Generative AI is rapidly transforming enterprise operations, with most forward-thinking organizations now exploring both customer-facing applications and internal productivity enhancements in parallel. While customer applications and content generation tools capture headlines, the most significant business impact often lies in modernizing internal operations—particularly in support functions where traditional automation falls short.
Across industries, companies are under immense pressure to reduce costs, improve efficiency, and scale operations without significantly increasing headcount. Legacy support models, particularly in IT and internal operations, often struggle to keep pace with employee demands, resulting in inefficiencies, bottlenecks, and rising service costs. Traditional rule-based automation tools have provided some relief, but they lack the adaptability required to handle complex, evolving queries.
Unlike static automation solutions, GenAI can process natural language, learn from interactions, and dynamically generate responses, enabling enterprises to modernize internal operations, accelerate problem resolution, and improve service experiences for employees.
Despite its promise, the challenge for many organizations isn’t just understanding AI’s potential—it’s knowing how to implement it in a way that delivers tangible business value. Without a structured adoption strategy, AI projects often fail due to poor integration with existing workflows, low user adoption, and unclear ROI metrics.
A Fortune 150 SaaS company exemplifies this strategic shift. Facing a 70% escalation rate in their IT Service Desk, leadership recognized an opportunity to leverage GenAI’s natural language processing and dynamic response capabilities to revolutionize employee support. This case study examines how their IT Director moved beyond theoretical AI potential to deliver tangible business value through a structured, results-driven implementation of a GenAI-powered Support Copilot.
The initiative demonstrates how enterprises can overcome common AI adoption challenges—poor workflow integration, low user adoption, and unclear ROI metrics—by applying disciplined product strategy to AI deployment. From business case development through scaled implementation, their approach provides executives and product leaders with a practical blueprint for driving meaningful AI transformation in enterprise operations.
The Challenge: IT Support Inefficiencies
The IT Service Desk faced mounting inefficiencies, including high ticket volumes, long resolution times, and limited self-service capabilities. Despite having an AI-powered virtual assistant, most IT support interactions still required human intervention, increasing operational costs and delaying response times. Employees struggled to resolve common IT issues independently, while support teams sought to optimize service efficiency and cost-effectiveness.
A key challenge was choosing the right service delivery method to balance support quality and savings opportunities. Over-relying on agent involvement increased costs, while excessive automation risked frustrating users if responses lacked depth or accuracy. The team needed a strategic mix of immediate answers, workflow automation, proactive notifications, agent support, and ticket creation to ensure efficiency without compromising user satisfaction.
The Solution: AI-Powered Support Copilot
To address these inefficiencies, the team developed a GenAI-driven Support Copilot capable of resolving routine IT issues without escalating to human agents. Unlike traditional rule-based chatbots, this solution leverages natural language processing and retrieval-augmented generation (RAG) to deliver context-aware responses and continuously improve through feedback.
By integrating seamlessly into existing ITSM workflows, the AI-driven Copilot aimed to reduce ticket volumes, accelerate resolution times, and enhance employee self-service capabilities. More importantly, the carefully designed service delivery strategy ensured that automation was applied where it maximized efficiency while agent support remained available for complex cases, creating an optimal balance between cost savings and high-quality IT support service.
Image Title: Sample Understanding Various Support Delivery Methods
Strategic Alignment and Roadmap Development
With a clear problem statement and well-defined objectives, the next step was to align strategy with engineering execution. The Service Desk Chat AI Copilot was designed to enhance IT support efficiency while ensuring a seamless user experience.
Before defining the solution, the team applied end-user-centric product design principles, focusing on who the solution was being built for, their roles, and the specific pain points in their workflow. By analyzing service desk data and gathering insights from IT support agents and employees, the team identified recurring issues that required AI-driven assistance. This user-first approach ensured that the AI Copilot was tailored to real-world needs rather than being driven solely by technological capabilities.
To minimize risk and maximize impact, the roadmap prioritized an MVP focused on high-value use cases. The initial phase centered on information retrieval within the company’s primary communication platform, providing immediate user benefits. Subsequent phases introduced capabilities such as request-based inquiries, asset provisioning, and advanced troubleshooting, progressively increasing AI’s role in IT support.
Selecting the Right Tech Stack & AI Model
Given security, scalability, and integration requirements, the Engineering team selected the company’s internal LLM with an advanced retrieval-augmented generation (RAG) mechanism. While external GPT-based models were considered, the internal solution provided greater control, improved security, and domain-specific accuracy.
To enhance AI performance, the team optimized retrieval mechanisms to handle ambiguous IT support queries effectively. The knowledge retrieval system was fine-tuned to reduce fallback rates to human agents, significantly improving response accuracy. Additionally, custom APIs were developed to enable seamless integration with ITSM workflows, allowing real-time interactions with ticketing and asset management systems.
