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6-steps-for-Conversational-Bot-success-A-primer 

Make the most of your conversational bot investment with our easy-to-follow guide featuring best practices that can be applied to your digital transformation journey.

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(Featured) 6-steps-for-Conversational-Bot-success-A-primer

Six steps for conversational bot success: A helpful primer for chatbot implementation

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Driving efficiency with digital automation for a financial services brand

Discover how TELUS International implemented a comprehensive intelligent automation
solution for a financial services client that enabled operational efficiency, a
reduction in employee effort and an overall increase in customer satisfaction.

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Artificial intelligence is set to define the future of banking. Learn about how banks are using machine learning, RPA and other AI-powered tech.

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Six AI technologies set to define the future of banking

Protecting your company's reputation in an evolving digital world isn't always easy. Between cyberattacks, phishing, fraud and other harmful behaviors, companies have a lot of ground to cover when it comes to preserving their brand.

These concerns are often compounded when working with a customer experience (CX) provider. Not only do you need to ensure that your brand promise is met in an outsourced environment, but also that your customer and company data is protected.

When selecting a customer experience provider, risk mitigation should be a top consideration. The right partner will make sure you're covering every angle to care for the security of both your brand and your customers — at every touchpoint.

What should you look for in an outsourcing provider?

When it comes to customer experience outsourcing, brands typically have two primary concerns: preserving customer data, and keeping all systems that might compromise operations tight as a drum, says Steve Jablonski, vice-president of information security at TELUS International. These concerns are often driven by an idea that outsourcing some aspects of your business means giving up control — but that's not the case when the right checks and balances are in place.

Companies and their outsourcing partners need to set clear parameters about how to proactively prevent harm, and how to respond if it ever occurs. Setting a strong communication foundation in your outsourcing relationship enables your brand to work in concert with your partner to avoid and mitigate risk. With that at the core, here are three additional factors to consider when assessing an outsourcing partner.

Training: Team members are the first line of defense

With both online reputation management and protecting customer data, organizations frequently wonder where to begin. The answer is: start with employees. Team members are often the first line of defense against security breaches and ever-present bad actors. "Think of your CX team as a human firewall," says Jablonski. "You have to prepare your customer care representatives for battle by providing them with comprehensive training."

Rather than taking a one-and-done approach to customer service training, partners should treat it as an ongoing process, every part of which can strengthen and enhance the customer care team. As threats become more sophisticated and ill-intentioned parties find new ways to infiltrate your defenses, it's important that your team's knowledge base has comparably advanced documentation.

That said, it isn't just outsiders that are cause for concern. When assessing a CX outsourcing partner, businesses often want to know what's being done to mitigate insider risk and to keep employees from jeopardizing the system. While it's smart to ask questions related to brand loyalty and engagement, you should also consider whether teams are being sufficiently trained to prevent all manner of attacks and breaches, adds Jablonski.

Outsourcing partners must have processes in place to mitigate risk from internal and external individuals intent on committing a crime and innocent insiders who fall victim to phishing links. Phishing simulations and security awareness training are useful protocols in a world where threats are subtle and ever-changing. Team members should also know what to do if they suspect a colleague is acting nefariously. Training employees to recognize patterns is crucial, Jablonski says, "because you can't always get ahead of the threat."

Technology: Taking advantage of economies of scale

According to recent research conducted by TELUS International and Statista, leaders named data security and compliance as the number one challenge impeding their digital customer experience goals in 2023. The same research found that transparency and data security were considered the most important qualities for an outsourcing provider.

Collaborating with the right digital CX partner can provide brands with access to best-in-class software with the proper security checks. "There needs to be solid due diligence when buying secure software," Jablonski says of outsourcing partners. "Robust patching, internal scans and penetration testing should occur on a regular basis." A good outsourcing partner not only does everything in its power to safeguard your company; it also takes the pressure off brands to identify and invest in the new technologies needed to deliver a secure and scalable CX.

Put security management processes under the microscope

Outsourcing providers should be investing in market intelligence to stay on top of potential threats. Jablonski suggests that the right partner will "leverage paid and public intelligence to stay current. Your provider should always be collecting information and taking action." They should also have a strong network in place for sharing brand and industry information, he adds.

Look for a partner that prioritizes security at all levels and makes a point of obtaining their third-party certifications demonstrating their commitment to protecting brands like yours. Regulatory compliance and certifications like ISO27001 offer a solid foundation for cybersecurity. NIST, the National Institute of Standards and Technology at the U.S. Department of Commerce, is a good resource for publications, reports and other information about the standardization process, while the NIST 800-53 framework serves as a comprehensive guide to strategies, systems and controls that businesses should employ based on risk, cost and an organization's capabilities. Other options include industry-specific certifications, like HITRUST in the healthcare space.

