Aviator Takes Flight: How OpenText is Powering the Future of Enterprise Service Management with Generative AI
In the rapidly evolving world of enterprise IT, generative AI is no longer a futuristic concept — it’s a transformative force reshaping how organizations deliver services, manage knowledge, and support their workforce. During the second day of the recent OpenText sessions, one theme stood out above the rest: Aviator, OpenText’s generative AI platform designed specifically for enterprise service management (ESM).
More than just another AI assistant, Aviator is a secure, context-aware, and deeply integrated solution that empowers teams to automate workflows, enhance knowledge creation, and deliver more personalized support experiences — all while maintaining enterprise-grade security and compliance.
The Principles Behind Aviator
At its core, Aviator is built around three guiding principles that define how AI should operate within an enterprise context:
Privacy and Security – In an era where data sovereignty and security are paramount, Aviator sets a new standard. Its private large language model (LLM) is hosted and operated entirely within OpenText’s SaaS environment, meaning your data stays yours. Importantly, the LLM is never trained on customer data — instead, it only accesses indexed information from your own Service Management (SM) tenant, with encryption at rest and in transit, and strict entitlement-based access controls.
Enterprise Intelligence – Aviator isn’t just intelligent — it’s contextually intelligent. Rather than relying on generic internet knowledge, it uses your organization’s data to deliver near real-time, relevant, and accurate answers tailored to your environment. This ensures that recommendations, ticket summaries, and automation actions are grounded in your actual business context.
Use Case–Driven Design – AI must solve real problems, not just answer questions. Aviator is built to drive measurable productivity gains, streamline agent workflows, and improve end-user experiences. Its capabilities are designed around real-world use cases, from ticket management and sentiment analysis to change risk scoring and guided automation.
Retrieval-Augmented Generation: The Engine Behind Aviator
Aviator leverages a powerful AI technique known as Retrieval-Augmented Generation (RAG) — a two-step process that elevates the quality and reliability of AI responses:
Retrieval – The system first gathers relevant information from your organization’s curated knowledge bases and data sources.
Generation – It then synthesizes that information into precise, context-aware responses.
The result is improved accuracy, higher relevance, and an AI experience you can actually trust — one that understands not just language, but the meaning behind your data.
Real-World Use Cases: AI at Work
Aviator isn’t just a theoretical concept — it’s already transforming how service teams operate. Here’s how:
Empowering Self-Service – Employees can resolve common issues on their own, from “how-to” queries to HR policy questions, without ever opening a ticket.
Boosting Agent Productivity – Agents can automatically summarize tickets, access all relevant knowledge in one place, and close incidents faster.
Sentiment Analysis & Prioritization – Aviator detects tone and sentiment shifts in user interactions, allowing teams to prioritize tickets more effectively and deliver personalized service.
Change Risk Analysis – Based on factors like downtime, urgency, and impact, Aviator generates dynamic risk scores and triggers additional workflows, such as approvals or backup planning.
Knowledge Creation & Enhancement – AI-powered digital workers draft knowledge articles, generate metadata for better searchability, and even review existing documentation for clarity and completeness.
AI-Enriched Workflows: Automation with Intelligence
Perhaps Aviator’s most powerful feature is its ability to seamlessly integrate into workflows. It can be triggered as part of standard processes — not just by direct user interaction — and automatically update records, initiate actions, or inform decisions.
For example:
A new ticket involving sensitive personal data can trigger an access control review.
A high-risk change request can automatically notify stakeholders or adjust backup plans.
Sentiment analysis on service requests can influence SLA prioritization — without human intervention.
This fusion of automation and intelligence helps organizations move from reactive ticket handling to proactive service delivery.
Building a Smarter Knowledge Ecosystem
The success of any AI initiative depends on the quality of enterprise knowledge behind it. Aviator not only leverages existing knowledge but actively helps improve it. With tools like Hot Topic Analytics, teams can identify trends, spot recurring issues, and continuously refine their support documentation.
AI-generated drafts give subject matter experts a strong starting point, reducing the time spent writing from scratch while ensuring content remains relevant and business-aligned.
Final Thoughts: A New Era for Service Management
Aviator is more than an AI tool — it’s a bridge between knowledge, automation, and human expertise. By combining robust security, contextual intelligence, and powerful automation, OpenText has created a solution that not only enhances today’s workflows but also paves the way for the future of service management.
In a landscape where efficiency, agility, and personalization define success, Aviator stands out as a transformative force — helping enterprises work smarter, resolve issues faster, and deliver better experiences across the board.