6 Best Practices for Using Artificial Intelligence in Service Management

Are you eager to make the most of artificial intelligence to enhance the way your organization delivers service management?

Ambitious claims are made about AI’s capacity to reshape IT operations. Whether it’s leveraging intelligent ticket routing or predictive incident prevention, the technology promises to transform service management from reactive firefighting to proactive problem-solving.

But deploying AI successfully requires discipline, planning, and a clear focus on tangible business outcomes.

This article explores six essential best practices to navigate governance challenges, measure real ROI, and build organizational capability for long-term success.

Summary of artificial intelligence service management best practices

#1 Focus on adding value and delivering measurable ROI

Before beginning the process of introducing AI into your service management environment, ask: What does success look like for your organization? 

The answer needs to be anchored in language your CFO and board relate to: revenue protection, margin improvement, risk reduction, and better customer and employee experiences.

For some organizations, AI is primarily a cost lever, cutting ticket volume, shrinking FTE requirements, and lowering cost-per-ticket. For others, the aim is better employee experiences: less drudge work for agents, more time on complex troubleshooting, and lower burnout. 

In more mature digital businesses, AI is a strategic weapon, accelerating service delivery, strengthening competitive differentiation, and lifting customer loyalty.

Begin by understanding key baseline metrics to benchmark performance after AI is deployed.

Key baseline metrics to measure before deploying AI:

• Ticket volume (broken down by category and team)

• Time to resolution and first-contact resolution rates

• Customer satisfaction (CSAT) and SLA compliance

• Agent productivity (tickets per agent per day)

• Cost per ticket

While it’s easiest to calculate direct savings such as time saved per ticket, reduced ticket volumes, and resulting hourly cost reductions, keep in mind that other, more nuanced outcomes often drive the real long‑term value of an AI enablement project.

• Indirect benefits such as higher engagement and capacity for strategic work

• Revenue impact through better retention, easier upselling, and fewer service failures

• Risk reduction via faster incident response, stronger compliance, and better auditability

Highlighting these other dimensions ensures you capture AI’s full strategic impact on resilience, experience, and growth, beyond obvious line-item savings.

#2 Determine specific use cases

Not every service management process needs an AI makeover. Some workflows are perfect for automation, while some demand nuanced human judgment. 

The goal is to deliberately target use cases and prove value quickly without adding unnecessary complexity. Start with two or three well-chosen use cases, build momentum, prove ROI, then expand.

Think about the tickets your team is tired of seeing, such as password resets, account unlocks, and simple connectivity issues. These are predictable, structured, and lend themselves well to automation.

How AI improves service management

Prioritize use cases that show clear patterns and repeatable logic, generate significant manual rework today, and have obvious success metrics. Incident triage and routing are classic examples: AI that reads short descriptions and assigns tickets to the correct queue eliminates misroutes and shaves minutes off every interaction.

The same is true for knowledge workflows. Use AI to draft articles from resolved incidents, surface relevant content to agents in context, and highlight gaps in your knowledge base.

An example of the power of AI knowledge bases can be found in Feshworks’ service management solution. Freddy AI, the solution’s proprietary artificial intelligence engine, analyzes ticket histories and chat transcripts to identify recurring issues and automatically generates relevant support articles that address emerging knowledge gaps.​

On the front line, AI-powered virtual agents deflect a meaningful chunk of inbound volume. In contrast, agent copilots accelerate resolution by suggesting subsequent actions, drafting responses, and summarizing history. They deliver faster outcomes without sidelining human expertise.

#3 Establish a framework for governance and risk management

It’s tempting to think of AI governance as red tape or a set of boxes to tick. But it’s much more important than that: it’s a structural foundation that permits teams to move fast safely by eliminating uncertainty about what's acceptable, what's risky, and who decides.

Well-designed guardrails let organizations operate confidently within clear boundaries. Organizations that establish robust, effective governance at the outset avoid expensive compliance rework later.

Effective AI governance requires ongoing monitoring, reviewing, and auditing

Ownership

Start by forming a cross-functional AI Governance Board spanning IT (ITSM, Infrastructure, Security), Finance, Legal/Compliance, and Data Management to own use case approval, performance reviews, and policy updates. It should have an executive sponsor [Chief Data Officer (CIO) or Chief Development Officer (CDO)] to unblock decisions and communicate upward.

Next, assign explicit AI system ownership. Every AI system needs both a business owner (accountable for outcomes and ROI) and a technical owner (accountable for quality and performance). These owners drive documentation, monitor drift, manage retraining, and respond to bias findings.

Usage

Draft an AI Acceptable Use Policy that clearly defines what AI can and can’t do. It should:

• Specify constraints (e.g., "AI cannot make access control decisions without human approval")

• Clarify data privacy expectations

• Establish escalation triggers. 

Publish it, make it accessible, and update it annually as regulations and capabilities evolve.

Risks

Build an AI Risk Register cataloging potential failure modes, such as model inaccuracy leading to SLA violations, data poisoning introducing bias, over-automation causing escalation failures, or model drift degrading performance over time.

