Generative AI

Generative AI

Generative AI

Generative AI

Echo AI Takeaways on Forrester Report: Generative AI Is The Catalyst For Change In The Contact Center

Echo AI Takeaways on Forrester Report: Generative AI Is The Catalyst For Change In The Contact Center

Echo AI Takeaways on Forrester Report: Generative AI Is The Catalyst For Change In The Contact Center

Echo AI Takeaways on Forrester Report: Generative AI Is The Catalyst For Change In The Contact Center

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Jul 16, 2024

Jul 16, 2024

Jul 16, 2024

Jul 16, 2024

Forrester Report on Gen AI
Forrester Report on Gen AI
Forrester Report on Gen AI
Forrester Report on Gen AI

Echo AI was recently included in the Forrester Report ‘Generative AI Is The Catalyst For Change In The Contact Center.’ Get our key highlights below, and download the report for more.

Generative AI (genAI) has evolved from being the latest in the AI hype to becoming a strategic tool that goes beyond efficiency and enhances every layer of the organization, from customer service to marketing to operations and beyond. It has single-handedly accelerated the urgency for contact centers to move away from long-standing reliance on legacy systems and embrace the migration towards an AI-augmented future.

Yet, there is a clear shift happening. GenAI has sparked an impetus to overhaul long-neglected tech stacks, reimagine strategies for navigating the tech evolution, and embrace an AI-augmented era with diverse, innovative genAI use cases—like the ones Echo AI offers— for richer insights that turn customer conversations into growth.

In its recent report ‘Generative AI Is The Catalyst For Change In The Contact Center,’ Forrester explores the major trends that contact center leaders must navigate as they lay their strategy for this new era. 

Read on for more findings from the report and explore how contact center leaders can integrate genAI into their organizations to deeply understand their customers, stay agile, and drive business outcomes.

Echo AI was recently included in the Forrester Report ‘Generative AI Is The Catalyst For Change In The Contact Center.’ Get our key highlights below, and download the report for more.

Generative AI (genAI) has evolved from being the latest in the AI hype to becoming a strategic tool that goes beyond efficiency and enhances every layer of the organization, from customer service to marketing to operations and beyond. It has single-handedly accelerated the urgency for contact centers to move away from long-standing reliance on legacy systems and embrace the migration towards an AI-augmented future.

Yet, there is a clear shift happening. GenAI has sparked an impetus to overhaul long-neglected tech stacks, reimagine strategies for navigating the tech evolution, and embrace an AI-augmented era with diverse, innovative genAI use cases—like the ones Echo AI offers— for richer insights that turn customer conversations into growth.

In its recent report ‘Generative AI Is The Catalyst For Change In The Contact Center,’ Forrester explores the major trends that contact center leaders must navigate as they lay their strategy for this new era. 

Read on for more findings from the report and explore how contact center leaders can integrate genAI into their organizations to deeply understand their customers, stay agile, and drive business outcomes.

Echo AI was recently included in the Forrester Report ‘Generative AI Is The Catalyst For Change In The Contact Center.’ Get our key highlights below, and download the report for more.

Generative AI (genAI) has evolved from being the latest in the AI hype to becoming a strategic tool that goes beyond efficiency and enhances every layer of the organization, from customer service to marketing to operations and beyond. It has single-handedly accelerated the urgency for contact centers to move away from long-standing reliance on legacy systems and embrace the migration towards an AI-augmented future.

Yet, there is a clear shift happening. GenAI has sparked an impetus to overhaul long-neglected tech stacks, reimagine strategies for navigating the tech evolution, and embrace an AI-augmented era with diverse, innovative genAI use cases—like the ones Echo AI offers— for richer insights that turn customer conversations into growth.

In its recent report ‘Generative AI Is The Catalyst For Change In The Contact Center,’ Forrester explores the major trends that contact center leaders must navigate as they lay their strategy for this new era. 

Read on for more findings from the report and explore how contact center leaders can integrate genAI into their organizations to deeply understand their customers, stay agile, and drive business outcomes.

