Infrastructure & Data: ready for tomorrow

The pain

Decision paralysis. Everyone shouts "cloud", but it looks expensive, and the current vendor pushes their own story. Meanwhile the data is scattered across the accounting package, Excel files, a CRM nobody uses and fifteen years of mailbox — and the vague ambition to "do something with AI" never gets off the ground.

The scan

The current infrastructure and data landscape is mapped, with an honest cost comparison (TCO) of staying versus migrating, and a target architecture in steps.

The result: an infrastructure roadmap (where you are now, where you want to go, in which steps, at what cost) plus a sober "data readiness" verdict.

Typical findings

• An on-prem server from 2017 that is end-of-life. The question is not "cloud, yes or no", but: what exactly runs on it, and what is the real cost of staying versus migrating? Often the right answer is hybrid — critical or heavy data local, the rest in the cloud — and not the all-in cloud the vendor is selling.
• Data fragmented without a single source of truth: customer data in the accounting package and in three Excels and in an unused CRM, with conflicting information between sources.
• Becoming "AI-ready" in practice means first consolidating, cleaning and structuring the data — before there can be any talk of a smart search function or assistant.
• Microsoft 365 is fully paid for but half used: SharePoint, Teams and OneDrive sit ready, while files still float around on local drives and in mailboxes.
• Vendor lock-in: a vendor who keeps everything in their own closed system, making switching artificially difficult.

The follow-up

This is the direct bridge to pillar 4: without organised data there is no meaningful AI. Companies that start here often request the AI Basics scan after the first phase.