Generative AI in IT Asset Management: How It Works and Where It’s Used Today

An IT Asset Management (ITAM) system has never lacked data. You can track almost everything from your IT ecosystem: what you own, where it is, who has it, what it costs, and the whole asset lifecycle (request, approve, procure, deploy, repair, retire). What’s often missing is time: time to read through everything, connect the dots, and explain what actually matters to the business.

That’s the role of Generative AI.

Generative AI in IT asset management focuses on understanding and communication. It helps teams summarize complex information, answer asset-related questions, and turn scattered data into clear, decision-ready insights.

In this article, we look at:

• what Generative AI means in the context of IT Asset Management,
• how it typically works on top of existing ITAM data,
• and how well-known IT asset management platforms are shipping it today.

Generative AI in ITAM

Generative AI (GenAI) in IT Asset Management (ITAM) typically refers to AI systems that understand, summarize, and generate information from existing sources to help people understand and communicate about assets more quickly. The existing sources could be: current IT asset data, tickets, and knowledge bases. It works by combining a language model with asset management-specific context.

Typical process of GenAI in IT asset management

At a high level, the process looks like this:

1. Start from a strong ITAM data foundation

GenAI is most useful when it can “see” the same data your asset team uses every day. That usually includes:

• asset inventory and asset lifecycle data
• request/case history and work notes
• CMDB relationships
• software entitlements vs deployments (for SAM/compliance)
• knowledge base content (policies, procedures, licensing terms)

2. A user asks for insight in plain language

GenAI gets triggered when someone asks something like:

• “Summarize this case/chat/call so I can take over.”
• “What laptops are underutilized?”
• “Summarize this licensing agreement.”
• “What’s our compliance position, and what should we focus on?”
• “Draft a policy/knowledge article from what we already know.”

This is an important shift: users are asking it to explain what matters, not display the data. Traditional ITAM tools are very good at showing data like asset lists, tables, and dashboards, etc. But with generative AI, the question shifts from “Show me all the data” to “Tell me what I should care about, and why”.

3. GenAI interprets intent

The AI analyzes the intent of the request, determining:

• the user’s goal (request, explanation, summary, insight)
• the user’s role (employee, asset manager, finance, IT agent)
• the scope (one asset, a request, a product, the entire estate)
• the relevant timeframe (current issue vs. historical trend)

4. GenAI synthesizes data into insights

Generative AI reads across multiple data sources and produces:

• concise summaries of long conversations or records
• synthesized explanations that combine data, policy, and context
• highlighted risks, issues, and optimization opportunities
• clear framing of what matters most right now

GenAI prioritizes (what’s most important), summarizes (what changed, what’s new, what’s risky), adds context (based on policies, history, and patterns), and frames next steps **for further actions.**

ITAM Generative AI in Action

To understand how Generative AI (GenAI) is applied in IT Asset Management today, let’s look at two concrete examples from widely used platforms: ServiceNow,** Jira Service Management**, and Freshservice.

Generative AI through Now Assist by ServiceNow

ServiceNow positions Generative AI (Now Assist) as a way to improve productivity through summarization, conversational assistance, and content generation.

In ServiceNow, asset records, tickets, ServiceNow CMDB data, knowledge articles, policies, and workflows already live in the same system (the Now Platform). Because of this, GenAI in Now Assist does not “look things up elsewhere.” Instead, it:

• reads live asset records
• reads case and request history
• reads knowledge and policy content
• understands who the user is and what they’re asking

This allows the AI to generate responses that are contextual and grounded in real ITAM data.

Key Capabilities of GenAI Now Assist

• Chat summarization: Helps agents quickly understand what has already been discussed in a conversation, so users don’t have to repeat themselves when a request is handed over from Virtual Agent to a human.
• Case summarization: Automatically reads through the full case history and creates a short, clear summary. This makes handoffs between teams easier and saves time when closing or escalating asset-related cases.

Now Assist by ServiceNow summarizes an incident ticket

• Post-call summarization: Turns call transcripts into usable notes as soon as a call ends. Agents spend less time writing follow-ups and more time helping the next user.
• Knowledge article generation: Drafts knowledge base content directly from resolved cases and work notes, making it easier to capture and share asset-related knowledge across the organization.

Generative AI through Atlassian Intelligence

In Atlassian’s ecosystem, Generative AI is delivered through Atlassian Intelligence, with asset-related work primarily happening in Jira Service Management and Atlassian CMDB (JSM Assets).

Rather than executing asset lifecycle actions directly, Atlassian Intelligence focuses on improving how asset work is requested, documented, and handled.

Key capabilities of Atlassian Intelligence in the context of IT asset management

• Virtual agent & AI answers: Uses Generative AI to read knowledge base content, asset integrations, and related context to generate natural-language answers to asset questions such as: “What laptop is assigned to me?” or “How do I request a new monitor?”
• Suggesting request types and fields for asset request forms: From a plain-language description of IT asset management tasks, AI suggests request types and relevant fields (such as asset picker, location, user, or department), helping teams design better intake forms.
• Generating automation rules from user description: Asset managers can describe flows like relocation, onboarding, offboarding, or warranty tracking in plain language, and AI generates draft automation rules that interact with Assets.

Atlassian Intelligence generates automation rules from prompt text

• Summarizing / generating / polishing content: Classic generative AI capabilities such as summarizing tickets, drafting user replies, rewriting internal notes, translating content, or adjusting tone.
• Generating KB articles from existing info: Uses existing tickets, notes, and policies to synthesize structured help articles, supporting self-service and consistent asset handling.

Generative AI in Freshservice (Freddy AI)

Another practical example of Generative AI in ITAM can be found in Freshservice, where GenAI is delivered through Freddy AI.

In the Freshservice CMDB environment, Freddy AI is used to:

• answer front-end asset questions in self-service portals,
• summarize incidents and service requests linked to assets,
• suggest responses for agents handling hardware and software requests,
• assist with knowledge article creation and improvement.

Freddy AI by Freshservice summarizes asset-related tickets and suggests resolutions

Here, GenAI plays a similar role to the previous examples: reducing the effort spent on reading, writing, and explaining asset information, while leaving execution and approvals to existing workflows.

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Generative AI in AssetLoom Roadmap

As generative AI becomes a natural part of modern IT asset management, AssetLoom plans to move in the same direction and evolve alongside the existing IT asset operation system.

Planned areas include:

• Natural language asset queries, so users can ask questions in plain language and get clear answers from asset data.
• Demand and cost forecasting, using GenAI to explore “what-if” scenarios for budgeting and planning.
• AI-generated summaries, to help teams quickly understand asset status, risks, and trends without manual reporting.

If this is a space you’re actively exploring, we’re always open to conversations - exchange ideas, get early feedback, or join the AssetLoom waitlist as we shape what comes next.