WhatTheyThink: 2025 marks your 25th anniversary major milestone. The industry has seen massive consolidation recently, with many MIS providers being acquired by private equity firms. As a company that has remained independent while evolving its focus and market, how does this influence your product roadmap and your long-term partnerships with clients?

Mariusz Sosnowski: It’s been 25 years…hard to believe it was way back in 2000 when my brother and I started developing management system for label converters and folding carton manufacturers! But it’s meaningful because it reflects decades of learning from packaging plants and building technology around their real workflows. We’re a family-owned company with no plans to be acquired, and that independence gives our customers something rare today: long-term stability and a partner they know will be here in the future. One thing our customers consistently say is that HiFlow feels personal. They know the team behind the software, they get hands-on support during migration, and they work with people who understand their plants inside and out.

We’ve seen a wave of consolidation, with many ERP providers being absorbed into larger interests and corporations. When that happens, priorities often shift—companies start thinking in terms of return cycles instead of customer needs. Products they’ve sold to customers are frequently phased out, forcing customers into expensive re-platforming. Innovation slows down and support is throttled.

HiFlow has taken a different path. Staying independent has allowed us to stay completely focused on the long game: building technology that actually solves the day-to-day challenges of packaging converters. Our roadmap isn’t dictated by investors—it’s shaped by our customers, by what we see on their production floors, and by where the industry is heading.

This independence gives us a lot of flexibility. We can innovate quickly, invest in AI, automation, and connected workflows, and roll out updates at a pace that keeps our clients ahead of the curve—not waiting years for the next release. Our customers know who we are, who’s behind the software, and who will be supporting them five, ten years from now.

The truth is, converters want stability. They want a partner that listens, that evolves with them, and that won’t disappear into a bigger corporate structure. Our independence lets us build those long-term relationships—real partnerships—and that’s been one of the biggest reasons we’ve been able to grow for 25 years.

WTT:  There is a lot of hype around Generative AI, but HiFlow’s strategy focuses on “Operational AI”—specifically for order processing, estimating, scheduling, and business intelligence. Can you walk us through the specific ROI your customers are seeing by adopting AI to make these core operations more efficient?

MS: There’s no question that generative AI gets most of the headlines, but for our customers, the real value comes from what we call Operational AI—AI that actually moves jobs through the plant faster, reduces manual labor, and gives leadership better visibility into their costs and capacity.

Take order processing. Many converters still key in purchase orders  by hand. Our AI-powered PO Processing module uses document recognition and validation logic to extract all job details automatically—quantities, SKUs, materials, dates, pricing—and push them directly into the ERP. What used to take up to 30 minutes per PO now takes three seconds. For some customers processing 2,000–4,000 POs a month, that’s hundreds of labor hours eliminated.

Estimating is another high-leverage area in any packaging business. It touches revenue, profitability, material planning, customer experience, and win rates. That’s why a dedicated estimating solution—especially one powered by AI—delivers some of the fastest ROI of any technology investment a converter can make. HiFlowQuote, our new independent estimating engine, brings that speed and accuracy to any operation, regardless of the ERP they use today.

The ROI in estimating really comes down to two things: speed and accuracy. AI allows teams to turn quotes around in minutes instead of hours, and that alone has a huge impact on win rates. In this industry, the first accurate quote usually wins the job—and AI gives our customers that advantage every single day.

Accuracy is the other big piece. AI looks at real production data—run speeds, material usage, tooling history, waste patterns—and it recommends the most cost-effective production route automatically. That protects margins and takes a lot of the pressure off estimators who used to rely on gut instinct. You end up with consistent, profitable pricing across the entire organization.

One of the things customers appreciate most is how AI captures the logic of their senior estimators. It helps newer team members perform at a much higher level and reduces the bottlenecks that happen when a few experts shoulder all the quoting work. So you’re not replacing people—you’re elevating the whole team. The result is a faster, smarter estimating operation that delivers measurable ROI in months, not years,

We’re also seeing big ROI gains right now in logistics. Logistics remains one of the most complex and time-consuming areas of any packaging operation. HiFlow’s strategy focuses heavily on that Operational AI—using AI to automate the entire Procure-to-Pay workflow.