Regardless of the approach your outsourcing partner opts to take, key controls like multi-factor authentication and virtual desktop infrastructure (VDI) should be standard. Consider your potential partner's stance on working from home and bring your own device (BYOD) management, too. From data leakage to loss and infection, related policies should be treated with the utmost caution and care.

Above all else, when selecting an outsourcing partner you should aim to find a company that values your brand and customers as much as you do. "Your customers are our customers," says Jablonski of TELUS International's mindset. A quality outsourcing partner should share your commitment to defending against threats, while also working to improve your customer experience.

Risk mitigation should be a top consideration when selecting an outsourcing partner. Learn what to look for to ensure your brand is protected.

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Protecting your customers and brand in an outsourced environment

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TELUS International, in partnership with Statista, surveyed 250 enterprise leaders to explore where they plan to invest in digital customer experience (CX) technology in 2023. Uncover the results.

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Survey results: Digital customer experience priorities for leaders in 2023

The digital world continues to shift rapidly. Discover what's next for content moderation to help you navigate the changes in this evolving industry.

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What's next for content moderation in 2023 and beyond?

Learn how TELUS International helped a leading European automotive machine learning team build high-quality datasets to power computer vision innovations at scale.

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Computer vision annotation to support autonomous driving systems

Artificial intelligence (AI) proponents around the globe believe that autonomous vehicles, enabled with smart sensors and intelligent algorithms, will significantly improve road safety in the upcoming years. On the other end of the spectrum, opponents feel that autonomous vehicles pose a greater risk to pedestrian and driver safety, requiring additional protective measures. To quell these fears, in July 2022, The Council of the European Union mandated advanced safety systems in all automated road vehicles, including cars, trucks, buses and vans.

Despite criticisms, automotive innovators aren’t hitting the brake pedals on innovation. According to a report by ResearchAndMarkets.com, the global autonomous vehicle market will grow to over $2.16 trillion by 2030. Self-driving cars and robo-taxis from front-runners like Tesla, Waymo and Cruise are already hitting the roads. As autonomous vehicles continue to grow in popularity, AI technologies — powered by high-quality labeled data — will play a key role in ensuring superior safety standards for autonomous vehicles.

The case for autonomous vehicles improving road safety

Despite considerable safety enhancements made over the past decade, recent reports published by the National Highway Traffic Safety Administration (NHTSA) in the U.S. estimated that there were over 40,000 traffic fatalities in the previous year, a 10.5% increase from 2020. Most of these accidents were attributed to human error and caused by distractions, negligence or split-second misjudgments. Thus leading self-driving car experts to believe enhancing the role of technology will save many lives.

Autonomous vehicles offer distinct levels of autonomy, ranging from Level 0 to Level 5 (L5), where each level reduces the amount of human intervention in driving. Today, many AI-enabled driver assistance technologies like adaptive cruise control, automatic emergency braking and lane departure or forward collision warning systems — considered Level 2 — offer built-in support for drivers to reduce accidents and the resulting injuries or fatalities caused by impact. They are common additions to new vehicles sold in the United States and Europe.

A study conducted using the RAND Model of Automated Vehicle Safety concluded that highly automated cars that are 10% safer than human-driven cars offer a better probability of reducing fatalities and saving more lives than waiting for systems to be 70% or 90% better. “We find that, in the short term, more lives are cumulatively saved under a more permissive policy (Improve10) than stricter policies requiring greater safety advancements (Improve75 or Improve90) in nearly all conditions, and those savings can be significant — hundreds of thousands of lives,” the study outlines.

The sheer potential of autonomous vehicles continues to push car manufacturers to pursue L5 autonomy, where autonomous vehicles drive and make critical decisions on the road without human intervention.

High-quality annotated data will fuel the autonomous vehicles of tomorrow

Autonomous vehicles, functioning via intricate multi-neural network systems, require large volumes of diverse and accurately-labeled datasets to execute complex actions in response to real-world stimuli. Supervised learning is one of the main machine learning (ML) techniques employed to train autonomous systems.

Primarily, computer vision models enhance visual senses for AI-powered mobility systems. Data collected from various sensors such as cameras, lidars and radars are labeled accurately to help autonomous vehicles develop machine vision and perception. These algorithms are trained using the annotated data to perceive its surroundings and detect other vehicles and pedestrians sharing the road.

The standard data inputs for computer vision models include image, video, point clouds and geo data. Data annotation procedures, such as image classification, object localization, detection and tracking, image or point-cloud segmentation, HD mapping and more, help train and familiarize models with the real world. From simple bounding boxes or polygons, to more complex 3D semantic segmentation, the type of labels used for the data varies from one use case to another. Case in point — a lane detection machine learning model will benefit from accurate lane markings made using polylines, and a pedestrian detection ML algorithm will produce better results using polygons.

