Implement AI for Customer Experience

AI’s unprecedented capabilities have the potential to redefine how organizations connect with their customers – for better and for worse. With the right strategic alignment, AI in CX can be a catalyst for deeper engagement, stronger loyalty, and sustainable growth. Our research can help you identify the right opportunities for AI in your CX strategy, define requirements and a go-live plan, and maintain your solutions after launch.

By leveraging advanced AI capabilities like predictive analysis, sentiment detection, and hyper-personalization, organizations can anticipate customer needs, resolve issues proactively, and build lasting brand strategy. AI in CX is not without risk, but in today’s competitive landscape, it is essential to delivering exceptional customer experiences.

1. Data should be one of your first stops.

Data is the lifeblood of AI for CX. Your data must be clean, relevant, and readily accessible. Devote your resources to data governance and integration up front and poise your future AI initiatives to deliver more accurate, contextual, and low-risk insights that truly enhance the customer journey.

2. Prioritize measurable benefits over AI wow factor.

Start your exploration with specific, measurable CX metrics that reflect on customer engagement at valuable junctions. Align the value of AI implementation to your customers’ needs and organizational goals, transforming isolated wins into a clear competitive advantage.

3. The devil is always in the details.

Like any other tool, the value of AI is in the way you use it. Your implementation approach can make or break your AI ambitions. Small ad hoc decisions, unpredicted pitfalls, and incongruent data can all significantly impact the tangible returns of AI for your customers. Fuel long-term scalability with a structured approach that considers scope, configuration, and ongoing optimization.

Use our step-by-step research to transform the moments that matter most to your customers

Use this blueprint as a comprehensive guide to strategically assess and evaluate potential AI implementation pilot projects. Shift your customer journey from reactive to transformative with our step-by-step methodology, templates, and tools to:

Identify high-impact use cases early: zero in on specific CX metrics – like first-contact resolution or NPS – that resonate with leadership and deliver visible value to customers.
Design for scalability from the start: define your project scope with precision – pinpoint the specific CX challenges, establish robust requirements, account for governance, risk, and compliance considerations – and involve cross-functional stakeholders from the outset.
Monitor and adapt continuously: track key performance indicators and user feedback in real time, then refine AI capabilities to regularly stay aligned with shifting customer needs and strategic goals.

Implement AI for Customer Experience Research & Tools

1. Implement AI for CX Storyboard

In this step-by-step guide, we will help you to:

Identify and select your highest impact CX-AI use case.
Define your CX-AI solution requirements and project scope.
Determine your solution path and build your “go-live” plan.
Establish your framework to manage and maintain your solution after launch.

2. CX AI Use Case Discovery Tool – A robust, Excel-based tool to help you identify the right AI use cases to explore for CX in your organization.

Use this tool to:

Assess customer impact and experience relevance across five CX capability dimensions.
Understand which CX capabilities require the most attention at your organization.
Identify problem statements that align to your CX pain points or high-impact opportunity areas.
Select the right AI use cases to take through value and feasibility rationalization.

3. KPI Mapping Tool – A comprehensive tool to help you identify the right AI use cases to explore for CX.

Use this tool to:

Identify KPIs that are relevant to your organization’s CX capabilities.
Assess those metrics to determine their overall health.
Identify the areas of concern and opportunity that an AI solution could improve.
Select the right AI use cases to take through value and feasibility rationalization.

4. AI Pilot Project Shortlisting Tool – A step-by-step tool for documenting your selection process and presenting the case for a chosen pilot.

Use this tool to:

Document a longlist of potential use cases from all departments.
Conduct a voting exercise to establish a shortlist of potential pilots.
Define your value and readiness scoring scales to assess pilots.
Score your pilot use cases against scales to select one or two high-ranking use cases.

5. Solution Path and Cost Estimation Tool – An Excel-based tool to help you identify the solution path and establish a size estimate for your project.

Use this tool to:

Identify KPIs that are relevant to your organization’s CX capabilities.
Assess those metrics to determine their overall health.
Identify the areas of concern and opportunity that an AI solution could improve.
Select the right AI use cases to take through value and feasibility rationalization.

6. Solution Development and Implementation Report – A comprehensive report template to document your outcomes for a fully scoped use case in a boardroom-ready presentation.

Use this report template to:

Explore a sample use case fully scoped end-to-end.
Document your decisions to communicate the recommended pilot project.
Present your findings to management and obtain formal approval.