Implement continuous monitoring to track risks and ensure mitigation. Set up dashboards to track model accuracy, false-positive rates, and decision patterns. Conduct monthly performance reviews and quarterly audits. Annual recertification determines whether each model continues, gets retired, or needs replacement. This ongoing discipline prevents surprise failures and keeps your AI trustworthy.

#4 Set an implementation strategy

A disciplined implementation strategy is what separates successful AI deployments from projects that stall in pilot purgatory.

Start by defining explicit accountability:

• Executive sponsor, likely the CIO, and definitely someone with budget authority and board access to unblock decisions

• Program manager who owns the timeline, scope, and resources

• Technical lead ensuring architecture and integration readiness

• Business lead accountable for use case selection and ROI measurement.

Create a clear decision-making framework that specifies who approves new use cases, who decides when to scale, and who has the authority to kill underperforming initiatives.

It’s essential to execute in deliberate phases. 

Months one to two focus on the foundation: 

• Audit data quality

• Evaluate process maturity

• Identify technical debt

• Establish governance

• Select one or two quick-win use cases

• Secure funding

• Form your core team. 

Months three to six pilot in shadow mode: AI makes recommendations but doesn't auto-execute, allowing humans to review and learn. Collect baseline metrics, train pilot users, monitor performance, and gather feedback. 

Months six to twelve expand to two or three additional teams: 

Transition from shadow to live operation, establish monthly performance reviews, and quarterly audits.

Comparing metrics before and after implementation ensures the impact of AI can be assessed effectively

Throughout, measure relentlessly. Document current-state metrics four weeks before pilot launch to provide a baseline that will be crucial for isolating AI's actual impact. 

Track AI accuracy, user adoption, satisfaction, and model performance. Post-implementation, calculate financial impact like labor hour savings, ticket deflection, and SLA improvements.

Taking a disciplined approach to implementation prevents surprise failures and maintains AI's trustworthiness.

#5 Anticipate and manage strategic disruption

Done well, AI becomes more than just a tool for squeezing efficiency gains; it fundamentally reshapes how service management operates and how organizations compete.

Service management has traditionally been reactive: incidents happen, get reported, triaged, and closed. AI inverts that, allowing the prediction and prevention of incidents before they occur.

This can have a significant impact, resulting in fewer outages, higher availability, and better user experiences. More importantly, IT stops being a cost center and becomes a business enabler.

Today, agents typically spend 60% of their time on repetitive work such as password resets, basic troubleshooting, and ticket routing. AI can absorb those tasks, leaving agents free to tackle the complex, judgment-heavy cases that actually require expertise. 

Their role can be transformed from “ticket processor” to “problem solver,” giving agents time for strategic work such as collaborating on chronic problems, implementing permanent fixes, and mentoring junior staff.

It's essential to decide how much workflow autonomy you want to give AI. Conservative teams allow AI to suggest actions, which humans then execute. A balanced approach is to let AI auto-execute routine work and escalate exceptions. 

Under an “aggressive” strategy, autonomous agents carry out most of the work, with humans reviewing only high-impact decisions.

Workflow automation using AI always involves a trade-off between speed and risk

#6 Take advantage of AI-enabled out-of-the-box solutions

Should you build custom AI or deploy out-of-the-box (OOB) solutions? For most organizations, OOB is the right move.

OOB solutions, such as Freshworks, are vendor-built and pre-trained, ready to deploy with minimal customization. They ship in weeks, not months. You get predictable subscription costs, not unpredictable professional services bills. 

They have already been tested on thousands of customers, so you inherit their battle-tested accuracy. Governance and compliance controls come baked in.

The vendor manages security, handles updates, re-trains models, and continuously improves the underlying algorithms by aggregating learning from its entire customer base. That's a real competitive advantage: your ticket routing improves as other organizations discover new patterns.

Custom-built AI makes sense in specific scenarios. For example, if your service management processes differ significantly from ITIL best practices, or if you've built proprietary systems that require custom connectors. Most organizations, however, use standard incident management, change management, and knowledge workflows. For those, OOB is faster, cheaper, and lower-risk.

The hybrid approach is most common in practice: deploy OOB for standard use cases (incident triage, ticket routing, knowledge management), build custom models for genuine differentiators, and use bespoke integrations to connect OOB AI to your proprietary systems. For instance, you might use the platform’s native virtual agent for common requests but build bespoke connectors to link that AI directly into your proprietary manufacturing systems.

For most organizations, it makes sense to start with OOB. Deploy fast, prove value, then decide if bespoke additions are needed.

Conclusion

The six best practices outlined here form an integrated strategy. Organizations implementing all six are more likely to achieve a successful AI implementation, while those who skip one or more typically struggle.

Always start with Practice #1: define what success means for your organization and let business value drive everything else.

Ready to build your AI service management strategy? The playbook is here. The platform capabilities exist. The competitive advantage awaits those who move now.

If you’re ready to advance on your AI implementation journey, Freshservice combines workflow automation, virtual agents, and intelligent routing with the flexibility to keep your team in control of the decisions that matter. 

If you're looking to reduce ticket volumes, speed up resolution times, and give your agents more time for meaningful work, book a demo today.