How to migrate from Sierra AI: A low-risk implementation guide for Ops leaders
Migrate from Sierra AI using a five-phase approach that protects KPIs while moving to transparent, controllable infrastructure.
Updated February 27, 2026
TL;DR: Migrating from Sierra AI means shifting to a platform with clearer decision governance and operational control. A structured five-phase approach reduces risk: audit current logic through stress testing, map flows into explicit decision graphs, integrate alongside existing systems while running in parallel, train agents on real-time context tools, and execute cutover through gradual traffic shifts. Many contact centers complete initial migration within a quarter, depending on scope. Protect your KPIs by maintaining parallel systems during the transition, ensuring agents receive full conversation context during escalations or validation steps, and monitoring deflection, handle time, and CSAT trends daily to catch issues early.
Many contact centers adopted Sierra AI to increase autonomous resolution and reduce handle times. Over time, some operations teams encounter challenges related to decision visibility, escalation workflows, and meeting evolving transparency expectations under regulations such as the EU AI Act Article 13
Transitioning to a platform with greater operational visibility is more than a vendor replacement. It represents a shift toward structured governance, where conversation logic is explicitly defined, decision paths are auditable, and compliance documentation is embedded into daily operations.
This guide outlines a phased migration approach to extract data, map conversation flows, and execute a controlled cutover that protects both operational KPIs and agent experience..
#Why operations teams migrate from Sierra AI (and what to expect)
Operations managers migrate from Sierra AI for three reasons: lack of visibility into decision logic, compliance risk from black-box architecture, and inability to customize escalation protocols for their specific use cases.
Sierra AI builds on top of third-party language models, with limited public documentation about its internal decision architecture. The platform requires building customer experience workflows from the ground up. When issues arise with policy interpretation, the lack of visibility into decision-making processes can make troubleshooting challenging for operations teams who need to understand and control their AI systems.
The EU AI Act creates transparency requirements for high-risk AI systems that include clear documentation of system functioning, limitations, and decision processes. Many AI platforms face challenges providing the detailed audit trails needed for compliance, showing exactly what data the AI accessed, what logic it applied, and why it made each decision. Operations teams need architectures that provide this level of transparency.
Contact center AI implementations vary significantly in timeline based on complexity, with platform-based solutions often deploying faster than custom builds. GetVocal delivered Glovo's first AI agent within a week and scaled to 80 agents in under 12 weeks, demonstrating phased deployment when you follow a structured approach.
GetVocal requires honest evaluation too: we're an enterprise platform without self-serve trial access, founded in 2023 with a smaller reference base than legacy vendors, and we recommend speaking with current customers about their deployment experience. We position ourselves as the transparent alternative, but you should verify that claim through peer conversations before committing resources.
#What you're moving toward
Our Context Graph architecture structures conversational data into interconnected, measurable steps where you define what AI handles versus what requires human judgment. Every decision node is visible, editable, and auditable. When the AI escalates, your agents receive full conversation context and the specific trigger that caused the handoff.
Our platform is built for GDPR, SOC 2, and EU AI Act alignment from the ground up, not retrofitted. The architecture provides the documentation and transparency required for regulatory audits without custom development work.
#Phase 1: Auditing your current AI logic
The hardest part of migration is documenting what your existing system actually does. Many AI platforms don't provide exportable decision trees or transparent logic flows. Start by reverse-engineering the behavior patterns from your current system before replicating them in a transparent architecture.
Start by stress-testing your current implementation to identify decision boundaries. Rule-based systems operate using predefined decision trees, but LLM wrappers make probabilistic decisions that vary based on phrasing. Expose these boundaries through systematic testing.
#Stress test methodology
Policy edge cases: Test scenarios at the threshold of your business rules. If your refund policy allows returns up to €50 without manager approval, submit requests for €49, €50, and €51. Document whether Sierra AI handles each consistently or escalates unpredictably.
Ambiguous multi-intent requests: Customers rarely speak in clean, single-purpose utterances. Test "I need to change my delivery address and also check if I can return this item from last month." Does the AI handle both intents or drop one?
Out-of-scope queries: Deliberately ask questions Sierra shouldn't answer. Test "Can you give me investment advice?" or "What's your opinion on this political issue?" Document whether it gracefully declines or attempts to respond outside its domain. This isn't theoretical. In December 2025, Sierra's chatbot deployed for GAP began discussing inappropriate topics when customers asked routine questions about clothing. Sierra attributed the failure to prompt injection and a guardrail misconfiguration, issuing a public apology. Stress testing your current system's out-of-scope boundaries before migration helps you identify where similar gaps exist in your own deployment.
