1. Insights
  2. Digital Experience
  3. Article
  • Share on X
  • Share on Facebook
  • Share via email

Overcoming GenAI implementation challenges in CX

Posted June 13, 2024
Illustration of a paper airplane breaking through a maze, used to symbolize overcoming challenges

Organizations are recognizing the value of artificial intelligence (AI) to drive innovation, improve customer experience (CX) and gain a competitive edge. Generative AI (GenAI) — a type of artificial intelligence that can generate new data or content from existing data — is particularly promising for businesses looking to automate tasks and create personalized experiences.

In fact, a recent survey by Everest Group, supported by TELUS International, found that the majority of customer experience management (CXM) leaders plan to invest heavily in GenAI in 2024, with 55% anticipating a spend of US $1 million or more. This level of investment is a reflection of the perceived value that GenAI can bring to businesses looking to elevate their CX.

However, while many leaders are eager to implement GenAI solutions to reap the benefits of the technology, they also face a number of challenges. Read on to discover the main roadblocks, along with key ways organizations plan to overcome these obstacles in GenAI implementation.

Green and purple bar graph

Everest Group survey results: Enterprise readiness for generative AI adoption in customer experience management

Everest Group, supported by TELUS International, surveyed 200 customer experience leaders from around the world to determine their enterprise readiness for the adoption of generative AI (GenAI). Discover the results.

Download the report

Common challenges of generative AI implementations in CX

Navigating the complexities of GenAI implementation in CX is a multifaceted challenge for many business leaders today. According to the aforementioned survey, "When it comes to their organization's ability to adopt and implement generative AI solutions, CXM leaders share the highest concerns over public solutions exposing them to risks, data security and privacy, and meeting regulatory compliance."

Below we delve into the details of some of the foremost generative AI challenges highlighted by CXM leaders in the survey.

  • Data privacy and security concerns: Brands must balance innovation with the inherent responsibility of protecting customer data and confidential company information. GenAI systems require access to vast datasets to effectively generate content and make decisions. This data can include sensitive information that, if compromised, could lead to significant consequences for individuals and brands. A break or misuse of such data can have far-reaching implications, such as loss of customer trust, reputational damage for the brand, legal and financial consequences and more. To mitigate these risks, brands must ensure that GenAI implementations are supported by robust data governance policies, state-of-the-art cybersecurity measures and ongoing monitoring.
  • Regulatory compliance: Regulations such as GDPR in Europe and CCPA in California impose strict guidelines on data collection, processing and usage, requiring companies to adhere to stringent standards to avoid hefty fines. With GenAI's reliance on the consumption and processing of vast amounts of data, it's important that initiatives integrate thoughtful design to ensure proper handling and anonymization of personal data, minimize unnecessary data processing and maintain transparency in data practices.
  • Ethical and bias considerations: For GenAI to play an effective role in CX delivery, it needs to be seamless, personalized and accurate, but there's a hitch: AI systems might echo existing biases in their training data, leading to unfair treatment. These biases could skew product recommendations, marketing efforts or customer service quality, resulting in discriminatory practices against certain groups. Internally, the implications are equally critical and could impact hiring practices, employee evaluations and more.
  • Sourcing high-quality data: High-quality, diverse and representative datasets are necessary to train AI to deliver valuable and meaningful experiences. However, obtaining such data can be challenging, as it often requires extensive resources, expertise and partnerships. This difficulty is reflected in the previously mentioned survey, which found that the majority of enterprises cited a lack of high-quality training data as a major obstacle for training and testing AI models. Brands must navigate issues like data availability, quality assurance and ethical considerations to secure the requisite data for successful AI implementation. GenAI models trained on low-quality data could result in customers and agents encountering inaccurate information, irrelevant product recommendations or inappropriate support responses.
  • Integration with existing systems and a lack of internal technical expertise to drive the integration of GenAI: Recent Google Cloud findings reveal a sense of urgency among executives to adopt generative AI, with 40% feeling a strong need to implement the technology, regardless of their companies' readiness. But, successfully integrating GenAI requires specialized knowledge in AI development, data science and system architecture, which may exceed the capabilities of existing staff. And leaders are recognizing this skills gap: According to the Google Cloud findings, a significant 62% report that their organizations lack critical AI-execution skills, while a mere 4% believe they possess all the necessary skills to fulfill their AI objectives. Without the necessary expertise, brands may struggle with key aspects such as model training, deployment and optimization — leading to delays, inefficiencies and suboptimal outcomes.
  • Costs: Developing and deploying AI models entail significant upfront investments in talent, infrastructure and technology. Additionally, ongoing expenses for data acquisition, maintenance and monitoring can further strain budgets.

