Embrace the Knowledge Chaos

Once we let go of the idea of a single perfect knowledge base, our actual knowledge creation and consumption skyrocketed – we saw a 10x increase compared to the traditional method of hiring technical writers.

Key takeaways

Why are we still chasing this elusive dream of a single knowledge base?

In our experience, no organization ever manages to consolidate all of its knowledge into one base. Functionally, it usually ends up being a blend of multiple software tools and formats.

This applies even for organizations that have purchased standalone knowledge base software – as these tools often overlook rich sources of information that aren't seen as traditional knowledge sources – such as email threads, Slack messages, and more. And when you're missing critical knowledge sources like email and communication apps, you're already creating new blind spots for your organization.

It makes sense that we want to just buy one knowledge base tool and be done. We crave order and organization, hate duplication, and above all, we want all our knowledge to be searchable in one place. But in practice, critical channels always get missed. What's more, user habits inevitably start to break down. If not immediately, then gradually, as new teams get onboarded, a new similar-looking tool gets added to your company's tech stack, or a business is acquired and integrated into your organization.

The solution? Embrace the knowledge chaos.

At Ask-AI, we’ve turned our entire organization into a knowledge base via our AI assistant.


Every support ticket, every Slack thread becomes a knowledge card that the AI assistant indexes, organizes, and surfaces, whenever it is applicable. With a universal AI assistant, where any given piece of knowledge lives becomes irrelevant, as long as it’s indexed by our work AI assistant.

And what about duplicate or outdated information?

That's where good AI comes in.

It should be savvy enough to consolidate knowledge and distinguish between new, verified knowledge and old unreliable cards.

Otherwise it's garbage in, garbage out.

Once we let go of the idea of a single perfect knowledge base, our actual knowledge creation and consumption skyrocketed – we saw a 10x increase compared to the traditional method of hiring technical writers. And we've achieved all this without needing to add the mental overhead of following knowledge creation rules or having dedicated Knowledge Managers create documentation for documentation.

The best part?

We no longer worry about which vendors we're using for knowledge, we don't bother having to create an endless rulebook of where information needs to be asked, created, or filed away.

In this new paradigm, it doesn’t really matter where knowledge is and where it's created. Our AI assistant will consolidate everything for us, and proactively make it available whenever it's relevant.

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Frequently Asked Questions

Get quick answers to your questions. To understand more, contact us.

How can generative Al improve customer support efficiency in B2B?

Generative AI improves support efficiency by giving reps instant access to answers, reducing reliance on subject matter experts, and deflecting common tickets at Tier 1. At Cynet, this led to a 14-point CSAT lift, 47% ticket deflection, and resolution times cut nearly in half.

How does Al impact CSAT and case escalation rates?

AI raises CSAT by speeding up resolutions and ensuring consistent, high-quality responses. In Cynet's case, customer satisfaction jumped from 79 to 93 points, while nearly half of tickets were resolved at Tier 1 without escalation, reducing pressure on senior engineers and improving overall customer experience.

What performance metrics can Al help improve in support teams?

AI boosts key support metrics including CSAT scores, time-to-resolution, ticket deflection rates, and SME interruptions avoided. By centralizing knowledge and automating routine tasks, teams resolve more issues independently, onboard new reps faster, and maintain higher productivity without expanding headcount.