How to Advance Your Help Desk AI Maturity

Feeling behind on AI adoption in employee or customer support? You’re actually right where most teams are. According to a recent Deskpro survey of over 220 support and IT leaders, only 37% of organizations say they have deployed some level of AI across multiple support functions, while 52% are still in the initial planning or pilot stages (and 11% aren’t using AI in support at all).

However, just because you’re in the same place as most of your peers doesn’t mean you should stay there. When deployed thoughtfully, AI has the potential to dramatically increase agent efficiency, improve self-service adoption, and reduce costs. By gradually advancing your AI maturity–from initial planning to small pilots to fully deployed solutions–you can realize those benefits while also maintaining data security, compliance, and trust.

What Should You Do When First Getting Started With AI?

Define What You Want To Achieve

Avoid adopting AI for AI’s sake: start with a key outcome you want to achieve and decide what success looks like. Your exact KPIs will depend on your use case and organizational goals, but some common examples we see in the help desk space include the percentage of tickets deflected using a chatbot, reduction in time to assign a ticket, and reductions in first response and overall resolution time.

Get Security and Compliance Buy-In

Your AI project won’t get far without buy-in from your security and compliance teams. In Deskpro’s survey, 81% of respondents rated security as either “very important” or “critical” when evaluating support technology, and 43% said they would not proceed with a purchase without strong security guarantees.

Bring your security and compliance teams into the evaluation process to ensure the solutions you’re vetting meet all your organization’s security requirements and any relevant data privacy regulations. Discuss where customer data will be processed, how it’s stored, who can access it, and what happens if you need to revoke access. Document agreements on acceptable use cases, data handling procedures, and audit capabilities.

Map Your Help Desk Data

Inventory your data: help center articles, ticket histories, internal runbooks, customer profiles, conversation transcripts, resolution documentation, and so on. Identify data sensitivity levels, including what’s public, what’s internal-only, and what contains customer PII, to determine what AI can safely index.

Plan for Regional Requirements and Industry Regulations

Map where your customers are located and what regulations apply to their data. If you support EU customers, you need to comply with GDPR. If you handle US healthcare inquiries, you need HIPAA controls.

Regional and industry regulations may impact how you deploy your help desk AI. If you’re in a highly regulated industry, you may be prohibited from using foundation AI model services operated in the public cloud, meaning you’ll need to look for help desk AI solutions that can be deployed in a virtual private cloud (or on-premise, if your organization operates its own data centers). If you’re in a region with strict data sovereignty requirements, you’ll need to look for a solution that can be deployed in the sovereign cloud for your region.

Document Roles and Responsibilities

Identify who will be responsible for what during the pilot phase, including who will manage the program, monitor for compliance, evaluate data quality, and review escalated incidents.

What Should You Do While Piloting AI for Support?

Index Your Public Content

Your AI capabilities can only go so far if you don’t connect content from your organization, such as help center articles, guides, and manuals. Without these data sources, you’ll get generic outputs with limited usefulness for your customers or employees.

Start by indexing public-facing support content. Because this content doesn’t contain proprietary information or sensitive data, it gives you a low-risk way to start testing an AI chatbot or retrieval-augmented generation (RAG).

Add Internal Support Use Cases

Launch your first AI pilot with internal support teams, such as HR support, or facilities management. These environments let you test AI assistance with lower compliance risk. Agents can search internal knowledge bases, get suggested responses for common IT/HR issues, and automate ticket routing. Once internal teams prove the value of AI, expand to customer-facing support.

Define Your Exit Criteria

Decide what thresholds your AI solution needs to hit to make a go/no-go decision on moving to production. You may set criteria related to both model accuracy and the KPIs you identified during the planning phase.

Educate Your Support Team

Help support agents understand what AI does in your help desk environment. Show them how AI suggestions work, when to use them, and when to rely on their own judgment. Address job security concerns directly by outlining how AI handles repetitive questions, so agents can deal with complex issues that require human empathy and expertise.

Forecast the Budget

Moving from pilot to production typically means increased costs due to the additional compute resources, licenses, and ongoing monitoring and maintenance. Work with your IT and finance teams to understand the additional costs and forecast the budget so there are no last-minute surprises.

What Should You Do After Deploying Your First AI Solution?

Scale Beyond Your Initial Use Case

If you started with a narrow scope during your pilot, this is your chance to expand to different teams, use cases, or AI feature sets. Keep in mind you don’t need to take an everything-all-at-once approach. You might gradually roll your AI solution out to different support teams or wait until you see success with one objective (e.g., using help desk AI to improve time to assign a ticket) before introducing new features to pursue another objective.

Index Additional Data Sources

Identify additional data sources, such as ticket histories, knowledge bases, and website content, that you can securely connect to your AI to improve your outputs. Work with your security team to determine what you can safely connect based on the data sensitivity level, industry regulations, and your AI deployment type. For example, if you’re in a highly regulated industry, you might be able to connect customer data sources to an AI model deployed in a virtual private cloud but not the public cloud.

Continue Monitoring and Measuring KPIs

Continue tracking the KPIs you set during the planning phase–and introduce others as needed to support new objectives as you scale. Set up dashboards to track progress and quantify your ROI. This will help you make the case for ongoing investment in your AI initiative.

Establish a Content Feedback Loop

Review tickets or chatbot transcripts where your AI solution couldn’t resolve a customer issue and escalated it to a human agent. Use this information to prioritize the content you need to create and use to tune the model so that it can successfully resolve these issues the next time they come up.

Continue Working With Security and Compliance Teams

Assign a named owner or small working group responsible for AI governance within the organization. This person or group should meet with stakeholders from your security, legal, compliance, and IT teams at regular intervals to review model changes, new data sources, expanded use cases, and any policy changes.

Taking a Thoughtful Approach to AI

AI adoption in support operations isn’t about being the first to implement the latest solutions. The organizations finding success are the ones taking a purposeful approach: they start with a clear use case and objective, work with compliance and security teams to avoid project shutdowns, start small with a pilot, prove the value, and scale gradually. They use performance insights to continuously improve their AI, indexing new content and delivering better outputs to employees and customers.

Rather than racing to deploy a solution and check the AI box, take the time now to put the right infrastructure, guardrails, and change management best practices in place. This will help you avoid some of the biggest stumbling blocks support teams are encountering and put you in a position to reap the long-term benefits of AI.

This blog post is an excerpt from “The Help Desk AI Maturity Journey: A Support Team’s Guide To Go From AI Experiments to Results.” Download the full guide here.