In today’s fast-evolving customer experience (CX) landscape, data is often referred to as the “new oil”—a critical resource powering innovation and efficiency.  

Nowhere is this truer than in contact centers, where data from customer interactions fuels everything from service delivery to strategic decision-making. With generative AI (gen AI) entering the scene, this data is not just an asset but a requirement. For contact centers, data is the lifeblood that enables artificial intelligence (AI) to deliver real-time, personalized experiences, optimize workflows, and streamline issue resolutions. 

But before you jump into implementing gen AI, there’s a key question to address: Is your data ready?

The success of AI initiatives hinges not just on the quantity of data but its quality, accessibility, and structure. Data readiness becomes the foundation for any successful AI deployment, ensuring that your contact center is prepared to harness AI’s full potential. But what exactly is data readiness, and how can you assess whether your contact center has it? Let’s dive deeper…

Reach out to discuss this topic in depth. 

What is data readiness?

Data readiness is the state of your organization’s data in terms of quality, quantity, structure, and availability to support AI-driven solutions. In the context of gen AI in contact centers, it refers to having the right kind of structured and unstructured data—voice transcripts, chat logs and customer relationship management (CRM) interactions, etc.—that AI systems can process, learn from, and use to generate intelligent insights or automate workflows.

Think of it like cooking a gourmet meal: You can have the best kitchen equipment (AI tools), but if your ingredients (data) are spoiled or mismanaged, the final dish will be a disaster. The same goes for AI implementation in contact centers — without properly prepared data, even the most advanced gen AI tools can underperform.

What should your data be like for gen AI success?

For gen AI to work effectively in contact centers, the data needs to meet several key criteria:

Exhibit 1: Checklist for data readiness 

Source: Everest Group

Stages of data readiness

Understanding where your organization stands in its data readiness journey is key to planning for gen AI. Here are typical stages:

Exhibit 2: Stages of Data Readiness 

Source: Everest Group

To identify your state in data readiness, answering the following questions will give you a clear picture of your current state:

Exhibit 3: Questions to identify your current state in data readiness 

Source: Everest Group

Tangible steps to improve data readiness

Once you identify your stage, you can follow the below approach to progress toward AI readiness:

Exhibit 4: Tangible steps to improve data readiness 

Source: Everest Group

Leveraging technology, automation, and AI to boost data readiness

Automation tools and AI can play a big role in speeding up the data readiness process. For instance:

• Automation for data cleaning: Deploy Robotic Process Automation (RPA) for cleaning and structuring large datasets, eliminating repetitive manual tasks

• AI-based feedback loops: Implement AI to constantly monitor data quality, identifying gaps and correcting inconsistencies in real-time

• Self-learning systems: As your gen AI models evolve, create a feedback mechanism where the AI itself learns from the data it processes, ensuring continuous improvements

Conclusion

Data readiness isn’t a destination—it’s a continuous journey that ensures your contact center is prepared to maximize the potential of gen AI.

Whether you’re in the early stages of organizing your data or already utilizing advanced AI systems, taking steps to improve your data’s quality, accessibility, and contextual richness will dramatically impact your AI’s effectiveness.

As gen AI continues to evolve, ensuring your data is prepared will not only enhance the customer experience, but also drive efficiency and innovation across your contact center operations. Don’t let poor data hold your AI back—start optimizing today!

If you found this blog interesting, check out our blog focusing on Generative AI In Healthcare – A Game Changer Or Another Fad? | Blog – Everest Group (everestgrp.com), which delves deeper into another topic regarding gen AI. 

If you have questions or want to discuss CX strategies and solutions, please contact Mohit Kumar or Chhandak Biswas. 

Adoption of Generative AI

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The landscape of Customer Experience Management (CXM) has witnessed a remarkable transformation within the advent of Generative AI (generative artificial intelligence). Based on periodic comprehensive studies conducted by Everest Group with customer experience (CX) leaders from over 300 enterprises globally, we present comparative insights that highlight the progress made in the past year (2023 to 2024).

Using two different primary studies, research has been conducted regarding gen AI in CXM operations, in the process providing our perspective on future developments.

Reach out to us to discuss this topic further with our expert analysts.

Adoption of digital CX solutions – 2023 vs 2024

Propelled by gen AI, a significant shift has been observed in the adoption of digital CX solutions such as automation, self-service, conversational artificial intelligence (AI), data and analytics, and migration to cloud contact centers.

