How We Helped a Global Packaging Technology Provider Transform Customer Service with AI
In complex manufacturing environments, AI customer service transformation is redefining how organizations handle technical support moving beyond basic ticketing to intelligent troubleshooting and automation. For a global packaging automation and logistics technology provider, customer service meant navigating highly technical workflows across fragmented systems, large machine data files, and manual processes.
To improve speed, accuracy, and scalability, they partnered with Collectiv to design an AI-powered customer service agent using Copilot Studio and Azure AI Foundry.
Modernizing Customer Support in a Complex Technical Environment
Customer service teams were responsible for handling a wide range of technical support requests, from troubleshooting machine issues to identifying replacement parts and initiating orders.
However, several challenges made it difficult to deliver fast, consistent support:
- Critical knowledge was spread across systems like knowledge management platforms and SharePoint, making it hard to access the right information quickly
- Diagnosing issues required analyzing large machine log files, often slowing down resolution times
- Parts identification relied heavily on manual expertise and disconnected image repositories
- Support workflows required manual updates across Salesforce and SAP, increasing effort and risk of error
- Historical case data was underutilized, limiting the ability to learn from past resolutions
As support demand grew, these inefficiencies created bottlenecks, impacting both customer experience and internal productivity.
Designing an AI-Powered Customer Service Agent
Collectiv delivered a scalable, AI-powered customer service agent solution designed to streamline technical support, automate workflows, and unify data across complex enterprise systems:
- Developed an intelligent agent using Copilot Studio and Azure AI Foundry to orchestrate workflows and enable advanced reasoning
- Enabled real-time troubleshooting through unified knowledge retrieval and machine log file processing
- Implemented image-based part identification with automated recommendations and SAP order draft creation
- Automated ingestion and structuring of Zoom call transcripts, syncing outputs directly into Salesforce with human-in-the-loop validation
- Integrated with Databricks to surface similar historical cases and enforce secure, governed data access
This solution brought together multiple capabilities; text, image, and structured data processing, into a single, cohesive support experience.
Connecting Systems for End-to-End Support Automation
A key differentiator of this solution was its ability to seamlessly integrate across the organization’s core systems.
The agent was designed to pull in data from multiple enterprise systems, including Salesforce, SAP, Databricks, SharePoint, internal knowledge bases, and image repositories, enabling a fully connected support workflow.
Instead of switching between systems, customer service representatives could now:
- Diagnose issues using AI-assisted insights
- Identify and confirm parts through image recognition
- Automatically generate draft orders in SAP
- Capture and structure case details without manual entry
This end-to-end integration significantly reduced friction in the support process while improving consistency and accuracy.
Unlocking Faster Resolution and Scalable Support
By embedding AI directly into customer service workflows, the organization accelerated its broader AI customer service transformation, changing how support is delivered across the business.
The new agent-enabled experience:
- Improved first-contact resolution through faster, more accurate troubleshooting
- Reduced manual data entry and administrative overhead
- Enabled teams to leverage historical data to guide decision-making
- Created a scalable foundation for future AI-driven service capabilities
- Provided a framework for continuous improvement through user feedback and performance monitoring
- Building the Future of AI-Driven Customer Service
This initiative represents more than a single use case, it’s a foundational step in the organization’s ongoing AI customer service transformation.
With a modular architecture built on Copilot Studio and Azure AI Foundry, the organization is now positioned to evolve toward a broader multi-agent ecosystem, supporting everything from advanced diagnostics to proactive service recommendations.
It’s not just about implementing AI, it’s about connecting systems, data, and workflows in a way that drives real business impact.
Ready to accelerate your AI customer service transformation? Reach out to the Collectiv team to get started.
FAQ
What is an AI-powered customer service agent?
An AI-powered customer service agent is a digital assistant that can understand user requests, retrieve relevant information, and automate workflows across systems. These agents can handle tasks like troubleshooting, answering questions, processing data, and even initiating actions such as creating orders or updating records.
What are the benefits of using AI in customer service?
Organizations that implement AI-driven customer service solutions typically see:
- Faster resolution times and improved first-contact resolution
- Reduced manual data entry and operational overhead
- More consistent, accurate responses across teams
- Better use of historical data and knowledge assets
- Scalable support without increasing headcount
How do Copilot Studio and Azure AI Foundry work together?
Copilot Studio acts as the front-end interface where users interact with the agent, while Azure AI Foundry powers the advanced reasoning, data processing, and model orchestration behind the scenes. Together, they enable scalable, enterprise-grade AI solutions that combine ease of use with deep technical capability.
What types of customer service processes can AI automate?
AI agents can automate a wide range of support workflows, including:
- Troubleshooting and diagnostics
- Knowledge retrieval from internal systems
- Call transcript summarization and case documentation
- Parts identification using image recognition
- Order creation and CRM updates
These capabilities help reduce manual effort and improve response times.
How do you ensure data security and governance with AI agents?
Enterprise AI solutions are built with strong security and governance controls, including role-based access, data encryption, and integration with existing platforms like Microsoft Entra ID and Azure security services. This ensures that AI agents only access approved data and operate within compliance requirements.
How does Collectiv approach AI consulting and implementation?
Collectiv helps organizations design and deploy AI solutions tailored to their business processes, leveraging tools like Microsoft Copilot, Azure OpenAI, and Azure AI Foundry. Their focus is on turning AI from a concept into real business impact through strategy, architecture, and implementation.
How do you get started with AI at Collectiv?
Getting started begins with an AI strategy session, where Collectiv helps assess your current environment, identify high-impact use cases, and define a roadmap for implementation. From there, Collectiv builds, tests, and scales AI solutions across your organization.
