Industrial IoT is no longer limited to machines, factories, and production lines. It is increasingly moving into supply chains where physical products are monitored, evaluated, predicted, and managed through connected data systems. In fresh produce logistics, value is not created by one sensor, one AI model, or one software feature alone. It is created by the way measurement data, image inputs, quality benchmarks, prediction models, calibration know-how, customer workflows, and supply chain feedback are combined into a system that can influence operational decisions at scale.

This creates a strategic IP challenge for AI-based Industrial IoT companies. A quality monitoring solution for fresh produce may become valuable because it helps growers, logistics providers, distributors, and retailers estimate freshness, reduce losses, improve routing, adjust storage conditions, and make more sustainable decisions about perishable goods. Yet the same transparency that customers, investors, and integration partners need for trust, deployment, security, compliance, and technical due diligence can also expose the system logic, data flows, model architecture, API structures, quality metrics, and operational know-how that make the business defensible.

This is closely connected to the shift described in the CEIPI IP Business Academy analysis “The IoT Strategy Gap”. The article shows that IoT is becoming a strategic control environment in which IP can no longer be reduced to patents or software rights. Connected-product companies need to decide what to protect, what to disclose, what to keep secret, what to license, what to open for integration, and where the technical architecture creates future bargaining power.

Here you find the findings of this study: “The IoT Strategy Gap: What Connected Product Companies Need, and What IP Advice Still Often Fails to Integrate”

Against this background, the CEIPI IP Business Academy integrates practice-based questions from industry into its teaching. These questions help students understand IP not only as a legal protection tool, but as a management instrument for strategic decision-making in digital innovation systems. In Industrial IoT, this means looking beyond isolated inventions and asking where the defensible control points of a connected business model actually lie.

We are therefore pleased to include this industry case study with Sid Koneti, Ph.D., Co-Founder of SkoneLabs and deep-tech entrepreneur working at the intersection of AI, IoT, fresh produce monitoring, and sustainable supply chains. His practical question focuses on a central issue for Industrial IoT startups: how to move from pilots to larger deployments with distributors, retailers, investors, and enterprise customers without losing control over the data, model logic, calibration know-how, and integration layer that turn freshness prediction into long-term business value. Here Sid Koneti’s assessment of this case:

“The use case reflects the reality of the strategic crossroads that AI – IIoT companies face when transitioning from prototype to commercial scale. The challenge is not the technology itself; rather how to orchestrate the IP protection that enables trusted integration, revealing enough to collaborate without surrendering the core logic and model intelligence which creates long-term value. In reality, the decisions made during the first deployment or the due diligence phase can determine the positioning of the company as a differentiated platform or a replaceable service provider. “ 

Mini Case Study

A DeepTech startup has developed an AI based quality monitoring system for fresh produce. Its solution combines sensor inputs, image or measurement data, quality prediction models and supply chain feedback to estimate product freshness, reduce losses and support more sustainable handling of perishable goods.

After successful pilots with growers and logistics partners, the company is now negotiating its first larger deployment with a food distributor and a retail group. The commercial opportunity is significant. The solution could become part of the customer’s operational infrastructure, influencing decisions on storage, routing, pricing, waste reduction and supplier performance.

The founders realize that the value of the business may not sit in one patentable invention alone. It may sit in the way sensor data, AI models, calibration know how, quality benchmarks, customer workflows and continuous learning from the supply chain are combined.

Exposure to the IP can also begin earlier to contract signature with a client. For example, during a funding round, venture capitalists conduct technical due diligence; they may require access to model architectures, data flows, training methods, IP ownership documentation, and competitive moat analysis, to assess whether the advantage is real and defensible. Similarly, when enterprise customers do pre-deployment security, integration feasibility, and GDPR compliance assessments, they also require visibility into API structures, data flows, access controls, system dependencies, and data management to satisfy their own compliance and vendor risk estimations. At both stages, if too much of this logic becomes visible or contractually accessible too early, the company may win the first rollout but lose control over the layer that makes the service scalable and defensible.

 Practical Question

When an AI based Industrial IoT solution for quality monitoring moves from pilot projects to supply chain deployment, which parts of the system should remain proprietary, which parts should be shared for integration, and which parts should be protected through contracts, trade secrets, patents or data governance to preserve long term bargaining power?

Why This Question Matters in Practice

This question becomes relevant when an Industrial IoT company moves beyond technical validation and enters commercial deployment with customers, logistics partners, retailers or platform operators. At that moment, the main issue is no longer whether the system works. The decisive question is whether the company can scale the solution without losing control over the data, interfaces, model logic and operational know how that create economic value.

It matters especially for startups, industrial software companies, sensor technology providers, food logistics companies, agritech ventures and manufacturers building data based services around physical products. It also matters for product managers, founders, IP managers, investors and business development teams who must decide what can be opened for adoption and what must remain controlled for differentiation.

The question is most critical under three conditions. First, the system depends on continuous access to customer or supply chain data. Second, integration requires technical transparency toward partners who may later become competitors or powerful gatekeepers. Third, the business model depends on recurring insights, predictive performance or platform effects rather than on selling a single device.

From an IP management perspective, the risk is not only imitation of the sensor or algorithm. The larger risk is that the company gives away the architecture of value creation: how data is collected, cleaned, interpreted, benchmarked and transformed into operational decisions. In Industrial IoT, competitive advantage often emerges from the combination of hardware, software, data access, process knowledge and trusted integration into the customer’s workflow.

The economic implication is clear. If the company protects too little, it may become a technical supplier whose intelligence is absorbed by larger partners. If it protects too much, it may block adoption and fail to become embedded in the supply chain. The strategic task is therefore to design an IP position that enables collaboration while keeping control over the core layer that turns industrial data into business value.

Sid Koneti

Sid Koneti, Ph.D. is a technology entrepreneur and deep-tech specialist based in Berlin, with a strong background in IoT, AI, product development, and business strategy. He is Co-Founder of SkoneLabs, where he works on AI-based quality monitoring solutions for fresh produce, aiming to extend shelf life, reduce produce losses, and support more sustainable supply chains. Alongside his entrepreneurial work, he has served as an expert mentor at Campus Founders and previously mentored start-ups at Techstars, combining technical expertise with business development and innovation strategy.

Before founding SkoneLabs, Sid worked as an IP consultant at aera in Copenhagen, supporting inventors from multinational companies, universities, and start-ups on patent applications, patentability opinions, IP analytics, and business opportunity evaluation. His scientific career includes research positions at Université de Rouen, the National University of Singapore, INSA Lyon, Institut Lumière Matière CNRS, and INL CNRS, with a focus on nanomaterials, energy storage, advanced electronics, electron tomography, microsystems, and microrobotics. He holds a Ph.D. in Material Science with a focus on Nanotechnology from INSA Lyon, as well as master’s degrees in Nanotechnology from École Centrale de Lyon and Université Claude Bernard Lyon 1.