Tag Archives: AI

Earning Executive Influence: Six Lenses for EHS Innovation and Payback

What if your EHS team had to earn the business before it could earn the influence? EHS leaders frequently talk about wanting greater influence with executive management. We want to be involved earlier in business decisions, have operating leaders seek … Continue reading →

Posted in Business Accumen, Career Skills, EHS Management, enterprise risk management, Innovation, Leadership, Leading at Scale, Sustainable Strategy | Tagged , , , , , , , , , , , , , , | Leave a comment

AI Won’t Need to Become Evil to Become Dangerous: What the AI Doomsday Debate Means for EHS

Artificial intelligence is rapidly moving from a tool that provides information to systems that can make decisions, take action, and interact with other agents. For EHS professionals, that shift should raise a familiar question: are capability and autonomy expanding faster than the controls designed to manage them? This article examines the AI “doomsday” debate through an EHS lens, connecting emerging AI risks to Management of Change, SIF prevention, human factors, inherited risk, and defense in depth. Continue reading →

Posted in AI, Artificial Intelligence, Design for Safety, Digital Twin, Innovation, Technical Skills | Tagged , , , , , , , , , , , | Leave a comment

If Everything Still Comes to You, You Haven’t Scaled Leadership

As leadership responsibility grows, working harder and staying involved in everything stops working. Drawing on experience scaling an organization from 15 to 300 people, this article explores how EHS leaders can multiply their impact through people, management systems, governance, delegation, digital tools, and AI—and build an organization capable of making thousands of good decisions every day. Continue reading →

Posted in Business Accumen, EHS Management, enterprise risk management, Leadership, Leading at Scale, operational integrity | Tagged , , , , , , , , , , | Leave a comment

Contractor Safety Management: The Contractor Operational Integrity Model

I’m pleased to share my latest article: “The Future of Contractor Safety Management: The Contractor Operational Integrity Model.”

The impetus for this piece came from my participation on a contractor safety panel at the Avetta annual conference in Chicago in mid-May. The discussion reinforced something I believe strongly: contractor safety management is moving into a new era.

For too long, contractor safety has often been treated as a compliance and prequalification process. Those elements still matter, but they are not enough. The future is about work readiness, verified control of work, and operational integrity at the point where risk is real.

In the article, I introduce the Contractor Operational Integrity Model, built around six core elements:

Critical Risk Definition
Capability and Capacity Verification
Control of Work Discipline
Field Verification and Leadership Cadence
Performance Intelligence
Corrective Learning and System Improvement

The central message is straightforward:

Compliance is the foundation. Operational integrity is the standard.

Contractor safety is not just a safety department issue. It is a test of how well safety, operations, procurement, maintenance, and contractors operate as one system under real field conditions.

I appreciate the Avetta team for hosting a strong discussion and creating space for practical dialogue on where contractor risk management needs to go next. Continue reading →

Posted in AI, Artificial Intelligence, contractor safety, enterprise risk management, Innovation | Tagged , , , , , , , , , , , , , , , | Leave a comment

Cyber-Physical Risk in the Age of AI: How Safety Professionals Help Directors Make Better Operational Technology Investment Decisions – Part 4

As the final article in this four-part series, Fay Feeney and I bring the conversation into the boardroom. Operational technology is no longer just an engineering concern—it is a governance test. As AI-enabled assets reshape industrial operations, directors are approving new risk profiles, resilience assumptions, and value-creation models. Continue reading →

Posted in AI, Artificial Intelligence, EHS Management, enterprise risk management, Leadership, Machine Learning, Sustainability Leadership | Tagged , , , , , , , , , , , , , , , | Leave a comment

Cyber-Physical Risk in the Age of AI: How Safety Professionals Identify and Manage OT Cyber Risk – Part 3

Part 3 moves from understanding the risk to executing against it. As cyber threats increasingly intersect with physical operations, organizations need a practical, structured approach to manage these risks at the system level. This section outlines how safety professionals—working with cybersecurity and engineering teams—can apply proven process safety methods to identify vulnerabilities, prioritize risk, protect critical systems, and strengthen operational resilience in the face of cyber-physical threats. Continue reading →

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Automated Reasoning for Human Error Detection in Industrial Operations

Most incidents aren’t caused by a single mistake—they result from conditions that made failure likely long before it happened.

In this article, I explore how automated reasoning can help EHS and operational leaders detect those conditions earlier, connect weak signals, and make more consistent, defensible decisions in real time. Just as important, I outline where technology stops—and where human leadership, trust, and judgment still determine outcomes.
The future of safety isn’t human or AI—it’s the integration of both to anticipate risk and act before it becomes reality. Continue reading →

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Cyber-Physical Risk in the Age of AI: How Safety Leaders and Boards Can Protect Operational Technology – Part 2

AI is changing the rules of cyber risk—and in OT environments, the consequences are no longer just digital.

From process instability to SIF potential, cyber threats are now operational threats. The real question isn’t if this risk exists—it’s whether we’re integrating safety, cyber, and operations fast enough to manage it.

Part 2 explores how AI is both accelerating the threat—and becoming a critical part of the defense. Continue reading →

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Cyber-Physical Risk in the Age of AI: How Safety Leaders and Boards Can Protect Operational Technology – Part 1

Artificial intelligence is reshaping cyber risk — and for industrial organizations, the stakes are no longer just digital.

Cyber attacks on operational technology (OT) can now disrupt physical processes, threaten worker safety, and create significant economic impact. Managing this evolving risk requires new thinking that connects plant-level realities with boardroom oversight.

I’m excited to share a new four-part thought leadership series I’ve co-authored with Fay Feeney, bringing together perspectives from industrial safety leadership and enterprise governance.

Together we explore how organizations can strengthen operational resilience, cyber-physical risk management, and strategic oversight in the age of AI.

Part 1 is now available — more to follow soon.

#OperationalTechnology #CyberSecurity #AI #IndustrialSafety #RiskManagement #BoardGovernance #Resilience Continue reading →

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Seeing Risk Before It Hurts: An Example of How Predictive Analytics Are Redefining Safety

Most safety systems are designed to explain injuries after they happen—not to prevent them while risk is forming. What if EHS leaders could see danger emerging in real time, understand why it’s happening, and intervene before someone gets hurt? This article outlines a bold predictive safety concept that uses AI, computer vision, and causal analytics to challenge traditional thinking about leading indicators and redefine what proactive risk management in manufacturing could look like. Continue reading →

Posted in AI, Artificial Intelligence, EHS Management, Injury Prevention, Machine Learning, psychological-safety | Tagged , , , , , , , , , , , , | Leave a comment