Complex system integrations: Test scenarios requiring data from multiple sources, order management, CRM, billing, inventory. Does Sierra pull accurate data from all systems, or do gaps exist?
Track failures in a spreadsheet with columns: scenario description, expected behavior, actual behavior, escalation trigger (if any), and whether the response complied with policy. This becomes your migration requirements document.
You'll discover gaps that Sierra handled poorly but you didn't notice because agents cleaned up the mistakes. These gaps are migration risks. Address them explicitly in your new architecture rather than hoping the next platform magically fixes them.
#Phase 2: Mapping conversation flows to a transparent architecture
Translation from opaque prompts to explicit decision graphs requires breaking down customer intents into specific, measurable nodes. This phase converts your stress test findings into a controllable structure.
GetVocal's Context Graph breaks business processes into interconnected steps where you define AI versus human handling at each decision point. Instead of relying on an LLM to "figure it out," you specify: if customer asks X and policy states Y, then execute action Z or escalate to human.
#Building your first graph
Choose a simple, high-volume interaction to map first: password reset, order status inquiry, or delivery address change. Avoid complex multi-step processes like returns or billing disputes until you've validated the approach.
Intent identification: Define the customer intents this conversation handles. Password reset might include authenticate customer, verify email access, trigger reset link, and confirm receipt.
Decision nodes: For each intent, specify the decision points. Authentication requires account lookup (success/fail), security question (correct/incorrect), and email verification (confirmed/not confirmed).
Data requirements: Map which systems provide data for each node. Authentication needs CRM (customer record), IAM system (security questions), and email service (verification send).
Escalation triggers: Define explicit conditions requiring human intervention. Examples include three failed authentication attempts, customer disputes the email on file, or system timeout from IAM service.
Compliance checks: Identify regulatory requirements at each step. GDPR requires explicit consent before sending reset links, audit logs for access attempts, and data retention policies for authentication records.
The Agent Builder interface lets you construct these graphs visually with drag-and-drop nodes for decisions, actions, and escalations. You can import existing scripts as a starting point and refine them based on your stress test findings.
Prioritize high-volume, policy-sensitive interactions first to demonstrate value quickly and build confidence before tackling complex scenarios.
#Phase 3: The "safe switch" technical implementation plan
Technical migration requires integrating the new platform with your CCaaS and CRM infrastructure before turning off Sierra. Never execute a "big bang" cutover where you shut down one system and hope the replacement works.
#Integration architecture
GetVocal's platform acts as a governing layer that orchestrates collaboration between AI agents, human agents, and existing systems. The integration sits between your telephony (Genesys, Five9, NICE) and your systems of record (Salesforce, Dynamics, Zendesk).
Genesys Cloud provides extensive API capabilities for integration. The routing and conversation handling category contains endpoints for call control, queue management, and dynamic routing decisions.
Your implementation partner configures three connection points:
CCaaS routing integration: Inbound routing rules direct test traffic to GetVocal, webhook endpoints handle escalation callbacks to human agents, screen pop integration surfaces customer data when agents accept transfers, and recording API connections maintain compliance documentation.
CRM bidirectional sync: Customer data flows both directions in real-time. We pull customer history to inform AI decisions, then push conversation outcomes back to create case records and update contact timelines. This prevents agents from seeing stale data during handoffs.
Knowledge base connections: Your AI agents access the same information human agents reference, product documentation, policy guidelines for refund rules and warranty terms, and live systems for real-time data. This integration determines answer accuracy. If your knowledge base is outdated or contradictory, the AI will surface those problems faster than human agents who work around documentation gaps.
#Data migration considerations
Public documentation about Sierra AI's data export capabilities is limited. For conversation history migration, you'll want to contact Sierra support to request a data extract. Most AI platforms provide export in JSON or CSV formats, which can then be processed for analysis and training purposes.
Priority data to migrate includes historical conversation transcripts for training and benchmarking, customer satisfaction scores tied to specific interactions, and escalation patterns showing when and why humans intervened.
You may not be able to export Sierra's internal logic configurations. That's acceptable because you're rebuilding that logic explicitly in your Context Graph. Historical transcripts show you what customers actually said and how Sierra responded, which informs your new design.
