Why Internal AI Projects Stall — and What It Takes to Scale
June 17th, at 10 am ET | 4 pm CET
Virtual Event
AI has fundamentally changed how software gets started. Almost any technically curious team can now build a working prototype in a matter of hours. But the bar for production hasn't moved. The question is no longer "Can we build it?", it's "Can we run it, reliably, securely, and at scale?"
Join us to see why most internal AI builds stall before production, where the real costs and risks accumulate, and how leading manufacturers are making the build-vs-buy decision in a world where AI has rewritten the opening of the conversation, but not the outcome.
In this webinar, you'll learn:
Why the first 80% of an AI project is the easy part — and what the remaining 20% actually requires to move from prototype to production
The true total cost of ownership of an internal AI build, including the hidden costs most teams underestimate
The structural risks of AI-generated code: security vulnerabilities, technical debt, and the 18-month wall
Why data and governance — not code — are the real barriers to scaling industrial AI
How "building" doesn't eliminate vendor lock-in — it just shifts the dependency somewhere less visible
The decision framework leading manufacturers use to evaluate build vs. buy at the executive level
AI has lowered the barrier to building a prototype — but production-grade requirements haven't moved: offline capability, security, audits, reliability every shift
The majority of AI pilots never make it from prototype to production, and the failure pattern is now well documented
Hidden costs — maintenance, integration, compliance, mid-shift support — are growing faster than internal teams can absorb them
Compliance, governance, and operational data are now where industrial AI projects succeed or fail — and they can't be bolted on later
The competitive advantage is shifting from "who can build" to "who can execute and scale"
The manufacturers pulling ahead won't be the ones who build the most software — they'll be the ones who deploy the right systems faster and execute better on top of them.
James Gardner is a manufacturing technology leader with global experience across 100+ plants. He’s passionate about bridging digital strategy with frontline execution to drive scalable, data-driven improvement.
Greg Breidenbach
AI Transformation Lead & Product Manager
Brings over a decade of experience driving digital transformation across manufacturing, telecommunications, and financial services. At Poka, he leads AI product strategy, developing practical, factory-ready AI solutions that improve knowledge access, training, and frontline decision-making. His core strengths include AI/LLM implementation, product strategy, solution architecture, and cross-functional leadership in B2B SaaS environments.
Who is this webinar for?
Built for Teams Facing:
Pressure to deliver AI results without a clear path from prototype to production
Internal AI initiatives that have stalled, plateaued, or quietly failed to scale
Difficulty quantifying the true cost of building vs. buying — beyond initial development
Concerns about security, governance, and compliance in AI-generated systems
The risk of trading visible vendor lock-in for invisible internal dependencies
Uncertainty about where to invest: internal builds, platforms, or a combination of both
