How to Deploy AI: Why 60% of Projects Never Reach Production

According to Gartner, 60% of business AI projects never reach production. The cause isn't money but data and business processes that aren't ready.

Why AI Pilots Fail

The typical picture: the board demands an AI transformation, a budget is allocated, contractors are brought in. A few months later it turns out the models simply have nothing to learn from. Tickets live in disconnected systems, there is no classification, and the request history is scattered across Excel spreadsheets, messengers and email threads.

Without structured data, even the most advanced language model doesn't deliver predictable results. The mistake isn't made when choosing a model or vendor. It is made before the pilot even starts, at the level of data architecture.

• 60% of AI projects don't reach production (Gartner data)
• The main cause is unprepared data, not a shortage of technology
• The problem recurs regardless of budget and team maturity
• Most companies launch a pilot without a single system for collecting service data

What ESM Is and Why AI Needs It

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

When all requests pass through one system with fixed fields and SLAs, the model gets what it was missing: a clean, labeled dataset with historical context. Instead of chaos across disconnected channels, there is a structured base on which training and inference behave predictably.

"If AI doesn't have quality data, the project is most likely doomed."

This conclusion recurs in most analytical reports on failures of enterprise AI initiatives.

That is why deploying ESM is called the "foundation" for later AI automation: without it, any pilot is building on sand.

How a Model Eliminates Routine Work

Once the ESM foundation is in place, automating routine work turns from an experiment into a manageable task. The first candidates are repetitive requests with a predictable resolution: password resets, standard access requests, routine purchases, template HR certificates.

A model trained on the structured request history from ESM can:

• automatically classify incoming requests without an operator
• suggest a solution based on similar cases from the history
• route a request to the right specialist while respecting SLAs
• escalate atypical situations with full context for a human

A significant share of incoming requests in service organizations is repetitive, and these become the first candidates for automation once properly labeled history exists in ESM. The result is lower operational load on service teams and shorter handling times for routine requests.

What This Means

Enterprise AI projects fail not because of bad technology but because of a lack of usable data. ESM platforms address this structurally: they create a single point for collecting service interactions and ensure the data quality on which models work predictably. Before launching the next AI pilot, a company should answer one question: is there already a system that stores the complete history of all service requests with their attributes?

Why Do AI Projects Fail to Get Deployed in Companies?

According to Gartner, the main reason is not a lack of budget but unstructured data. Tickets are stored in disconnected systems, there is no classification, and the request history is scattered across Excel spreadsheets, messengers and email threads. Without structured data, a neural network doesn't deliver predictable results.