Experts predict artificial intelligence augmentation will create $2.9 trillion in business value globally in 2021.
One way artificial intelligence (AI) can create value for your business is via your employee support system. AI ticket deflection, which enables a virtual assistant bot to respond to frequent and repetitive requests, improves your employee experience and cuts costs.
Calculating Your Ticket Deflection Ratio
Does your help desk need support? Chances are, it could benefit from using AI ticket deflection as a first line of support, leaving human analysts to solve more complex problems. To understand the opportunity and demand for AI deflection, let’s look at a metric you want to track: ticket deflection ratio (or self-service score).
How to calculate ticket deflection ratio: the total number of users being supported divided by the total number of users who submit tickets. You’ll get a ratio like 5:1, which means that for every five employees who try to resolve their own issues using self-service, one submits a support request.
The higher the ratio, the higher number of people who found solutions through self-service.
Using AI for Ticket Deflection
Artificial intelligence (AI) and machine learning can help your business encourage self-support in several ways.
Identify Knowledge Gaps
If you want employees to use your self-service portal, you need to have a knowledge base that answers their questions accurately. Finding answers should only require minimal effort on employees’ part.
Your knowledge base should be:
- As comprehensive as possible
- Accurate with expert verification
- Up-to-date
- Use appropriate rich content, like rich-text, images, videos or audio
- Universally searchable
AI can help you identify knowledge gaps, so that you can improve the quality of your knowledge base. AI uses algorithms that can identify trends in employees’ searches. Natural language processing can find questions that appear frequently in search query data. By curating search data, you can see categories emerge, topic popularity and topics that aren’t covered in the knowledge base.
Recommend Relevant Content
AI can provide suggestions of relevant content based on an employee’s intent and context. If an employee starts a chat or creates a support ticket, AI is working in the background. The AI virtual assistant bot uses deep learning and natural language processing to scan the employee’s text. It replies to the employee with suggestions of help center articles that they can use to resolve the issue themselves, often surfacing useful content that the employee might not have seen yet.
Benefits of AI Ticket Deflection
Using AI to increase ticket deflection improves IT support and employee experience. It also lowers your support costs and gives you better support data.
Better IT and Employee Experience
Employees often prefer self-service support, because they can resolve problems faster. They don’t need to contact an agent and wait in the queue to get an answer to their question.
Operational Efficiency
AI and machine learning let you automate many high-frequency, low-touch interactions. Your support agents have more time to handle complicated issues.
Every support contact costs money and resources, whether to staff the phone line, assist over chat or reply to email. Ticket deflection means that you are reallocating resources to create greater efficiency in IT and speeding response times to employees.
Improved Data Collection Process
Getting accurate data on self-service support operations can be difficult. How do you count the tickets that didn’t happen? AI gives you one way to get concrete data.
When an employee starts a chat or sends an email requesting help, AI sends an automated response. The suggested resources should help the employee resolve the issue themselves.
If the employee successfully finds a solution, an agent doesn’t need to follow up. If the employee needs further assistance, a help desk analyst will contact them to resolve the issue and close the ticket. You can clearly see when an interaction with AI prevented the need for a ticket. This data can help you understand what information should be highlighted or more visible in the knowledge base.
Start Increasing Case Deflection With AI
Using AI can encourage self-service support and ticket deflection, which improves employees’ experience and speeds efficiency. Choosing the right self-service software helps you maximize the benefits you get from ticket deflection.
If your business uses Office 365, Tikit provides a great way to use AI deflection to delight IT analysts and employees. Tikit integrates seamlessly with Microsoft Teams, providing a native help desk experience in Teams. Schedule a demo today or sign up for a free trial. You’ll see the difference the right ticketing solution can make.
That AI is changing how we work across nearly every industry is no secret. Gartner projects that global AI spending in 2026 will leap 44% year-over-year. That’s a staggering number, but for IT leaders, the more interesting question is where the return actually shows up in their organizations’ IT services.
One of the clearest, most measurable payoffs is happening right at the service desk. AI-powered ticket deflection has matured well beyond the keyword-matching bots of a few years ago. Today, large language models (LLMs) and generative AI can understand employee intent, synthesize answers from internal documentation, and resolve requests before a human analyst ever gets involved. The result is faster support, less noise in the ticket queue, and a better experience on both sides of the conversation.
What Is Ticket Deflection — and How Do You Measure It?
Ticket deflection is exactly what it sounds like: support requests that get resolved through self-service, without requiring an agent to step in. The metric to track is your ticket deflection ratio — total users supported divided by total users who submit tickets. A 5:1 ratio means five employees found answers on their own for every one who opened a ticket.
The higher the ratio, the more your self-service layer is working. AI raises that ceiling considerably — but only when it’s implemented thoughtfully.
How AI Has Changed the Deflection Equation
Early virtual agents were brittle. If an employee phrased a question slightly differently than expected, the bot returned irrelevant results or nothing at all. That failure mode eroded trust in self-service — and drove users straight to the phone or chat queue.