We now extend AI automation across estimating, purchase order processing, supplier deliveries, BOL validation, and accounts payable. This removes repetitive data handling and gives manufacturers instant accuracy and full traceability. As I often say: We’re helping customers move product, not paperwork.

And when you connect procurement, logistics, and finance—the whole Procure-to-Pay chain—AI becomes a real force multiplier. Purchase order validation, supplier delivery registration, BOL confirmation, invoice matching… all of it flows through one intelligent workflow. Manufacturers get accuracy, visibility, and efficiency across every supplier transaction.

So when we talk about ROI in operations, it’s not theoretical. We’re seeing reductions in manual entry, faster quoting, better capacity planning, fewer production bottlenecks, and higher throughput. Operational AI takes the friction out of the daily workflow—and that’s where our customers feel the impact immediately.

WTT:  As a designated Microsoft AI Cloud Partner, how does this ecosystem enable you to deliver faster and deeper AI capabilities—such as computer vision and predictive analytics—compared to standalone solutions?

MS: Being a Microsoft AI Cloud Partner fundamentally accelerates how we deliver AI to the packaging industry. We’re not building AI in isolation—we’re building within an ecosystem that already includes world-class computer vision, machine learning, natural language processing, and predictive analytics capabilities. That gives us a massive advantage over other solutions without Microsoft partnerships.

For example, our PO Processing engine uses models to extract structured data with high accuracy. We then layer HiFlow’s industry-specific logic on top—estimating rules, materials validation, SKU intelligence, pricing, production data—which creates a solution that is both extremely fast to deploy and incredibly accurate in real-world packaging environments.

The partnership also gives us access to scalable compute power for AI training and inference. Tasks like route optimization, predictive scheduling, forecasting material consumption, or analyzing historical production patterns require large data processing capacity.

Another major benefit is speed. Our partnership with Microsoft allows us to continuously release new AI capabilities—updated vision models, language models, and predictive analytics tools—that we adopt and integrate into our quarterly release cycle. That means our customers see innovation much faster and sooner than they would from other vendors.

Finally, the security and governance framework of the Microsoft ecosystem is critical. Packaging manufacturers handle customer data, supply chain data, and often confidential artwork or product information. With Microsoft’s enterprise-grade security, audit controls, and compliance certifications, we can deliver advanced AI without compromising data integrity or trust.

In short, the Microsoft partnership lets us combine the power of a global AI  with the specialized workflows and domain expertise of HiFlow—giving our customers deeper, faster, and safer AI capabilities than standalone solutions can realistically deliver.

WTT:  One of the biggest threats to the industry is the “Silver Tsunami”—the retirement of skilled workers who hold decades of tribal knowledge. How are your customers utilizing your AI tools to “institutionalize” this knowledge, allowing them to maintain high efficiency with a younger, less experienced workforce?

MS: You are right. The Silver Tsunami is one of the biggest challenges facing packaging and print today. We’re watching an entire generation of highly skilled operators, estimators, and schedulers retire—people who’ve run these plants for 30 or 40 years and carry enormous tribal knowledge. When that expertise walks out the door, productivity, accuracy, and continuity are all at risk.

What our customers are doing with HiFlow’s AI tools is essentially institutionalizing that knowledge so it doesn’t vanish with retirement. We take what was once stored in someone’s head—estimating logic, press behavior, make-ready shortcuts, substrate preferences, historical job patterns—and embed it into the system.

For example, our AI-assisted estimating captures the decision patterns senior estimators have relied on for decades. It learns from historical quotes, machine capabilities, material usage, and past job performance. Instead of a new estimator guessing, the system guides them toward the same logic an experienced senior estimator would use.

The same thing is happening in scheduling. Veteran schedulers know intuitively which jobs should run together, what causes bottlenecks, and how to avoid certain constraints. Our predictive scheduling engine models those same patterns—press behavior, changeovers, run speeds, crew availability—and automates that decision-making. That means a newer scheduler can produce a strong, efficient schedule without needing 20 years of tribal experience.