Echo AI was recently included in the Forrester Report ‘Generative AI Is The Catalyst For Change In The Contact Center.’ Get our key highlights below, and download the report for more.

Generative AI (genAI) has evolved from being the latest in the AI hype to becoming a strategic tool that goes beyond efficiency and enhances every layer of the organization, from customer service to marketing to operations and beyond. It has single-handedly accelerated the urgency for contact centers to move away from long-standing reliance on legacy systems and embrace the migration towards an AI-augmented future.

Yet, there is a clear shift happening. GenAI has sparked an impetus to overhaul long-neglected tech stacks, reimagine strategies for navigating the tech evolution, and embrace an AI-augmented era with diverse, innovative genAI use cases—like the ones Echo AI offers— for richer insights that turn customer conversations into growth.

In its recent report ‘Generative AI Is The Catalyst For Change In The Contact Center,’ Forrester explores the major trends that contact center leaders must navigate as they lay their strategy for this new era. 

Read on for more findings from the report and explore how contact center leaders can integrate genAI into their organizations to deeply understand their customers, stay agile, and drive business outcomes.

GenAI has Sparked an Overhaul of Long-Neglected Technology Foundations

The rush to adopt genAI has uncovered major gaps in the operational and technological underpinnings of the contact center. It’s no secret that older technology systems have contributed greatly to poor customer service delivery, but genAI has opened the door for much needed knowledge base face-lifts, AI-enhanced agents (from workspaces to workflows), and a closer look at outdated measurement practices (AHT, anyone?) that are missing the mark on more contextual KPIs (such as those surfaced with conversation analytics.)

As stated in the report, “Contact center leaders must prioritize analytics that provide insights into the contributions of human agents and their AI counterparts, allowing them to understand performance and customer experience (CX) more comprehensively.”

The rush to adopt genAI has uncovered major gaps in the operational and technological underpinnings of the contact center. It’s no secret that older technology systems have contributed greatly to poor customer service delivery, but genAI has opened the door for much needed knowledge base face-lifts, AI-enhanced agents (from workspaces to workflows), and a closer look at outdated measurement practices (AHT, anyone?) that are missing the mark on more contextual KPIs (such as those surfaced with conversation analytics.)

As stated in the report, “Contact center leaders must prioritize analytics that provide insights into the contributions of human agents and their AI counterparts, allowing them to understand performance and customer experience (CX) more comprehensively.”

The rush to adopt genAI has uncovered major gaps in the operational and technological underpinnings of the contact center. It’s no secret that older technology systems have contributed greatly to poor customer service delivery, but genAI has opened the door for much needed knowledge base face-lifts, AI-enhanced agents (from workspaces to workflows), and a closer look at outdated measurement practices (AHT, anyone?) that are missing the mark on more contextual KPIs (such as those surfaced with conversation analytics.)

As stated in the report, “Contact center leaders must prioritize analytics that provide insights into the contributions of human agents and their AI counterparts, allowing them to understand performance and customer experience (CX) more comprehensively.”

The rush to adopt genAI has uncovered major gaps in the operational and technological underpinnings of the contact center. It’s no secret that older technology systems have contributed greatly to poor customer service delivery, but genAI has opened the door for much needed knowledge base face-lifts, AI-enhanced agents (from workspaces to workflows), and a closer look at outdated measurement practices (AHT, anyone?) that are missing the mark on more contextual KPIs (such as those surfaced with conversation analytics.)

As stated in the report, “Contact center leaders must prioritize analytics that provide insights into the contributions of human agents and their AI counterparts, allowing them to understand performance and customer experience (CX) more comprehensively.”