There was a 15-30% increase in the number of enterprises having deployed these solutions from 2023 to 2024.

Generative AI awareness and its potential

Noteworthy changes in the awareness and potential of various gen AI use cases were also observed during this analysis. In 2023, while most enterprises had a good understanding of applications such as text, image, and code generation, few had robust knowledge of other application areas.

However, this scenario changed significantly in 2024. The majority of enterprises across industries now report having a solid working knowledge of various gen AI applications. Many are even considering synthetic data generation and audio and video generation as high-potential applications for gen AI in CXM.

The role of third-party providers

The role of third-party providers has become pivotal for enterprises, as they look to navigate complexities. Their importance is increasingly becoming more significant as enterprises realize the various nuances required in developing these solutions.

In 2024, there is a significant uplift in enterprises opting for tech-heritage or specialized AI companies, to use for implementation of gen AI, to be able to leverage their expertise in this technology and achieve faster time to market.

Additionally, more enterprises are outsourcing to contact center providers for gen AI integrations, capitalizing on their CXM domain expertise to better customize customer journeys and improve productivity and CX metrics.

Conversely, there has been a notable decline in the hybrid approach to gen AI development which combines both in-house and outsourced development. From a whopping 70% in 2023, the percentage of enterprises preferring this mode has reduced to only around 30%. This decline, accompanied by a decline in internal development, can be attributed to the change in business priorities for organizations and their need to have eagle-eyed focus on improving their core competencies and achieving their business objectives of revenue improvement, cost reduction, and adapting to new business challenges.

Enterprises choosing to invest wisely in their long- and short-term approach to Gen AI

From a financial perspective, enterprises exhibited a more optimistic stance toward generative AI adoption in 2023, with nearly two-thirds planning to invest over US$1 million in the next 12-18 months on gen AI solutions in CXM.

However, as the technology has matured, enterprises now have a clearer understanding of the returns these investments can generate. Over the past year, many enterprises observed that a significant number of gen AI pilots failed to progress to the deployment phase.

Consequently, in 2024, enterprises have taken a more cautious approach toward gen AI adoption. They now prefer to evaluate each application on a use-case basis before committing to full-scale investments. This shift is reflected in the investment budgets for gen AI, with only half of the enterprises (down from two-thirds) now planning to spend more than US$1 million on these initiatives. This decrease in investments on gen AI is propelled further by the current difficulties in the macroeconomic and business environments, where organizations are placing cost reduction and revenue enhancement as their top priority.

This cautious stance, however, does not mean that there is a decrease in the perceived potential of gen AI. 2025 continues to hold promise of a booming gen AI adoption. In fact, more than 80% of the enterprises plan to invest more than US$1 million in 2025. As gen AI continues to demonstrate its potential and deliver its promised outcomes, enterprises are likely to embrace it with increased enthusiasm.

If you found this blog interesting, registrations are now open for our Gen AI Unhyped: How It Is Evolving And How To Plan For Success | LinkedIn Live – Everest Group event LinkedIn Live event on September 11, 2024!

If you have questions or want to discuss CX strategies and solutions, please contact Mohit Kumar at mohit.kumar@everestgrp.com or Aishwarya Barjatya at Aishwarya.Barjatya@everestgrp.com.

By strategically partnering with third-party providers, enterprises can fully harness the potential of gen AI in customer experience management. Learn insights from our latest survey on enterprise readiness for gen AI adoption and how collaborating with providers can help overcome the major obstacles.

Reach out to us for more information or to further discuss this topic.

Generative Artificial Intelligence (gen AI) is emerging as a game-changer in customer experience management (CXM) by offering the potential to personalize customer interactions, enhance operational efficiency, and provide a competitive edge. As enterprises adopt gen AI, third-party providers have an increasingly vital role. Let’s explore this further.

Current demand for gen AI

Demand for gen AI solutions is skyrocketing among enterprises across industries. According to a recent Everest Group survey of top executives and CXM leaders of 200 enterprises worldwide, nearly 75% believe gen AI will significantly impact their CXM strategies within the next two years.

Enterprises highlighted personalized interactions, cost reduction, and operational efficiency as the top three drivers for gen AI adoption.