Image Title: Sample User Persona of an IT Support Copilot End-User
Agile Product Development Process
The Engineering team worked in iterative sprints, ensuring continuous improvements throughout development. The Product Manager collaborated closely with Engineering to conduct feasibility and impact assessments for each proposed use case.
To align technical execution with user expectations, the Product Manager provided detailed UX flows, ensuring clarity in AI responses, expected interactions, and integration within ITSM workflows. Regular feedback loops allowed for rapid iteration, resolving engineering challenges while refining AI performance.
This collaborative and agile approach enabled the team to move quickly, ensuring the AI-powered Copilot delivered measurable impact from early-stage deployment.
With the MVP ready for production, the focus shifted to minimizing friction, validating product performance, and gathering real-world insights.The team launched a controlled pilot, targeting a subset of users who were engaged and likely to provide valuable feedback.
The two-week pilot phase allowed the team to monitor system stability, track AI accuracy, and refine the experience based on user feedback. Users who encountered issues were followed up with directly, ensuring the AI model could quickly adapt and improve before full deployment across the entire organization.
Performance Metrics and Expansion
Early results demonstrated the AI solution’s effectiveness, with escalation rates to human agents dropping by 85% compared to the legacy system. Encouraged by this outcome, the team expanded the rollout across additional departments to further validate performance.
As adoption scaled, KPIs were closely tracked. Although escalation rates were slightly higher in broader deployments compared to the pilot, the AI Copilot still far outperformed the legacy system. With approximately 40% of IT support case volume resolved by AI —even in its MVP state— the solution presents a meaningful opportunity to drive further efficiency. As adoption grows and capabilities expand, the department is well-positioned to realize up to a 30% reduction in cost per ticket—freeing up capacity, reducing operational overhead, and enabling the IT Service Desk to focus on higher-value, more complex support needs.
Image Title: Product & AI Metrics Aligned to the User Journey
Validating Long-Term Success
The team continuously monitored AI accuracy, resolution speed, and user satisfaction, refining the model based on performance data. Customer Satisfaction (CSAT) scores consistently exceeded 4.6, significantly outperforming other GenAI applications deployed within the company.
With penetration testing completed and system stability confirmed, the organization fully decommissioned the legacy solution one week after the global rollout, marking a successful transition to AI-powered IT support at scale.
This initiative demonstrated the potential business impact of AI-driven IT support, setting the stage for future iterations and expansion into additional use cases. The success of this deployment also provided a blueprint for accelerating AI adoption across other business functions.
Image Title: CSAT, Deflection, and Cost Reduction Metrics
What Worked Well?
One of the most critical factors behind the success of this GenAI initiative was the alignment between strategy, engineering, and execution. From the outset, the IT Service Desk, Engineering, and Product teams worked in lockstep, fostering trust and transparency through open communication, shared accountability, and clear goal alignment. This ensured that the vision for the AI-powered Copilot was clearly communicated and executed against well-defined objectives. By maintaining a collaborative and transparent approach, the team was able to address challenges proactively and make informed decisions that kept development on track.
Once the MVP was released to a pilot department, the continuous iteration process became another key success factor. Real-world feedback from users enabled the team to refine responses, optimize AI interactions, and improve the product’s UX/UI. The ability to make data-driven enhancements early on ensured that the solution was not only functional, but also intuitive and effective in real-world support scenarios.
Challenges & How We Overcame Them
Managing Stakeholder Expectations
As a high-profile AI initiative, the project attracted significant executive attention and expectations. Managing this required a well-defined business case and PRD, which provided a structured rationale for decision-making, ensuring strategic alignment and continued buy-in throughout development and deployment.
Handling AI Bias & Hallucinations
Another challenge was ensuring that LLM-generated responses were accurate, relevant, and free from bias or hallucinations—a common issue in AI-powered applications. Since reliable AI outputs were essential for maintaining trust in the system, the team adopted a two-pronged testing strategy:
1. Golden Data Set Approach: The IT Service Desk team curated a golden dataset of expected responses, allowing engineers to manually track AI accuracy by comparing generated outputs against validated answers.
2. Leveraging Product Analytics: As the solution matured, product analytics were leveraged to monitor whether users were successfully resolving issues with AI-generated answers. This helped the team identify patterns of failure, enabling targeted fine-tuning of the model.
This proactive testing and monitoring approach allowed the team to mitigate AI-related risks and ensure that the Copilot provided reliable, high-quality responses to users.
Best Practices for Business Leaders
For business leaders looking to drive successful GenAI implementations, three key principles emerged from this initiative:
Clearly defining the problem, opportunity, and expected impact secures early buy-in and aligns the product strategy with business objectives.
