June 18, 2026
Operationalizing AI in Customer Experience Workflows
With the right foundation, organizations can turn artificial intelligence’s potential into measurable results by enabling seamless agent support and personalized customer experiences.
Artificial intelligence (AI) holds transformative potential for customer experience, yet many organizations struggle to move beyond initial deployments and achieve measurable value at scale. As customer expectations increase, however, AI has become an operational imperative. Organizations must deliver faster resolution, multimodal support and personalized interactions — and operationalized AI provides a path to get there.
Deploying and scaling AI successfully requires clean, well-structured data; seamless system integration; and strong governance. Organizations also need clearly defined outcomes, which should reflect strategic alignment across IT, operations and business leadership. Organizations that build on these foundations are positioned to move from isolated pilots to fully operationalized AI that delivers consistent results across every channel and touchpoint. When AI is embedded effectively, it reduces friction, improves first-call resolution and drives customer satisfaction, loyalty and operational efficiency that enable long-term competitive advantages.
Advance your artificial intelligence initiatives and elevate customer experiences with the right foundation and an expert partner.
Artificial intelligence (AI) holds transformative potential for customer experience, yet many organizations struggle to move beyond initial deployments and achieve measurable value at scale. As customer expectations increase, however, AI has become an operational imperative. Organizations must deliver faster resolution, multimodal support and personalized interactions — and operationalized AI provides a path to get there.
Deploying and scaling AI successfully requires clean, well-structured data; seamless system integration; and strong governance. Organizations also need clearly defined outcomes, which should reflect strategic alignment across IT, operations and business leadership. Organizations that build on these foundations are positioned to move from isolated pilots to fully operationalized AI that delivers consistent results across every channel and touchpoint. When AI is embedded effectively, it reduces friction, improves first-call resolution and drives customer satisfaction, loyalty and operational efficiency that enable long-term competitive advantages.
Advance your artificial intelligence initiatives and elevate customer experiences with the right foundation and an expert partner.
AI is widely recognized as a transformative force for customer experience, but many organizations find it difficult to turn AI’s potential into genuine improvements for agents and customers. A Boston Consulting Group study found that only 5% of organizations have achieved value from AI at scale, while 46% are struggling to move past initial deployments. Meanwhile, 85% of customers say they will leave brands that can’t resolve their issues on the first contact. In this landscape, AI has become an operational imperative for CX.
In modern CX environments, AI is already delivering value in repeatable workflows, such as intelligent routing, agent-assist tools and front-door virtual agents handling low-complexity requests. These use cases demonstrate that AI’s value is most tangible when applied to clear friction points, such as wait times and redundant data collection. Yet many organizations are still figuring out how to leverage AI for more complex solutions.
Common challenges include poor data quality, fragmented tools and lack of integration. Many leaders are unsure of the best way to proceed — for instance, whether to adopt new solutions or use the built-in tools of an existing platform. They may need guidance on the full scope of AI capabilities. Above all, organizations need clear objectives and an overarching strategy that aligns IT and business perspectives.
When implemented effectively, AI helps organizations expand their focus from efficiency metrics to experience quality. Reduced friction, improved first-call resolution and more empathetic agent interactions are the prevailing success indicators, driving customer loyalty and operational cost efficiency. To get there, organizations must approach AI not as a product, but as a capability that requires a holistic rethinking of the AI-enabled workflow.
Organizations that prioritize data quality, frictionless integration and robust governance are well positioned to embed AI into daily operations, enabling consistent outcomes across every channel and touchpoint.
72%
The percentage of consumers who say they have experienced the benefits of AI and automation in customer service
Source: nice.com, “NiCE Global Happiness Index 2025,” June 17, 2026
CDW can help you rethink your workflows to take full advantage of artificial intelligence.
AI is widely recognized as a transformative force for customer experience, but many organizations find it difficult to turn AI’s potential into genuine improvements for agents and customers. A Boston Consulting Group study found that only 5% of organizations have achieved value from AI at scale, while 46% are struggling to move past initial deployments. Meanwhile, 85% of customers say they will leave brands that can’t resolve their issues on the first contact. In this landscape, AI has become an operational imperative for CX.
