ManageEngine, a division of Zoho Corporation, announced a major GenAI release in the cloud version of its unified service management platform, ServiceDesk Plus, to transform service experiences and foster greater productivity levels for end users, technicians, and process owners.
ManageEngine's AI strategy for ServiceDesk Plus revolves around providing customers with the flexibility to choose the AI providers of their choice for various AI capabilities. Customers can leverage Zia LLM without any additional pay-per-usage costs or choose between public AI providers like ChatGPT and Azure OpenAI. The company believes that this approach helps customers leverage the best model suitable for various AI functionalities while also optimizing the costs involved.
"As AI initiatives across enterprises accelerate, it is important to ensure that they seamlessly integrate into the enterprise IT ecosystem and learn from and adapt to workflows to harness the true potential of these initiatives," said Umasankar Narayanasamy, VP at ManageEngine. "With the Ask Zia virtual agent and the other embedded AI-enabled capabilities available now in ServiceDesk Plus, IT and enterprise service management teams can leverage them at no additional cost across their high-value employee and customer success use cases. At ManageEngine, we will continue to invest in and build our proprietary, end-to-end AI technology stack to help customers achieve a rapid time-to-value on their AI initiatives while ensuring the highest standards of data security and privacy."
AI-powered ITSM for ServiceDesk Plus: Upgraded Virtual Agent, GenAI-powered Workflow and Script Generation, and More
ManageEngine's customers benefit from a breadth and depth of AI-driven ITSM capabilities across editions, without any additional licensing complexity. The latest update to the cloud version of ServiceDesk Plus includes a host of AI features, including:
- Ask Zia: Now sporting an LLM-style interface with multi-modal support, the GenAI-powered Ask Zia virtual agent empowers end users, technicians, and process owners to accomplish their goals conversationally. It delivers answers instantly, searches across the service desk, extracts and summarizes KB articles, and performs ticketing actions—functioning as the primary touchpoint for every persona.
- Ask Zia Workflow Assist: Acting as a personal workflow expert, it helps process owners move from theoretical blueprints to full-fledged workflows in seconds. Workflow Assist understands descriptive requirements and images and generates visual workflows with suggestions for automations, condition checks, and fixing disconnected nodes.
- Other GenAI enhancements: Leverage the ability to generate resolutions from ticket conversations, notes, and past tickets; create checklists; generate custom JavaScript snippets for template customizations; and recommend solutions when users create or edit tickets.
The Latest
AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...
Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...
Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...
When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...
In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...
Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...
AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...
Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...
For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...
Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...