AI value realization in service

The next evolution of service operations is a shift from legacy automation to agentic service ops, where digital employees handle complex execution while humans focus on strategic oversight. As of 2026, 88% of organizations have adopted AI, but only 6% are high performers that drive significant EBIT impact through fundamental workflow redesign. To capture real value, organizations need to move beyond feel-good metrics and transform their workforce into agent leaders.

The strategy shift: from automation to autonomy

True transformation requires moving away from linear automation toward autonomous agents.

Establish agentic squads

Deploy digital employees with specific roles and performance reviews. Many leading research organizations, including Gartner, identify agentic AI as a top strategic trend for 2025, enabling autonomous decision-making that reduces unnecessary manual intervention. How to deploy a minimal viable organization has become the next milestone for operational excellence.

Adecco Group, a leading UK employment agency, has moved beyond treating AI as a search tool and is actively deploying digital recruitment agents that function as members of the team. They analyzed their entire recruitment lifecycle to determine where a digital employee could add value versus where a human is essential. This resulted in a goal to have 50% of global revenues powered by agentic AI by the end of 2026. By treating the agent as a teammate who can keep learning and improving through task repetition, rather than a software tool, they transitioned human recruiters into agent leaders who focus on high-level talent consulting and interview finalization.

Measure true value

Focus on actual value-capture results such as cost savings and revenue growth rather than resolution velocity. McKinsey reports that AI-driven transformation can improve customer satisfaction by 45% and reduce the cost to serve by 20 to 30%.

Klarna is the gold standard for shifting the focus from vanity metrics like chat volume to hard financial value capture. Instead of just measuring speed and surface-level efficiency gains, Klarna tracked $40 million in annual profit improvement and a 25% reduction in repeat inquiries, a direct proxy for resolution quality, through deploying an OpenAI-powered assistant. Klarna automated 67% of all customer service chats, the equivalent workload of 700 full-time human agents. This shift allowed them to maintain a lean operating model during a high-growth phase, proving that agentic service ops is a driver of EBIT, not just a cost-reduction exercise.

Enable agentic autonomy

Autonomy requires unlearning legacy processes that were designed for heavy human involvement and limited AI capabilities. Leading organizations on AI investment ROI are 2.8 times more likely to report fundamental workflow redesign compared with peers who merely automate existing tasks.

Siemens Energy demonstrates how to redesign workflows to be AI-native, especially in complex brownfield industrial environments. They implemented location-aware agentic AI through their askPixi system to move away from manual status updates. The AI agent uses real-time location data from 1,500 tracked assets to trigger workflow actions automatically in SAP. This redesigned autonomous loop led to a 90% reduction in search time and a 20% increase in parts throughput. By unlearning the habit of manual data entry, they enabled the agent to manage the execution layer entirely, leaving humans to manage exceptions.

Key differentiators: transforming roles and processes

Organizations must bridge the scaling gap by focusing on people and process re-engineering.

People and role transformation

Reskill operators to become agent field leaders. Deloitte reports that 66% of organizations see productivity gains from AI, but success depends on senior leadership actively shaping governance rather than delegating it to IT. In order to drive an effective people transformation, some common best practices include:

• Onboard AI as a new teammate: treat AI agents like human hires. Provide them with agent operating procedures, natural language instructions that define their logic and voice.

• Macro to copilot transition: audit your existing knowledge setup, for example your Zendesk macros. Identify repetitive responses that require minor human personalization and convert them into AI agent suggestions. This allows the human to move from writing to editing and approving.

• Exception hand-off protocol: define clear escalation triggers where the AI hands off a ticket not just when it is hard, but when it is high emotion. For example, use Zendesk advanced AI sentiment analysis to automatically route low-emotion tickets to AI agents and high-frustration or VIP tickets to human agent leaders immediately.

What good looks like

• 80% of routine queries, such as "Where is my order?" resolved autonomously by AI agents

• Human agents see a 25% reduction in average handle time on escalated tickets due to copilot's proactive internal note summaries

• Human agents spend more time on higher-order work, such as analyzing AI performance logs and coaching the AI on edge cases

API-first process design

Use value stream mapping to separate heavy-cognitive tasks, human-led, from heavy-execution tasks, agent-led. A few best practices have been identified:

• Outcome-first goal mapping: strip away intermediate triage steps. If a process requires three human handoffs to get an approval, collapse those into a single automated policy check within the Zendesk AI agent’s reasoning path.