In modern CX environments, AI is already delivering value in repeatable workflows, such as intelligent routing, agent-assist tools and front-door virtual agents handling low-complexity requests. These use cases demonstrate that AI’s value is most tangible when applied to clear friction points, such as wait times and redundant data collection. Yet many organizations are still figuring out how to leverage AI for more complex solutions.
Common challenges include poor data quality, fragmented tools and lack of integration. Many leaders are unsure of the best way to proceed — for instance, whether to adopt new solutions or use the built-in tools of an existing platform. They may need guidance on the full scope of AI capabilities. Above all, organizations need clear objectives and an overarching strategy that aligns IT and business perspectives.
When implemented effectively, AI helps organizations expand their focus from efficiency metrics to experience quality. Reduced friction, improved first-call resolution and more empathetic agent interactions are the prevailing success indicators, driving customer loyalty and operational cost efficiency. To get there, organizations must approach AI not as a product, but as a capability that requires a holistic rethinking of the AI-enabled workflow.
Organizations that prioritize data quality, frictionless integration and robust governance are well positioned to embed AI into daily operations, enabling consistent outcomes across every channel and touchpoint.
CDW can help you rethink your workflows to take full advantage of artificial intelligence.
How Organizations See AI for CX
41%
The percentage of customer interactions resolved by AI without requiring live agent support
Source: Metrigy Research, “AI for Business Success: 2025-26,” March 2025
83%
The percentage of CX leaders who say that AI-enabled memory context across channels greatly reduces customer effort and frustration
Source: Zendesk, “CXtrends 26: Leading in the AI Era,” November 2025
66%
The percentage of IT decision-makers who say they are using AI-enabled chatbots and virtual agents to improve their contact center operations
Source: CDW, “New CDW Research: How Organizations Optimize the Digital Experience,” October 2025
How Organizations See AI for CX
41%
The percentage of customer interactions resolved by AI without requiring live agent support
Source: Metrigy Research, “AI for Business Success: 2025-26,” March 2025
83%
The percentage of CX leaders who say that AI-enabled memory context across channels greatly reduces customer effort and frustration
Source: Zendesk, “CXtrends 26: Leading in the AI Era,” November 2025
66%
The percentage of IT decision-makers who say they are using AI-enabled chatbots and virtual agents to improve their contact center operations
Source: CDW, “New CDW Research: How Organizations Optimize the Digital Experience,” October 2025
- DESIGNING AI AROUND BUSINESS OUTCOMES
- THE FOUNDATIONS OF SCALABLE AI
- AI PILOTS VS. PRODUCTION-READY AI CX
CX transformation is most effective with a holistic approach that integrates technology, processes and business outcomes. From the start, IT, operations and executives must be aligned on the AI adoption strategy. A fragmented or siloed approach, where teams experiment independently without alignment, often leads to inefficiencies and missed opportunities.
FIRST, DEFINE OUTCOMES: Organizations achieve the best results with AI when they define business and customer outcomes and then determine where AI can enable them. When organizations attempt to insert AI into existing workflows without rethinking the customer journey, they can limit impact and even worsen customer experiences. Organizations need a clear understanding of what “better” looks like for customers — for example, 68% have rising expectations for fast responses, and 79% want share media easily.
Outcome-driven design starts by identifying key CX challenges, such as long wait times, inconsistent service or high agent workloads, and mapping them to measurable goals: improved first-call resolution, higher satisfaction and reduced average handle time.
PRIORITIZE USE CASES: Clearly defined outcomes help organizations prioritize AI use cases that deliver high value with low complexity. In addition to customer satisfaction score, net promoter score and other standard key performance indicators (KPIs), organizational data can point to additional use cases tailored to specific environments.
Organizations typically prioritize AI-powered agent assistance with the understanding this also improves the customer experience. For example, AI can enable a centralized view that surfaces and integrates relevant data in one place, from call transcripts to customer journeys. When agents can quickly access the information they need without putting customers on hold or searching multiple systems, issue resolution becomes smoother and more efficient.
