Tech limitations in the print-on-demand industry

The print-on-demand industry faces five key challenges: supply chain fragility, inconsistent print quality, inefficient order routing, high-volume customer service pressure, and growing environmental reporting requirements. Each of these, if taken individually, is manageable. But together, they create an operational load that conventional automation simply cannot handle.

But the application of specially tailored Agentic AI, as fully autonomous systems that can pursue goals across multiple processes and systems without waiting for a human trigger, offer a practical path to the automation of each of these challenges. This article explains what this looks like in practical terms: covering how AI Agents can manage supplier disruptions in real time, catch quality defects before they reach the customer, route orders intelligently across production networks, resolve customer inquiries autonomously, and embed sustainability tracking into every fulfilled order.

We also address what separates implementations that successfully deliver ROI from the significant number of implementations that do not. Gartner projects that more than 40% of agentic AI projects will be cancelled by 2027, primarily because organisations skip process mapping and feasibility work which make deployment viable. We share practical steps and recommendations on avoiding that pitfall below.

The operational reality of running a print-on-demand business

There is a version of the print-on-demand model that looks straightforward at a glance: a customer orders a product, the facility prints it, and it ships. No need for complex inventory. No wasted stock. Clean, responsive, and lean.

But this model’s core promise, to produce only what is ordered, creates a chain of critical decisions that must be made, every time, across four variables that each move simultaneously: print quality consistency, supply chain reliability, customer acquisition cost, and competitive positioning. Managing all four manually is not viable at scale, and it becomes less viable with every stage of growth.

Consider where POD operations actually break down:

• Breakdowns in supply
• Problems in quality control
• Limited production capacity
• Customer service at scale
• Sustainability compliance

These are not problems that stem from a lack of effort or insufficient tools, but from a structural mismatch: the complexity of POD operations quickly outgrow the traditional automation methods that most businesses deploy.

While most POD companies have automation, very few have systems capable of making inter-connected decisions across supplier management, production quality, order routing, and customer communication, a gap perfect for the application of Agentic AI tailored to the task.

Why standard automation falls short

It is worth being precise about what traditional automation actually does, because the distinction matters for understanding what Agentic AI is capable of.

Most POD businesses have deployed some combination of the following:

• Rule-based order routing scripts: to direct client requests to specific facilities based on geography or product type
• Basic chatbots: that handle order status queries in a fixed decision and conversation tree
• File processing pipelines: to check submitted artwork against technical specifications
• Integration layers: which pass order data between storefronts, print management systems, and logistics providers.

These are useful tools. They are also fragile in a specific way: they only execute instructions. When a condition arises that falls outside the defined workflow, like a supplier goes offline, a file format triggers an unexpected error, or a demand spike overwhelms routing logic, the system fails and a human must intervene.

And these interventions are often slow, because the human is not monitoring that system in real time, and the solutions are often incomplete, because the human addresses the symptom rather than the pattern of failure, solving that customer’s need in that instance but not addressing how the system failed to manage on its own.

Agentic AI operates differently. An AI Agent is an autonomous system that pursues a defined goal, not by executing predefined instructions, but by monitoring conditions, reasoning through what actions are available, taking those actions, and adjusting its strategy based on what happens next.

In the context of POD operations, this difference has real consequences. A rule-based order routing system moves a job to Facility B when Facility A is at capacity. An AI Agent managing order routing monitors capacity in real time across a network of facilities, cross-references material availability at each location, factors in the customer’s delivery deadline, weighs the cost difference, and makes the final routing decision. and if Facility B encounters a production issue, it detects that too and responds before the order is compromised.

“AI agents may soon autonomously monitor production signals, evaluate external events and even propose adjustments — all before a human even opens their planning tool.” – World Economic Forum, From Shock to Strategy: Building Value Chains for the Next 30 Years, 2025

This is not a theoretical capability. Gartner projects that 15% of all day-to-day enterprise work decisions will be made autonomously through agentic AI by 2028, up from base zero in 2024. The businesses that are building toward that now are not doing so because the technology is new and exciting. They are doing so because the operational advantage enables real scalability.

Five operational challenges, five agentic applications

The POD challenges described above do not require five separate applications and investments. They represent five areas where a coordinated set of AI Agents, each designed for a specific operational domain, each sharing information with the others, can enhance the combination of manual effort, reactive scripts, and human escalation that currently manages them.

What follows is a practical account of what each application looks like and what it may cost to leave it unaddressed.