How to Navigate The Shifting Sands Of Tech Evolution

The contact center tech landscape is evolving at whiplash speed. The market is merging, ecosystem boundaries are becoming more fluid, and there is greater opportunity for contact centers to apply flexible, hybrid approaches to adopting new technologies. This applies to everything from pricing to procurement to integration capabilities. For example, the infamous build vs. buy conundrum doesn’t have to be black and white. It’s advantageous to partner with, or “buy” genAI vendors who have built their own LLM pipeline analysis to ensure high accuracy and reduce costs. However, this approach can harmoniously connect with existing in-house tools or tech systems, enabling contact centers to adopt new technologies while not ripping out the systems they have today. 

“Savvy teams will direct their energies to where they have inherent competitive advantage and expertise and identify strategic partnerships with vendors known for  domain-specific excellence and flexible integration capabilities."

The contact center tech landscape is evolving at whiplash speed. The market is merging, ecosystem boundaries are becoming more fluid, and there is greater opportunity for contact centers to apply flexible, hybrid approaches to adopting new technologies. This applies to everything from pricing to procurement to integration capabilities. For example, the infamous build vs. buy conundrum doesn’t have to be black and white. It’s advantageous to partner with, or “buy” genAI vendors who have built their own LLM pipeline analysis to ensure high accuracy and reduce costs. However, this approach can harmoniously connect with existing in-house tools or tech systems, enabling contact centers to adopt new technologies while not ripping out the systems they have today. 

“Savvy teams will direct their energies to where they have inherent competitive advantage and expertise and identify strategic partnerships with vendors known for  domain-specific excellence and flexible integration capabilities."

The contact center tech landscape is evolving at whiplash speed. The market is merging, ecosystem boundaries are becoming more fluid, and there is greater opportunity for contact centers to apply flexible, hybrid approaches to adopting new technologies. This applies to everything from pricing to procurement to integration capabilities. For example, the infamous build vs. buy conundrum doesn’t have to be black and white. It’s advantageous to partner with, or “buy” genAI vendors who have built their own LLM pipeline analysis to ensure high accuracy and reduce costs. However, this approach can harmoniously connect with existing in-house tools or tech systems, enabling contact centers to adopt new technologies while not ripping out the systems they have today. 

“Savvy teams will direct their energies to where they have inherent competitive advantage and expertise and identify strategic partnerships with vendors known for  domain-specific excellence and flexible integration capabilities."

The contact center tech landscape is evolving at whiplash speed. The market is merging, ecosystem boundaries are becoming more fluid, and there is greater opportunity for contact centers to apply flexible, hybrid approaches to adopting new technologies. This applies to everything from pricing to procurement to integration capabilities. For example, the infamous build vs. buy conundrum doesn’t have to be black and white. It’s advantageous to partner with, or “buy” genAI vendors who have built their own LLM pipeline analysis to ensure high accuracy and reduce costs. However, this approach can harmoniously connect with existing in-house tools or tech systems, enabling contact centers to adopt new technologies while not ripping out the systems they have today. 

“Savvy teams will direct their energies to where they have inherent competitive advantage and expertise and identify strategic partnerships with vendors known for  domain-specific excellence and flexible integration capabilities."

Understanding Top Gen AI Use Cases for The AI-Augmented Era

GenAI has changed how contact centers interact with customers as well as how they analyze these interactions. The initial excitement (and horror, for some) around the chance to fully automate customer conversations has worn off as limitations of genAI in the contact center have made themselves known. 

It’s also clear that post genAI strategies require holistic data insights that can improve every layer of the organization. Post-call summarization stands out as one of the most straightforward applications of genAI for contact centers, offering clear, attributable ROI. 

While basic free-text summaries streamline post-call note taking for agents, vendors like Echo AI have designed solutions that convert unstructured conversations into meaningful structured data. For example, Echo AI can analyze millions of data points from calls, emails, tickets and more, and then identify and tag contact sub-reasons that would be tedious if done manually. Echo AI’s AI Analysis Summary offers a rich yet easy to digest overview of insights.

