multiple teams are already connected and data sources are well integrated.

You can apply AI agents across a broad range of use cases, but the key is to look for workflows that are both repetitive and time-consuming. Agents need to become familiar with the types of information they’re working with and clearly understand what they’re expected to deliver. Think about the routine tasks you or your team always handle and wish you could do faster.

Enterprise and business AI agent use cases

There are many ways to adopt AI agents across the enterprise, but you’ll want to ensure that you build agentic workflows across the right infrastructure, with clean data, and provide agents with the context they need to be effective. Here are a few common applications, many of which benefit from keeping a human in the loop:

1. Lead and account research, and prioritization

Agents can automatically research target companies and prospects — pulling firmographic data, recent news, funding history, and competitive signals — and provide summaries so that sales reps can go into calls feeling confident and prepared. Taking things a step further, agents can also surface the strongest leads for follow-up and even route them to specific reps.

2. Personalized outreach at scale

Agents can enrich account data in your customer relationship management (CRM) solution, pulling from research or data from other connected tools, and generate tailored email drafts for every prospect on behalf of your sales and/or marketing team. Outputs are more personalized and relevant, relying less on generic templates.

3. Customer feedback triage

Agents can ingest support tickets, survey responses, and product reviews, and then classify, tag, and prioritize feedback — surfacing anything that needs immediate attention and feeding patterns into product roadmaps. As an example, eBay uses Airtable to analyze and process massive amounts of product feedback, a manual process that used to take months but now happens in real-time. This means eBay can now act on behalf of more customers, much faster.

4. Weekly business intelligence briefing

Briefings are often an easy thing to automate, especially because they’re not customer facing. Still, you want to make sure they’re accurate for executives. These can be a good candidate for a multi-agent pipeline, where one agent collects market data, another analyzes it for trends, and a third writes a polished executive summary.

5. Contract and document data extraction

Agents can read PDFs, contracts, and proposals and convert unstructured content into structured, queryable records (e.g., dates, names, contract details) — eliminating manual data entry from legal and procurement workflows, and making it easier to review contracts and flag risks.

6. Campaign brief generation

Agents can take a product launch or campaign objective and generate structured creative briefs that include audience parameters, messaging angles, and channel guidance. This is especially considering that briefs typically draw from multiple sources of information.

7. PRD and documentation drafting

Routine, detailed, in-house documents are well-suited for agent help. Agents can quickly generate first-draft product requirements documents, pulling from feature requests, customer feedback summaries, and taking into account engineering constraints. This leaves product managers in a position to apply their judgement rather than drafting.

8. Brand and compliance reviews

Agents can audit creative assets against brand guidelines and regulatory requirements, removing a huge burden from your brand team, who may struggle to provide oversight at scale. This helps flag issues early, before an asset goes live and potentially damages or erodes your brand reputation.

9. Image generation at scale

Some agents are designed for image generation to help produce on-brand assets at scale. Image generation agents work best when they draw from brand guidelines and clearly defined creative parameters for repetitive workflows, such as producing header images for blog posts or images for social campaigns.

10. Content localization

Agents can generate market-specific copy variants with localized language and cultural adaptations for regional teams to quickly review and adjust, enabling global teams to run regional campaigns without waiting for lengthy manual localization processes.

11. Resource and capacity planning

Operational teams can use agents to monitor project loads, flag over-allocated team members, and suggest ways to rebalance workloads. They can also automatically assign owners to tasks, set due dates, and send status updates, taking a load off project leads who struggle to maintain a live view into each project’s status and team capacity.

Industry-specific AI agent use cases

Challenges and use cases aren’t necessarily the same across industries. Here’s a few use cases where specific fields are finding value from agentic workflows.

12. Healthcare: Clinical documentation

Agents can draft clinical notes and patient summaries from structured intake data, so that care teams spend less time on documentation and giving more time with patients. Human review remains essential before any clinical record is finalized, however.

13. Financial services: Earnings and market research

Finance teams use agents to monitor earnings releases, regulatory filings, and analyst reports. Agents can summarize key data points and flag material changes in real time.

14. Retail and e-commerce: Product catalog management

Agents can generate and update product descriptions, tag images, monitor inventory signals, and flag listings that need attention to keep catalogs accurate across thousands of SKUs.