AI agents in manufacturing supply chain disruption and materials sourcing

The most consequential supply chain lesson from the past five years is that disruptions do not announce themselves with enough warning for procurement teams operating on weekly planning cycles. By the time a paper mill announces reduced allocations, or a port reports congestion delays, or a key supplier’s performance data shows a degrading trend, the window for a measured response has usually already closed.

A supply chain AI Agent approaches this differently. Rather than receiving reports from suppliers on a scheduled basis, it continuously monitors for changes: ingesting supplier performance data, raw materials price indices, logistics status feeds, geopolitical risk signals, and capacity utilisation metrics across the production network in real time.

When a disruption signal is detected, the agent does not wait. It evaluates available alternatives against predefined criteria, such as cost, lead time, quality certification, geographic proximity to affected print facilities, etc, and either executes a reorder from a pre-approved backup supplier or escalates to a procurement manager with a ranked set of options and the data behind the selection of each one.

Additionally, an agent can track performance over time, including quality consistency, on-time delivery rate, or responsiveness to queries, and flags degrading trends before they become failures. A procurement manager receiving this information from an internal Agent is in a fundamentally better position than one who discovers a supplier issue when customer complaints arrive.

The adoption of AI Agents in this field cannot be denied. A 2025 ABI Research survey of 490 supply chain professionals found that 76% see direct potential for autonomous AI agents to handle supplier reordering and shipment rerouting. While IBM’s 2025 supply chain research reports that organisations with higher AI investment in this domain see revenue growth 61% greater than peers (IBM Institute for Business Value). Meaning that early adopters have the potential to outstrip their competition.

For instance, the geopolitical disruptions that many POD companies treat as troubles to be endured become, for businesses running agentic supply chain management, a source of competitive edge. The ability to re-source faster than the market compresses supplier cost and protects fulfilment commitments while the competition is left scrambling.

AI agents in print quality consistency and defect detection

Quality problems in POD share something that makes them particularly costly: they are often discovered too late. A colour variance that is ‘within spec’ by a platform’s tolerance parameters may still be visible and disappointing to a customer who ordered a premium product. While a trim inconsistency that occurs in one batch of a run is invisible to an automated pipeline that approved the file before printing began.

The current state of quality monitoring in most POD operations is a combination of file pre-flight checks to catch technical errors before printing, which cannot predict production variances, and reactive customer service, which only catches quality failures after the customer receives them. Neither addresses the core problem.

A tailored AI Agent can address this pitfall by analysing each printed item against the original digital file, checking colour fidelity, trim alignment, print registration, and surface consistency, at the speed of production. When a deviation is detected, the agent traces the pattern. This analysis determines whether the appropriate response may be and allows it to take informed action.

An agent can also communicates laterally. When it identifies a defect pattern linked to a specific substrate batch, that information is sent to the supply chain agent, which updates the supplier performance record. When it detects a recurring file-related issue, it is sent down the file processing pipeline. With proper integration, quality monitoring becomes a source of operational intelligence rather than an isolated inspection step, with real-world implementations reporting up to 40% reduction in waste and 25% faster inspection cycles (AI-Innovate, 2025).

Agentic AI in order routing and production capacity management

Every order that comes in includes a set of constraints: geographic proximity to the customer, required delivery window, materials requirements, machine compatibility for the product type, and cost parameters. In a single-facility operation, routing is simple. In a network operation or for a POD platform serving multiple production partners, it becomes a complex problem that only grows with volume.

The static routing rules that most POD companies deploy handle predictable conditions reasonably well. And they break down at exactly the moments that matter most: when demand spikes unexpectedly, when a facility sees a production drop, when offers are temporarily unavailable at one location, or when a time-critical order arrives and operations are already at capacity.

An AI Agent tasked with routing order can maintain a live picture of the production network, including the capacity of each location, material availability, machine status, current production queue length, etc, and routes each incoming order against that picture, rather than against a static rule set. When conditions change mid-production, like when a machine goes offline or a shipment is delayed, the agent detects the change and re-evaluates affected orders, routing them to alternative facilities and communicating the change to the customer service agent, who may then take a proactive role with the affected customers.

A well tuned agent can also handle demand forecasting by analysing order velocity trends, seasonal patterns, and the social media signals that precede demand spikes along with external data sources, identifying elevated demand before it arrives and taking action to prepare for the spike: such as pre-staging materials, alerting procurement to increase stock, and pre-positioning production capacity.