By combining genAI with conversation intelligence, Echo AI helps contact centers gain full visibility into interactions, deeply understand churn drivers, and act automatically to address them. By analyzing every touchpoint and customer data point, Echo AI can identify patterns and pinpoint specific issues that lead to customer frustration, all without the need to configure tags or keywords. This is the generative component of the technology; insights are surfaced, categorized, and analyzed autonomously.

Let’s explore some real ways that contact centers have used genAI insights to reduce churn, improve conversions, and protect customer satisfaction.

GenAI has changed how contact centers interact with customers as well as how they analyze these interactions. The initial excitement (and horror, for some) around the chance to fully automate customer conversations has worn off as limitations of genAI in the contact center have made themselves known. 

It’s also clear that post genAI strategies require holistic data insights that can improve every layer of the organization. Post-call summarization stands out as one of the most straightforward applications of genAI for contact centers, offering clear, attributable ROI. 

While basic free-text summaries streamline post-call note taking for agents, vendors like Echo AI have designed solutions that convert unstructured conversations into meaningful structured data. For example, Echo AI can analyze millions of data points from calls, emails, tickets and more, and then identify and tag contact sub-reasons that would be tedious if done manually. Echo AI’s AI Analysis Summary offers a rich yet easy to digest overview of insights.

By combining genAI with conversation intelligence, Echo AI helps contact centers gain full visibility into interactions, deeply understand churn drivers, and act automatically to address them. By analyzing every touchpoint and customer data point, Echo AI can identify patterns and pinpoint specific issues that lead to customer frustration, all without the need to configure tags or keywords. This is the generative component of the technology; insights are surfaced, categorized, and analyzed autonomously.

Let’s explore some real ways that contact centers have used genAI insights to reduce churn, improve conversions, and protect customer satisfaction.

GenAI has changed how contact centers interact with customers as well as how they analyze these interactions. The initial excitement (and horror, for some) around the chance to fully automate customer conversations has worn off as limitations of genAI in the contact center have made themselves known. 

It’s also clear that post genAI strategies require holistic data insights that can improve every layer of the organization. Post-call summarization stands out as one of the most straightforward applications of genAI for contact centers, offering clear, attributable ROI. 

While basic free-text summaries streamline post-call note taking for agents, vendors like Echo AI have designed solutions that convert unstructured conversations into meaningful structured data. For example, Echo AI can analyze millions of data points from calls, emails, tickets and more, and then identify and tag contact sub-reasons that would be tedious if done manually. Echo AI’s AI Analysis Summary offers a rich yet easy to digest overview of insights.

By combining genAI with conversation intelligence, Echo AI helps contact centers gain full visibility into interactions, deeply understand churn drivers, and act automatically to address them. By analyzing every touchpoint and customer data point, Echo AI can identify patterns and pinpoint specific issues that lead to customer frustration, all without the need to configure tags or keywords. This is the generative component of the technology; insights are surfaced, categorized, and analyzed autonomously.

Let’s explore some real ways that contact centers have used genAI insights to reduce churn, improve conversions, and protect customer satisfaction.

GenAI has changed how contact centers interact with customers as well as how they analyze these interactions. The initial excitement (and horror, for some) around the chance to fully automate customer conversations has worn off as limitations of genAI in the contact center have made themselves known. 

It’s also clear that post genAI strategies require holistic data insights that can improve every layer of the organization. Post-call summarization stands out as one of the most straightforward applications of genAI for contact centers, offering clear, attributable ROI. 

While basic free-text summaries streamline post-call note taking for agents, vendors like Echo AI have designed solutions that convert unstructured conversations into meaningful structured data. For example, Echo AI can analyze millions of data points from calls, emails, tickets and more, and then identify and tag contact sub-reasons that would be tedious if done manually. Echo AI’s AI Analysis Summary offers a rich yet easy to digest overview of insights.

By combining genAI with conversation intelligence, Echo AI helps contact centers gain full visibility into interactions, deeply understand churn drivers, and act automatically to address them. By analyzing every touchpoint and customer data point, Echo AI can identify patterns and pinpoint specific issues that lead to customer frustration, all without the need to configure tags or keywords. This is the generative component of the technology; insights are surfaced, categorized, and analyzed autonomously.