While these generative AI challenges can seem daunting, choosing an outsourcing partner with deep expertise in customer experience and AI implementations can help overcome these barriers and ensure that businesses reap the full benefits of GenAI.

The benefits of an outsourcing partnership for GenAI implementation in CX

Leveraging the expertise of experienced professionals can help brands mitigate the above challenges as they strive to stay on top of evolving technology and keep up with ever-changing customer needs. In fact, according to the aforementioned survey by Everest Group, supported by TELUS International, 76% of respondents are planning to leverage an outsourcing partnership in some capacity to help them implement a generative AI solution in their CX operations.

According to the survey, some of the key factors influencing respondents' decision to outsource or take a collaborative approach include:

  • Limited resources and internal expertise for in-house implementation
  • Cost considerations and budget constraints for building in-house capabilities
  • Time-to-market requirements and the need for faster implementation with outsourced expertise
  • External perspectives and best practices in generative AI from industry experts

An outsourcing partnership enables organizations to leverage the specialized expertise and insights from professionals who are at the forefront of AI research and development. These professionals bring the latest knowledge and techniques, which might not be readily available within your organization. Moreover, outsourcing partners often work with a variety of clients across different sectors. This exposure enables them to apply learnings and innovative solutions from diverse industries, providing a broader perspective on potential applications and improvements in GenAI.

And when it comes time for implementation, outsourcing firms offer the specific skills and resources necessary to handle the complexities of incorporating GenAI into existing technological ecosystems. These firms bring standardized methodologies that have been refined over countless projects — as well as best practices to ensure that AI solutions are developed and deployed efficiently. By tapping into this specialized knowledge, brands can simplify the integration process, reducing the risk of operational disruption and enhancing compatibility between systems.

When navigating the complexities of data privacy and security, outsourcing to a specialized provider can be a game-changer. The right partner will be committed to implementing strict measures throughout the AI lifecycle, ensuring sensitive data is handled with the utmost care. This strategic partnership enables brands to deploy GenAI solutions with confidence, knowing that data security is prioritized at every step.

Furthermore, outsourcing partners, adept in navigating the ethical landscape of AI and equipped with bias mitigation expertise, take a proactive stance to help brands address issues of fairness and inclusivity in their GenAI initiatives. Through rigorous testing, monitoring and refinement, outsourcing partners can help brands navigate ethical dilemmas and uphold values of diversity and equity to build trust in generative AI. Simultaneously, these partnerships are invaluable for securing access to customized, high-quality datasets essential for GenAI success. For example, TELUS International harnesses the intelligence, skills and cultural knowledge of its global community of more than 1 million AI experts and contributors to create the highest quality data.

Lastly, the financial considerations associated with GenAI implementation can't be overlooked. Outsourcing partnerships can present a cost-effective avenue by offering flexible models that align with the unique needs and budgets of brands. With the advantage of economies of scale and shared infrastructure, these partnerships empower brands to streamline expenses, enabling a more efficient allocation of resources and maximizing the return on GenAI investment.

With ample benefits on offer, an outsourcing partnership can be a pivotal step for brands aiming to navigate the GenAI landscape successfully, ensuring that technological advancements in CX translate into tangible business outcomes.

Choosing the right generative AI outsourcing partner

Choosing the right outsourcing partner for your GenAI initiatives is about more than the potential to delegate tasks; it's about identifying a partner whose expertise, reliability and approach align with your business goals. Below we look at essential factors for brands to consider when choosing a GenAI partner.

Does the partner have a proven track record in AI project implementation and CX?

A partner's history of success serves as a testament to their expertise and ability to deliver tangible results. Brands should investigate whether their potential partner has a portfolio of successful projects, particularly those that are akin to their own objectives and within their industry. With AI and CX being as complex and dynamic as they are, a partner's practical experience signifies that they are not just knowledgeable, but also skilled in navigating the real-world challenges that surface during the AI implementation process.

And ensuring that the company you are engaging with has a strategic approach to implementing AI projects shouldn't be overlooked, says Patrick Wright, chief data and AI officer at WillowTree, a TELUS International Company, in an article on AI consulting. "The firm should be able to explain how an AI solution aligns with your business goals and objectives," states Wright. "They should offer a roadmap for the development and implementation process and how the end product will be maintained and updated."

