How Bigblue Reimagined Logistics Support with Agentic AI

Welcome to Agents of Change by Ema, our monthly newsletter spotlighting enterprise leaders at the forefront of AI-powered transformation every month.

Past guests have included digital transformation veterans like Mahi Inampudi (CTO, Envoy Global), Venkat Narayan (Head of CX, MoneyView), and Michael Burian (Founder and CEO, New Digital Intelligence)—each sharing real stories of AI implementation (Gen AI, AI Agents) in the enterprise today.

Today, we’re excited to share insights from Laetitia Leghzaoui and Benjamin Karouby, who lead Customer Experience at Bigblue. 

Bigblue is a logistics tech platform that powers over 600+ omnichannel brands across Europe, enabling order fulfillment, delivery, and returns. 

They combine the best of logistics and technology—integrating an advanced logistics network with its proprietary WMS and (Transport Management System) TMS—to deliver an unmatched branded delivery and returns experience, reflected in their 96% customer satisfaction rate.  

Handling over 1 million parcels a month from DTC and B2B clients, Bigblue’s customer support team plays a mission-critical role in ensuring millions of buyers get accurate, fast resolutions to their queries—at scale.

Expectations from customer support have only increased over the last decade, with the rise of social media and instant connectivity. Laetitia and her team turned to Agentic AI to solve one of the most pressing problems in logistics customer support today:

• Delivering multilingual, nuanced, on-brand, accurate responses to
• High-volume, often unpredictable, multi-party customer queries—which are also time-sensitive and complex. 

This is the story of Bigblue’s journey to AI-powered customer support automation, where response time went from 2 hours to under 90 seconds, without hiring more human agents— while also increasing response quality. 

They share lessons learned along the way, talk about the future of customer support, and share relevant advice for other leaders navigating the same problems and questions about customer support and AI.

Key Takeaways:

• Customer support in modern logistics is a high-stakes, high-complexity function—ripe for Agentic AI. As digital commerce scales across borders, languages, and systems, CX teams face growing pressure to deliver fast, accurate, culturally sensitive support that eliminates the inefficient, operationally taxing solutions of the past. 
• The future of CX is human + AI, not human vs. AI. The most forward-thinking teams aren’t using AI to replace agents—they’re using it to create space for better work. AI employees can take on repetitive, time-consuming tasks so human agents focus on strategy, escalation, and empathy. 
• Leaders building AI-powered support orgs must rethink both tools and team design.  Success with AI starts with identifying the right use cases, codifying brand tone and workflows, and tracking metrics that go beyond deflection. But it also requires internal storytelling—building trust, co-creating with teams, and treating AI as a new colleague embedded in the system, not just a feature in a tool

Why eCommerce and Logistics CX is a hard problem

E-commerce logistics serves as a critical competitive differentiator today. Consumers expect real-time updates, instant resolutions, fast returns, and seamless experiences, even when packages cross multiple borders and service providers.

Bigblue operates as the connective tissue between carriers, warehouses, brands, and customers. In the absence of Bigblue, e-commerce brands would have to manually tie up with local carriers to ship their products and manage returns, provide delivery status updates, and address product concerns. 

But Bigblue takes care of all of this, so that e-Commerce brands can focus on building delightful products and succeed. 

As Bigblue scaled, the volume and complexity of support queries, from questions about order deliveries and order status to processing returns, naturally grew at a high rate. Issues often required investigation across multiple systems and partners, making timely resolutions all the more challenging.

• Growing, unpredictable volume of support tickets: Depending on the time of the year, around festive or holiday seasons, there are spikes in incoming customer tickets. But relying on additional staffing to handle this growth quickly becomes cost-prohibitive and operationally taxing, underscoring the need for a more scalable, cost-effective, reliable approach. 
• Specific Issue types: From delayed or lost shipments to damaged products, each customer support issue requires a specific, accurate response, starting with verifying the claim by triangulating the issue across stakeholders, and providing a resolution that meets the customer’s needs in a timely fashion. 
• Cultural and linguistic nuances: Another challenge for Bigblue is managing a Europe-wide customer base that submits inquiries in various languages, including English, Spanish, and French.
• Integrations with Multiple Systems: Bigblue partners with more than 50 carriers and numerous warehouses, with much of the customer information stored in CRMs and internal databases. It must seamlessly integrate with each of these platforms, ensuring the right information can be retrieved to resolve every ticket efficiently.

Designing for Exceptional Customer Experience

Bigblue's core mission revolves around empowering e-commerce brands by transforming their logistics operations into a growth lever rather than a bottle-neck. This mission implies delivering high-quality logistics that can become a competitive wedge against industry leaders, while preserving the unique brand identity of each client.

Bigblue's customer care team thus plays a pivotal role in their mission. They are responsible for managing carrier investigations, serving as the primary point of contact for a significant portion of the customer base, and providing fast, accurate customer support for both logistical and technical challenges. 

• Customer-Centric Excellence: Bigblue’s mission requires a deep understanding of the evolving needs of their merchant partners. They strive to not only meet expectations but anticipate and consistently exceed them.
• Growth Enabler: Biglue strives to help increase the important metrics for an e-commerce brand, such as conversion and retention, by offering high-quality logistics and tech. They are particular about championing independent brands so they can compete with giants like Amazon. The aim is to let brands run logistics hands-off, so they can focus on brand-building and customer acquisition.
• Operational Scalability: Bigblue recognizes the importance of building support infrastructure that can adapt and grow with the expanding needs of their clients and the rapidly evolving e-commerce landscape.
• Team Empowerment: Bigblue believes in fostering a culture where customer care team members are equipped with the knowledge, tools, and autonomy to deliver exceptional service. This includes ongoing training and development opportunities.
• Innovation: Bigblue is committed to staying ahead of the curve by constantly exploring new technologies and strategies that can enhance the customer experience. This includes investing in cutting-edge AI and automation tools, and fostering a culture of innovation with the team.