THINK LONG-TERM: Starting with the end in mind helps leaders think strategically about what they want to gain from AI over time. While pilots and proofs of concept demonstrate initial value, organizations should plan to scale from the outset. Scaling effectively means developing a roadmap to expand AI capabilities and using AI to replicate successes through automation. Organizations that align AI efforts with strategic objectives are more likely to achieve measurable ROI and sustainable improvements.
Ongoing optimization is essential for long-term success. After implementing an AI-powered front door, for example, leaders should assess its value and use those insights to extend it and drive the next round of improvements. AI is iterative, and meaningful business outcomes should continually inform its deployment and refinement.
REFINE METRICS: Metrics are inherent to CX operations, but organizations may need stronger alignment between what they currently measure and what they actually need to understand. For example, minor tweaks in AI verbiage can generate notable improvements in customer sentiment when organizations can assess the impact of those adjustments. High-maturity organizations are significantly more likely to measure their success in using AI automation: 66%, versus 21% for low-maturity organizations. As organizations deploy AI, humans must stay in the loop of real-time feedback systems to ensure customer interactions deliver the desired results.
Click Below To Continue Reading
While many organizations have adopted AI, few have the internal expertise to move efficiently from idea to execution. A partner that understands AI solutions, deployment best practices and the unique needs of the CX industry can help organizations achieve faster results, avoid pitfalls and maximize ROI. CDW’s wide range of services lets organizations engage help when and where they need it.
In strategic consulting engagements, CDW CX experts work with line-of-business leaders to clarify objectives, identify AI use cases and build strategic roadmaps that can evolve as business needs change.
CDW can help customers resolve specific roadblocks and move AI initiatives forward. Examples include prioritizing use cases, selecting the right platforms, addressing integration challenges and mapping AI-enabled customer journeys.
Full lifecycle services support customers throughout their AI journeys, ensuring that proper solutions and governance strategies are in place, from initial deployments to ongoing optimization.
CDW’s Cloud Foundation Services help organizations establish the right foundation for AI by modernizing data, applications and platforms to increase agility and enable automation.
While many organizations focus on AI tools, fewer address the foundational requirements necessary for those tools to function effectively at scale: clean and structured data, seamless systems integration, and strong governance. Organizations that prioritize these elements are better equipped to operationalize AI beyond isolated use cases and into enterprisewide workflows.
DATA QUALITY: AI performance in CX is only as strong as the data and systems that support it, which makes data quality a primary determinant of success. AI systems need accurate, consistent and well-organized information to generate reliable outputs. Poorly structured knowledge bases, conflicting data sources or outdated content lead to incorrect responses that diminish trust in AI tools and degrade customers’ experiences. Centralized data can serve as a “single source of truth,” while agent and customer feedback loops enable continuous improvement.
DATA LIFECYCLE MANAGEMENT: Data lifecycle management is crucial, including cleaning, tagging and maintaining content to ensure relevance and accuracy over time. Organizations need a consistent methodology for surfacing data properly, correcting data and making updates as information and workflows evolve. Organizations must store, format and encrypt data in ways that are usable for AI systems. For instance, organizations may need to update their metadata with appropriate tags to identify what files contain and which audiences they are for. Without these cues, AI tools will struggle to locate and surface the right data at the right time. While AI can be used to clean data — for example, surfacing newer information and expunging or tagging data that is old or invalid — humans should validate these outputs.
SYSTEMS INTEGRATION: The integration of systems and data is imperative, yet it remains a challenge for 47% of organizations across industries, according to CDW research. To enable meaningful interactions, AI must connect to core systems such as customer relationship management platforms, ticketing systems and knowledge repositories. Without these integrations, AI tools will be isolated and unable to deliver personalized, actionable insights. Seamless integration also enables agent efficiency and productivity through dashboard views that proactively put relevant information at their fingertips. Many organizations need to prepare their systems for AI-focused integration. That can be a valuable opportunity to analyze systems and potentially reduce sprawl, thereby increasing ROI and simplifying data management.
GOVERNANCE AND OVERSIGHT: Governance provides the necessary controls to scale AI responsibly, including implementing guardrails to prevent inappropriate outputs, ensuring compliance with privacy regulations and monitoring performance through human oversight. While guardrails are critical, they aren’t sufficient to ensure that AI systems do not wrongly access or provide personally identifiable information and other sensitive data. Organizations must also employ best practices such as creating limited-purpose agents that have access only to the data they need to perform specific activities. Another best practice is limiting the number of conversational turns that a bot will engage in before transferring the customer to a human, which can prevent bad actors from using chatbots to gain unauthorized information.