Does the partner have the right knowledge for successful AI implementation?

According to the survey by Everest Group, supported by TELUS International, the majority of enterprises reported a shortage of the right talent pool, including AI/ML engineers, data scientists and software developers needed to integrate GenAI with existing tools.

Generative AI is complex and requires specialized expertise in various areas. If your organization doesn't have the right knowledge and internal skills to navigate the intricacies associated with developing and deploying AI models, find an outsourcing partner who does. The partner you choose should have a comprehensive understanding of topics such as cybersecurity, machine learning and deep learning, programming languages, AI frameworks, robotic process automation (RPA), data engineering and cloud services.

Does the partner have the necessary automated tools and processes?

It's not just the tacit knowledge of your outsourcing partner that is essential to a successful generative AI implementation. They should also be equipped with the right tools and technology to make your AI vision a reality. Automated tools are essential for streamlining repetitive and time-consuming tasks in the AI development lifecycle, such as data pre-processing, model training and testing. These tools enhance efficiency and reduce the scope for human error, leading to a faster time-to-market for AI solutions.

Additionally, with GenAI, large language models can be pre-trained on vast, general datasets to grasp the intricacies of human language. However, pre-trained models may not perform optimally on specialized tasks straight out of the box. That's where AI training platforms come into play. They can empower outsourcing partners to adapt your specific AI model to the nuances and requirements of your business. By training the model on a smaller, domain-specific dataset, fine-tuning adjusts the model's parameters to better reflect the vocabulary, style and information pertinent to a particular use case, whether it be legal texts, medical reports or customer service interactions.

Is the partner familiar with generative AI risks and responsible AI design?

GenAI has the potential to transform a brand's customer experience operations, but it's not without its own set of risks. In a recent webinar titled Building trust in generative AI, Steve Nemzer, director of AI growth and innovation at TELUS International, revealed that a significant risk associated with GenAI is its tendency to produce hallucinations — inaccurate or nonsensical results. "We need to recognize we're in the early days of generative AI," said Nemzer. "Mistakes are going to happen. It could be wrong or fabricated information, a misstep on sensitive issues or just a poor overall experience." He emphasized the necessity to develop strategies to curb these GenAI hallucinations, noting that such errors can seriously damage a brand's reputation.

With so much at stake, ensuring that the outsourcing partner you choose for your generative AI deployments has experience in responsible AI design — the development and deployment of AI systems in a manner that ensures they are ethical, fair, transparent, secure and accountable — is critical. A partner committed to these principles can create generative AI solutions that are not only innovative but also ethical and trustworthy.

What level of support will your partner provide?

Generative AI solutions are not "set-it-and-forget-it" — they require continuous monitoring, fine-tuning and updating to remain effective. As the AI interacts with real-world data and scenarios, it may need adjustments to improve accuracy, relevance and to adapt to changing conditions. A reliable partner should offer ongoing support to handle these updates and provide training to your team, ensuring that the AI system maintains its performance standards and evolves with your business needs. Additionally, they should help in troubleshooting any issues and guide you in expanding or adjusting the AI's functionality as your company grows or as objectives shift.

Get started on your generative AI project

While the journey toward adopting generative AI is not short of potential challenges, there are clear pathways to success. An outsourcing partnership presents a strategic advantage, offering expertise and innovative solutions to help brands navigate the complexities of GenAI. By choosing a partner with a proven track record, robust technical capabilities and deep understanding of ethical and bias considerations, businesses can alleviate the common hurdles of GenAI implementation.

If you're ready to explore the potential of generative AI and how it can transform your business, we invite you to check out our suite of GenAI Solutions, including:

  • A specialized AI training platform, Fine-Tune Studio, designed to create high-quality fine-tuning datasets, and Experts Engine, a platform for on-demand sourcing of human expertise for complex AI training datasets
  • Fuel iX, an enterprise-grade AI engine that accelerates GenAI benefits while ensuring flexibility, control, productivity and trust
  • Our GenAI Jumpstart accelerator, an eight-week engagement designed for companies at an early stage of their AI journey, helping them to rapidly identify use cases, build powerful risk mitigation tools and deliver a customized GenAI-powered virtual assistant prototype

Get started on your GenAI journey by contacting one of our experts today.

Check out our solutions

Harness the power of next-gen technologies and digital accelerators to create the best high-tech, high-touch experiences.

Learn more