From an HR standpoint, this is critical. Plants aren’t going to solve the workforce shortage overnight. But technology like HiFlow gives younger employees a foundation to succeed—by putting expertise, best practices, and tribal knowledge inside the workflow, not in someone’s memory.

Customers consistently tell us: AI isn’t replacing the expert—it’s capturing their knowledge so the entire organization can benefit from it. And that’s how you maintain high efficiency even when the workforce becomes younger and less experienced.

WTT: We are seeing a shift where brands and regulators are starting to require granular data on sustainability and carbon footprints. Do you see a future where AI is essential not just for making the box, but for reporting on it? How is HiFlow evolving to become a compliance engine for these data-heavy buyer requirements?

MS: Absolutely—we’re already living in a world where brands and regulators expect granular sustainability and carbon data, and that requirement is only going to intensify. It won’t be enough to simply manufacture a package. Converters will need to prove how it was made, what materials were used, how much energy was consumed, how much waste was generated, and what the carbon impact was across the entire workflow.

HiFlow has been preparing for this shift for years. Our platform already captures deep operational data in real time—material usage, substrate specifications, machine performance, run speeds, waste levels, energy consumption, labor inputs, and changeover activities.

And because brands and regulators are demanding more transparency than ever, HiFlow can generate compliance documents, traceability reports, and customer-specific sustainability scorecards with a few clicks. For carbon tracking HiFlow pushes data (from materials, production processes, transportation) to third party carbon footprinting software plug-ins that calculate and track greenhouse gas emissions and provide reporting. Results flow back into  HiFlow’s ERP to provide carbon calculations for quotes, customer reporting, compliance documentation, and sustainability tracking.

AI plays a major role here as well. It helps normalize data across different machines, flags anomalies, predicts where waste or energy spikes will occur, and auto-generates the reporting that brands and regulators increasingly require. For many of our customers, what used to be a manual, spreadsheet-heavy effort is now a real-time, automated process.

So yes—the future isn’t just about using AI to make workflows more efficient—it’s about using AI to document every step of every process. To prove compliance, manufacturers must capture data at every point in the workflow: material sourcing, substrate certifications, recycled content, machine energy usage, waste output, labor inputs, shipping documentation, and carbon impact. When AI collects and connects all of this information automatically, it helps converters stay ahead of rapidly changing sustainability expectations.

WTT:  As supply chains become increasingly connected, we see a tug-of-war between legacy EDI requirements from big retailers and modern API connections for e-commerce. What is your advice for companies trying to bridge this gap? How should they structure their IT stack to satisfy retail mandates while remaining agile enough for modern web-to-print workflows?

MS: This is one of the biggest technology tensions in packaging today. Large retailers still rely heavily on EDI because it’s standardized, proven, and embedded deeply into their procurement systems. At the same time, the rise of e-commerce and web-to-print workflows depends on modern APIs that deliver real-time data exchange. Many converters feel stuck in the middle, trying to support 30-year-old protocols while also enabling instant digital ordering.

Our advice is: don’t choose one or the other—build an architecture that supports both. EDI isn’t going away anytime soon, especially with the big-box retailers. But APIs are the future because they allow for speed, automation, and richer data. The key is to create a middleware layer or integration hub that can translate between these worlds and keep your ERP as the source of truth.

This is where HiFlow invests heavily. We provide a robust integration framework that handles legacy EDI transactions while also supporting modern, REST-based APIs. Our technical support team works closely with customers to configure and maintain these connections—not just at go-live, but throughout the life of the system. Retailers change specs, e-commerce platforms evolve, and we stay with our clients every step of the way to ensure nothing breaks.

From a strategic standpoint, your ERP should be flexible enough to receive data in multiple formats—and process all of it consistently. That’s how companies stay compliant with retail mandates while remaining agile enough to support fast, automated workflows for web-to-print or short-run digital production.

So, the IT stack of the future isn’t either/or. It’s a connected ecosystem where EDI and APIs coexist. Our role at HiFlow is to make that easier—to provide the integration technology, the expertise, and the ongoing support so converters don’t have to worry about what’s on the other end of the connection. That’s what keeps plants agile, accurate, and ready for whatever the supply chain demands next.