Workshop: Implement AI for Customer Experience

Workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn't enough, we offer low-cost delivery of our project workshops. We take you through every phase of your project and ensure that you have a roadmap in place to complete your project successfully.

Module 1: Identify and Select Your Highest Impact CX-AI Use Case

The Purpose

Assess your core CX capabilities to identify high impact areas of opportunity to implement AI solutions.

Key Benefits Achieved

Understanding where AI will drive the most value to supercharge your core CX capabilities.

Activities

Outputs

1.1

Assess your core CX KPIs to identify any challenging areas that need to be addressed.

1.2

Diagnose your CX capabilities to identify priority focus areas and aligned AI solutions.

CX Capability Diagnostic Assessment

1.3

Assess a candidate list of potential use cases on value and feasibility.

Candidate portfolio of CX-AI use cases

1.4

Select your pilot CX-AI project.

Prioritized list of CX-AI solutions

Module 2: Define Your CX-AI Solution Requirements and Project Scope

The Purpose

Fully scope your AI solution to include a robust problem statement and business context, the right solution success criteria, well defined user and data requirements, and a comprehensive framework to manage governance, risk, and compliance for your solution.

Key Benefits Achieved

A fully scoped CX-AI solution that can be leveraged to find vendor/solution fit or handed off to a development team to build and deploy.

Activities

Outputs

2.1

Articulate the business context and a clear problem statement.

Definition of the problem and business context for your solution.

2.2

Define solution objectives and success metrics.

Defined objectives and success criteria.

2.3

Define high-level user requirements.

Defined user requirements for your solution.

2.4

Define your project data requirements.

Defined data requirements.

2.5

Define solution governance and compliance requirement.

A GRC framework to your guide your project.

Module 3: Determine Your Solution Path and Build Your “Go-Live” Plan

The Purpose

Develop an implementation plan to guide configuration and deployment of your CX-AI solution. Ensure you make the right choice around build or buy and built out your solution roadmap.

Key Benefits Achieved

A robust and highly detailed plan outlining all critical aspects and planning considerations to build, test, deploy, and scale your CX-AI solution.

Activities

Outputs

3.1

Determine your solution path and estimate your solution size.

A first-pass solution path and size estimation.

3.2

Establish an execution framework to develop your Proof of Concept (POC).

Requirements to guide POC development for your use case.

3.3

Establish your deployment plan to bring your solution to production.

A deployment plan to launch your solution into production.

3.4

Define your communication plan.

A communication plan and roadmap to define key milestones.

3.5

Define your project roadmap.

A deployment plan to launch your solution into production.

Module 4: Establish Your Framework to Manage and Maintain Your Solution After Launch

The Purpose

Build the framework to manage, monitor, and continuously improve your AI solution after it’s gone live.

Key Benefits Achieved

A framework to ensure that your solution delivers as promised, it’s being adopted in the right way, and the impact it’s driving is being captured and communicated to your key stakeholders.

Activities

Outputs

4.1

Establish your post-production monitoring plan.

4.2

Develop and adoption plan to guide your solution.

A monitoring and adoption plan for your solution.

4.3

Establish a framework to capture the ROI of your solution.

A defined ROI capture methodology.

Implement AI for Customer Experience

Use AI to supercharge your customer experience outcomes and propel your brand forward.

Analyst perspective

Use AI to supercharge your customer experience outcomes and propel your brand forward.

AI presents an extraordinary opportunity to redefine how organizations connect with their customers, turning routine interactions into high-value experiences that drive loyalty and growth. By leveraging advanced capabilities like predictive analytics, sentiment detection, hyper-personalization, and embedding AI within their contact center, companies can anticipate customer needs, resolve issues proactively, and build lasting brand advocacy. The real story here isn't just about technology - it's about shifting the entire customer journey from being reactive to being genuinely transformative.

Capturing this opportunity requires more than a technology rollout. Organizations must align AI initiatives with concrete business outcomes, secure cross-functional buy-in, and maintain a relentless focus on the human element - both employee training and customer empathy. Those who successfully navigate these challenges will find that AI can be the cornerstone of a modern, scalable customer experience (CX) strategy that keeps pace with changing customer expectations and delivers measurable returns.

The future is bright for CX leaders who fully embrace the transformative potential of AI. With the right strategic alignment, AI becomes more than a piece of the technology puzzle - it becomes the catalyst for deeper engagement, stronger loyalty, and sustainable growth. Embrace this shift with confidence, because a well-executed AI for CX strategy has the power to elevate the entire organization and define how customers experience your brand for years to come.