The Q1 2025 Gelato State of Print Production Report found that 80% of print professionals now consider AI-driven automation essential for workflow improvement (Gelato, 2025). McKinsey’s research puts the broader business case plainly: companies that digitise supply chain operations report growth in incomes 3.2% higher than peers, which is the largest improvement on record for digitising any single business area.

Agentic AI enabling customer service at scale: from reactive ticket handling to proactive resolution

The need for customer service in POD becomes more costly the faster a business grows. The majority of inbound queries are routine: where is my order, why has my delivery date changed, my product arrived damaged, how do I submit a replacement file? These queries require access to live order data and production system information, but they do not require human judgment. And yet they consume human capacity anyway, because the alternative, which is traditionally a basic chatbot with a fixed decision tree, cannot access live systems, cannot process exceptions, and cannot resolve anything more complex than a standard FAQ.

The consequence is that operational teams spend a disproportionate amount of their work time handling queries that are interruptive, low-value, and demotivating, while the genuinely complex cases that require experienced judgment sit in the queue.

An AI Agent designed for customer service is built on live system integration, not scripted responses. It pulls real-time order status from the production management system, checks current logistics carrier data for delivery estimates, evaluates reprint eligibility against defined policy parameters, and resolves the queries end-to-end without the need to escalate to a human unless the case falls outside its defined authority. For cases that do require escalation, the agent compiles a complete case file before passing it into human hands: including order history, customer tier, the nature of the issue, recommended solutions, and the data backing them. The human agent who receives that case can then react immediately, rather than spending time gathering critical information.

The proactive dimension is where a customer service agent can create value that reactive systems cannot. When the order routing or quality monitoring agent flags a production hiccup, like a delay, a quality hold, or a shipment reroute, the customer service agent can be triggered automatically. Allowing it to contact the affected customer before they contact support. The customer experience shifts from discovering a problem at delivery to receiving a managed update, which has a measurable effect on retention.

The operational efficiency evidence is clear. The work we did with Mixam in the development of their AI-assisted order completion system demonstrates how connecting agents to live product data, rather than static documentation, produces recommendations that are actually actionable, with our agent helping 70% of new customers find their desired product. The 87% of U.S. consumers who report frustration with slow or disconnected service are not a market research finding to be acknowledged and set aside, but a warning of what happens when customer service capacity does not scale with operational volume.

Agentic AI in sustainability compliance and environmental reporting

Environmental responsibility in POD began as a marketing differentiator and is rapidly becoming a procurement requirement. Enterprise customers and B2B platforms increasingly request carbon footprint data per order, paper sourcing certifications, and evidence of waste reduction practices. In some markets, particularly in Northern and Western Europe, this information is moving from ‘nice to have’ to ‘required for onboarding.’

The challenge for most POD operators is not that they lack environmental responsibility, but that they lack the operational infrastructure to generate accurate, auditable sustainability data. Determining carbon footprint per order depends on shipping distance, energy sources, packaging materials, and substrate sourcing, information that lives in four different systems. Reporting on those data points traditionally requires a manual process that is slow, expensive, and prone to error.

While an environmental monitoring AI Agent can embed sustainability tracking into every operational decision rather than treating it as a separate reporting exercise. At the point of order, the agent considers carbon cost alongside delivery cost and speed, it does not optimise exclusively for carbon, but it makes the carbon cost visible and factors it into the final decision. It can track paper stock certification status continuously, flagging sourcing decisions that would compromise sustainability claims as well as assemble carbon footprint data at order level, so that a customer or partner requiring a quarterly sustainability report receives it in the form of live operational data.

A NielsenIQ 2024 report confirms that sustainability is a key purchasing driver, particularly in European markets, where regulatory pressure on environmental reporting is also most advanced. The POD’s industry’s inherent advantage in producing only what is ordered, eliminating overproduction waste, is a genuine sustainability claim and an environmental monitoring agent makes it measurable and auditable.

What a multi-agent POD operation looks like in practice

The five applications described above are more valuable as a coordinated system of agents than as five separate deployments. The value of agentic AI in POD operations is not just that each individual agent handles its domain better than manual processes, it is that the agents share information, and that shared information enables responses that no single-agent or human-monitored system can produce at speed.

