About Knowledge Intelligence

Let me introduce you to a Knowledge Intelligence (KI) framework. The name might still evolve, but I believe it will soon resonate just like Business Intelligence (BI). Why?

Let’s start with some context. Everyone’s talking about AI. You’ve seen the headlines: AI reads faster than any human, works 24/7, never takes a day off. Sounds like the perfect employee, right?

And that’s exactly what many companies are banking on. Capgemini reports that 80% have already ramped up AI investments. KPMG says 97% of executives expect quick ROI from GenAI.

When AI is applied to a well-defined problem with clear inputs and outputs, you get an AI Agent. And these agents are everywhere. They can do amazing things when they know what they’re doing and why.

But in a real business, that’s rarely the case. The agent doesn’t know your policies, your ways of working, or where to even find the right information. So what happens?

• You end up re-explaining things.

• Cleaning up messy outputs.

• Trying again. And again.

Eventually, people lose trust. Not because the tool is broken, but because it doesn’t understand your business.

At FLO, we’ve seen the same pattern again and again across industries: most company knowledge is locked away. Some in SharePoint or Google Drive, some in CRMs or ERPs, some in data warehouses, and a lot of it still in people’s heads.

You can’t expect AI to deliver real value without seeing the full picture. Without the right context, even the smartest model is just guessing. And business decisions shouldn’t be based on guesswork.

That’s where Knowledge Intelligence (KI) comes in — a smarter, more connected approach.

To understand the value of KI, it helps to look at the DIKW pyramid.

To better understand the added value of KI, let's first look at the DIKW pyramid.

At the base: raw data — numbers, tables, entries. As you move up, data becomes information (meaning), then knowledge (context), and finally wisdom (application). That’s when real decisions can be made. In most companies, moving from raw data to smart decisions still means a lot of manual effort — people interpreting spreadsheets, re-explaining context, and digging up rules from PDFs or old emails.

Traditional BI tools usually stop halfway. They turn data into dashboards, but humans still need to do the heavy lifting. With Knowledge Intelligence — and AI’s ability to understand text, unstructured data, and even reason over it — we can automate more of that journey. Not just crunching numbers, but understanding and applying them too. That means less guessing, faster answers, and better decisions.

With Knowledge Intelligence, you can use AI to analyse data, understand it and apply it.

Here’s how we typically bring Knowledge Intelligence to life:

1. Map your internal knowledge sources

2. Connect them into a single, accessible knowledge base

3. Clean and structure the content, and set clear governance

4. Prepare it for AI — with the right access, format, and logic

We don’t just dump everything into a model and cross our fingers. Instead, we build a smart, structured layer between your tools and your AI — one that understands what matters and delivers the right answers at the right moment.

If this resonates with you, I’d love to hear your thoughts. And if you're curious, I will dive into the technical side on my LinkedIn profile next week — how it all actually works, under the hood.