Let’s explore some real ways that contact centers have used genAI insights to reduce churn, improve conversions, and protect customer satisfaction.

Uncovering Hidden Insights to Prevent Churn

Customer churn is one of the most important metrics that businesses use to gauge health. Companies must focus on enhancing customer satisfaction and loyalty, sure. Quick response times, personalized interactions, and effective issue resolution can significantly improve customer satisfaction levels.

Generative insights allow business leaders to get to the root drivers behind why customers leave in the first place, as soon as issues arise. 

For example, Wine Enthusiast implemented Echo AI to better understand its customers. Within hours, their team discovered a manufacturing defect that was causing rusting on electrical components which was impacting their wine fridges. This insight helped Wine Enthusiast swiftly resolve the manufacturing issue to prevent any future customer churn, ensuring their quality standards were upheld.

Customer churn is one of the most important metrics that businesses use to gauge health. Companies must focus on enhancing customer satisfaction and loyalty, sure. Quick response times, personalized interactions, and effective issue resolution can significantly improve customer satisfaction levels.

Generative insights allow business leaders to get to the root drivers behind why customers leave in the first place, as soon as issues arise. 

For example, Wine Enthusiast implemented Echo AI to better understand its customers. Within hours, their team discovered a manufacturing defect that was causing rusting on electrical components which was impacting their wine fridges. This insight helped Wine Enthusiast swiftly resolve the manufacturing issue to prevent any future customer churn, ensuring their quality standards were upheld.

Customer churn is one of the most important metrics that businesses use to gauge health. Companies must focus on enhancing customer satisfaction and loyalty, sure. Quick response times, personalized interactions, and effective issue resolution can significantly improve customer satisfaction levels.

Generative insights allow business leaders to get to the root drivers behind why customers leave in the first place, as soon as issues arise. 

For example, Wine Enthusiast implemented Echo AI to better understand its customers. Within hours, their team discovered a manufacturing defect that was causing rusting on electrical components which was impacting their wine fridges. This insight helped Wine Enthusiast swiftly resolve the manufacturing issue to prevent any future customer churn, ensuring their quality standards were upheld.

Customer churn is one of the most important metrics that businesses use to gauge health. Companies must focus on enhancing customer satisfaction and loyalty, sure. Quick response times, personalized interactions, and effective issue resolution can significantly improve customer satisfaction levels.

Generative insights allow business leaders to get to the root drivers behind why customers leave in the first place, as soon as issues arise. 

For example, Wine Enthusiast implemented Echo AI to better understand its customers. Within hours, their team discovered a manufacturing defect that was causing rusting on electrical components which was impacting their wine fridges. This insight helped Wine Enthusiast swiftly resolve the manufacturing issue to prevent any future customer churn, ensuring their quality standards were upheld.

Turning Insights into Conversions

For sales-driven contact centers that are measured by their ability to acquire new business, meeting goals depends heavily on lead quality.

One way to improve funnel progression is by analyzing conversation data. This information can be used to enhance AI models that determine the likelihood that leads will progress through the funnel, and in turn help businesses identify worthwhile targets.

For example, Centerfield, a leading global contact center, turned to generative insights to improve its customer acquisition strategy. These insights, categorized by key demographics like age, gender, and region, provided Centerfield with a deep understanding of customer interests and preferences.

Centerfield integrated these insights directly into Google and Facebook audience targeting, updating segments dynamically. This gave Centerfield a view into what customers cared about, and which leads were likely to be most interested in key services or offerings.

With a better understanding of which leads to pursue, Centerfield has improved conversions of high-intent leads by 10%, creating a quality pipeline that closes more business.

"Echo AI has become a centerpiece in our strategy to supercharge customer acquisition. Every customer conversation contains insights that can improve personalization, drive a conversion, or prevent churn." —Aniketh Parmar, EVP of Sales, Centerfield

For sales-driven contact centers that are measured by their ability to acquire new business, meeting goals depends heavily on lead quality.

