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Next-generation KPIs: Redefining customer experience measurement - Hero

Customer experience (CX) is evolving — and your measurement strategy should too. Discover how next-generation KPIs can offer deeper insight into CX performance, from customer effort and journey satisfaction to agent engagement and digital maturity.

Next-generation KPIs: Redefining customer experience measurement

Discover how TELUS Digital transformed customer support for a leading cryptocurrency platform, scaling the program within weeks while achieving high customer satisfaction scores.

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Reduction in Average Handle Time within 3 months

Reduction in Customer First Response Time

Elevating the customer experience for a leading cryptocurrency platform

See how TELUS Digital helped a leading global fintech enhance their sales operation with enterprise lead qualification, delivering 47.6% conversion rate growth and $121 million in operating net revenue.

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B2B Sales Transformation for Global Fintech

Transforming a global fintech’s sales operations with enterprise lead qualification

Discover the importance of data neutrality in today’s AI ecosystem, and why partnering with an ethical, trust worthy and impartial data provider has never been more important.

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Why data neutrality is more critical than ever

Join industry experts from TELUS Digital and Everest Group as they explore how leading technologies, such as generative AI, automation, analytics and more, are delivering value for organizations without compromising service quality.

CX Efficiency How to Balance AI, Cost, and Human Talent (trailer)

CX Efficiency How to Balance AI, Cost, and Human Talent (Full webinar)

Efficient CX delivery: A tech-driven approach

As the telecom industry faces mounting costs and shifting customer expectations, AI can turn challenges into opportunities. Learn more about the use cases.

Photo of two people in a restaurant interacting with their phones and smiling, meant to symbolize generative AI applications in the telecom industry

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How telecom brands can seize industry opportunities with AI

Join us for an enlightening discussion with two pioneers in the field of AI: Tommy Guy, principal applied researcher at Microsoft Copilot Studio and Steve Nemzer, senior director of AI growth and innovation at TELUS Digital.

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Overcoming the challenges of AI agent creation, training and evaluation

Explore new CX outsourcing opportunities in this article by Jim Mitchell, vice president of customer experience and digital innovation at TELUS Digital.

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Transforming CX delivery in a cost-conscious world

If you’re still looking at your contact center through the lens of a cost-center model, I have tough news. Your strategy is already a legacy artifact.

For years, the contact-center-as-a-service (CCaaS) promise was simple: Automate to save. Customer experience (CX) leaders chased deflection as if it were the only metric that mattered.

But as we move deeper into the era of AI, we’re seeing the fallout of that narrow vision. Customers are stuck in circular chatbot loops, and human agents are drowning in the Alt-Tab dance between disjointed systems.

Efficiency is no longer the ceiling, it’s the floor. The brands winning the moments that matter right now are those treating their CCaaS ecosystem as a growth engine — a place where data, agentic AI and human empathy collide to drive actual revenue.

Here’s how you pivot your CCaaS strategy from a maintenance mindset to a growth-first vision.

1. Let go of legacy thinking: Focus on growth over savings

Most CX leaders look at their CCaaS strategy and ask, “How much money can this save me?” That’s the wrong question. The real question now is, “How can this interaction drive the next sale?”

We need to stop viewing the contact center as a place for fixing problems and start viewing it as a massive repository of customer intelligence. When you use AI to analyze every interaction in real-time, you aren’t just closing tickets. You’re identifying churn signals and expansion opportunities that your marketing team hasn't even seen yet.

In the following clip, I break down why the cost-center model is dead and how to use your customer data to fuel growth.

According to a 2026 Gartner survey, 91% of customer service leaders are under pressure to implement AI, but the goal is shifting away from back-office efficiency toward improving customer satisfaction and first-contact resolution (FCR).

Of those CX leaders who implement AI-driven CCaaS strategy, the real winners won't just pocket savings — they’ll reinvest them into total experience (TX) strategies that link customer and employee satisfaction.

2. Relieve agent burnout by automating after-call work (ACW)

We talk a lot about the customer experience, but we often ignore the cognitive tax agents pay. Contact center burnout isn’t just about high call volumes. It’s about the mental load of navigating five different windows while trying to sound human.

This reality underscores why CX Today concludes “agent well-being has become a CX performance issue, not an HR one.”

To maximize your CCaaS ROI while saving agents’ sanity, start by automating the boring stuff. AI note-taking and automated after-call work (ACW) are the lowest-hanging fruits with the highest impact. When the AI handles the documentation, the agent can actually listen to the customer.

