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AI Smart Operations

AI in tSM helps business users get answers, spot risks, and act faster—directly in their daily views (listings, tickets, customers, dashboards).
No query syntax, no data hunting, just clear guidance and proactive insights.

Context-Aware Chatbot (in-app)​

ℹ️ Rollout alongside other AI features; modules integrate gradually

The chatbot is embedded directly inside the tSM application and understands the context of the screen you are working on:

  • When looking at a ticket, you can ask:
    • “Why is this ticket escalated?”
    • “Summarize this ticket in 3 sentences.”
    • “What should I do next?”
  • On a customer page, you can ask:
    • “What’s the full history of this customer in the last 6 months?”
    • “Summarize their active contracts and open issues.”
  • In a listing view, you can ask:
    • “Show me all urgent orders created last week.”

The assistant combines application data with documentation and project-specific knowledge, giving users immediate, contextual answers and recommended actions.
It also allows technicians to control and update tickets directly through chat, turning conversation into action.

Natural-Language Filtering & Search​

ℹ️ Pilots starting early; deeper integration in later releases

Filtering in Listings often requires complex conditions. AI allows users to type natural sentences instead of building query logic:

  • “Tickets opened in the last 14 days by VIP customers in Germany.”
  • “Orders with value greater than €10,000 pending approval.”
  • “All unresolved incidents escalated more than once.”

The system translates these sentences into structured filters and applies them instantly.
Users can refine queries conversationally (“Only show those assigned to Team A”) without resetting filters.
This makes complex filtering accessible to everyone and will be piloted across selected use cases first.

Dashboards & Intelligent Reporting​

ℹ️ Templates available in V2.4; conversational reporting in V2.5

Dashboards evolve from static visualizations into interactive analysis tools:

  • Ask in plain language – “Show average ticket resolution time by team for Q2.”
  • Adaptive dashboards – highlight anomalies (e.g., sudden SLA breaches) without configuration.
  • Explained insights – “Resolution times increased 20% last week due to more escalations in Support Team B.”
  • Templates – ready-made packs (SLA compliance, team utilization, customer churn, KPIs).
  • Conversational refinement – drill down by asking, “Split by product line” or “Show only enterprise customers.”

This makes reporting faster to build, easier to understand, and directly actionable.

AI Assistant for Customer & Ticket Work​

ℹ️ Available with chatbot integration; extended gradually per module

AI reduces manual effort in customer and service interactions:

  • Prepared Answers – when a customer asks a common question, the assistant drafts a reply, grounded in your knowledge base and past tickets.
  • Summaries – tickets or customer histories condensed into key facts.
  • Guidance – “Escalate, schedule follow-up, or notify another department.”
  • Contextual Q&A – “Has this customer had similar issues before?”
  • Draft text generation – replies and notes prepared automatically from the ticket context and similar historic cases.

This leads to faster responses, more consistent communication, and improved customer experience.

Predictive Insights & ML Signals​

ℹ️ Future roadmap; delivered incrementally with pilots

AI is not only reactive but also predictive, surfacing risks and opportunities before they become problems:

  • Churn Prediction – highlight customers with high likelihood of leaving so account managers can act early.
  • Related Tickets & Recommendations – when viewing one issue, the system suggests similar cases or proven solutions.
  • Risk Scoring – tickets or orders flagged as likely to fail, delay, or escalate.
  • Pre-calculated Attributes – AI adds searchable fields like ChurnRisk=High.
  • Warnings & Alerts – surfaced directly on dashboards or listings.

Pilot projects will explore complex, text-based queries (e.g. combining operational and customer data) to see what can be served directly in tSM and what is better suited for BI tools.

Application support & Automation​

ℹ️ Future pilots and gradual rollout

AI will also help support teams by automating technical diagnostics and responses:

  • Automated process problem evaluation – detect failed or stuck processes, recommend or perform corrective actions such as automatic restart.
  • Event & log correlation – scan logs and events for anomalies, group related signals, and highlight probable root causes.
  • Guided resolution – suggest proven recovery steps or link to similar historic cases.

This reduces the time L2 teams spend digging through logs or manually restarting flows, allowing them to focus on solving root issues and ensuring higher uptime.

Vision​

AI Smart Operations in tSM are designed for business users first.
They don’t need to understand query languages, reporting frameworks, or machine learning. Instead, they:

  • Ask questions in plain language.
  • Get actionable insights, summaries, and prepared responses instantly.
  • Control tickets directly through chat.
  • See predictions, recommendations, and warnings without hunting for data.
  • Rely on AI-driven support that monitors processes, evaluates logs, and suggests resolutions automatically.

The outcome: every user has an AI co-pilot that improves decision-making, boosts productivity, and delivers better business outcomes.