Modern AI doesn’t work that way. LLMs understand natural language: intent, context, and nuance. An employee asking “my laptop won’t connect to the VPN” and another asking “I can’t access the network remotely” are asking the same question, and today’s AI recognizes that. The ability to match intent rather than exact phrases is what makes deflection actually stick.
There’s also been a meaningful shift in how AI responds. Rather than returning a list of links and hoping the employee clicks the right one, generative AI synthesizes a direct answer — specific, step-by-step, grounded in your organization’s own documentation. That difference in experience is what drives self-service adoption. Employees use tools that actually help them.
Three Ways AI Drives Higher Deflection Rates
1. Surface Knowledge Gaps Before They Become Tickets
Your knowledge base is only as useful as its coverage. If employees can’t find an accurate answer, they’ll skip self-service entirely. AI changes how teams maintain that coverage.
By analyzing support interactions — what employees ask, how the virtual agent responds, and whether the issue escalates to a human — AI can identify topics that consistently fall through the cracks. Categories without adequate coverage, articles that underperform, questions that generate tickets despite appearing in search results. This kind of feedback loop turns your knowledge base into a living system rather than a static document library.
A strong knowledge base should be:
- Comprehensive, with articles covering high-volume request categories
- Verified by subject matter experts and kept current
- Enriched with clear formatting, steps, and relevant media
- Universally searchable across the tools employees already use
2. Deliver Answers, Not Just Links
One of the most meaningful advances in AI deflection is the shift from retrieval to generation. Traditional systems matched a keyword to an article and surfaced the result. Generative AI reads the context of the employee’s request and composes a direct response based on relevant documentation — no article-hunting required.
This approach, often powered by retrieval-augmented generation (RAG), keeps responses grounded in your actual organizational knowledge. The AI draws from internal sources rather than generic training data, which means answers are accurate, company-specific, and far more useful.
3. Understand Context, Not Just Keywords
Conversational context matters. An employee who says “I already tried restarting it” in a support chat is signaling something important. A well-implemented AI picks up on that and adjusts — skipping the standard first-tier troubleshooting steps and moving to the next resolution path.
This kind of contextual reasoning reduces the back-and-forth that makes self-service feel like work. When the virtual agent tracks the conversation and responds intelligently, employees are more likely to see it through to a resolution rather than abandoning the thread and escalating.
The Business Case for AI Ticket Deflection
The operational efficiency argument is straightforward — fewer tickets means less analyst time on repetitive, low-complexity requests. But the case for AI deflection goes further than headcount math.
Employee experience. People expect fast answers. A support system that makes them wait in queue for a password reset or a software license request creates friction that compounds over time. Self-service done well feels like a feature, not a workaround.
Analyst capacity. When repetitive volume is deflected, IT teams have genuine room to focus on complex issues, proactive problem management, and strategic projects — work that creates more organizational value than Tier 1 troubleshooting.
Measurable data. One of the historic challenges with self-service is tracking outcomes. AI creates a clean interaction log: what was asked, what was returned, whether the employee resolved the issue or escalated. That data becomes a feedback mechanism — informing knowledge base improvements and demonstrating ROI to leadership.
AI Ticket Deflection with Tikit + Microsoft Teams
For organizations already running Microsoft 365, Tikit provides a practical, low-friction path to AI-powered ticket deflection — without rebuilding your support infrastructure.
Tikit is built natively for Microsoft Teams, which means employees interact with the Virtual Agent in the same environment where they already work. There’s no separate portal to navigate, no new login to remember. Support feels like a natural extension of the Teams experience — and that familiarity drives adoption.
Tikit’s Virtual Agent handles deflection in two layers. First, it draws from your configured knowledge base and templates — articles and responses your team has explicitly curated. When a match is found, those responses take priority. When no match exists, Tikit’s integration with OpenAI on Azure fills the gap with generative AI responses built from documentation you upload directly to your Azure environment.
Because the OpenAI integration deploys within your own tenant — using Azure OpenAI, Azure AI Search, and Azure Blob Storage — your data never leaves your organization. That’s a meaningful distinction for teams with compliance or data residency requirements. And with support for scheduled retraining, the AI stays current as your documentation evolves — new policies, updated processes, software changes — without requiring manual intervention every time something shifts.
Tikit also supports multi-department ticketing, meaning IT, HR, Facilities, Finance, and other teams can all benefit from the same deflection and self-service infrastructure. One platform, consistent experience, across the organization.
You can explore the full range of Tikit’s features to see how deflection fits into a broader ITSM strategy — from automated routing to SLA tracking to Power BI reporting.
Making Self-Service Actually Work
AI deflection isn’t a switch you flip — it’s a capability you build. The organizations that see the highest deflection ratios are the ones that treat the knowledge base as an ongoing investment, review interaction data regularly, and tune their virtual agent based on what’s happening in conversations.
The good news is that the tools available today make that process far more manageable than it was even two or three years ago. Generative AI surfaces gaps automatically. Retraining can be scheduled rather than manual. And with a platform like Tikit, that infrastructure lives inside the Microsoft environment your team is already using every day.
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