Human-First AI for Industrial Frontline Workers: Tulip Shares Its Vision for the Future of Manufacturing

This conversation between Chris Luecke, Host of Manufacturing Happy Hour, and Natan Linder, Founder and CEO of Tulip Interfaces explores that question through the lens of what’s happening across the industry right now.

Across regions and industries, manufacturing is at an inflection point. Productivity has stalled despite decades of investment in automation. Supply chains remain volatile. Skilled labor is harder to find and harder to retain. At the same time, AI has moved from experimentation to real deployment, creating both opportunity and uncertainty about how it should be applied in physical operations.

The next phase of manufacturing will be defined by how well the industry learns to combine people, processes, machines, and software in environments that are increasingly complex, constantly changing, and AI-enabled.

For years, automation was positioned as the answer to productivity challenges. The assumption was that if enough systems were installed and enough processes standardized, output would scale on its own. This was true to a point, but there is a diverse, emergent, and complex longtail of challenges that account for the remaining productivity and innovation challenges. There is no “management machine” that can be switched on to replace the judgment, adaptability, and problem-solving required on the factory floor.

But real productivity looks different. It shows up when experienced engineers can oversee more lines, when operators can resolve issues without escalation, and when teams can make better decisions faster. This next phase of productivity is about increasing the leverage of the people who already understand the system, not about removing people from the system. It’s a phase of human-driven innovation.

This reality points to the deeper issue in manufacturing. It’s not the case that there are too many people. There are too few people able to orchestrate operations, resulting in wasted talent. Frontline engineers, operators, and quality teams carry deep operational knowledge, but for decades, the software designed to support them has been rigid, centralized, and disconnected from how work actually happens. Large transformation projects often failed, pushing teams back to spreadsheets, whiteboards, and paper because those tools, however imperfect, reflected reality better than monolithic systems ever did.

The result is distrust in these systems and in structural inefficiency.

AI has reopened the conversation, but the industry needs to avoid repeating its past failures with this new technology and with the realities of manufacturing in mind. Generic AI tools struggle in physical operations because they lack context. They don’t understand machines, materials, quality constraints, regulatory requirements, or the cost of getting it wrong.

For AI to be useful, it has to live inside production systems: grounded in real data and real workflows, with humans responsible for decisions and outcomes. In that environment, AI becomes a tool for acceleration, helping teams analyze issues, build workflows, translate documentation into action, and respond to change without removing accountability.

This shift is giving rise to a new role across manufacturing: the AI process engineer. These are not software developers. They are process engineers, quality engineers, and operations leaders who already understand their environment and now have the ability to turn that understanding into working systems. With AI embedded in low- and no-code tools, they can create applications, automate decisions, and improve processes directly within production, rather than waiting on centralized IT projects.

Tulip has set an initial goal of enabling 5,000 AI process engineers as part of this next phase. The goal is to build practical capability in people, helping manufacturing teams safely and effectively apply AI where it matters most.

As manufacturing systems become more software-driven, the challenge moves beyond automation to orchestration. Modern operations require coordinating people, machines, data, and workflows in real time, across plants, suppliers, and regions. No single system or vendor can address every need. Flexibility and openness become critical.

This is why open, composable architectures matter. Manufacturing environments are inherently heterogeneous and constantly evolving. Systems must be able to integrate, adapt, and change without forcing teams into rigid or proprietary models that limit long-term progress.

Partnerships, including Tulip’s recent investment from Mitsubishi Electric, reflect this shift. Rather than consolidation, the focus is on alignment: shared customers, shared operational realities, and a shared belief that transformation is continuous, not one-time. Automation at the machine layer and orchestration at the operational layer must work together, not compete.

Ultimately, the future of manufacturing will be shaped less by technology headlines and more by accountability. Trust between software providers, partners, and customers is built through transparency, reliability, and respect for the complexity of real operations. Technology may account for only part of the solution; the rest lies in how organizations empower people to adapt, improve, and lead change over time.

This moment in time is a step forward in a longer conversation about how manufacturing evolves next, and how AI, applied responsibly and in context, can help the industry move forward.