60% of AI Projects Fail to Reach Production — How ESM Creates a Foundation for Success

According to Gartner, 60% of corporate AI projects never reach production — and the reason is not a lack of budget, but unpreparedness of data and business processes.

Why AI pilots fail

A typical scenario: the board of directors demands AI transformation, budget is allocated, contractors are hired. After a few months, it turns out that models simply have nothing to learn from. Requests are stored in fragmented systems, there is no classification, request history is scattered across Excel spreadsheets, messengers and email chains.

Without structured data, even the most advanced language model produces unpredictable results. The mistake is not made when choosing the model or vendor — it was made before the pilot even started, at the level of data architecture.

• 60% of AI projects fail to reach production — Gartner data
• The main reason is data unpreparedness, not a technology deficit
• The problem reproduces regardless of budget and team maturity
• Most companies launch a pilot without a single unified system for collecting service data

What is ESM and why AI needs it

Enterprise Service Management (ESM) is a platform that extends ITSM methodology beyond the IT department and unites all service processes: HR, finance, procurement, facilities, legal. For AI initiatives, its main role is to create a single point of request collection with unified attributes, categories and interaction history.

When all requests go through a single system with fixed fields and SLA, the model gains what it was missing: clean, labeled data with historical context. Instead of chaos from fragmented channels — a structured database where model training and inference work predictably.

"If AI lacks quality data, the project is likely doomed" — this

conclusion is repeated in most analytical reports on corporate AI initiative failures.

This is precisely why ESM implementation is called the "foundation" for subsequent AI automation: without it, any pilot is building on sand.

How models eliminate routine

Once the ESM foundation is established, routine automation transforms from an experiment into a manageable task. The first candidates are repetitive requests with predictable solutions: password resets, standard access requests, typical procurement orders, template HR certificates.

A model trained on structured request history from ESM is capable of:

• automatically classifying incoming requests without operator involvement
• proposing solutions based on similar cases from history
• routing requests to the right specialist accounting for SLA
• escalating atypical situations with full context for human review

A significant portion of incoming requests in service organizations are repetitive in nature — these become the first candidates for automation when proper labeled history exists in ESM. The result is reduced operational load on service teams and faster processing time for standard requests.

What this means

Corporate AI projects fail not due to poor technology — but due to lack of suitable data. ESM platforms solve this problem structurally: they create a single point of collection for service interactions and ensure data quality on which models work predictably. Before launching the next AI pilot, a company should answer one question: is there already a system where the complete history of all service requests with attributes is stored?