Welcome to the Trend Detection podcast, brought to you by Senseye
Predictive Maintenance – which gives you visibility and insights
into all your assets, from single machines to full plants to help
you reduce downtime, increase knowledge sharing and accelerate
digital transformation across your organization.In this episode of
the Trend Detection podcast, we’re joined by Christian Zillner, who
leads global AI deployment for Digital Industries Automation at
Siemens, to explore what it really takes to scale industrial AI
from experiments to real shop‑floor impact.Drawing on hands‑on
experience across industries, Christian shares practical lessons on
what works, what doesn’t, and why many AI initiatives struggle to
move beyond pilots, including:What industrial AI deployment really
means—going beyond algorithms to include business cases, ownership,
services, and organisational changeWhy many AI pilots fail to
scale, from unrealistic expectations to non‑serviceable, custom
architecturesThe human side of IT/OT convergence, and how unclear
roles and ownership can derail progressHow to choose between cloud,
edge, or hybrid AI based on latency, security, cost, and
operational constraintsThe role of partners and ecosystems in
taking AI from the lab to productionWhere AI delivers real value
today—and where expectations still need groundingWhy standardising
the deployment platform early is critical to long‑term
scalabilityPractical advice for moving from experimentation to
production with a small set of repeatable, high‑value use casesA
refreshingly realistic discussion on industrial AI for anyone
responsible for digitalisation, automation, or AI strategy in
manufacturing.You can find out more about how Senseye Predictive
Maintenance can reduce unplanned downtime and contribute towards
improved sustainability within your manufacturing plants, by
visiting: www.siemens.com/senseye-predictive-maintenanceConnect
with Christian on
LinkedInhttps://www.linkedin.com/in/christian-zillner/
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