Ryan Brunet, Principal Research Director

Ryan Brunet
Principal Research Director
Info-Tech Research Group

Executive summary

Key Benefits

Your Challenge

Info-Tech's Approach

Personalized, proactive engagement: Imagine reaching out before your customer even realizes there's an issue. AI detects patterns in real time, turning reactive support into proactive, personalized experiences that set your brand apart.

Empowered agents, faster resolutions: Intelligent assistance tools guide agents with real-time prompts and knowledge, significantly cutting resolution times. Freed from routine inquiries, they can focus on empathy and problem-solving that create a lasting impression.

Boosted customer loyalty and NPS: Delivering consistent, timely, and personalized service naturally fuels positive sentiments and long-term loyalty. When AI is fully aligned with customer-centric goals, organizations routinely see double-digit gains in satisfaction and advocacy metrics.

Balancing operational efficiency with genuine customer delight: Pursuing automated cost savings can backfire if it undercuts the personalization and empathy customers crave, leading to churn and missed revenue opportunities.

Navigating the containment vs. resolution dilemma: Focusing on quick call deflection or speedy self-service may lower handle times, but leaving deeper customer issues unresolved undercuts trust and can increase follow-up costs.

Scaling AI beyond the pilot phase: High agent turnover, fragmented tech stacks, and inadequate training hamper efforts to expand successful AI pilots into sustained, enterprise-wide improvements.

Identify high-impact use cases early: Zero in on specific CX metrics - like first-contact resolution or NPS - that resonate with leadership and deliver visible value to customers.

Design for scalability from the start: Define your project scope with precision - pinpoint the specific CX challenges, establish robust requirements, account for governance, risk, and compliance considerations - and involve cross-functional stakeholders from the outset.

Monitor and adapt continuously: Track key performance indicators and user feedback in real time, then refine AI capabilities regularly to stay aligned with shifting customer needs and strategic goals.

Info-Tech Insight

AI can be transformative for customer experience, but only when tied to genuine customer outcomes and supported by well-prepared teams. Pursuing AI's "wow factor" without linking it to customer needs can yield flashy pilots but few lasting benefits. By tackling both the technology and the people side of change, organizations can convert one-off AI experiments into a powerful, organization-wide CX advantage.

Your challenge

As organizations rush to integrate AI into their customer experience (CX) strategies, they often encounter significant obstacles that can undercut real value. From data fragmentation to real-time data latency, and from insufficient oversight to maintaining reliable AI models at scale, each obstacle can undermine the seamless experiences today's customers expect.

AI's promise in CX hinges on delivering interactions that reflect each customer's history, preferences, and real-time context. However, inconsistent or siloed data often leads to generic or irrelevant responses. Furthermore, customers expect seamless experiences across chat, email, social, voice, and even in-person touchpoints. Yet behind the scenes, each channel may run on separate technologies, APIs, or data structures, making it hard for AI systems to unify interactions in real time.
AI models thrive on comprehensive, high-quality data, but capturing and sharing it securely across multiple systems is no small feat. Weak encryption, inadequate data governance, and immature integration architectures can expose sensitive customer information to breaches or misuse. These challenges grow as AI tools multiply and compliance requirements intensify, demanding rigorous oversight, robust APIs, and enterprise-wide security policies.
While black-box AI can produce rapid results, it often leaves both agents and customers in the dark about how decisions are made. This lack of transparency can breed distrust, hinder compliance efforts, and impede effective troubleshooting when errors arise. Building in mechanisms for explainability - such as interpretable model outputs and clear escalation paths - helps maintain user confidence and ensures that AI-driven insights remain actionable rather than mysterious.

"While 99% of respondents recognize the positive impacts generative AI can have on their organization, 89% of respondents report that their use of generative AI is being slowed."

Source: Elastic, 2024.

"The model [ChatGPT 3.5] was trained using text databases from the internet. This included a whopping 570GB of data obtained from books, web texts, Wikipedia, articles and other pieces of writing on the internet. To be even more exact, 300 billion words were fed into the system."

Source: BBC Science Focus, 2023.

