AI readiness: How to get your organization agent ready

AI agents are here, and here to stay. According to PwC’s 2025 AI Agent Survey, 79% of enterprises have already adopted agents. Yet as PwC points out, broad adoption doesn’t necessarily mean that companies are seeing deep impact. At least, not yet. 

In the span of only a few short years, we’ve moved from simple AI assistants to multi-step agentic workflows to goal-oriented AI agents that operate autonomously and reason across tools and data. The bottleneck to seeing deeper impact isn’t intelligence. Leading LLM models are already capable enough to handle complex, autonomous work. The next stage is multi-agent models collaborating across complex cross-functional workflows — and, like humans, these agents must be set up to succeed. 

The challenge lies in preparing your operations for AI readiness: creating the right way to orchestrate your model, ensuring agents have access to the right data, and building a deployment layer that allows you to manage them. Here’s how to build an operational foundation to benefit both humans and agents across your entire organization.

What is AI readiness?

AI readiness is how prepared your organization is to adopt AI and actually realize value from it. But as AI evolves from chat-based assistance to agents that can take action and collaborate with humans, the definition is changing. The readiness that matters now is agent readiness—your ability to support autonomous work across your business.

That comes down to your operational foundation: connected, trusted data; systems agents can operate within; and clear governance with human oversight. While individual AI usage is exploding, that alone doesn’t translate to organizational impact. Without this foundation, work stays fragmented and outputs are hard to trust. With it, agents move from isolated experiments to reliable execution at scale.

9 tips to prepare your org for AI agents: An AI readiness framework 

Preparing your organization for effective human–agent collaboration doesn’t require a massive transformation effort. It’s about focusing on the right fundamentals.

Use these nine tips as a practical framework to strengthen your agent readiness—so agents have the context, structure, and oversight they need to deliver real results.

1. Evaluate your operations

Before adding an AI tool, document how work is accomplished within your organization. Where does key data live? Which workflows are automated, and which still rely on spreadsheets or manual approval chains? Where are gaps in knowledge, and current bottlenecks? 

Being the earliest adopter doesn’t guarantee success with AI. The organizations that will be hardest to catch are the ones who built the right operational foundation from the beginning of their AI journey. Your most important workflows need to be structured, visible, and connected to provide AI agents with the best chance to be useful; anywhere data is fragmented or key tools are disconnected means agents can’t access all the context they need.

2. Identify clear use cases

Don’t try to automate everything at once, or pour energy into orchestrating a one-time special project. Instead, identify the teams or processes where AI can deliver a fast, visible win and deploy agents to handle tasks your team does repeatedly. Often these are time-consuming tasks that become difficult to manage at scale: document extraction, data enrichment, compliance checking, and more. 

Wondery, for example, started by using agents to automatically identify and analyze organic brand mentions in podcast transcripts — capturing sentiment, quotes, and speaker context that would have been time-consuming to find manually. By turning these moments into structured data, their sales team can quickly spot opportunities and tailor pitches to potential advertisers. Or, take MGA Entertainment, which uses agents to turn unstructured creative briefs into separate, tagged tasks, helping project managers plan.

These workflows are good starting points because they’re well-defined, high-volume, low-risk, and provide clear, ongoing outcomes — which helps build the case for agents to handle more complex use cases over time. 

3. Get more people using AI regularly

Teams have different feelings about AI. You might have one power user and a few team members that are hesitant, overwhelmed, or even outright resistant. But the next wave is about more than using AI for personal assistance on individual projects. The teams that get the most value from AI are the ones where everyone uses it, and where AI becomes embedded in shared workflows, gaining context across the org.

Incentivize and accelerate agent adoption by helping employees see it as an upskilling opportunity. Share successful use cases so that teams see how agents can help make their jobs easier or enhance the work they’re doing — and to understand how they don't replace human judgment. Agents are not just a tool some people are using; they're becoming a central part of how work is accomplished within a business.

4. Structure your data and workflows better 

There’s no way around this step. AI readiness, particularly agent readiness, depends on effective workflow management and structured data with explicit relationships, current state, and business context. Without this, outputs are likely to be generic or even inaccurate. 

If you’re using Airtable, this looks like clean fields, linked records, and clear classifications and naming. Even if you use another system of record, your data, taxonomy, and logic need to be clearly thought through, and cleaned up so that there aren’t duplicate records or missing fields. With this foundation in place, you’re in good shape to make every future collaboration model possible. 

5. Build shared views for humans and AI agents

Once your data is structured, the next step is making it usable — for both humans and agents. Teams work best when they operate from shared context, and agents need that same visibility to be effective.

That means connecting all your systems — your CRM, customer feedback tools, email platforms, and more — into a central system of record. Take eBay, for instance — every piece of customer feedback, whether it comes through surveys, account managers, customer support tickets, community forums, or social media flows into one shared view. 

With everything flowing into one place, data stays up to date, workflows stay connected, and both humans and agents can see, trust, and act on the same information.

6. Move agents out of chat windows and into real workflows

Modern agents don’t just answer questions; they take action. They create records, update data, trigger workflows, assign tasks, and send communications. But to do that effectively, they need a system they can write back to, not just read from.

That’s why agents need to operate inside an operational layer, not a chat window. When outputs flow directly into structured workflows, work doesn’t get stuck waiting for manual follow-up — it moves forward automatically. For example, a marketer can ask Claude to generate content ideas and have the best ones written directly into Airtable as fully structured records, complete with owners, deadlines, and status. The result is immediate execution, not just output.

7. Align on how much autonomy agents should have 

One of the most important decisions you’ll make as you scale agent adoption is how much autonomy agents should have — and where humans need to stay in the loop. For some tasks, agents can handle the full workflow and humans review exceptions. For others — especially those touching compliance, customer communication, or financial decisions — you might want every output to pass through a human review step before it’s final. In other cases, you might just sample deterministic outputs (when the outputs are always expected to be the same) to ensure there isn’t any variance.

Build approval workflows and role-based permissions in your system of record (another reason this operational layer is so vital) to explicitly reflect these distinctions and ensure there’s always a clear step for human review, determined by you.

8. Set up security and governance early 

The approval chains and governance you build now becomes the infrastructure for more autonomy later. Governance and security are required at scale and it becomes more difficult to rebuild your systems later.

Begin by thinking about deploying a system of record that allows for enterprise audit trails so that every agent action is logged and traceable. Define escalation paths for edge cases that agents shouldn’t handle on their own. Build compliance controls that match your industry requirements. Work with IT early to make sure your AI tooling meets your security standards — not after you’ve already deployed agents across business-critical workflows. The time to get this right is now so that you can move faster, and with confidence in your models, later.

9. Share and scale agent expertise across teams

As teams work with AI agents, they develop real expertise—what instructions work, how to keep agents on task, and how to get reliable outputs. These are agent skills, and they shouldn’t stay siloed.

Create a shared system to capture and organize these skills by use case, owner, and last-tested date. When teams can reuse what’s already been proven, they move faster, avoid starting from scratch, and scale what works across the organization.

Get your org AI agent ready with Airtable 

AI readiness requires building an operational foundation for success. Using Airtable as your system of record means your agents can access structured data and shared workflows so that they have everything they need to do impactful work, and you have insight into how that work is done.

Airtable is a platform for human-agent collaboration, where agents and humans work from the same data, current state, and are accountable to the same goals. Any team can manage agents, with the assurance of clear governance. 

What you build today, agents will run tomorrow. 

Get started now.