Increasingly, innovators are also experimenting with speech recognition and natural language processing applications to enable voice-assisted features in autonomous vehicles to refine the overall driving experience. Text and audio data annotation such as transcription, translation, text categorization, linguistic annotation, classification and more provide inputs for these models. The key to unlocking all of these possibilities lies with high-quality and accurately labeled data.

There’s still a long road ahead before self-driving cars are fully capable of making judgments without human intervention. Better autonomous vehicle perception requires high-quality data spanning several different real-world scenarios. However, sourcing training datasets that accurately represent a large volume of edge cases and safety-critical scenes can be challenging. Moreover, the dynamically evolving nature of our world and human unpredictability on roads calls for advanced and sophisticated mobility systems trained on accurately annotated data.

Generating comprehensive datasets for fail-safe models demands labor-intensive processes and sophisticated data solutions to collect and label data, test and validate quality, and ensure data diversity and volume of edge cases. TELUS International is a leading AI Data Solutions provider with years of experience building high-quality datasets for autonomous driving use cases. Talk with our experts and leverage our expertise to accelerate your autonomous vehicle projects.

Experts believe autonomous vehicles will substantially improve road safety. Discover how large, diverse and accurate datasets are enabling this vision.

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Does data hold the key to autonomous vehicle safety?

Machine learning demands massive amounts of clean, structured, labeled data. Learn the fundamentals of data labeling for successful AI training.

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data labeling ml

Data labeling fundamentals for machine learning

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Outsourcing Locations E-book Feature

Discover some of the top outsourcing destinations for consideration and the factors to keep in mind as you make your decision.

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Selecting the best outsourcing destination for your business

Driver monitoring systems (DMS) are proving to be effective in helping prevent accidents due to driver drowsiness or inattention. Learn all about these AI-backed systems.

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Driver monitoring systems: Needs, regulations, popular use cases and trends

Keep up with the latest in digital customer experience in 2023. Check out this list of important stats to kick-start your CX planning for the year ahead.

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23 digital customer experience stats to know for 2023

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Streamlining operations and kickstarting the digital transformation journey - preview image

Want to speak with a Process Consulting expert?

Uncover how TELUS International’s business process consultants helped a leading financial services brand optimize its operations, enhance its customer experience (CX) and begin its digital transformation journey.

Streamlining operations and kickstarting the digital transformation journey of a leading financial services brand

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In this IDC Info Snapshot, learn about the three key pillars for any successful AI implementation and the advantages of partnering with a trusted advisor to create and deliver your AI vision.

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Quality, quantity, and responsibility: Three key data pillars for successful AI implementations

Natural language processing (NLP) is the term used to describe a machine’s ability to interpret and respond to language data. Learn why it is a key AI concept.

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What is Natural Language Processing?

What is natural language processing?

Video annotation enriches video data by adding object information tags across every frame. Discover different video annotations, their popular computer vision applications and the benefits of video labeling automation.

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Video annotation for computer vision

TI resource eBook 800x450 The essential guide to training data 

Discover best practices for the sourcing, labeling and analyzing of training data from TELUS International, a leading provider of AI data solutions.

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The essential guide to AI training data

Player support outsourcing isn't just for Triple-A studios. Learn about the benefits of outsourcing for growing games companies.

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What growing games companies can gain from outsourcing

Germany's diverse and talented workforce make the European nation an ideal CX outsourcing destination. Read up on all the benefits.

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Don't overlook Germany as a CX outsourcing destination

Learn how TELUS International helped a leading automotive lidar systems provider create high-quality flash-lidar 3D datasets with our fully-managed data annotation solutions.

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Industry - Automotive - Solutions - Image annotation 

Building high-quality 3D flash lidar datasets for autonomous vehicles

If words like "amortization" get your customers feeling anything but amore, you're not alone.

Many would-be customers grapple with the complexities of financial decision-making. It's easy for them to become paralyzed by choice: should they save? Invest? Pay down debt?

These anxieties are compounded by an increasing sense of financial vulnerability amid rising inflation and costs of living. According to the American Psychological Association's 2022 Stress in America™ survey, 65% of respondents say money is the most significant stressor in their life — a steady rise from the previous survey results from 2021.

Automotive

Does data hold the key to autonomous vehicle safety?

Does data hold the key to autonomous vehicle safety?

Building high-quality 3D flash lidar datasets for autonomous vehicles

Driver monitoring systems: Needs, regulations, popular use cases and trends

Video annotation for computer vision

Computer vision annotation to support autonomous driving systems

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