What's New — June 16, 2026

This month's updates move your contact center analytics from isolated data points to a unified, strategic view of performance. We're introducing powerful new ways to visualize quality across your entire organization, automate the AI feedback loop, and connect disparate metrics to uncover deeper truths about your team's effectiveness. It's about spending less time hunting for data and more time driving impactful, data-backed improvements.

Executive Summary

New Quality Dashboard — Get an instant, organization-wide view of scorecard performance with a new managed dashboard that compares results across all teams and agents.

Automated AI Topic Improvement — Streamline AI model training with a redesigned, single-page Topics workbench that automatically measures and improves topic accuracy after you finish a review batch.

Quadrant Performance Charts — Visualize agent and team performance across two metrics at once, instantly spotting top performers and coaching opportunities in a new four-zone scatter chart.

Granular Genesys Cloud Integration — Take precise control over your data ingestion by selecting multiple Genesys divisions and narrowing the scope to specific queues, all editable at any time without a reinstall.

Enterprise SSO Reliability — Strengthened authentication provides more resilient and reliable single sign-on access for customers using OIDC identity providers.

Highlights

New Quality Dashboard — Get a Unified View of Scorecard Performance

Comparing quality scores across different scorecards, teams, and agents often required manual data exports and spreadsheet gymnastics. Without a central dashboard, it was difficult to spot organizational trends or identify which teams were falling behind.

▸ Benchmark teams and agents: See side-by-side results for every scorecard across your entire organization in two new managed reports: Scorecards by Team and Scorecards by Agent.

▸ Compare performance at a glance: Instantly see how many conversations fall into Pass, Borderline, or Fail zones with a new compact "Score-Zone" view, perfect for executive reviews.

▸ Drill into the details: Click any score in the matrix to jump directly to the underlying conversations, with all the right filters pre-applied for a seamless root cause analysis.

Before: Scorecard results were siloed, requiring manual work in spreadsheets to compare teams or agents.
After: A single, managed Quality dashboard shows all scorecard results in one matrix, with toggles for manual, automated, or combined scoring and a compact score-zone view.
In practice: A QA Director opens the Quality dashboard for a weekly review, switches to the Score-Zone view, and immediately spots that the "Tier 1 Support" team has a high number of "Borderline" scores on the "Compliance" scorecard, signaling a need for targeted coaching.

Get started by navigating to your dashboards list — the Quality board appears automatically. Find the Scorecards by Team card and use the settings icon to switch between Detailed view and Short view.

Automated AI Topic Improvement — Go from Raw Data to High Accuracy Faster

Tuning a Gen AI Topic used to be a fragmented, multi-step process. You had to navigate between different tabs to define, review, and evaluate a topic, then manually trigger improvement and evaluation steps after each review cycle, slowing down the path to accurate AI.

▸ A guided, single-page workbench: A new single-page design consolidates all tools—definition, review, and version history—into one place, with a clear "Accuracy Journey" stepper that tells you exactly what to do next.

▸ Automated improvement cycles: Finishing a review queue now automatically triggers the "Improve" and "Evaluate" steps. The system does the work in the background while you see live status updates, eliminating two manual clicks from every cycle.

▸ Surface the best versions: The Topics list now highlights when a new version of a topic significantly outperforms the active one. Inside the workbench, a "Promote" banner shows the exact accuracy gain, so you can activate better versions with confidence.

Before: A three-tab editor required manual clicks to trigger improvement and evaluation after each review, with no clear signal when a better version was ready.
After: A single-page workbench automatically triggers an improvement cycle when a review is finished, and a badge on the topics list tells you which topics have a better version ready to promote.
In practice: A QA Manager finishes labeling the last conversation for a new "Billing Complaint" topic. The system automatically starts improving and measuring the new version. Minutes later, they see an "Improvement ready" badge on the Topics list and promote the new, more accurate version with one click.

Get started by opening any Gen AI Topic from the Topics list. The new single-page workbench and Accuracy Journey are active now.

