Judith Apshago, Chief Digital Officer at Amtrak, shares her journey through IT leadership across industries like rail and biotech. She reveals Amtrak's innovative approach to predictive maintenance using IoT and AI, aimed at boosting reliability. Judith emphasizes the importance of collaboration between IT and business, advocating for iterative prototyping and a product mindset. She also discusses challenges in AI integration and provides valuable insights for IT professionals on building effective business partnerships. Tune in for her forward-thinking strategies!
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Sensor-Driven Predictive Maintenance
Amtrak is building a sensor-rich, multi-source data ecosystem to enable condition-based and predictive maintenance for trains and wayside infrastructure.
Integrating sensors, AI anomaly detection, and a modern asset management system aims to reduce downtime and improve on-time performance.
insights INSIGHT
Leverage Partner Data Sharing
Data-sharing partnerships extend capabilities where assets cross external boundaries, like Amtrak's freight partners providing wayside data.
Combining partner and internal sensor data enriches models and reduces duplication of effort.
question_answer ANECDOTE
From Paper Tickets To Integrated Logistics
At U.S. Silica Judith led a full-stack digital dispatch and mobile solution that replaced paper tickets with integrated ERP, kiosks, scales, and driver apps.
The system quadrupled volumes, saved over $1M annually, sped invoicing by 30%, and grew market share from 10% to 25%.
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In this episode, Markus Zirn and Judith Apshago explore the dynamic realm of IT leadership and the impact of technology on organizational transformation. Judith takes us on a journey through her illustrious career spanning industries from rail to biotech. Delving into the heart of process innovation, she reveals Amtrak's cutting-edge advancements in predictive maintenance, aimed at revolutionizing operational efficiency and elevating customer experience. She also unveils her unique approach to IT leadership, where business-IT collaboration, agility, and strategic integration of data and AI take center stage. From her groundbreaking work at U.S. Silica to her current role at Amtrak, Judith shares invaluable insights on leveraging technology for transformative impact across many industries.
Timestamps
*(00:00) Episode Start *(03:15) Judith's career at Amtrak *(10:15) Judith's process innovation at U.S. Silica *(17:10) Advice on business process redesign (BPR) *(21:10) Role of IT in solution design and business partnership *(26:55) Identifying cross-industry experience for process improvement *(32:22) Challenges and opportunities in AI-driven process improvements *(36:06) How to lead organizational transformation *(39:45) Judith's advice for IT professionals
Episode Key Takeaways
Start Small and Iterate: When implementing new technologies or processes, begin with a manageable scope. Test the waters, gather feedback, and refine your strategies accordingly to ensure they address pertinent business challenges effectively.
Leverage Data and AI: AI can help you extract valuable insights from your data, enhancing decision-making and operational effectiveness. Reimagine processes with innovative, data-driven approaches to drive fundamentally better solutions.
Align IT with business goals: Ensure seamless alignment between tech initiatives and overarching business goals. Get IT and business teams in sync, collaborating closely to make sure every tech move delivers real business value.
Foster curiosity and innovation: Cultivate a workplace culture that champions continuous learning and inventive problem-solving. Empower your team to explore emerging technologies and brainstorm solutions to address business challenges effectively, fostering a culture of continuous improvement.
Top Quote
“The people who are most valuable to me in the organization are the dot connectors who can see things in different places and say, ‘if that works over here, maybe we can leverage it over there.’ Back to that example I gave about the data that we're collecting from the trains and the tracks – we're doing that for a specific purpose to improve our maintenance processes, but that data is also valuable in other places in the business. So how can I connect dots and see where else might this information be valuable and what other information might be valuable to this process? So again, dot-connecting is a kind of key skill in this area.
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The New Automation Mindset: AI + Automation + Integration 
