Artificial Intelligence is one of the moment’s biggest conversations. Tremaine Hartranft, Vice President,
Technical Growth and Strategy at Reading Bakery Systems, shares with Baking+Biscuit International the company’s perspective on AI, its current status and vision for opportunities and further advances.
BBI: What are the highlights of your R&D work in AI over the past three years?
Tremaine Hartranft: Over the past few years, our R&D efforts have focused on building a strong foundation for AI-enabled production by advancing our systems with Industry 4.0 capabilities. This includes enhancing data collection, connectivity, and control architecture so our equipment is ready to support future AI and machine learning applications.
At the same time, we’ve been closely aligned with leading industrial automation providers such as Rockwell Automation and their Encompass partners. By staying current with proven automation platforms and emerging technologies, we ensure our solutions are both forward-looking and grounded in reliable, scalable systems.
BBI: In what production areas have you already incorporated AI features into your technology?
Tremaine Hartranft: Our AI-related capabilities are currently most advanced in vision detection systems. These systems can identify product defects, inconsistencies, and out-of-spec conditions in real time, providing valuable data for trending and process optimization.
While full AI-driven control is still evolving, these vision systems lay the groundwork for more advanced applications by enabling data-driven decision-making. They support operators with actionable insights and are increasingly being integrated with automated rejection systems to improve product quality and reduce waste.
BBI: What are the most frequent requests you are receiving?
Tremaine Hartranft: One of the most common requests we receive is for systems that reduce reliance on highly skilled labor. As experienced bakery operators are becoming harder to find, manufacturers are looking for equipment that is easier to operate, more intuitive, and less dependent on deep process knowledge.
We’re also seeing strong demand for vision-based inspection systems and automated product rejection. Customers want to ensure consistent product quality while minimizing manual intervention, and they are increasingly interested in systems that not only detect issues but also help guide operators toward corrective actions.
“AI has the potential to automate decision-making across key quality metrics such as moisture, color, size, and oil content. This would enable more proactive process control, reduce waste, and improve overall efficiency – especially in
high-volume production environments.”
Tremaine Hartranft, Vice President, Technical Growth and Strategy
BBI: How is machine learning defined to optimize various production steps in real time?
Tremaine Hartranft: Machine learning in industrial baking is focused on using real-time data to identify patterns, trends, and process deviations, and then applying that insight to optimize production. This can include monitoring variables such as temperature, moisture, color, and size, and correlating them with final product quality.
Rather than replacing operator expertise, machine learning enhances it, providing recommendations or controlled adjustments that help maintain consistency. Over time, these systems can ‘learn’ optimal process conditions and support more stable, repeatable production.
BBI: What are the challenges with AI decision-making algorithms and how are consistent results ensured?
Tremaine Hartranft: AI in industrial snack production is still in the early stages, even with support from established automation platforms like Rockwell Automation. One of the key challenges is ensuring that AI-driven systems remain stable and predictable over time.
Like many AI-based systems, there is a risk of unintended behavior or process drift if models are not properly monitored and validated. To address this, a measured approach is essential, starting with limited, supervised automation and allowing time for observation, trend development, and validation.
Consistency is ensured through strong oversight, controlled implementation of automated adjustments, and maintaining human interaction and decision-making. This balance helps prevent unintended outcomes while building confidence in the system.
BBI: What opportunities do you see in expanding AI use in bakeries?
Tremaine Hartranft: There is abundant opportunity for AI in areas such as product defect detection, automated rejection, and process optimization. Vision systems combined with machine learning can not only identify defects but also provide insights into why they are occurring and what adjustments can be made to bring the product back into specification.
This would enable more proactive process control, reduce waste, and improve overall efficiency – especially in high-volume production environments.
“One of the key challenges is ensuring that AI-driven systems remain stable and predictable over time.”
Tremaine Hartranft, Vice President, Technical Growth and Strategy
BBI: What does your R&D prioritize in further AI advancements? And how are quality and safety concerns covered?
Tremaine Hartranft: Our R&D priorities focus on advancing AI capabilities while maintaining the highest standards for safety, reliability, and product quality. One of the
biggest considerations is ensuring that AI-driven decisions – especially those affecting line control – do not introduce risk.
There are important safety and liability concerns when allowing automated systems to make process adjustments that traditionally require human judgment. To
address this, we emphasize rigorous validation through simulation and controlled testing before any field implementation.
Ultimately, our approach is to combine AI innovation with proven engineering practices, ensuring that any advancements enhance performance while maintaining safe, predictable, and consistent operation.