• Personalized customer interactions: Gen AI can produce customized content, product recommendations, dynamic pricing, and marketing campaigns tailored to individual customer preferences, enhancing customer satisfaction and loyalty
• Increased efficiencies and automated CXM processes: Gen AI can empower customer support agents with intelligent tools such as agent assist, next-best-action recommendations, language translation, and accent neutralization. These tools enhance productivity, reduce response times, and enable agents to focus on more complex and value-added tasks. Using gen AI to automate routine tasks and repetitive processes can further boost operational efficiency
• Reduced cost: By automating repetitive tasks, streamlining processes, and enabling advanced data and analytics capabilities, gen AI adoption can increase operational efficiency and productivity, optimizing cost

Current enterprise capabilities

Despite growing gen AI demand, the technology is still nascent. Most enterprises and service providers are investigating gen AI operations use cases or piloting solutions to test feasibility. Given the technology’s evolving nature, enterprises currently face or will likely encounter challenges in adopting and implementing it effectively.

Our survey asked enterprises about their readiness to adopt gen AI to identify significant areas where enterprises need specialized support. Below are the results of the preparedness of enterprises across industries and key parameters:

EXHIBIT 1, Enterprise readiness for gen AI by industry, Source: Everest Group (2023)

Challenges in gen AI implementation

Based on the readiness levels and significant challenges identified by enterprises in the survey, the following overarching issues for enterprises emerged:

• 55% of enterprises reported a shortage of the right talent pool, including AI/ML engineers, data scientists, and software developers needed to integrate gen AI with existing tools and create agent interfaces
• 56% of enterprises highlighted the scarcity of high-quality training data required for training and testing models
• Between 45-50% of enterprises expressed concerns about computing power required for gen AI adoption and their ability to scale
• Regulatory compliance concerning the fairness of the output, data security, and privacy, and misuse of the models also stood out as significant issues for enterprises

How can enterprises navigate these challenges?

The gaps in enterprise capabilities identified in our survey underscore the need for strategic partnerships to ensure successful implementation. While executing gen AI in-house offers greater control over the development and implementation process and allows enterprises to tailor solutions to specific needs, it requires significant internal expertise and resources that may be limited. Outsourcing gen AI implementation can provide access to specialized knowledge and resources, accelerating the implementation process and reducing the time to market for these solutions.

Our survey revealed that nearly 89% of the enterprises are looking to either outsource the implementation of gen AI in customer experience management to specialized AI companies or contact center providers or follow a hybrid approach involving in-house and outsourced development.

EXHIBIT 2, Implementation plan for Gen AI solutions within CXM, Source: Everest Group (2023)

The top three areas where enterprises are seeking service providers’ support are:

• Building gen AI solutions and enhancing technical capabilities: Enterprises need partners who can help train gen AI models on enterprise data, customize foundational models to build tailored solutions, and set up necessary computational systems to run these solutions
• Integrating gen AI solutions with existing technologies: Enterprises need partners who can create necessary bridges to integrate gen AI with existing business intelligence tools, providing a more comprehensive view of customer interactions
• Supporting and complimenting in-house teams: Enterprises need partners who can support in-house technical teams, aid in maintenance, troubleshoot activities, and supervise the functioning of gen AI solutions

Future outlook

Gen AI has the potential to revolutionize CXM, enabling enterprises to deliver personalized, efficient, and innovative customer experiences. However, successfully adopting and implementing gen AI requires overcoming expertise, data quality, and change management challenges. Strategic alliances with third-party providers can bridge these gaps, providing enterprises with the necessary industry-specific knowledge, resources, and guidance to unlock the full potential of gen AI.

Third-party providers have a crucial role in helping enterprises translate the perceived potential of gen AI into practical applications. These partners can help enterprises identify specific CXM use cases where gen AI can deliver tangible benefits, develop gen AI strategies, design and implement solutions, and provide ongoing support to ensure successful gen AI adoption.

Read Everest Group’s Generative AI in CXM: Assessing Enterprise Readiness for this Disruptive Transformation to better understand gen AI in Customer Experience Management and how providers can help enterprises adopt gen AI. If you have questions or want to discuss digital CX strategies and solutions, contact Anubhav Das at anubhav.das@everestgrp.com or Mohit Kumar at mohit.kumar@everestgrp.com.

Don’t miss our LinkedIn Live, How Will Next-gen Technologies Be Financed in CXM Delivery?