How AI is Transforming Managed Services

Not long ago, talking about artificial intelligence in managed services environments sounded like science fiction. A distant concept, reserved for tech giants. But today, things have changed… a lot.

In my daily work with clients, I see how AI is becoming a real, useful, and accessible tool that helps teams work better, reduce repetitive tasks, and offer a more agile and personalized service.

And no, it’s not about replacing people. It’s about allowing people to focus on adding value while AI takes care of the mechanical tasks.

In this article, I want to share how we are applying AI in service management, what tools we use, and most importantly, a real success story that can help you identify opportunities in your own organization.

Why Bet on AI in Managed Services?

If you have ever been in charge of a managed service (or are managing one now), you will know that there are tasks that are repeated every day:

• Tickets that arrive misclassified or without context.
• Frequent queries that take up valuable team time.
• Processes that could be automated but no one has reviewed.
• Reports that are generated manually with scattered data.

→ This is where AI starts to make a difference: not because it is futuristic, but because it solves what we already do poorly or reluctantly.

Use Cases That Are Already Working

1. Intelligent Ticket Classification

With generative AI (like Azure OpenAI or Copilot Studio), we can now:

• Read and understand the content of the ticket.
• Automatically detect the category and urgency.
• Assign it to the appropriate team.

→ This reduces time, errors, and improves the experience from the first contact.

2. Copilots for Technicians

Thanks to Microsoft 365 Copilot, technicians can:

• Consult suggested responses based on internal documentation.
• Summarize long conversations to quickly gain context.
• Get recommendations on next steps.

→ Less time searching, more time solving.

3. Proactive Incident Detection

With Power BI and Sentinel, we can:

• Identify patterns before they become problems.
• Launch automatic alerts.
• Propose preventive actions.

→ Pasamos del clásico “apagar fuegos” a anticipar lo que puede fallar.

Real Case: Smarter Internal Support in a Retail Company

A retail company with more than 2.000 employees asked us for help to improve their internal IT support. They received more than 600 tickets a month, many on the same topics:

• Account lockouts.
• Problems with Teams.
• Poorly managed access.

The team was overwhelmed, users were frustrated, and the entire model was reactive and manual.

What Did We Do?

1. Analyzed History and Processes

We reviewed a history of tickets to identify:

• Repetitive typologies.
• Bottlenecks.
• Possible automations.

→ We found that 42% of incidents were repetitive and perfectly automatable.

2. Automated Classification with AI

We used Azure OpenAI so that the system:

• Read the ticket (email, form, or Teams).
• Identified the category and urgency.
• Automatically assigned it to the correct team.

→ Result: fewer human errors and classification in seconds.

3. Created a Bot in Microsoft Teams

A virtual assistant in Teams capable of responding in natural language to typical questions like:

• “How do I unlock my account?”
• “Where do I change the password?”
• “Why can’t I see my network drive?”

→ The bot resolves almost 30% of requests without human intervention. And the best part: it is available 24/7.

4. Automated Repetitive Tasks

With Power Automate, we created flows that:

• Detect standard requests (example., account unlock).
• Automatically validate the user.
• Execute the action (in Azure AD or Microsoft 365).
• Record the result in the system.

→ This reduced the resolution time of many tasks from minutes to seconds.