Common obstacles

These barriers make this challenge difficult to address for many organizations:

When your technology is fractured, delivering seamless AI-driven CX feels impossible. Outdated platforms, siloed CRMs, and systems that won't talk to each other force teams to spend more time wrangling data than serving customers. The opportunity lies in modernizing your infrastructure - through real-time data pipelines, cloud migrations, and robust APIs - so every upgrade accelerates business impact rather than holding it back.
Conflicting priorities across leadership, operations, and IT often dilute AI initiatives before they gain traction. A finance-focused C-suite might push cost-cutting strategies, while frontline teams clamor for rapid resolution times, and IT quietly worries about security. The opportunity is to align stakeholders under one unifying vision for AI in CX, ensuring that each function's goals amplify rather than contradict each other, and ultimately driving cohesive, high-value outcomes.
Underestimating the people side of transformation can turn even the best technology investments into missed opportunities. AI may offer predictive analytics and hyper-personalization, but without clear roles, robust training, and genuine cross-functional buy-in, momentum stalls. The opportunity is to treat AI as a team sport, bringing everyone on board early, championing a culture of ongoing learning, and fostering an environment where each advancement in AI resonates across your entire organization.

Common barriers to AI-enabled transformation

AI implementation presents several common challenges that organizations should navigate.

Lack of Skills

57% of organizations believe they have a shortage of internal expertise to implement AI solutions.

(Source: ITSM.tools, 2023.)

Legacy Systems

43% of organizations have challenges with outdated and legacy applications, making AI adoption difficult.

(Source: ITSM.tools, 2023.)

Employee Resistance

62% of managers have reported that their employees are concerned that AI will replace their jobs.

(Source: The HR Director, 2024.)

Data Security Concerns

55% of data leaders are concerned about exposure of sensitive data by large language models (LLMs).

(Source: Immuta, 2025.)

Info-Tech's methodology to implement an AI solution for customer experience

	1. Identify and Select Your Highest-Impact CX AI Use Case	2. Define Your CXAI Solution Requirements and Project Scope	3. Determine Your Solution Path and Build Your "Go-Live" Plan	4. Establish Your Framework to Manage and Maintain Your Solution After Launch
Phase Steps	Assess your core CX KPIs to identify any challenging areas that need to be addressed. Diagnose your CX capabilities to identify priority focus areas and aligned AI solutions. Assess a candidate list of potential use cases on value and feasibility. Select your pilot CX AI project.	Articulate the business context and a clear problem statement. Define solution objectives and success metrics. Understand current process, target-state process, and high-level user and data requirements. Establish a risk assessment and mitigation strategy. Define a solution governance and compliance requirements.	Determine your solution path and estimate your solution size. Establish an execution framework to develop your proof of concept (POC). Build a robust testing framework for your solution. Establish your deployment and scaling plan to bring your solution to production. Build your change management, training, and communication plans. Define your project roadmap.	Establish your post-production monitoring plan. Develop and adoption plan to guide your solution. Establish a framework for continuous improvement of your solution. Establish a framework to capture the ROI of your solution.
Phase Outcomes	CX Capability Diagnostic Assessment Candidate portfolio of CX AI use cases Prioritized list of CX AI solutions	A business-centric solution requirements and scoping document	A robust configuration and deployment framework and corresponding action plans to bring your solution "live"	A monitoring and adoption plan for your solution A continuous improvement framework for your solution A defined ROI capture methodology

Insight summary

Overarching Insight

AI for CX can transform the way organizations connect with their customers, but success hinges on more than just technology. By channeling AI's capabilities into the moments that matter most to customers, you transform each interaction from a mere transaction into a genuinely memorable experience - and in doing so, outshine competitors in a crowded market.

Elevating CX Pays Off

While AI can address countless business goals, organizations that zero in on the customer experience often see the fastest and most substantial payoffs. Every improvement - whether it's quicker resolutions, more personalized support, or proactive engagement - instantly resonates with customers and fuels brand loyalty. Companies that treat CX as the primary beneficiary of AI investments report significant boosts in retention, satisfaction, and revenue, underscoring why a CX-first AI strategy is simply too powerful to ignore.

Metrics Matter

Organizations often chase AI's "wow" factor, neglecting to tie solutions back to the CX metrics that truly matter. In fact, 66% of organizations claim that establishing ROI on potential opportunities is a top challenge for AI implementations.* A deliberate approach clarifies how AI will improve key outcomes - like increasing loyalty or reducing call volume - and ensures each initiative aligns with broader success objectives. By weaving these AI successes together, you transform isolated wins into a clear, customer-centric competitive advantage.

Do It Well and Win

Once you've identified the right problem to solve, your implementation approach can make or break your AI ambitions. A structured, step-by-step methodology - from scope and configuration to ongoing optimization - wards off pitfalls that leave 74% of organizations struggling to see tangible returns.* It's the small details - clean data, robust testing, and clear metrics - that fuel long-term scalability and impact. Quick fixes may be tempting, but they're seldom transformative. Those who invest in doing it right ultimately raise the bar for their entire market.