AI’s true value emerges when it is operationalized and embedded thoughtfully into workflows. Moving from experimentation to production requires technical readiness and organizational maturity in managing and optimizing AI. Expert partners and internal centers of excellence can help organizations build the expertise and fluency they need to achieve meaningful results.
WHAT ‘GOOD’ LOOKS LIKE: Operationalized AI in CX is characterized by consistency, scalability and measurable impact. Systems are actively monitored using defined KPIs such as first-call resolution, sentiment analysis, average handle time and escalation rates. These metrics provide visibility into performance and enable organizations to identify opportunities for improvement.
High-maturity organizations often establish an AI center of excellence to ensure that efforts and investments are holistic, strategic and aligned with business objectives. AI CoEs provide centralized coordination and accountability among stakeholder groups, as well as oversight and ownership of governance activities. Leaders who combine strategy, centralized oversight and ongoing optimization are most likely to succeed in leveraging AI’s potential and maximizing ROI.
AI LIFECYCLE MANAGEMENT: AI requires continuous training and iteration, both within the tools and in how organizations deploy them. Accordingly, AI lifecycle management is a key indicator of maturity. It reflects the fact that AI systems should not be static, but rather evolve continuously based on new data, customer preferences and business priorities.
Organizations should plan for long-term management from the outset, with a clear strategy for how they will measure, support and optimize AI deployments over time. Processes for ongoing evaluation should include regular audits of AI performance, updates to knowledge bases and workflow refinements. A mature lifecycle governance plan is essential for long-term success and improves the customer experience by ensuring that AI systems remain accurate, reliable, secure and compliant, enabling better personalization, faster service, consistent performance and continuous improvement.
HUMAN–AI INTERACTIONS: Every organization must establish an appropriate balance between time-saving automation and human involvement that protects accuracy and quality. This is especially true for CX organizations, which are in the business of supporting customers at key inflection points in their journey. AI should augment, rather than replace, human agents. When implemented effectively, AI lets humans focus on higher-value interactions that require empathy, judgment and complex problem-solving and ensures that CX remains both efficient and personalized.
Well-planned change management practices can help employees adopt AI successfully. CDW research found that across industries, just over 50% of organizations encounter employee resistance to new platforms. Research also shows that high-maturity companies are four times more likely to provide structured learning opportunities to help employees adopt AI effectively. Training, support and employee feedback are critical.
CLEAR BUSINESS OUTCOMES: Organizations that effectively operationalize AI for CX embed it seamlessly into daily operations, enabling consistent outcomes across channels and touchpoints. Organizations that reach this stage are not only experimenting with AI but leveraging it as a core capability to drive customer satisfaction, operational efficiency and long-term business value. They are able to align interactions with customers’ evolving expectations — for speed, personalization and multimodal support — and they routinely deliver experiences that are intelligent, responsive and personalized. These capabilities build customer loyalty and help organizations differentiate themselves on one of the most valued qualities in the market: exceptional service.
- DESIGNING AI AROUND BUSINESS OUTCOMES
- THE FOUNDATIONS OF SCALABLE AI
- AI PILOTS VS. PRODUCTION-READY AI CX
CX transformation is most effective with a holistic approach that integrates technology, processes and business outcomes. From the start, IT, operations and executives must be aligned on the AI adoption strategy. A fragmented or siloed approach, where teams experiment independently without alignment, often leads to inefficiencies and missed opportunities.
FIRST, DEFINE OUTCOMES: Organizations achieve the best results with AI when they define business and customer outcomes and then determine where AI can enable them. When organizations attempt to insert AI into existing workflows without rethinking the customer journey, they can limit impact and even worsen customer experiences. Organizations need a clear understanding of what “better” looks like for customers — for example, 68% have rising expectations for fast responses, and 79% want share media easily.
Outcome-driven design starts by identifying key CX challenges, such as long wait times, inconsistent service or high agent workloads, and mapping them to measurable goals: improved first-call resolution, higher satisfaction and reduced average handle time.