WTT: For a company running on a legacy system, the risks are often invisible until it’s too late. What are the hard questions they should be asking potential software partners about modern IT stacks, cybersecurity, data sovereignty, and the 'change management' support needed to ensure migration doesn't cripple operations?

MS: When a company has been running on a legacy system for 20 or 30 years, the risks creep in slowly—and they’re invisible right up until something breaks. It might be a cyber incident, a database corruption, a lack of API connectivity, or simply the fact that the one person who knows how the system works is about to retire. Legacy systems will limit the ability to take advantage of cutting edge developments like AI. That’s why companies need to ask much harder questions when evaluating a new software partner.

The first thing I always tell people is: don’t just ask what the software can do—ask whether the technology underneath it is modern, secure, and built to evolve. Ask how deeply AI is integrated into the system. Ask about their architecture, their update cycle, how they handle APIs, how they protect your data, and whether they understand the realities of migrating off a platform that’s been in place for decades.

The second area is cybersecurity and data sovereignty. Too many legacy systems were built in a pre-cyber-threat era. You need to know where your data is stored, how it’s encrypted, and who owns it. It can mean deciding to host the system on site rather than in the cloud. These are important decisions that protect your data in the long run.

And third—and this is where HiFlow really shines—you need to ask how they will support your people during the transition. Replacing a legacy system can feel overwhelming for many manufacturers, and the biggest fear is operational disruption. That’s why our entire implementation methodology is built around stability, transparency, and hands-on support. We don’t just install software—we guide people through the transition.

We start by learning exactly how each plant operates—its workflows, machines, and approval steps—so we can configure HiFlow to match the way your business actually runs. We also take on the heavy lifting of data migration, cleaning and validating decades of pricing, tools, materials, and customer information to ensure accuracy from day one. And when it comes to training, we work side-by-side with your team so they learn the system through real tasks, not just classroom sessions. By go-live, your staff feels confident and capable, and the transition happens without disrupting operations.

And finally, ask about future support, after the implementation.  Are you handed off to a generic support center? With HiFlow, you work with the same implementation specialists who already know your plant and your workflows. That continuity is essential to a smooth transition.

If you ask those hard questions, you very quickly separate the vendors who have a true modern platform and a real implementation methodology from the ones trying to retrofit old technology. Our customers tell us that the reason HiFlow stands out is not only because we replace legacy systems—it’s because we make the transition feel achievable, not overwhelming.

WTT:  Looking 2-3 years out, do you see the role of the ERP shifting from a “system of record” to a “system of decision”? Are we moving toward autonomous operations where the software not only tracks the job but actively negotiates the schedule and raw material procurement without human intervention?

MS: We’re absolutely seeing the ERP evolve beyond a passive system of record. The next generation of platforms—including HiFlow—is becoming a true decision-support system. AI can analyze thousands of variables in real time, recommend the optimal production sequence, predict material shortages, or flag margin risks long before a human could see them. That’s incredibly powerful.

But I want to be very clear: we are not heading toward a world where software replaces people or runs the plant on its own. What we are heading toward is a world where manufacturers can do far more with the team they already have. AI removes the repetitive, manual, error-prone tasks—so schedulers, estimators, and production managers can focus on higher-level decision making.

For example, AI can propose the best schedule based on machine availability, crew skills, changeovers, and delivery dates. It can forecast raw material interruptions and even suggest when to reorder. But a human still approves the plan. People remain at the top of the decision-making chain—we’re simply giving them better intelligence and faster insights.

Given the workforce shortages in this industry, that’s critical. Plants aren’t expanding their teams; they’re trying to stay competitive with the staff they have. AI gives them leverage. A junior estimator can perform like a senior one. A new scheduler can rely on the system to avoid bottlenecks. A production manager can make decisions based on real-time data instead of gut feel.

So yes, ERP is evolving—from a system that records what happened to a system that guides what should happen next. But fully autonomous operations? That’s not the goal. The goal is augmented operations, where people are supported by data-driven intelligence and can make smarter decisions at speed. AI is an advisor, not a replacement.