Tactical Insight

Data is the lifeblood of AI for CX - it must be clean, relevant, and readily accessible. By devoting the right resources to data governance and integration up front, your AI models are poised to deliver more accurate, contextual insights that truly enhance the customer journey.

Tactical Insight

Governance, risk, and compliance (GRC) can't be an afterthought when AI is making real-time decisions that shape your customers' experiences. Establish transparent policies that define how AI tools handle personal information, adhere to regulatory requirements, and enforce ethical boundaries.

*Source: BCG, 2024.

Blueprint deliverables

Each step of this blueprint is accompanied by supporting deliverables to help you accomplish your goals:

CX AI Use Case Discovery Tool
Identify the right opportunities and challenges for your CX AI pilot project.

Solution Path and Size Estimation Tool
Determine if you will buy, build, or outsource your CX AI solution and determine a high-level initial cost estimation.

AI Pilot Project Shortlisting Tool
Assess candidate opportunities based on value and feasibility and select your pilot CX AI use case.

KPI Value Mapping Tool
Analyze your key CX KPIs and map them to potential use cases.

Key deliverable

CX AI Solution Development and Implementation Report

A highly detailed and compelling report outlining your project scope and delivery plan.

Quantify your strategic approach

Use the following metrics throughout this process to track AI's impact on improving customer experience and driving measurable outcomes:

NPS Score: Used to measure customer loyalty and satisfaction by gauging how likely customers are to recommend your company or products to others.

Agent Utilization Rate: Calculated by measuring the percentage of time that service desk agents spend on IT support tasks as opposed to their available time.

Cost Per Ticket: Average cost of ticket resolution, which will be essential to analyzing the ROI of AI implementation in the service desk.

Customer Satisfaction: Measurement of end-user satisfaction done through surveys, to demonstrate service desk's timeliness and effectiveness.

Organizations that have adopted AI have reported positive impacts on support and processes:

Implementation of generative AI at the service desk leads to 75% reduction of resolution times (Rezolve.ai, 2024).

Access to an AI-powered conversational assistant increases worker productivity by 15% (Arxiv, 2023).

AI implementation leads to 35% reduction of customer support costs (Plivo, 2024).

Case study

Inside Target's Real-Time Personalization Solution - A lesson in AI optimization

INDUSTRY
Retail

SOURCE
Target Blog

Challenge	Solution	Results
Target, a major retail brand, faced the challenge of meeting customers' growing expectations for personalized, relevant shopping experiences in real time. As Target's customer base increasingly engaged online, the company needed a data solution that could support personalization at scale, balancing the need for speed and reliability with robust data management. Target's data teams were tasked with unifying data sources, ensuring data quality, and developing infrastructure that could deliver personalized recommendations, promotions, and experiences to customers in real time, without compromising on operational efficiency or scalability.	Target's technology teams developed a real-time personalization platform capable of ingesting and analyzing data as guests interacted with digital touchpoints. By leveraging AI models at scale, the platform evaluated each user's context - such as browsing history and item preferences - to surface dynamic, customized recommendations. Teams rolled out this solution in phases, first piloting it on a limited scope of products and experiences, then refining its algorithms based on guest feedback.	Target's real-time personalization strategy led to significant improvements in customer engagement and satisfaction. With personalized recommendations and offers delivered promptly, Target saw an increase in online and in-store conversions, reflecting a strong alignment between customer needs and the brand experience. The phased rollout provided essential lessons for continuous improvement. As the platform evolves, it can seamlessly incorporate new data sources and handle greater personalization demands, ensuring Target stays ahead in delivering meaningful guest experiences.

Info-Tech offers various levels of support to best suit your needs

DIY Toolkit	Guided Implementation	Workshop	Executive & Technical Counseling	Consulting
"Our team has already made this critical project a priority, and we have the time and capability, but some guidance along the way would be helpful."	"Our team knows that we need to fix a process, but we need assistance to determine where to focus. Some check-ins along the way would help keep us on track."	"We need to hit the ground running and get this project kicked off immediately. Our team has the ability to take this over once we get a framework and strategy in place."	"Our team and processes are maturing; however, to expedite the journey we'll need a seasoned practitioner to coach and validate approaches, deliverables, and opportunities."	"Our team does not have the time or the knowledge to take this project on. We need assistance through the entirety of this project."

Diagnostics and consistent frameworks are used throughout all five options.

Guided implementation

What does a typical GI on this topic look like?