Early and ongoing collaboration between Product and Engineering ensures that technical feasibility, model performance, and user experience are considered holistically, leading to a more effective solution.
AI deployment isn’t just about launching a system—it’s about ensuring users understand, trust, and benefit from it. Leveraging product analytics and user feedback loops enables continuous refinement, increasing engagement and long-term success.
By following these best practices, organizations can maximize GenAI’s business impact while ensuring strong adoption and sustained value.
The successful deployment of the GenAI-powered IT Support Copilot demonstrates that effective AI implementation requires more than cutting-edge technology—it demands strategic vision, disciplined execution, and continuous refinement. This case study reveals a clear path forward: an 85% reduction in escalations, dramatically improved resolution times, and CSAT scores consistently above 4.6 all point to measurable business impact that extends far beyond IT.
For executive leaders, the strategic implications are clear:
1. Act with urgency, but execute with precision. The window for competitive advantage is narrowing as GenAI capabilities mature. Begin by conducting a comprehensive assessment of your enterprise workflows to identify high-value, low-risk opportunities for AI augmentation.
2. Build for scale from day one. While starting small is prudent, architect your AI initiatives with enterprise-wide deployment in mind. Ensure your technology stack can accommodate growing data volumes, expanding use cases, and increasing user expectations.
3. Integrate AI into your talent strategy. The most successful organizations are redefining roles to leverage AI-enhanced productivity. Invest in upskilling programs that enable your workforce to collaborate effectively with AI systems rather than merely responding to automation.
4. Establish cross-functional AI governance. Form a dedicated team spanning IT, legal, HR, and business units to address emerging questions of data privacy, accuracy standards, and appropriate AI use cases.
The time for theoretical discussions about AI’s potential has passed. Organizations that systematically implement GenAI solutions today will create substantial operational advantages that compound over time. By applying the structured approach demonstrated in this case study—clear business case development, collaborative engineering partnership, and metrics-driven refinement—you can transform GenAI from an experimental technology into a core driver of enterprise productivity and innovation.
The question is no longer whether to adopt GenAI, but how quickly you can scale it effectively across your organization.
In today’s digital landscape, where user engagement directly correlates with product longevity, the stakes for developing user-centric products have never been higher. Success demands not just initial user understanding, but a continuous cycle of testing, learning, and iterating to refine products based on real-world usage and feedback. When technology product teams rush to build solutions before establishing this foundational cycle, they overlook critical user needs, mistakenly believing they’re accelerating delivery. Organizations that bypass this iterative approach don’t just risk poor adoption rates—they jeopardize their entire product lifecycle and, ultimately, their market position. Yet despite these evident risks, many companies still find themselves in reactive modes, struggling to retain customers and maintain market relevance.
This challenge stems from a complex interplay of organizational dynamics that extends far beyond simple oversight. When technology product teams craft their strategic vision, they often rush to build solutions before fully understanding what users truly need, mistakenly believing this accelerates delivery. This approach, while seemingly efficient, leads to misaligned products that fail to resonate with their intended audience. The reality is that user requirements must be the foundation of product strategy—not an afterthought—and should be shaped by those closest to the user experience.
Our work with organizations across industries has revealed a consistent pattern: companies that do not invest adequate time in upfront user understanding and ongoing iteration inevitably pay the price in form of multiple product iterations and customer dissatisfaction. When combined with aggressive timelines and insufficient user data, this creates a perfect storm that not only undermines product success but also strains organizational resources and team morale.
Through careful analysis of successful product-centric transformations, we have identified five fundamental steps that enable organizations to pursue customer centric product design. These steps aren’t meant to be executed once and forgotten, but rather form a continuous cycle of learning and improvement:
By embracing this framework, organizations can transform their approach to product development. Instead of navigating uncertain returns and user indifference, teams can create products that consistently delight users and drive sustainable business growth. This systematic approach not only enhances product success rates but also establishes a foundation for continuous innovation and market leadership.
To illustrate how these steps transform product development in practice, let’s take a look at a product team tasked with reimagining the customer checkout experience for an eCommerce platform. Composed of Design, Engineering, and Product roles, the team recognizes that successful product development requires seamless collaboration across these critical functions—each bringing unique perspectives that collectively unlock user-centric innovation. Guided by executive leadership’s strategic mandate to reduce customer drop-off rates and improve conversion metrics, the team oversees both web and mobile interfaces and faces a critical challenge: high customer abandonment after cart additions, directly impacting revenue and customer satisfaction. With clear directives from senior management to develop a comprehensive strategy that balances user needs with business objectives, the team must rapidly identify pain points and craft innovative solutions. Under the guidance of an experienced Product Manager, they embark on a systematic journey to uncover user challenges and transform the checkout experience from a point of friction to a competitive advantage.