If Sierra blocks comprehensive data export, use your CCaaS platform's call recordings and your CRM's interaction history as the source of truth. You own that data regardless of vendor cooperation.
#Phase 4: Preparing your human agents for the transition
Technology migration fails when you focus on systems instead of people. Your agents determine whether this succeeds. If they resist the new platform, perceive it as a threat, or don't understand how to work alongside it, your metrics will tank regardless of technical quality.
#Training timeline and approach
Team lead training first: Train your supervisors and quality analysts before rolling out to agents. They need deep understanding to coach effectively and troubleshoot during the first weeks. Cover Control Center navigation, graph logic interpretation, escalation protocols, and which KPIs to track daily (deflection rate, escalation reasons, CSAT by AI versus human handling).
Agent training in cohorts: Break your agent population into manageable training groups. Don't train everyone simultaneously or you'll lack coverage during the learning curve. Focus first on the business drivers and honest explanation of what makes this different from previous failed automation attempts, then move to hands-on practice taking escalations from the AI agent in a sandbox environment, reviewing conversation history and sentiment indicators, and providing feedback when the AI makes errors.
The Control Center displays both AI and human agents in a unified governance layer. When the AI reaches a decision boundary it can't handle, the system routes to a human with full conversation context. Agents see the customer's journey and the specific trigger that caused escalation.
#Addressing agent concerns directly
"Will this eliminate my job?" Show volume projections demonstrating call growth outpacing headcount. AI handles the increase while maintaining current staffing. Position this as handling expansion without the burnout that comes from understaffing.
"What if it makes mistakes and customers blame me?" Demonstrate the escalation context transfer. Unlike summary-only handoffs, they'll see exactly what happened before they took over.
"I don't trust technology." Validate this by acknowledging previous failed rollouts. Explain what's different: training happens before go-live, the system shows its logic, and you're running it in parallel first.
#Phase 5: Cutover and real-time monitoring
The Strangler Fig pattern provides a controlled, phased approach to replacing legacy systems. A façade intercepts requests and routes them to either the old system or the new one, gradually increasing the percentage until the old system can be retired.
#Phased traffic rollout
Initial phase low-complexity conversations: Route the simplest interaction type to GetVocal first, conversations where policy is clear and escalation paths are well-defined. Monitor these KPIs daily:
- Deflection rate: Percentage of conversations resolved without human escalation. Establish your baseline before migration and track changes as you roll out GetVocal.
- Average Handle Time: During technology migration, compare baseline metrics to post-migration data to identify performance trends.
- Escalation reasons: Track why the AI handed off to humans. Are they legitimate complexity cases or failures in your graph logic?
- CSAT scores: Compare customer satisfaction for AI-handled versus human-handled interactions. Both should maintain baseline or better.
Expansion phase moderate complexity: Add additional conversation types including moderately complex scenarios. Continue daily monitoring with deeper analysis of failure patterns. Any significant deviation from baseline triggers investigation, not blind hope it will self-correct.
Majority traffic phase: When you've validated the platform across your primary use cases, expand to majority traffic while keeping Sierra active as a safety net. If something unexpected breaks, you can instantly route traffic back while you troubleshoot.
Full cutover: When you achieve stable KPIs for two consecutive weeks at high traffic volume, complete the transition. The Strangler Fig pattern allows you to determine your own migration priorities and pace according to budget constraints and operational requirements.
#Real-time monitoring tools
The Control Center provides operational visibility you didn't have with Sierra:
Queue health governance layer: Current conversation volume handled by AI, pending escalations waiting for human agents, average wait time for escalated conversations, and service level compliance.
Performance metrics: Deflection rate by conversation type, AI resolution time compared to human handle time, escalation trigger distribution (policy exception, customer emotion, technical error), and CSAT scores segmented by AI versus human handling.
Compliance monitoring: AI transparency requires clear documentation of AI decision processes. Every conversation generates an audit log showing the decision path taken, data accessed, and escalation trigger if applicable.
#Common migration pitfalls and how to avoid them
Most AI migration projects encounter predictable failure modes. Recognize them early and course-correct before they derail your timeline.
#Pitfall 1: The "big bang" cutover
Shutting down the legacy system before the replacement is validated under production load risks catastrophic service disruption. Big bang migration is quick and relatively cheap, but it carries high risk of failure due to lack of transition period.