15. Media and publishing: Content performance analysis

Agents can track article and campaign performance across channels, identify what's resonating with which audiences, and surface optimization recommendations for editorial and distribution teams.

16. Legal: Contract review and risk flagging

Agents can scan contracts for non-standard clauses, missing terms, and potential risk indicators — giving legal teams a head start before manual review, and helping sharpen focus on where contracts need attorney judgment.

17. Manufacturing: Supplier and procurement monitoring

Agents can monitor supplier performance data, track delivery timelines, and flag deviations from expected patterns so that procurement teams have early warnings before disruptions escalate.

18. Education: Personalized learning support

Agents can analyze student progress data and generate individualized practice recommendations, giving instructors a clearer picture of where each learner is struggling.

Personal and daily life AI agent use cases

Agents aren't just for enterprise workflows. Individuals can also take advantage of the efficiencies that AI agents provide, especially if you’re managing complex schedules, research, or projects in the workplace, or at home.

19. Personal research assistant

Agents can monitor topics you care about, whether that’s industry news, competitors, or specific subjects you’re interested in, and deliver curated summaries on a schedule.

20. Travel or purchase planning

Agents can aggregate flight options, hotel availability, local itineraries, and travel advisories into a single organized plan, saving hours of tab-switching and comparison shopping. And this is true for any other large purchase you’re looking to make, whether you’re buying a house, car, or a new refrigerator.

21. Meeting preparation

Before important calls, agents can pull in relevant background on attendees, companies, and prior conversations so that you show up prepared.

22. Email drafting and management

Agents can draft replies, flag high-priority messages, and organize inboxes by category so that you don’t miss anything important.

23. Budget and expense tracking

Agents can categorize transactions, identify spending trends, and flag anomalies so that your personal finance reviews are faster and more accurate.

24. Creative project development

Writers, designers, and other creatives use agents to brainstorm, outline, research, and refine ideas — often as a thought partner rather than an executor, which helps to move through early stages of development faster.

25. Learning and skill development

Agents can build personalized study plans, recommend resources, and quiz you on material to help build and strengthen new skill sets.

Keeping humans in the loop

It’s important not to compromise your productivity gains by moving too fast. Take the time to set up protective guardrails around what agents can and can’t do. While some tasks can be fully automated without worry, other workflows need human oversight or built-in approval steps to ensure success.

Within a structured system of record, you can design human-in-the-loop (HITL) workflows by creating fields like "Agent recommendation," "Approved," and/or "Override" to prevent an agent from executing until someone has reviewed and approved. This helps your team build confidence in the system over time.

Agent observability is also important. When agents are operating across multiple workflows, you need visibility into what they're doing and why. That means having access to audit logs, status fields that reflect agent actions, and clear error states when something goes wrong. Ideally, agents and humans are working across the same operational surface, in a solution like Airtable, where everyone can see each other’s actions and the outcomes.

H2: The future of AI agents

Most organizations today are using agents within isolated workflows. Our research found that 56% of organizations are in the earliest stages of human-agent collaboration, where AI is primarily used for personal assistance instead of for wider-team productivity gains. In the future, this will look more like adoption of advanced orchestration across multi-agent systems, where multiple specialized agents hand off work to each other with minimal human intervention. Of the 1,001 companies we surveyed, only 3% of organizations have reached the multi-agent, cross-system orchestration stage, but if they’re pulling ahead now, consider just how fast they’ll be able to run in the future.

Cross-functional agent orchestration requires org-wide investment in your infrastructure and true AI readiness. Many companies aren’t there yet — Gartner found that only 20% of executives believe their workplace is AI-ready. Even so, industry analysts see AI agents as a workforce here to stay, with Gartner predicting that “by 2028, organizations that adopt and sustain an AI-first strategy will achieve 25% better business outcomes than competitors.” Being AI-first doesn’t mean being AI-everything, however, as Gartner noted. Companies can start building the necessary foundation for AI agent success now and being experimenting across specific use cases.

Airtable is where humans and AI agents actually work

Agents need structured data, clear workflows, and your unique operational context to produce reliable results. Airtable gives your agents a home — a centralized AI workflow platform where your data is organized, your workflows are live, and your team can see exactly what's happening. Whether you're building your first agent workflow or orchestrating a multi-agent pipeline, Airtable provides the tools and governance to make it work at scale. You can start out using Airtable’s prebuilt Field Agents or build a custom agent of your own.

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