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.
As digital reliability becomes increasingly critical to business success, organizations must mature their application support operating models to mitigate risks and enable a seamless customer experience. Without a well-defined framework, businesses risk significant financial losses, operational inefficiencies, and diminished customer trust. The 2024 CrowdStrike IT outage, which reportedly caused an estimated $5B in direct losses[1] for Fortune 500 companies, highlights the growing financial risks of digital failures. While this incident stemmed from a faulty software update, it underscores the need for organizations to have resilient disaster recovery plans and adaptable support models to minimize downtime and disruption.
A robust support operating model enhances developer productivity by enabling engineering teams to increase their focus on innovation while maintaining critical business functions, ultimately improving talent retention. New AI tools are aiding this process and improving employee experience as well.
In an era where both internal and external stakeholders demand efficiency and reliability, organizations that invest in resilient support models position themselves for long-term success. To achieve this resilience, organizations must first understand the different approaches available and where they stand on the maturity spectrum.
There is no one-size-fits-all approach to application support. The right model depends on several factors, including business need, size, and complexity. Based on our experience with Fortune 500 clients, we have identified three common approaches, ranging from the least to most mature:
In this traditional model, a dedicated support team is responsible for incident response, troubleshooting and maintenance. While this approach reduces operational burden on development teams, it often results in slower resolution, knowledge gaps, and inefficiencies.
This approach involves collaboration between development and support teams, improving response times. There can be multiple flavors within this category:
At the highest level of autonomy, development teams take complete ownership of applications, including support and maintenance. Enabled by self-service platforms and automation, this model facilitates the fastest incident resolution times and drives continuous improvement by ensuring developer accountability. Growing use of AI-driven observability, self-healing systems, and other automation tools can reduce burnout risk for teams handling both development and on-call support. However, this model may not be ideal for every organization due to the potentially higher costs associated with hiring skilled developers who also have operational expertise.
Each of these models has trade-offs, and organizations must evaluate priorities and capabilities to determine the best approach and target maturity level (the highest may not be the best for every organization). Many organizations may operate in a hybrid model. The key to success lies not just in choosing a model, but in adapting it over time to balance efficiency, productivity and business resilience.
Regardless of which model an organization chooses, success depends on how effectively it is implemented. Here are five best practices to optimize your support model:
When designing an application support model, it is crucial to understand the needs of both internal (developers, business teams) and external (end-users) customers. Organizations should consider:
By adopting a customer-centric approach, organizations can ensure alignment with business needs.
As organizations grow, especially through mergers and acquisitions, support responsibilities often become fragmented, with multiple teams managing the same capabilities across different business units. For one hospitality client, this disjointed structure often led to confusion, inefficiencies, and delayed resolutions. The Metis Strategy team applied a capability-driven approach to ensure teams were organized to deliver business value with the right skills, expertise, and processes. By aligning support teams with core competencies, such as platform reliability, security, or incident management, organizations can reduce redundancies and streamline workflows.
As organizations increasingly modernize and move to the cloud, the application and infrastructure layers become increasingly intertwined. Cloud environments abstract and automate many traditional infrastructure concerns, which can require platform engineering teams to bridge the gap between development, infrastructure and operations by providing automation, self-service tools, and best practices for deployment, observability and cost optimization.
This evolution reshapes understanding of application support. Instead of solely addressing application issues, support teams must take a full-stack, proactive approach—leveraging platform engineering to monitor, automate, and secure both applications and infrastructure. Support teams must collaborate across disciplines to troubleshoot across the entire tech stack to prevent downtime and optimize performance.
Effective application support requires collaboration across multiple functions. Organizations should proactively seek to understand some key perspectives with each group of stakeholders:
By addressing these questions upfront, organizations can avoid roadblocks and create a more sustainable model.
Shifting to a mature support model isn’t just about processes and tools. It requires a cultural shift, making executive storytelling and change management critical. Metis Strategy recently worked with a client shifting to a new model by:
A well-communicated change plan can foster trust, reduce friction, and accelerate adoption.
As organizations redefine their application support model, it is important to recognize that simply implementing the latest AI solution is not enough. Success depends on balancing the right tools with the right people and processes. When properly integrated, AI and human expertise can reduce costs, improve efficiency, and enhance customer satisfaction. Now is the time to build a support model that ensures long-term success.
Future articles will explore how AI is transforming roles, responsibilities, and workflows within different application support models.
[1] https://www.reuters.com/technology/fortune-500-firms-see-54-bln-crowdstrike-losses-says-insurer-parametrix-2024-07-24/