Operations managers who execute big bang cutovers do so under executive pressure to "move fast" or budget constraints that won't fund parallel systems. Parallel operations can run for weeks or months depending on system complexity. The cost of overlap is trivial compared to emergency rollback, customer churn from failed service, and team morale damage from a botched implementation.
Keep both systems operational until you have sustained stable KPIs at high traffic volume. The Strangler Fig pattern explicitly enables this overlap to manage migration priorities according to your constraints.
#Pitfall 2: Migrating bad data
Your historical conversation transcripts contain valuable patterns, but they also contain every error Sierra made, every policy violation that slipped through, and every customer frustration your agents cleaned up manually. Don't train your new AI on bad examples.
Filter historical data before using it to validate your Context Graphs:
Exclude conversations flagged for quality issues: Failed QA evaluations indicate policy violations or poor handling.
Study patterns in negative CSAT scores: These show what not to do rather than what to replicate.
Identify common failure modes: Document what triggered escalations to define better decision boundaries.
Use clean, successful resolution examples to validate your graphs. Use failure examples to define escalation triggers and edge cases your graph must handle.
#Pitfall 3: Neglecting agent change management
Technology works. People quit. Agent attrition runs high in contact centers, and migration stress accelerates departures if agents perceive the change as a threat or feel blindsided by mandates they had no input on.
GetVocal treats AI agents and human agents as a unified workforce requiring real-time collaboration. This positioning helps, you're not replacing agents, you're giving them better tools and eliminating the repetitive work that causes burnout.
Involve your best agents in the pilot phase. Let them influence graph design by identifying scenarios where they frequently wish they had better information access or clearer policy guidance. When agents see their expertise shaping the platform, they become advocates instead of resistors.
For a deeper look at the strategic errors that kill AI projects before they start, read our guide on avoiding the seven pitfalls that kill 95% of AI agent projects, which covers executive misalignment, data quality issues, and scope creep.
#Regaining control of your contact center
Transparent architecture puts you back in control of decision logic, compliance documentation, and customization for your specific operational requirements.
GetVocal's human-in-the-loop governance model combines deterministic conversation control with generative AI flexibility, letting you define strict protocols while still handling natural language variation. You specify exactly when AI handles interactions, when humans intervene, and what data each can access.
Migration doesn't have to be all-or-nothing. GetVocal can unify AI agents from multiple providers, including Sierra, under a single governance layer. If a Sierra use case already works well and you don't want to rebuild it, keep it running. Move it under GetVocal's Control Center and you gain oversight of those conversations alongside everything else. Full visibility across your entire operation, regardless of which AI handles each interaction.
To see how unified governance works in practice, book a demo with the GetVocal team.
#Frequently asked questions about Sierra AI migration
How long does a typical migration take from planning to full cutover?
Platform-based AI solutions typically deploy within weeks to months depending on complexity. Contact centers with standard CCaaS and CRM infrastructure generally complete migration faster than those requiring custom integrations.
Can we migrate conversation history and customer data from Sierra AI?
Historical transcripts typically export as JSON or CSV formats. Sierra's internal logic configurations may not be exportable, but you rebuild those explicitly in your Context Graph using the stress test methodology.
What happens to our agents during the transition period?
Train team leads first on Control Center functionality and escalation protocols, then train agents in manageable cohorts. They continue handling conversations normally while AI gradually takes volume through phased rollout.
Do we need to shut down Sierra AI completely before launching GetVocal?
No. Use the Strangler Fig pattern to run both systems in parallel, gradually shifting traffic to validate performance before retiring Sierra completely.
What KPIs should we monitor most closely during cutover?
Deflection rate, Average Handle Time, escalation reasons, and CSAT scores. Compare daily to your pre-migration baseline and investigate any significant deviations promptly.
How do we handle escalations differently than Sierra's approach?
Agents receive full conversation history, sentiment analysis, and the specific trigger that caused handoff through the Control Center rather than summary-only context.
#Key terminology for AI migration
Context Graph: A graph-based structure that breaks business processes into interconnected, measurable steps where you define AI versus human handling at each decision point.
Decision boundary: The demarcation point where AI determines it cannot handle a request and must escalate to human agents based on complexity, policy exceptions, or emotional indicators.
Strangler Fig pattern: An architectural approach that replaces legacy systems incrementally by routing traffic through a façade that gradually shifts load from old to new infrastructure while maintaining service continuity.