Phase 1	Phase 2	Phase 3	Phase 4	Final
Call #1: Initiate KPI mapping and CX capability diagnostic. Call #2: Review capability diagnostic and create list of candidate use cases. Call #3: Assess candidate use case and select pilot project.	Call #4: Discuss objectives and success metrics. Call #5: Define current and target state and requirements. Call #6: Establish GRC framework for project.	Call #7: Determine solution path and size estimation. Call #8: Establish POC and testing framework. Call #9: Establish deployment and scaling plan.	Call #10: Define roadmap, training, and communication plans. Call #11: Establish adoption and monitoring framework. Call #12: Define a framework for continuous improvement.	Call #13: Establish ROI measurement and review Solution Development and Implementation Report.

Phase 1

Phase 2

Phase 3

Phase 4

Final

Call #1: Initiate KPI mapping and CX capability diagnostic.

Call #2: Review capability diagnostic and create list of candidate use cases.

Call #3: Assess candidate use case and select pilot project.

Call #4: Discuss objectives and success metrics.

Call #5: Define current and target state and requirements.

Call #6: Establish GRC framework for project.

Call #7: Determine solution path and size estimation.

Call #8: Establish POC and testing framework.

Call #9: Establish deployment and scaling plan.

Call #10: Define roadmap, training, and communication plans.

Call #11: Establish adoption and monitoring framework.

Call #12: Define a framework for continuous improvement.

Call #13: Establish ROI measurement and review Solution Development and Implementation Report.

A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.

A typical GI is 8 to 12 calls over the course of 2 to 3 months.

Implement AI for CX - Workshop overview

Contact your account representative for more information.
workshops@infotech.com
1-888-670-8889

	Pre-Workshop	Session 1	Session 2	Session 3	Session 4	Post-Workshop
Activities	Understand and Define Key Inputs to Project Selection	Identify and Select Your Highest Impact CX AI Use Case	Define Your CX AI Solution Requirements and Project Scope	Finalize Project Scope and Define Your "Go-Live" Framework	Finalize "Go-Live" Plan and Define Monitoring and Maintenance Plan	Next Steps and Wrap-Up (Offsite)
CXO to: Identify CX AI working group. Complete CX KPI use case mapping exercise. Review and complete first draft of CX Capability Diagnostic Assessment. Schedule participants.	1.1 Assess your core CX KPIs to identify any challenging areas that need to be addressed. 1.2 Diagnose your CX capabilities to identify priority focus areas and aligned AI solutions. 1.3 Assess a candidate list of potential use cases on value and feasibility. 1.4 Select your pilot CX AI project.	2.1 Articulate the business context and a clear problem statement. 2.2 Define solution objectives and success metrics. 2.3 Understand current-state process and define your target-state process. 2.4 Define high-level user requirements.	3.1 Define your project data requirements. 3.2 Establish a risk assessment and mitigation strategy. 3.3 Define a solution governance and compliance requirements. 3.4 Determine your solution path and estimate your solution size. 3.5 Establish an execution framework to develop your proof of concept (POC).	4.1 Establish your deployment plan to bring your solution to production. 4.2 Define your communication plan. 4.3 Define your project roadmap. 4.4 Establish your post-production monitoring plan. 4.5 Establish a framework to capture the ROI of your solution.	5.1 Complete remaining sections of Solution Development and Implementation Report. 5.2 Generate workshop deliverables. 5.3 Set up review time for workshop report and to discuss next steps.
Outcomes	CX AI working group. CX KPI value maps. CX Capability Diagnostic Assessment.	Candidate portfolio of CX AI use cases. Prioritized list of CX AI solutions. Selection of your CX AI pilot project.	Definition of the problem and business context for your solution. Defined objectives and success criteria. Defined user requirements for your solution.	Defined data requirements. A GRC framework to your guide your project. A first pass solution path and size estimation. Requirements to guide POC development for your use case.	A deployment plan to launch your solution into production. A communication plan and roadmap to define key milestones. A framework to monitor and measure the performance of your solution.	Completed workshop deliverables. Provide exercise tools leveraged in workshop with content entered in workshop (optional).

Pre-Workshop

Session 1

Session 2

Session 3

Session 4

Post-Workshop

Activities

Understand and Define Key Inputs to Project Selection

Identify and Select Your Highest Impact CX AI Use Case

Define Your CX AI Solution Requirements and Project Scope

Finalize Project Scope and Define Your "Go-Live" Framework

Finalize "Go-Live" Plan and Define Monitoring and Maintenance Plan