When you order a book on Amazon, that book is often printed after you place the order. Amazon Books Print on Demand (POD) runs 19 manufacturing sites across North America, Europe, Asia, and Australia — so tens of millions of titles are always available and printed close to where you are. The operation goes from order to finished book in hours. For authors and publishers, this means reaching a global audience without the cost and complexity of traditional inventory.
In this post, you’ll learn how Amazon Books (POD) built what it calls an AI-native execution system using Amazon Quick — and how it’s changing the way cross-functional teams coordinate and deliver complex projects. By connecting project artifacts into a living knowledge base and layering AI agents on top, the team reduced deliverable creation time by 50–75% and accelerated the pace at which critical project milestones are reached.
Amazon Books POD constantly invents on behalf of readers and authors — offering a greater selection of printing formats, reducing time to print and deliver books to customers, improving book quality, and lowering manufacturing costs. To do this, POD deploys next-generation automation equipment, launches manufacturing facilities in new marketplaces, builds AI-powered manufacturing optimization models, and integrates new device capabilities across its global equipment fleet.
Delivering these innovations is incredibly complex. Each project requires coordinating 10–15 cross-functional teams across 50+ connected deliverables, on tight schedules, with core team members spanning from Seattle to London and many locations in between.
To give a sense of how requirements evolve in practice: an equipment vendor in Switzerland sends updated specifications. A hardware engineer in London realizes the change affects how the equipment interfaces with production systems. That triggers a software requirements change, which changes the process design, the training plan, and raises a safety question — and five different teams need to be on the same page.
Many team members work across three or more projects simultaneously. They might have only a few hours a week dedicated to a given project — and they’re often spending the majority of that time just getting up to speed because of the context gap since the last touch point. The result is a lot of meetings. Instead of doing their best work, people are chasing context.
Instead of chasing context through meetings, Amazon Books POD flipped the coordination model. The team built an AI-native execution system — where teams have continuous access to the latest project context and AI-powered tools to reason over it — using Amazon Quick as the orchestrator.
At the foundation is a living knowledge base. Through Quick Spaces, the team connected all project artifacts being generated across teams in real time. Quick’s action connectors pull from SharePoint for equipment specifications, requirements, and technical documentation. They pull from Slack and email for day-to-day conversations and decisions, so when the team resolves an open question in a Slack thread, that context is captured automatically. They also pull from Asana for milestones and tasks. Critical decisions, open questions, risks, and meeting outcomes all flow into a single, living source of truth.
Because everyone now operates from the same shared context, the Quick Chat Agent becomes incredibly powerful. Team members don’t need to schedule a meeting or send an email and wait for a response. They can go to the agent first — and because it has access to everything across the project, they can get what they need without switching between applications or waiting on people.
When Amazon Books POD first deployed the Quick Chat Agent, team members were curious. They started asking basic questions — what are the latest equipment specifications? What decisions were made last week? — and were surprised by how useful the responses were, with citations back to the source. For example, a data scientist ramping onto one of the projects mid-stream used the agent to get up to speed on weeks of technical decisions, equipment constraints, and open questions — self-service instead of requiring several meetings with team members.
In another example, a senior leader who oversees multiple projects across the global portfolio uses the agent to make the most of his limited time with each team. While attending a critical project review meeting, he quickly got up to speed using the agent — so instead of spending limited time on background, the discussion went straight to evaluating options and making a critical decision in a 30 minute meeting.
But then something more powerful emerged. Team members started thinking with the agent. A data scientist needed to evaluate whether the team should build intelligent routing logic for how books are assigned to new binding equipment. She described the problem to the agent — and it pulled together relevant context from the equipment specifications, the process flow design, and the operational constraints. It surfaced three different routing approaches with trade-offs. What would have taken several meetings to even frame the problem properly became a structured starting point in one conversation with the agent.
Even more impactful is team members using the Quick Chat Agent and Quick Flows to create starting points for deliverables — so teams never have to start from scratch.
One of the product leaders needed to draft an Interface Controls Document — one of the many deliverables that connects evolving requirements across hardware, software, and operations. Creating it required pulling together the latest vendor specifications, software requirements, and cross-team decisions into a single aligned artifact. Previously, this took roughly 10 hours of effort spread over two weeks — deep-diving into vendor specs, cross-referencing requirements, and scheduling meetings across teams. She went to the Quick Chat Agent first. It synthesized context from across the knowledge base and produced a first draft. She refined it into a solid document in under two hours. The software team could start their work two weeks sooner — and she reduced her time on it by 80%.
The team also built a Quick Flow that generates comprehensive factory acceptance test (FAT) plans for new equipment. The Flow pulls in curated best-in-class examples, quality standards, safety requirements, and machine specs — synthesizing them into a structured test plan that would typically take engineering and quality teams 15 hours to produce manually. It cuts that to less than 5 hours — a 67% reduction. What the team didn’t expect: an AI-guided test case optimization step in the Flow identified opportunities to consolidate redundant test cases, reducing on-site test execution time by 10% — while still capturing every required data point. Quick didn’t just help produce the deliverable faster — it produced a better one.
Similar results are emerging across other deliverable types — executive review preparation dropping from 6 hours to under 2, process designs going from a full calendar week to half a day — where the agent pulls together all the context so people can get into a flow state and actually produce, rather than spending their time gathering information.
The most compelling moment came about 6 weeks after the system was deployed. The team had a critical Go/No-Go review for a major project milestone. It gained approval — and the project leader said: “We never would have hit this milestone at the pace we were moving without this system.”
That’s the real shift. Not just faster deliverables — but faster milestones.
Amazon Books POD built an AI-native execution system using Amazon Quick. Deliverables that used to take days or weeks now take hours. But the most important result isn’t any single metric — it’s the flywheel. Every deliverable that goes through the system makes the knowledge base richer, the agent smarter, and the next milestone faster. Every week, team members discover new ways to use the system — new questions, new workflows, new deliverables they can accelerate.
Ready to transform your project coordination? Get started with Amazon Quick or explore our technical documentation to learn how to build your own system.
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