The brands implementing this approach are already reaping the benefits. Case in point: TELUS, parent company of TELUS Digital, reduced ACW by 23% through AI-powered contact center transformation.

Watch this video to see how AI note-taking tools kill the Alt-Tab dance and let agents breathe.

When you reduce agents’ cognitive load, you don't just get faster calls — you get better outcomes. A burnt-out agent cannot deliver a premium experience.

3. Put up empathy guardrails: Know where automation begins and ends

This is where the modern CCaaS strategy gets nuanced. We shouldn’t automate everything just because we can. A successful CCaaS strategy requires applying Humanity-in-the-Loop principles.

Think of it as a strategy of empathy guardrails. You need to be creative about where AI begins and ends. For a simple password reset or an order status update? Let a bot handle it. But for a customer who is calling about an urgent medical case or a high-value retention conversation? That requires a human soul.

Learn how to break down high-value versus low-value interactions in the next video. The goal isn’t 100% automation, it’s 100% appropriate routing.

The strategy is simple: Use AI to handle the mundane tasks so your humans are freed up for the moments that matter. If your bot is trying to handle a sensitive escalation, you aren't being efficient — you’re being reckless with your brand. 

4. Replace talking with doing: Deploy large action models (LAMs)

The biggest shift we’re seeing right now is the move from AI that just chats to agentic systems that can plan and execute. For the last two years, we’ve been obsessed with large language models (LLMs) that can talk. That’s great, but talking alone doesn’t resolve a customer’s issue.

Live from Genesys Inspire, I dive into LAMs and explain why doing is the new talking in the world of CX. 

LAMs power agentic AI agents that don’t just explain a policy, they execute it. These agents can:

Autonomously identify goals from a conversation

Create a plan to resolve the customer’s issues

Execute the plan and follow up, no human required

That means navigating digital interfaces and triggering workflows across your backend systems autonomously. If a customer needs to reschedule a complex multi-leg shipment, a LAM doesn't just tell them how — it goes into the system and does it.

5. Upskill your frontline with AI personas

Finally, you have to modernize your training. You can’t put human agents in 2026 through a 2015 training program and expect them to thrive.

AI lets us build a safe space for failure. By using tools like the Fuel iX™ Agent Trainer, you can create AI personas that simulate difficult, angry or complex customer scenarios. This AI-powered sandbox allows new hires to practice de-escalation and learn next-best action (NBA) strategies with immediate feedback before they ever take a live call.

See how AI simulations train agents up to 50% faster while improving retention. 

The takeaway: Accelerating proficiency doesn’t just make agents more competent, it also makes them stick around longer.

Moving from a legacy mindset to an agentic, growth-first model doesn't happen overnight. It requires a roadmap that balances technical capability with human ethics.

The effortless experience we’ve all been chasing is finally within reach, but it requires us to stop obsessing over cost-savings and start obsessing over agent empowerment and autonomous execution.

At TELUS Digital, we aren't just service providers. We’re advisors for the future of tech in the contact center. We spend our days thinking about what happens when the next big thing becomes the current standard.

Is your CCaaS strategy ready for the agentic era? Let’s build your 2026 roadmap together. Reach out to get started.

Unlock growth with a modern CCaaS strategy. Learn five steps to move from efficiency to customer-centric innovation in your contact center.

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The modern CCaaS strategy: Five steps for turning efficiency into growth

Discover why consumers are choosing AI assistants over traditional apps and how brands can adapt to this shift in digital experience.

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Why consumers prefer AI assistants to apps, and how brands should respond

Join Steve Nemzer, director of AI growth and innovation for TELUS Digital, as he unveils the transformative world of agentic artificial intelligence (AI). Learn what makes AI “agentic”, various use cases and implementation best practices to benefit your organization.

Screen shot of Steve Nemzer, director of AI growth and innovation for TELUS Digital.

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 Are you ready for the era of agentic AI?

On this episode, we explore AI orchestration in contact centers — and what CX leaders need to know to move from digitized chaos to operational clarity.

Image displaying the title text "CX AI: From chaos to clarity" and a "Podcast" label, along with headshots and names for Nuri Gocay and Abby Spahich, as well as logos for TELUS Digital, Zendesk, and Questions for now. 