• Transition from flow-based to intent-based logic: audit decision trees. If they have more than five branches, they are too complex for a legacy bot but perfect for an agentic AI that can navigate a knowledge base and API set to find its own path to resolution.

• Closed-loop execution: redesign processes so resolution happens inside the ticket. Integrate AI capability, for example Zendesk, with your backend systems such as Shopify or JIRA so the AI can perform the action, such as updating a shipping address. Good looks like the customer never leaving the chat window and the ticket closing as resolved, not pending.

What good looks like

• More than 80% autonomous resolution rate, not just deflection, but tickets closed without human intervention in a manner that meets defined quality metrics

• Zero-friction continuity. The customer feels like they are talking to one expert who just knows their history, even during a handoff

Tiered autonomy risk management

Implement guardrails based on discernment analysis. With 51% of organizations experiencing negative consequences like inaccuracy from AI, safe execution and accountability are critical leadership questions. Balancing the speed of AI with the safety of the brand requires a set of best practices:

• Triage, assist, solve framework:

  • Triage: AI classifies and prioritizes every incoming ticket, 100% coverage

  • Assist: AI provides drafts and summaries for human review, medium risk

  • Solve: AI acts autonomously on pre-approved, high-confidence intents, low risk

• Runtime guardrails: use a guardrail model that sits outside the main agent. If the main agent tries to say something toxic or perform an unauthorized action, the secondary guardrail model stops the process instantly.

• Continuous adversarial testing: regularly red-team your AI agent’s logic. If the AI can be tricked into giving a 100% discount via a prompt, you must refine the agent operating procedure guardrails.

What good looks like

• Less than 1.5% hallucination or error rate

• Auditability. In a QBR, you can point to any single action taken by an AI agent and show exactly which policy, API call, action flow triggered it, and which human supervisor authorized it

The implementation roadmap: the first 90 days

Phase 1: foundation and pilot, days 1 to 30

• Map value drivers and identify use cases to automate 80% of heavy execution tasks, such as ticketing and how-to queries

• Initiate culture change by moving from operators to agent leaders

• Run early adopter workshops to build initial action flows

Phase 2: refine and track, days 31 to 60

• Refine data and discernment settings to reach the 80% automation goal

• Establish weekly metrics meetings with business and IT leaders

• Develop career paths for agent leaders and implement coaching-the-coaches to scale adoption

Phase 3: scale and strategize, days 61 to 90+

• Hold the first quarterly business review to reallocate resources based on value tracking

• Launch a second discovery phase to extend AI into finance, HR, and supply chain

• Establish internal AI thought leadership and expand cohorts by function

Immediate next steps

Before deploying autonomous agents, leadership must bridge the gap between legacy processes and agentic potential. These four steps ensure that your organization is not just launching a bot, but architecting a new operating model.

AI readiness assessment: data, tech stack, and people

• Audit your current service ecosystem

• Determine whether your knowledge base is machine-readable

• Check whether your APIs are robust enough for execution

• Assess whether your staff has the orchestration mindset

Zendesk can support this by helping identify gaps in your help center using AI tools. If the AI does not have a high-quality source of truth, it cannot be agentic. Audit integration services by mapping backend systems such as billing, shipping, and CRM. Review reporting data to identify high-volume, low-complexity intents, which are prime candidates for the first wave of automation.

Leadership alignment: vision, impact, and metrics

• Align leadership on a vision where the goal is resolution autonomy, not just traditional operational metrics such as AHT

• Establish a holistic value case for AI transformation with clear milestones for value capture

• Diagnose the intent waterfall by volume to show where the business can de-layer processes and save immediate costs

Identify champions: pinpoint early adopters

• Find front-line staff already using hacks or AI to speed up their work

• Use the sandbox so champions can test the agent’s logic safely before production

• Deploy AI solutions such as Zendesk Copilot to a small alpha group of top-tier agents

• Use QA solutions such as Zendesk QA to monitor how champions interact with AI-generated drafts

Define the guardrails: security and policy alignment

• Work with legal and security teams to define the automated threshold, the maximum dollar amount or risk level an AI agent can handle before a human intervenes

• Set up data protection and privacy tools so PII is automatically redacted before processing by the LLM

• Configure AI agents to recognize out-of-bounds topics, such as legal threats or medical advice, and trigger an immediate high-priority human handoff

Want more on agentic service change management? Check out the Agentic Service Playbook