Embracing AI and Agentic Business Automation for Customer Support

Bigblue needed to reduce time-to-first-response and handle increasing query volumes across languages and stakeholders. The challenge was doing this without compromising on accuracy, empathy, and brand voice.

Bigblue began with the traditional forms of customer support automation. They tried generating automatic contextualized answers based on static data. An AI tool was built into their ticketing system, HelpScout, which leveraged only ticket history and the Help Centre for its responses. 

But while this was free, already part of their existing HelpScout subscription, and allowed for self-service updates to answer templates—the solution couldn’t scale.

Static data doesn’t go very far in logistics tech. Shipment and order status change continuously, so using static data for responses makes them potentially outdated or irrelevant. Further, the HelpScout tool couldn’t pull live information from carriers or respond dynamically based on real-time updates. It depended on how customers categorized their tickets—leading to misfires in both context and resolution. 

Bigblue needed automation that would provide answers based on real-time data, with the ability to integrate with multiple external information systems (such as Bigblue’s carrier extranet) and use that information to efficiently and autonomously solve customer inquiries—at scale.

With Ema’s Agentic AI-powered Customer Support AI Employee, Bigblue found a solution that continuously learnt from past support cases and relevant SOPs and documentation. It autonomously adapted its responses based on delivery and shipment information being received real-time. 

Response times at Bigblue dropped from an average of ~2 hours without Ema to under 90 seconds with Ema. Nuanced responses, specific to each customer support issue, are delivered in multiple languages, with empathy and in brand voice, at scale. Some of Ema’s answers are now being used to train human agents—to be more accurate, on-brand, and efficient. 

Bigblue has also been able to avoid seasonal hiring to tackle unpredictable support volumes. Ema synthesizes pain points across tickets, helping leadership improve carrier workflows and reduce repeat queries.

The Future of CX

For Bigblue, the future of customer experience isn’t a choice between humans or AI. It’s about building a partnership where both do what they do best.

Their team sees AI employees as powerful complements—not replacements—for human agents. The role of automation, they believe, is to free up valuable human time by taking over repetitive, low-impact tasks. When an agent no longer needs to pull shipment data manually, answer the same “Where is my order?” queries, or sift through outdated macros, they can focus on what really moves the needle: handling complex inquiries, optimizing support processes, and delivering truly personalized, high-touch service to Bigblue’s merchants.

This mindset also shapes their hiring and team culture. Rather than viewing AI as a threat, Bigblue’s team has embraced it as a force multiplier—one that allows each human agent to do more of the strategic, creative, and satisfying parts of the job. AI handles the busywork; humans bring the nuance.

Looking ahead, Bigblue envisions a future where CX isn’t just reactive—it’s predictive. They’re excited about AI’s ability to identify customer pain points before they escalate, to route issues intelligently based on tone or urgency, and even to suggest improvements to logistics workflows across carriers and routes.

In short: support won’t just be faster. It’ll be smarter, more personalized, and more proactive than ever before.

Updates from Ema

• Ema has been named one of America’s Greatest Startup Workplaces 2025 by Newsweek. The list spotlights 250 US companies that have beaten typical start-up odds—scoring high on growth, employee experience, investor confidence, and long-term vision. If you’d like to learn more, check out our careers page.
• Gartner Recognizes Ema as a SaS Leader: Ema was named a representative provider in Gartner’s latest Innovation Insight on Service-as-Software (SaS), spotlighting our role in transforming enterprise operations with Agentic AI. The report places Ema at the forefront of outcome-driven enterprise automation, creating the category of the emerging SaS market.
• Our blog on “The DeepSeek Effect”, from Everest Group authors, Vaibhav Bansal, Vershita Srivastava, and Yusuf Ahsan—it explores how foundation model commoditization is reshaping the Agentic AI industry. From accuracy to cost to control, it also breaks down why blended models are the future of intelligent automation.

Catch up on Ema’s recent events:

• Agentic Transformation in the Enterprise: How are enterprises working with AI agents today? What are the real-world change management strategies and principles at work in large enterprises?—featuring Edoardo Tealdi, Swami Chandrasekaran, and Ian Barkin. 
• Conversational AI Employees that Supercharge Every Stage of the Customer Lifecycle. See how Ema’s Customer Support AI Employees autonomously handle complex customer interactions end-to-end. From responding to tickets with nuance and expert-level accuracy, to boosting customer satisfaction, driving upsells, and reducing churn — these AI Employees supercharge every stage of the customer lifecycle.

From Ema’s Desk: Must Reads This Month

Stay ahead on everything Agents, AI, and Enterprise Tech with our recommended reads:

• The Death of Customer Service: Why Has It Become So, So Bad?, The Guardian: A compelling exploration of the decline in customer service quality and the urgent need for empathetic, efficient solutions.
• The Future of AI in Customer Service,  IBM: An insightful look into how AI is set to revolutionize customer service by enhancing personalization and efficiency.
• Customer Experience in the Age of AI, Harvard Business Review: An examination of how AI technologies are redefining customer experience across various sectors.

Thank you for reading! Write back with your thoughts on this edition of Agents of Change by Ema. We’ll see you in the next one.

Surojit Chatterjee

Founder & CEO, Ema

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