One way to improve funnel progression is by analyzing conversation data. This information can be used to enhance AI models that determine the likelihood that leads will progress through the funnel, and in turn help businesses identify worthwhile targets.

For example, Centerfield, a leading global contact center, turned to generative insights to improve its customer acquisition strategy. These insights, categorized by key demographics like age, gender, and region, provided Centerfield with a deep understanding of customer interests and preferences.

Centerfield integrated these insights directly into Google and Facebook audience targeting, updating segments dynamically. This gave Centerfield a view into what customers cared about, and which leads were likely to be most interested in key services or offerings.

With a better understanding of which leads to pursue, Centerfield has improved conversions of high-intent leads by 10%, creating a quality pipeline that closes more business.

"Echo AI has become a centerpiece in our strategy to supercharge customer acquisition. Every customer conversation contains insights that can improve personalization, drive a conversion, or prevent churn." —Aniketh Parmar, EVP of Sales, Centerfield

For sales-driven contact centers that are measured by their ability to acquire new business, meeting goals depends heavily on lead quality.

One way to improve funnel progression is by analyzing conversation data. This information can be used to enhance AI models that determine the likelihood that leads will progress through the funnel, and in turn help businesses identify worthwhile targets.

For example, Centerfield, a leading global contact center, turned to generative insights to improve its customer acquisition strategy. These insights, categorized by key demographics like age, gender, and region, provided Centerfield with a deep understanding of customer interests and preferences.

Centerfield integrated these insights directly into Google and Facebook audience targeting, updating segments dynamically. This gave Centerfield a view into what customers cared about, and which leads were likely to be most interested in key services or offerings.

With a better understanding of which leads to pursue, Centerfield has improved conversions of high-intent leads by 10%, creating a quality pipeline that closes more business.

"Echo AI has become a centerpiece in our strategy to supercharge customer acquisition. Every customer conversation contains insights that can improve personalization, drive a conversion, or prevent churn." —Aniketh Parmar, EVP of Sales, Centerfield

For sales-driven contact centers that are measured by their ability to acquire new business, meeting goals depends heavily on lead quality.

One way to improve funnel progression is by analyzing conversation data. This information can be used to enhance AI models that determine the likelihood that leads will progress through the funnel, and in turn help businesses identify worthwhile targets.

For example, Centerfield, a leading global contact center, turned to generative insights to improve its customer acquisition strategy. These insights, categorized by key demographics like age, gender, and region, provided Centerfield with a deep understanding of customer interests and preferences.

Centerfield integrated these insights directly into Google and Facebook audience targeting, updating segments dynamically. This gave Centerfield a view into what customers cared about, and which leads were likely to be most interested in key services or offerings.

With a better understanding of which leads to pursue, Centerfield has improved conversions of high-intent leads by 10%, creating a quality pipeline that closes more business.

"Echo AI has become a centerpiece in our strategy to supercharge customer acquisition. Every customer conversation contains insights that can improve personalization, drive a conversion, or prevent churn." —Aniketh Parmar, EVP of Sales, Centerfield

Improving Agent Behaviors Swiftly

The lifeblood of the contact center continues to be the agent workforce. Training agents, coaching on behaviors, and improving overall performance depends on the quality of the data being used. With genAI, agent conversations are analyzed by numerous LLMs, each one specific business questions around tone, sentiment, and different types of contact drivers. The result is more accurate transcripts, and a faster response time when agent intervention is required.

For example, NativePath overhauled its entire QA system with Echo AI, replacing manual scorecards with autoQA, and using genAI-powered insights to surface time-sensitive information that helped them curtail a bad customer service experience and prevent potential churn.

Their call review time has also been reduced by 90% across the board.

The lifeblood of the contact center continues to be the agent workforce. Training agents, coaching on behaviors, and improving overall performance depends on the quality of the data being used. With genAI, agent conversations are analyzed by numerous LLMs, each one specific business questions around tone, sentiment, and different types of contact drivers. The result is more accurate transcripts, and a faster response time when agent intervention is required.

