TL;DR

90% of organisations aren’t ready for agentic AI because their people, processes, and technology are still fragmented. Before building copilots or custom agents, companies must become data-first organisations: establishing strong governance, integrating foundational systems, and replacing manual processes with structured, interconnected workflows. With agentic blueprints and a proven methodology grounded in value, architecture, and design patterns, Microsoft-centric organisations can gain AI value faster and more safely.

90% of organisations aren’t AI-ready. Are you?

Everyone is talking about AI agents — or copilots — that summarise meetings, answer questions, trigger workflows, and automate the routine.

Most organisations believe they’re “ready for AI” because they use ChatGPT or Copilot. But agentic AI only becomes valuable when the foundations are in place — governed data, consistent processes, and interconnected systems.

In reality, around 90% of organisations aren’t there yet. Their data lives in silos, processes run on spreadsheets, and collaboration happens in unstructured ways that AI cannot interpret.

So before you rush to “add AI,” stop for a moment. Is your organisation truly ready for an agentic AI strategy, or are you still running on Excel?

From automation to augmentation

Many companies start here: Sales teams track opportunities in Excel. Documents live in personal folders. Collaboration happens over email or private Teams chats.

It works until it doesn’t. Because when you ask, “Can we plug AI into this?” the answer depends entirely on how your work is structured.

For AI to deliver value, your processes and data need to be consistent and governed. If information sits in silos or moves around without clear ownership, no Copilot will sort it out for you.

Step 0: Look at how you work

AI can only operate within the workspace it lives in. Before talking about technology, ask a simple question: How does your team actually get work done every day?

• Where do we keep track of our work, such as opportunities, sales/purchase orders, contacts, customers, and contracts?

• Who updates them?

• How are documents stored, shared, and versioned?

If the answer includes “Excel,” “someone keeps a list,” or any other manual step, that’s not AI-ready. Manual tracking makes automation impossible and governance invisible.

When we assess readiness, we start by examining your value patterns: how your teams create value across people, process, and technology. These patterns reveal which activities need to be structured into systems that log every action consistently. Only then can an agent analyse, predict, and assist.

Microsoft’s modern workspace is AI-ready by default

Microsoft’s modern workspace, including SharePoint, Teams, Loop, Dataverse, and Copilot, is already agent-ready by design.

Chat, files, and meeting notes create structured, secure data in the cloud. When your team works in this environment, an AI agent can see what’s happening, and safely answer questions like:

• “What was decided in the last project meeting?”

• “Show me invoices from vendors in Q3.”

• “Which opportunities need follow-up this week?”

With even basic tools, you can achieve impressive results. A simple SharePoint automation can pull in invoices, let AI read and structure them into columns (supplier, invoice number, amount), and feed the data into Power BI, all in an afternoon.

Step 1: governance first, AI second

When someone logs into Copilot and asks a question, Copilot will find everything they can access. That’s both the promise and the risk, without strong data loss prevention, AI may surface information you never intended to expose.

This is why governance is the first pillar of any AI readiness strategy, and it’s the foundation of our value–architecture–design pattern methodology. Without clear ownership, access, and data controls, no agent can operate safely.

When we run readiness audits, the first questions aren’t about models or copilots — they’re about access and accountability:

• Who owns each SharePoint site?

• Who has edit rights?

• Is sensitive data over-shared across Teams?

• What happens if a site owner leaves the company?

The good news is that Microsoft’s audit tools automatically flag ownership gaps, oversharing, and risky access patterns so you can act before an AI ever touches the data.

Step 2: structure your business data

Even with strong governance, your data still needs structure. AI can read unstructured notes and spreadsheets, but it can’t extract meaningful insights without a consistent data model.

This is where Microsoft's data ecosystem helps. Their tools connect sales, service, finance, and other processes into a single, governed data layer. Every record — contact, invoice, opportunity — sits in one place with shared logic.

Structuring business data turns your architecture patterns into reality. When CRM, ERP, SharePoint, and collaboration systems are interconnected, you create the unified backbone that agentic workflows rely on.

And that’s where agentic AI truly begins. You can build agents that review opportunities, identify risks, and recommend next steps based on the clean, consistent data flowing through Microsoft 365.

Step 3: from readiness to reality

Once the foundation is solid, the strategy becomes clear:

1. Audit your workspace and permissions.

2. Standardise how data is collected and stored.

3. Govern collaboration and access through Teams and SharePoint admins.

4. Enable your first agent — a Copilot, chatbot, or custom agent using Copilot Studio — to assist in everyday processes.

From there, you can start to ask more ambitious questions:

• Which of our processes could an agent safely automate?

• How do we combine Copilot with custom workflows to handle domain-specific tasks?

• What guardrails do we need so that AI doesn’t just act, but acts responsibly?
  

You also don’t have to start from scratch. Our proprietary baseline agents for Microsoft ecosystems cover common enterprise scenarios and act as accelerators, reducing implementation time and giving you a proven foundation to tailor AI behaviour to your organisation.

Want to learn more? Come to our free webinar.

The right question isn’t “how fast”, it’s “how ready”

Every organisation wants to move fast on AI. But the real differentiator isn’t how early you adopt, it’s how prepared you are when you do.

For small teams, AI readiness can be achieved in weeks. For large, established enterprises, it’s a transformation touching governance, data models, core systems and ways of working.

So before asking “How soon can we deploy an agent?” ask instead:

• “Would our current systems help or confuse the AI?”

• “Can we trust the AI to find, but not expose our data?”

• “Do our people know how to work with agents, not against them?”

That’s what an agentic AI strategy really is. Not just technology, but the deliberate design of trust, control, and collaboration between humans and AI.

Before you deploy agents, build the trust they need to work

AI adoption is no longer about experimentation. It’s about building the right foundations —  governance, structure, and readiness — so your first agents don’t just answer questions, but deliver real, secure value.

Agentic AI starts with readiness. Once your systems, data, and people are ready, intelligence follows naturally.

We help Microsoft-centric organisations move from AI-curiosity to real impact, creating environments where AI agents can operate safely, efficiently, and intelligent.

Join our free 45-minute webinar — we’ll walk you through how to get AI-ready in 90 days.

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