Questions for now - CX AI: From chaos to clarity image

 How do you move from chaos to clarity with CX AI? (feat. Zendesk)

Assessing data readiness for AI involves 1) defining a vision, 2) prioritizing use cases, 3) doing an initial assessment and 4) diving into critical gaps.

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How to assess data readiness for AI in four steps: A guide for executives

B2B Long-tail management guide - Preview Image

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From neglect to nurture: A five-step guide to transforming your long-tail customer strategy

On this episode, we explore strategies that transform customer experience into a revenue engine — and how to communicate CX wins with language that resonates with C-suite executives.

Episode cover image for Questions for now, a TELUS Digital podcast, featuring the heading "Monetizing CX initiatives" with guests Cami Ferreira and Richard Bledsoe, next to a close-up of two people shaking hands.

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Are you leaving revenue on the table in customer experience?

Unlock AI ROI with proven delivery leadership strategies. Learn how to reduce costs, drive value, and transform skeptics into champions.

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Stop wasting AI investment: How delivery leadership can slash costs and drive ROI

Execs Guide to Agentic AI-CTA

Execs Guide to Agentic AI-Cover

The executive's guide to agentic AI: Enhancing workflows to unlock high-value cost savings

Mapping data is essential for the automotive navigation ecosystem as it enables autonomous vehicles (AV) and advanced driver assistance systems (ADAS) to understand four critical things: where it is, what surrounds it, where it’s headed and the best path to get there.

Mapping requires creating digital representations of the road environment to support safe and efficient driving. In contrast to traditional navigation maps, modern automotive maps feature precise lane-level detail and rich semantic information, such as lane boundaries, road markings, signs and intersection geometry. By providing detailed, precise and centimeter-level accurate digital representations of the road environment, digital maps offer vehicles a human-like context that enhances what their sensors perceive, essentially building a rich, 3D view of the world around them.

These maps are critical for a range of vehicle functions, including:

Augmenting sensor capabilities to see beyond perception systems, with existing maps and recorded features to assist extraction of information, surpassing real-time detection, especially in adverse weather and obstructed visibility conditions.

Better localization through the use of onboard sensors combined with mapped features instead of relying on the global positioning system (GPS) alone.

Improved path planning with information on lane connectivity, traffic rules and road geometry to make safer decisions in complex scenarios like intersections or merges.

Three critical stages for mapping annotation

Since mapping plays a critical role in how vehicles understand and navigate their surroundings, building accurate context via annotations requires a structured, multi-step process. There are three key stages in this process that require careful attention and specialized expertise. Let’s walk though these steps on Ground Truth Studio (GTS), our proprietary annotation platform.

Step 1: Raw data transformation and pre-processing

Once raw data is collected, global navigation satellite system (GNSS) and inertial measurement unit (IMU) measurements are used to estimate the vehicle’s position and orientation over time. Using this data, sensor outputs such as lidar point clouds and images are transformed from the ego-vehicle coordinate frame into a common world reference frame to ensure spatial consistency.

For lidar data captured as timestamped frames, each frame is aligned using ego-motion data to account for the vehicle’s movement.

The processed point cloud and corresponding imagery are spatially and geometrically aligned, enabling accurate interpretation of map features. Multiple aligned consecutive frames are combined to generate an aggregated point cloud (APC), forming a unified spatial view of the environment.

The aggregated point clouds or keyframes need to be enhanced using the following methods:

Environmental and sensor-induced noise is removed using in-house filtering algorithms to improve clarity and accuracy.

The detection and removal of dynamic objects such as vehicles and pedestrians, isolating static features necessary for mapping.

Aggregated frames may contain motion blur due to vehicle movement. Using ego motion compensation (EMC) algorithms can correct such distortions, ensuring sharper, more reliable point clouds.

Step 2: Loading high-density point clouds

For making/editing and quality control (QC) workflows, Ground Truth Studio supports tens of millions of high-density point clouds, allowing teams to work with detailed spatial data without performance compromise.

Optionally, in-house smart downsampling algorithms can be used to label strategic frames, ensuring a smoother annotation experience for huge point clouds when needed. These algorithms optimize point density by preserving high-resolution details like road markings, in critical regions, while downsampling less relevant areas like, for example, vegetation.