For example, NativePath overhauled its entire QA system with Echo AI, replacing manual scorecards with autoQA, and using genAI-powered insights to surface time-sensitive information that helped them curtail a bad customer service experience and prevent potential churn.

Their call review time has also been reduced by 90% across the board.

The lifeblood of the contact center continues to be the agent workforce. Training agents, coaching on behaviors, and improving overall performance depends on the quality of the data being used. With genAI, agent conversations are analyzed by numerous LLMs, each one specific business questions around tone, sentiment, and different types of contact drivers. The result is more accurate transcripts, and a faster response time when agent intervention is required.

For example, NativePath overhauled its entire QA system with Echo AI, replacing manual scorecards with autoQA, and using genAI-powered insights to surface time-sensitive information that helped them curtail a bad customer service experience and prevent potential churn.

Their call review time has also been reduced by 90% across the board.

The lifeblood of the contact center continues to be the agent workforce. Training agents, coaching on behaviors, and improving overall performance depends on the quality of the data being used. With genAI, agent conversations are analyzed by numerous LLMs, each one specific business questions around tone, sentiment, and different types of contact drivers. The result is more accurate transcripts, and a faster response time when agent intervention is required.

For example, NativePath overhauled its entire QA system with Echo AI, replacing manual scorecards with autoQA, and using genAI-powered insights to surface time-sensitive information that helped them curtail a bad customer service experience and prevent potential churn.

Their call review time has also been reduced by 90% across the board.

Looking Ahead: Using Gen AI Wisely

The potential for genAI will continue to evolve and expand as businesses move from experimentation to value creation. Efficiency gains are important, but genAI is a strategic lever that will help contact centers optimize how they deliver customer experiences with technology that has the chance of living up to its promise, perhaps for the first time ever.

But, it will still be critical for leaders to choose their technology partners wisely. Navigating the new genAI landscape with the help of the right vendors will guide contact center leaders to make effective decisions that not only improve operational agility, but also get a richer look into what their customers need most.

The potential for genAI will continue to evolve and expand as businesses move from experimentation to value creation. Efficiency gains are important, but genAI is a strategic lever that will help contact centers optimize how they deliver customer experiences with technology that has the chance of living up to its promise, perhaps for the first time ever.

But, it will still be critical for leaders to choose their technology partners wisely. Navigating the new genAI landscape with the help of the right vendors will guide contact center leaders to make effective decisions that not only improve operational agility, but also get a richer look into what their customers need most.

The potential for genAI will continue to evolve and expand as businesses move from experimentation to value creation. Efficiency gains are important, but genAI is a strategic lever that will help contact centers optimize how they deliver customer experiences with technology that has the chance of living up to its promise, perhaps for the first time ever.

But, it will still be critical for leaders to choose their technology partners wisely. Navigating the new genAI landscape with the help of the right vendors will guide contact center leaders to make effective decisions that not only improve operational agility, but also get a richer look into what their customers need most.

The potential for genAI will continue to evolve and expand as businesses move from experimentation to value creation. Efficiency gains are important, but genAI is a strategic lever that will help contact centers optimize how they deliver customer experiences with technology that has the chance of living up to its promise, perhaps for the first time ever.

But, it will still be critical for leaders to choose their technology partners wisely. Navigating the new genAI landscape with the help of the right vendors will guide contact center leaders to make effective decisions that not only improve operational agility, but also get a richer look into what their customers need most.

Download The Report

Download the Report to learn more about the key themes discussed in this blog and ways contact center leaders are navigating them.

Download the Report to learn more about the key themes discussed in this blog and ways contact center leaders are navigating them.

Download the Report to learn more about the key themes discussed in this blog and ways contact center leaders are navigating them.

Download the Report to learn more about the key themes discussed in this blog and ways contact center leaders are navigating them.