PRIORITIZE USE CASES: Clearly defined outcomes help organizations prioritize AI use cases that deliver high value with low complexity. In addition to customer satisfaction score, net promoter score and other standard key performance indicators (KPIs), organizational data can point to additional use cases tailored to specific environments.
Organizations typically prioritize AI-powered agent assistance with the understanding this also improves the customer experience. For example, AI can enable a centralized view that surfaces and integrates relevant data in one place, from call transcripts to customer journeys. When agents can quickly access the information they need without putting customers on hold or searching multiple systems, issue resolution becomes smoother and more efficient.
THINK LONG-TERM: Starting with the end in mind helps leaders think strategically about what they want to gain from AI over time. While pilots and proofs of concept demonstrate initial value, organizations should plan to scale from the outset. Scaling effectively means developing a roadmap to expand AI capabilities and using AI to replicate successes through automation. Organizations that align AI efforts with strategic objectives are more likely to achieve measurable ROI and sustainable improvements.
Ongoing optimization is essential for long-term success. After implementing an AI-powered front door, for example, leaders should assess its value and use those insights to extend it and drive the next round of improvements. AI is iterative, and meaningful business outcomes should continually inform its deployment and refinement.
REFINE METRICS: Metrics are inherent to CX operations, but organizations may need stronger alignment between what they currently measure and what they actually need to understand. For example, minor tweaks in AI verbiage can generate notable improvements in customer sentiment when organizations can assess the impact of those adjustments. High-maturity organizations are significantly more likely to measure their success in using AI automation: 66%, versus 21% for low-maturity organizations. As organizations deploy AI, humans must stay in the loop of real-time feedback systems to ensure customer interactions deliver the desired results.
Click Below To Continue Reading
While many organizations have adopted AI, few have the internal expertise to move efficiently from idea to execution. A partner that understands AI solutions, deployment best practices and the unique needs of the CX industry can help organizations achieve faster results, avoid pitfalls and maximize ROI. CDW’s wide range of services lets organizations engage help when and where they need it.
In strategic consulting engagements, CDW CX experts work with line-of-business leaders to clarify objectives, identify AI use cases and build strategic roadmaps that can evolve as business needs change.
CDW can help customers resolve specific roadblocks and move AI initiatives forward. Examples include prioritizing use cases, selecting the right platforms, addressing integration challenges and mapping AI-enabled customer journeys.
Full lifecycle services support customers throughout their AI journeys, ensuring that proper solutions and governance strategies are in place, from initial deployments to ongoing optimization.
CDW’s Cloud Foundation Services help organizations establish the right foundation for AI by modernizing data, applications and platforms to increase agility and enable automation.
While many organizations focus on AI tools, fewer address the foundational requirements necessary for those tools to function effectively at scale: clean and structured data, seamless systems integration, and strong governance. Organizations that prioritize these elements are better equipped to operationalize AI beyond isolated use cases and into enterprisewide workflows.
DATA QUALITY: AI performance in CX is only as strong as the data and systems that support it, which makes data quality a primary determinant of success. AI systems need accurate, consistent and well-organized information to generate reliable outputs. Poorly structured knowledge bases, conflicting data sources or outdated content lead to incorrect responses that diminish trust in AI tools and degrade customers’ experiences. Centralized data can serve as a “single source of truth,” while agent and customer feedback loops enable continuous improvement.
DATA LIFECYCLE MANAGEMENT: Data lifecycle management is crucial, including cleaning, tagging and maintaining content to ensure relevance and accuracy over time. Organizations need a consistent methodology for surfacing data properly, correcting data and making updates as information and workflows evolve. Organizations must store, format and encrypt data in ways that are usable for AI systems. For instance, organizations may need to update their metadata with appropriate tags to identify what files contain and which audiences they are for. Without these cues, AI tools will struggle to locate and surface the right data at the right time. While AI can be used to clean data — for example, surfacing newer information and expunging or tagging data that is old or invalid — humans should validate these outputs.
