Artificial intelligence is generating excitement across regulated industries, from manufacturing and healthcare to government and critical infrastructure. Organizations are piloting AI to automate knowledge work, improve decision-making, and increase operational efficiency. Yet despite promising demonstrations, many AI initiatives never become dependable parts of day-to-day operations.
Why?
The challenge is rarely the model itself.
This session explores the gap between a successful AI pilot and sustainable enterprise adoption. Drawing on real-world experiences from regulated environments, including manufacturing, quality management, regulatory operations, and compliance workflows, the presentation examines why organizations struggle to move AI from experimentation into governed, scalable use.
Participants will explore five common barriers that prevent AI systems from achieving lasting business value:
• Lack of organizational and business context
• Fragmented and poorly governed information
• Inadequate evaluation and success criteria
• Weak integration into existing workflows and systems of record
• Unclear ownership of outputs, risks, and decisions
The session introduces the concept of the review paradox: AI can generate work in seconds, but without trusted evaluation methods, traceable evidence, and clear accountability, organizations often spend more time reviewing AI outputs than they save creating them.
Attendees will learn why scaling AI is not simply a technology challenge but an operational, governance, and trust challenge. The presentation offers practical questions leaders can use to assess whether an AI use case is truly ready to move from pilot to production.
Learning Objectives
By the end of this session, attendees will be able to:
1. Explain why successful AI pilots often fail to scale into sustainable business processes.
2. Identify common organizational, governance, and operational barriers that slow AI adoption.
3. Evaluate whether an AI use case has the necessary controls, context, and success criteria to support production deployment.
4. Recognize the role of evaluation, workflow integration, and human oversight in building trust in AI-enabled processes.
5. Apply a practical readiness framework to assess whether an AI initiative is ready to move from experimentation to operational use.