Quadrant Charts — Compare Two Metrics in a Single View

Analyzing the relationship between two different metrics—like agent efficiency and quality—required flipping between separate charts or exporting data to a spreadsheet. It was impossible to see at a glance who was a top performer in both dimensions versus who was sacrificing one for the other.

▸ Visualize performance in four zones: The new Quadrant chart plots entities (like agents or teams) on a two-axis grid, instantly sorting them into four performance zones based on the average of each metric.

▸ Identify outliers instantly: Spot top performers (high/high), agents needing coaching (low/low), and those with specific trade-offs (high/low or low/high) without cross-referencing multiple reports.

▸ Drill down with one click: Click any point on the chart to drill directly into the associated conversations for that agent or team, making it easy to investigate the "why" behind the data.

Before: Comparing two metrics required looking at two separate charts and manually trying to connect the dots.
After: A single Quadrant chart visualizes the relationship between two metrics, automatically highlighting who is above or below average on both.
In practice: A Team Lead plots Average Handle Time vs. Quality Score for their agents. They immediately see one agent in the "High AHT, Low Quality" quadrant, identifying a clear coaching opportunity to improve both efficiency and effectiveness.

Get started by editing any analytics chart. Add exactly two datasets, then select the new Quadrant icon from the chart type selector.

Granular Genesys Cloud Integration — Ingest Only the Conversations You Need

Previously, configuring our Genesys Cloud integration was an all-or-nothing choice per division, and the scope was locked in at install time. For large organizations, this meant ingesting unnecessary data or performing complex workarounds to focus on specific teams.

▸ Multi-division and per-queue scoping: Select multiple divisions to ingest from, and for each one, you can optionally narrow the scope to only include specific queues.

▸ Edit scope anytime: Your data ingestion needs can change. You can now adjust your division and queue selections at any time via a new "Manage data fetching" dialog, no re-installation required.

▸ Smarter, safer setup: Choose divisions and queues from searchable, pre-populated lists—no more copy-pasting IDs. The system now validates your configuration on save, preventing silent ingestion failures from typos.

Before: Ingestion was limited to a single division, and the scope was locked at install, requiring a full reinstall to change.
After: You can select multiple divisions and specific queues during setup and modify the scope at any time.
In practice: An administrator for a multinational company sets up ingestion for their North America and EMEA contact center divisions, but filters each to only include their primary support queues, excluding all sales and back-office activity.

Get started during a new Genesys Cloud integration setup, or for existing integrations, find the "Manage data fetching" option to adjust your scope.

Enterprise SSO Reliability — Hardened Authentication for OIDC Users

For organizations using single sign-on, login isn't just a feature—it's a mission-critical utility. Unexpected failures or unclear error messages can disrupt operations and erode trust, especially when the root cause is a minor network hiccup or a configuration mismatch.

▸ More resilient sign-in: We've improved how our platform communicates with OIDC identity providers (like Okta, Azure AD, or Keycloak). The system is now more tolerant of transient network issues and URL formatting differences.

▸ Clearer error handling: In the rare event an authentication issue does occur, you will now see a clear sign-in error instead of a generic server error page, making it easier for your IT team to diagnose the issue.

▸ Improved stability: These changes ensure that authentication remains stable and reliable, preventing login failures that could previously occur during routine system maintenance or discovery refreshes.

Before: Certain SSO login attempts could fail with a generic server error, making troubleshooting difficult for admins.
After: The login flow handles identity provider issues more gracefully, providing clear authentication errors if a login cannot be completed.
In practice: A manager at an enterprise using a corporate identity provider can sign in reliably, even if their provider's discovery endpoint is temporarily unavailable, and their IT team gets an actionable error message instead of a vague one.

Get started by doing nothing at all. This improvement is now active for all customers using OIDC-based single sign-on.

Improvements

▸ Smoother login when switching organizations — If you work across multiple sub-organizations, opening a link to a different one in your browser now shows a clean sign-in prompt instead of a confusing "wrong account" error, letting you easily switch contexts.