SYSTEMS INTEGRATION: The integration of systems and data is imperative, yet it remains a challenge for 47% of organizations across industries, according to CDW research. To enable meaningful interactions, AI must connect to core systems such as customer relationship management platforms, ticketing systems and knowledge repositories. Without these integrations, AI tools will be isolated and unable to deliver personalized, actionable insights. Seamless integration also enables agent efficiency and productivity through dashboard views that proactively put relevant information at their fingertips. Many organizations need to prepare their systems for AI-focused integration. That can be a valuable opportunity to analyze systems and potentially reduce sprawl, thereby increasing ROI and simplifying data management.
GOVERNANCE AND OVERSIGHT: Governance provides the necessary controls to scale AI responsibly, including implementing guardrails to prevent inappropriate outputs, ensuring compliance with privacy regulations and monitoring performance through human oversight. While guardrails are critical, they aren’t sufficient to ensure that AI systems do not wrongly access or provide personally identifiable information and other sensitive data. Organizations must also employ best practices such as creating limited-purpose agents that have access only to the data they need to perform specific activities. Another best practice is limiting the number of conversational turns that a bot will engage in before transferring the customer to a human, which can prevent bad actors from using chatbots to gain unauthorized information.
AI’s true value emerges when it is operationalized and embedded thoughtfully into workflows. Moving from experimentation to production requires technical readiness and organizational maturity in managing and optimizing AI. Expert partners and internal centers of excellence can help organizations build the expertise and fluency they need to achieve meaningful results.
WHAT ‘GOOD’ LOOKS LIKE: Operationalized AI in CX is characterized by consistency, scalability and measurable impact. Systems are actively monitored using defined KPIs such as first-call resolution, sentiment analysis, average handle time and escalation rates. These metrics provide visibility into performance and enable organizations to identify opportunities for improvement.
High-maturity organizations often establish an AI center of excellence to ensure that efforts and investments are holistic, strategic and aligned with business objectives. AI CoEs provide centralized coordination and accountability among stakeholder groups, as well as oversight and ownership of governance activities. Leaders who combine strategy, centralized oversight and ongoing optimization are most likely to succeed in leveraging AI’s potential and maximizing ROI.
AI LIFECYCLE MANAGEMENT: AI requires continuous training and iteration, both within the tools and in how organizations deploy them. Accordingly, AI lifecycle management is a key indicator of maturity. It reflects the fact that AI systems should not be static, but rather evolve continuously based on new data, customer preferences and business priorities.
Organizations should plan for long-term management from the outset, with a clear strategy for how they will measure, support and optimize AI deployments over time. Processes for ongoing evaluation should include regular audits of AI performance, updates to knowledge bases and workflow refinements. A mature lifecycle governance plan is essential for long-term success and improves the customer experience by ensuring that AI systems remain accurate, reliable, secure and compliant, enabling better personalization, faster service, consistent performance and continuous improvement.
HUMAN–AI INTERACTIONS: Every organization must establish an appropriate balance between time-saving automation and human involvement that protects accuracy and quality. This is especially true for CX organizations, which are in the business of supporting customers at key inflection points in their journey. AI should augment, rather than replace, human agents. When implemented effectively, AI lets humans focus on higher-value interactions that require empathy, judgment and complex problem-solving and ensures that CX remains both efficient and personalized.
Well-planned change management practices can help employees adopt AI successfully. CDW research found that across industries, just over 50% of organizations encounter employee resistance to new platforms. Research also shows that high-maturity companies are four times more likely to provide structured learning opportunities to help employees adopt AI effectively. Training, support and employee feedback are critical.
CLEAR BUSINESS OUTCOMES: Organizations that effectively operationalize AI for CX embed it seamlessly into daily operations, enabling consistent outcomes across channels and touchpoints. Organizations that reach this stage are not only experimenting with AI but leveraging it as a core capability to drive customer satisfaction, operational efficiency and long-term business value. They are able to align interactions with customers’ evolving expectations — for speed, personalization and multimodal support — and they routinely deliver experiences that are intelligent, responsive and personalized. These capabilities build customer loyalty and help organizations differentiate themselves on one of the most valued qualities in the market: exceptional service.
CDW can help you manage and optimize your artificial intelligence initiatives to achieve meaningful results for customer experience.
Ken Drazin
Director of Digital Experience, CDW
