95% of AI Projects Fail

The problem

• Retrieval is capped (max. 20 sources per agent) and bound to the SharePoint filing structure – it mostly finds what is filed cleanly and named correctly.
• Good for single documents, but contract families – lease, amendments, annexes, protocols – are seen as isolated files.
• Extraction is flat: isolated values like name, date, amount, without linkage to the property.
• Citations stay at document level, and reach ends at Microsoft 365 – ERP, DMS, and network drives stay out.

The right approach

• A gif-IDA-compliant data model with over 28 document types: leases, amendments, invoices, energy certificates, building permits, and more.
• Relational extraction across entire contract families: connections between person, contract, property, and terms can be recognized automatically.
• True hybrid search across your entire portfolio, independent of filing discipline – every answer cited down to the page.

The problem

• Five or more systems – ERP, DMS, SharePoint, network drive, email – and none of them talks to the others.
• Key-person risk: knowledge leaves with the person instead of going into the system; insights live in private chats and single files.
• Isolated AI pilots achieve little because the data layer underneath is missing – reporting stays manual.

The right approach

• An open data layer on top of your existing systems that connects sources instead of replacing them – no system replacement, no migration mega-project.
• Three-layer model: originals are preserved, AI enrichment runs automatically, analytics are BI-ready – open formats, self-service BI directly on the data lake.
• First make scattered knowledge accessible with sources, then attach AI – available to everyone, even when the knowledge holder is out of reach.

The problem

• Whoever stays at stage 1 measures small efficiency gains and concludes "AI doesn't deliver" – even though the real potential was never touched.
• Putting new technology on an old process only makes it faster, not better.

The right approach

• Pick one process with real leverage and rebuild it consistently around the AI – step by step, not everything at once.
• The open data layer (see mistake 2) is the prerequisite for putting AI at the center.

The problem

• A generic AI always sounds convincing – even when it is wrong. Without tight guardrails, quality fluctuates.
• Citations stay at document level: you have to find the relevant passage yourself. That costs time and trust.

The right approach

• AI with tight guardrails: an assistant that knows the real estate domain and receives the facts from your systems directly in context, answers based on real data.
• Every answer is additionally checked against the sources – which increases factual accuracy.
• Citations down to the exact page: verifiable in seconds instead of searching through a hundred pages.

The problem

• The EU Data Boundary sounds safe but allows exceptions: US subprocessors are possible with third-party models.
• What often weighs heavier than a potential fine is whether compliance can be demonstrated to regulators, investors, and tenants at all.

The right approach

• EU-only hosting: the data lake belongs to you, rented in your name at the hyperscaler and under your full access control – the encryption keys stay with you.
• No data transfers to third countries, no customer data used for AI training.
• Open formats and open interfaces instead of proprietary storage: your data stays accessible and exportable at any time – no lock-in.

The problem

• Per-user models scale linearly with headcount.
• A portfolio quickly spans hundreds of thousands to millions of pages – even small per-page differences add up, especially when the vendor earns on every page.
• On top of that come hidden items: implementation, internal operations, and custom training for real estate documents.

The right approach

• Flat rate per module, independent of user count – processing only adds pure infrastructure costs, at hyperscaler conditions without markup.
• An intelligent pipeline classifies document types first; only relevant files go through extraction – which can keep costs low.
• Managed service: no internal AI or Azure team needed, operations and further development included.

The problem

• According to Gartner, only around 5% of organizations moved from a Microsoft Copilot pilot to broader deployment.*
• New tools mean new logins and new habits that get lost in day-to-day business.
• Without before-and-after measurement, the value stays invisible – and what nobody sees does not get used.

The right approach

• No new hurdle: the assistant lives directly in Microsoft Teams, with single sign-on – no extra tool, no new login.
• Fast, visible first value: an initial use case that can typically make a noticeable difference within weeks.
• Make the value demonstrable: before-and-after comparison and clear KPIs from day one; managed service keeps usage going.