Image 1: Cross-Functional Team Integration: Ensuring seamless collaboration between Product, Design, UX, and Engineering to aid the product discovery process
To develop customer-centric products, it’s essential to first understand who the product team is designing the experience for. Defining the customer persona is not just a step in design thinking—it is the foundation of a successful product strategy. By clearly outlining user preferences, challenges, and motivations, teams can ensure their solutions are relevant and impactful. Keep in mind that customer persona can represent either a customer or an employee, depending on the product’s focus.
Once a comprehensive persona is created, teams should map out the user’s current experience. Journey maps visually represent the customer’s interactions with the product, enabling teams to identify friction points and tailor enhancements.
For example, the checkout team’s persona might be a 37-year-old working mother of two, often shopping in a rush. Her journey could include:
By understanding these stages, product teams can address user preferences holistically, ensuring that improvements align with both immediate and long-term goals.
After mapping the stages and understanding how customers engage with the app, the next step is to evaluate the pain points they encounter. The ‘jobs-to-be-done’ framework can help product teams understand that users choose products to accomplish specific tasks. By outlining these jobs, product teams can define desired outcomes, segment them and devise strategies to address them. This approach not only fosters empathy for user challenges but also provides actionable insights to define the desired future state.
For the checkout team, this might involve analyzing app usage, metrics, and customer feedback to pinpoint issues like:
Mapping these pain points to the identified journey stages, enables the product team to create a comprehensive overview of specific pain points across the customer journey.
With a clear understanding of user personas and pain points, teams can envision the ideal future-state experience. This critical step requires a nuanced approach that balances user preferences with technical feasibility and business constraints. While user feedback is invaluable, product teams must recognize that not every user suggestion represents a viable or optimal solution—some ideas may introduce unintended complexity or misalign with broader product strategy.
For the checkout team, crafting an ideal experience means carefully prioritizing enhancements that deliver maximum user value while remaining technically and strategically sound:
During a recent engagement with a leading technology company’s mobile application team, this strategic approach enabled the client to boost user engagement and streamline upselling opportunities. By defining an ideal experience that balanced user desires with technical constraints, the team could allocate resources effectively and build toward a cohesive vision.
Once the future state is defined, product teams must identify the capabilities required to bridge the gap. Developing these capabilities proactively—rather than merely reacting to user feedback—empowers teams to anticipate needs and deliver innovative solutions that surprise and delight users.
Leadership support is essential to ensure teams have the resources and authority to pursue these enhancements efficiently. For the checkout team, this might mean prioritizing:
By focusing on proactive development, teams can release iterative changes more rapidly and achieve a continuous cycle of improvement.
Finally, measuring success is as critical as defining the vision. Product teams must identify key performance indicators (KPIs) that align with the desired user experience and overarching business goals. Well-defined KPIs enable teams to validate assumptions, track progress, and refine strategies in real time, making them an integral part of a mature dual-track discovery and delivery process.
For the checkout team, KPIs such as site loading times and drop-off rates provide tangible benchmarks for success. These metrics not only indicate the immediate health of the product but also serve as early signals of deeper issues or opportunities.
Establishing a metrics-driven approach not only elevates the team’s problem-solving capabilities but also creates a culture of accountability and continuous learning. When both product teams and leadership actively engage with KPIs, they foster an environment where decisions are based on evidence rather than assumptions, paving the way for long-term success and innovation.
Through the application of these five steps, the eCommerce checkout team transformed their approach to product development. By deeply empathizing with their users, mapping the customer journey, and methodically addressing pain points, they gained invaluable insights that traditional feature-driven development would have missed. Their systematic approach to defining the future state and identifying necessary capabilities ensured that every enhancement addressed user needs, ultimately resulting in meaningfully lower drop-off rates and higher customer satisfaction.
This framework, though straightforward, opens doors to broader strategic considerations. Teams must handle capability prioritization, cross-functional responsibilities, and resource allocation—challenges that extend beyond any single product team’s domain. Yet these challenges are worth tackling, as they lead to more cohesive, user-centric solutions.
The path to user-centric product development requires more than just following steps—it demands a fundamental shift in how organizations approach product strategy. Success is dependent on leadership’s commitment to empowering teams with both the framework and the organizational support to execute it effectively. By establishing a dual-track discovery and delivery process, teams can maintain their user focus while delivering tangible results. This balance between user needs and business objectives, supported by clear vision and strong change management, creates the foundation for products that truly resonate with users and drive sustainable business growth.
Image 2: Illustrative E2E Product Discovery & Delivery Product for Product Teams