Consider a practical scenario. A creator’s storefront receives an order at 11:47 PM. The sequence that follows is largely invisible:

• The file validation agent checks the submitted artwork against print specifications. It detects a colour profile mismatch that would produce a visible variance in the final output. It flags the file and queues a customer notification requesting a corrected submission.
• While that notification is being drafted, the order routing agent has already begun evaluating production options for when the corrected file arrives, pre-reserving capacity at the optimal facility based on the customer’s location and delivery deadline.
• The quality agent at that facility is operating on a current batch. It has identified a paper stock lot running slightly outside the colour tolerance range specified for the product type, and has already adjusted the machine calibration parameters accordingly. It logs the adjustment.
• The sustainability agent logs the carbon cost of the planned routing decision, accounting for local production in the customer’s region and domestic shipping, and records the certified paper stock source for the order.
• The customer service agent, once the file correction is submitted and the order confirmed, sends a proactive production update to the customer.

No human was involved in any of those steps. The customer receives a higher-quality product, with proactive communication, at a lower carbon cost, without a support ticket being opened. The operations team receives a log of the event, with all decision rationale documented and easily traceable.

This is the human-in-the-loop principle in application. Agentic systems do not remove human judgment, they redirect it. Operations managers stop handling routine decisions and start receiving curated exceptions with recommended actions. Quality issues that required a manager’s attention only retroactively are handled by the system in real time. The cases that reach a human are the ones that genuinely require their experience and judgment, while the quality of those decisions improves because the person making them is not fatigued by an overwhelming volume of decisions that should never have required them in the first place.

Most POD businesses do not start with five coordinated agents. They start with one, typically in order routing or customer service, to prove measurable ROI on a defined process, and build up from there as scale increases. The coordination between these agents develops as the transformative impact of agentic AI becomes real. This is how durable multi-agentic systems are built.

Where to begin and what to expect

Gartner’s June 2025 projection that more than 40% of agentic AI projects will be cancelled by 2027 deserves to be understood rather than glossed over. The projects that fail do not fail because the technology does not work. They fail because the problem was not clearly defined before the solution was designed, because the data required to run the agent was not clean or accessible, or because the business case was built on enthusiasm rather than on a measurable process with a quantifiable cost.

The practical criteria for identifying a good starting agentic use case in a POD operation are straightforward:

• The process is high-volume and repeatable. Agentic AI delivers value by handling many instances of a defined situation better and faster than a human or traditional automation. A process that occurs once a week is not a strong starting point. Order routing, quality inspection, and customer service ticket handling all qualify.
• The data already exists in a structured form. An agent needs to interact with real systems. If the data required to make a routing decision, such as facility capacity, material availability, delivery windows, only lives in a spreadsheet that someone updates manually each morning, the first investment is not in an AI Agent, but in data infrastructure.
• The cost of the current manual approach is measurable. The business case for an AI Agent is straightforward when the baseline is clear: X hours of staff time per week on routing decisions, Y% of customer service tickets that are routine and repeatable, Z reprint rate per month driven by quality defects that visual inspection would catch. Without that baseline, ROI cannot be demonstrated, and the project loses internal support at the first sign of complexity.

Order routing and customer service typically meet all three criteria first, which is why they are the most common entry points for POD operations. Quality monitoring follows, once production data is in place.

The implementation sequence matters. Process mapping and use case feasibility come before any agent architecture decisions. The process has to be documented and understood before automation is introduced, as an agentic AI cannot fix poorly defined workflows, but scales effective operations.

From feasibility test with PoV to a production-ready agent typically takes between eight and sixteen weeks for a well-scoped first use case, assuming the data infrastructure is in place. That timeline extends when the foundational data work has not been done, which is another reason to assess it critically before committing to an implementation timeline.

What does the ROI trajectory look like? Order routing optimisation and customer service deflection both show measurable cost reduction within the first quarter of production deployment. Quality monitoring produces ROI through reprint cost reduction and customer retention improvement, which typically becomes visible across quarters. Supply chain management delivers value that compounds over time as the agent builds a richer picture of supplier performance and disruption patterns, with the the early wins being faster responses to disruptions and the longer-term wins being disruptions that are anticipated and avoided.

Summary

POD businesses that are investing in agentic AI are building a structural business advantage that can only grow. The efficiency gains from a well-deployed order routing agent become the data foundation for a demand forecasting agent six months later, the quality monitoring system becomes a source for supplier performance scoring the following quarter, and operational intelligence begins to build on itself. Each application holds the potential to dramatically scale up operations and the distance between businesses that started building early and those that waited grows with every passing year.