Every support leader we talk to wants the benefits of AI but almost none of them think they're ready to roll it out. They're stuck waiting for perfect documentation, flawless processes, and complete system overhauls.

But what we've learned, deploying AI at companies from scrappy startups to complex enterprises, is that the companies winning with AI aren't the ones with perfect conditions. They're the ones who start yesterday and iterate today.

The six pillars that underpin successful deployments

Based on our experience of customer deployments, we've identified what actually determines AI success. Spoiler: It's not perfect preparations.

1. A "good-enough" knowledge base (that gets better)

Your knowledge base probably sucks. Most do.

But here's the thing: You don't need perfection to start. You need commitment to iteration.

What you're aiming for:

• Up-to-date information without contradictions

• Explicit explanations with minimal room for inference

• Examples that show, not just tell

The real requirement: Someone who owns making your documentation more AI-centric. The beauty of that commitment is it pays off on the double – making content AI-friendly ultimately makes it more customer-friendly. If an AI can't understand your knowledge base, your customers are probably confused too.

Arbor got up and running in a week with a custom Notion integration syncing their state-specific energy content, but has been improving their knowledge base ever since based on analysis of the AI agent's responses. It's all about starting where you are and iterating from there.

2. Strategic use cases

Most companies either try to automate everything at once or pick random low-hanging fruit (we're all for quick wins, but if you want to build confidence in your AI efforts it also needs to be high impact as well as low risk).

The framework that works:

• Identify high-volume, time-consuming processes in your support queue

• Prioritize which ones to tackle first based on:

  • Volume (what processes get the most tickets?)

  • Time taken (what takes your team the longest?)

  • Complexity (what's feasible to automate?)

• Clearly define what good looks like for each use case

• Document step-by-step processes you want AI to follow

• Build iteratively - design common paths first, then edge cases

• Don't try to design for everything at once - that's inefficient

Critical Question: What processes, if automated, would free up your support team to do significantly more meaningful and valuable work?

3. Give AI programmatic access to the same tools and information as your agents

We've encountered companies that expect AI to tell customers about their order status without access to all of their order systems. Would you hire a human agent and not give them system access? Then why expect AI to work without it?

Giving AI access to your tools and data means programmatic - the AI can't navigate UI like a human. That means you'll need technical resources, although the specifics of that will very much depend on your current infrastructure. You need API integrations. You need to plan for this upfront.

We see this constantly – implementations are delayed or under-deliver not because the AI isn't smart enough, but because nobody planned to connect it to their systems. If you want AI to deliver experiences that match your human team, give it the same data access your human team has.

4. An iteration mindset

AI often doesn't work the way you think it should. The only way to figure out what works is to test, measure, and iterate.

The first prompt you write won't work perfectly. Neither will the second. This isn't failure – it's the process.

What this means practically:

• Build QA processes for ongoing optimization

• Develop skills for iterative improvement

• Accept that perfection comes from iteration, not planning

• Allocate resources specifically for this work

Summ automated their refund workflows during tax season. It didn't work perfectly on day one. But because they committed to iteration, they achieved 97% faster resolutions within weeks.

5. A platform built for your reality

Out-of-the-box solutions are largely theater, rather than technology. Real AI agents emerge when you can bring your business logic to the platform.

What to look for:

• Deep customization capabilities

• Ability to incorporate your specific workflows

• Flexibility to adapt to your processes (not vice versa)

Yes, this means more work upfront. People ask us "how quickly can we get this running?" Wrong question. This isn't plug-and-play. It's building an agent that actually works for your business.

6. The right team

You need people who are customer-obsessed but technically curious. They don't need to be engineers, but they need to be comfortable with:

• Reading API logs

• Understanding prompt engineering

• Building evaluation frameworks

• Moving fast and breaking things (safely)

We call them CX Automation Specialists. The best AI teams we see are former support agents who've gotten excited about technology, not technologists trying to understand support.

The myths holding you back

Time to challenge some assumptions:

"We need perfect documentation first" No. Flex trained their AI while discovering gaps in their documentation. The AI helped them identify what was missing.

"Our processes need to be fully mapped" Start with what you have. Linktree built workflows reflecting how support actually works, not what the SOPs said.

"It's all or nothing" The best AI knows what it doesn't know. Better to handle 50% of tickets well and leave the rest to the humans than attempt 100% and fail on half.

"We need to redesign everything" Eucalyptus used AI to analyze historical data and found issues early enough to proactively communicate with customers. No redesign required.

Start before you're ready

Let's face it – the companies with perfect documentation and processes probably don't need AI as urgently. It's the messy, fast-growing, resource-constrained teams who benefit most.

You don't need perfect conditions. You need:

• A good enough knowledge base (that gets better over time)

• Select and prioritize a strategic use case

• A plan for providing system access for your AI agents

• An iteration mindset and ongoing resources

• A customizable platform built for your reality

• The right team to make it all work

Stop waiting for perfection. Your competitors started iterating yesterday.