Given the complexity and precision required in automotive maps, the annotation process is a vital step in ensuring the final output meets accuracy standards for autonomous or assisted driving applications. To manage the scale of mapping large geographic areas, data is segmented into smaller, more manageable units known as map tiles. Each tile contains:

Associated imagery (from multiple RGB or grayscale cameras)

Calibration metadata for multi-sensor alignment

Pose and timestamp data to enable spatial-temporal validation

Once the tile is set up, annotators create vector representations of real-world features, primarily using 3D polylines and 3D cuboids:

3D polylines are used for road boundaries, lane lines, crosswalks, curb edges and stop lines

3D cuboids may represent traffic signs, poles, signal lights or physical obstacles

Each geometric annotation can then be enriched with attributes, which help define the semantics of a feature, for instance, speed limits or the type of traffic sign. After all map tiles are fully annotated and validated, they are stitched together to form a seamless automotive map.

Key features for 3D polyline annotation platforms on Ground Truth Studio

A full-featured annotation platform, as well as expert annotators trained in mapping, play a significant role in accurate mapping annotations. Smart features are a huge part of ensuring this accuracy with operational efficiency.

Ground Truth Studio offers comprehensive support for automotive mapping, including advanced features to enhance productivity and quality. Using our tool, annotators can create precise 3D polylines and 3D cuboids directly on the point cloud. The platform supports automatic 2D projections on camera images using calibration data if users need to view 2D projections for both cuboid and polyline annotations. For dedicated 2D annotations, our sensor fusion tool enables 2D polyline, rectangle creation and 3D-2D annotation linking across sensor modalities.

Ground Truth Studio offers a wide range of attribute configurations such as single or multi-select and text input for accurate metadata annotation

Parent-child linking (Hierarchical): Used when one annotation depends on another.

Two-way linking (Non-hierarchical): Used for representing peer-to-peer relationships where elements are connected but not strictly dependent in a hierarchical sense. (e.g., linking a virtual lane class (VLC) to associated lane lines).

Each link can include a property, such as specifying if a lane line is on the left or right of a VLC.

To significantly enhance the efficiency, accuracy and ease of use for annotators working with 3D data, our platform is built with a suite of productivity features specifically designed to streamline the 3D polyline annotation workflow.

Automated ground anchoring with ML-generated meshes

Manually setting the height (Z-value) for each point in a 3D polyline can be tedious and error-prone, particularly when working with complex terrain or large datasets. To simplify this, our platform uses machine learning-generated hybrid ground meshes as anchor surfaces. The first annotation made by an annotator automatically snaps to the ground, reducing the need for manual height adjustments and improving both speed and precision.

Our axis locking tools help maintain geometric accuracy by allowing annotators to lock lines to specific orientations, perfectly vertical (Z-axis lock) or horizontal (XY-plane lock). This feature prevents accidental deviations and ensures cleaner, more reliable annotations, eliminating the need for constant manual corrections.

Many real-world road environments contain repetitive structures like parking spaces, building edges and road markings are common. Instead of re-annotating from scratch, our platform allows annotators to duplicate existing polylines and make fine adjustments to individual points. This saves significant time while preserving accuracy.

Line direction visualization and line connection

To provide immediate clarity on the flow or intended direction of an annotation and to connect two 3D polyline annotations to form continuous paths or networks, our platform includes line direction visualization and line connection respectively. This intuitive visual feedback enhances understanding and reduces errors in path-based annotations.

Scaling mapping annotations through partnership

Automotive maps, especially semantic maps enriched with geospatial data, provide the critical context autonomous systems need to make safe, confident decisions on the road. They enhance perception, enable smarter path planning and support scalable operations by helping autonomous systems anticipate road conditions and traffic scenarios.

In contrast, choosing a mapless approach shifts the entire burden of navigation and safety onto real-time systems. This creates several significant challenges:

Your vehicle must constantly process large volumes of raw sensor data, demanding costly and complex onboard hardware

Without prior map knowledge, rare or unpredictable scenarios become harder to handle safely

Proving the safety and reliability of a mapless system across an infinite variety of real-world scenarios is a significant regulatory and engineering challenge

Handling the operational demands of large-scale mapping projects can be highly complex, where efficiency plays a crucial role.

At TELUS Digital, we make mapping scalable and cost-effective. Backed by our team of expert annotators, we combine advanced tools with deep domain knowledge to deliver exceptional accuracy at scale. With tile-based collaboration, smart productivity features and intuitive UX, Ground Truth Studio enables seamless, conflict-free contributions from multiple users working in parallel.

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Computer vision annotation to support autonomous driving systems

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