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AI compliance demands a fundamentally new mindset. Many organizations fall into one of two limiting perspectives: either viewing compliance primarily through the lens of corporate compliance, focusing on training and audits, or treating it as a purely technical challenge within the domain of cybersecurity. Both approaches, while valuable, ultimately miss the mark. Neither alone is sufficient to ensure AI delivers real benefits in a safe and responsible manner. When it comes to AI, the stakes are exceptionally high, with both significant risks and opportunities emerging at unprecedented speeds. This environment demands real-time AI governance, supported by programs, systems, and processes that work in harmony. Traditional approaches to building compliance programs – which often focus on developing individual components in isolation with the hope of future integration – are inadequate. While such approaches might address basic obligations, they fail to create the integrated, responsive systems needed for effective safe and responsible AI. When it comes to AI, what we need instead are compliance programs that function as a system from day one and capable of evolving over time. The Lean Startup Approach This is where the Lean Startup methodology (developed by Eric Ries and adapted by Lean Compliance) proves invaluable, as it aligns naturally with how AI itself is being developed. This approach is what compliance must also follow to reduce friction and keep up with the speed of AI risk. The core principle is maintaining an operational compliance program with essential capabilities working together (a Minimal Viable Program or MVP) that can be continuously improved through learning and iteration. Think of it like transportation technology: you might start with a scooter, progress to a bicycle, then to a car, and beyond. At each stage, you have a functional system that delivers the core value proposition of transportation, rather than a collection of disconnected parts that might someday become a vehicle. This approach mirrors how technology itself is developed and represents how compliance must evolve to keep pace with AI advancement. Applying Lean Startup to AI Compliance in Practice The Lean Startup approach for AI compliance focuses on three key principles: Build-Measure-Learn: Create a minimal viable program that can be quickly implemented and tested. Gather data on its performance and effectiveness and use these insights to make informed improvements. Validated Learning: With AI regulations being actively drafted and enacted globally, organizations can't wait for complete regulatory clarity. Instead, they must implement practical compliance measures and learn from their application in real-world scenarios. This hands-on experience helps organizations understand how to operationalize regulatory requirements effectively, identify potential gaps or challenges, and develop practical solutions before regulations are fully enforced. This learning becomes invaluable input for both improving internal compliance programs and engaging constructively with regulators as they refine their approaches. Compliance Accounting : Establish clear metrics for measuring the success of your compliance program, focusing on meaningful outcomes rather than just traditional compliance checkboxes. In practice, this might mean starting with a basic set of AI compliance capabilities, then iteratively advancing monitoring tools, governance structures, and audit capabilities based on real-world experience and feedback. The key is maintaining a functional system at every stage while continuously improving its capabilities and sophistication over time. This approach ensures that organizations can begin managing AI risks immediately while building toward more capable compliance programs. It's a pragmatic and rapid response to the challenge of governing evolving technology, allowing companies to stay on mission, between the lines, and ahead of risk. Lean Compliance has adapted the Learn Startup Approach to support implementation of compliance programs across all obligations: safety, security, sustainability, quality, and so on. This approach ensures compliance programs are operational - able to deliver the outcomes of compliance.

While your organization may be committed to practising safe and responsible AI, what about your third-party partners? From suppliers and contractors to vendors and service providers, every external entity that your business relies on could introduce AI-related risks into your operations. Managing these risks is crucial to maintaining compliance and safeguarding your reputation. Here’s how to approach third-party AI risk management and how Lean Compliance can support you along the way. Understanding the Risks Third-party AI risks arise when the AI systems, algorithms, or data used by external partners don’t meet your organization’s standards for safety, ethics, or regulatory compliance. These risks could manifest in several ways: Data Privacy Violations : If partners don’t adequately secure personal or sensitive data, your organization could face compliance penalties. Algorithmic Bias : AI models may unintentionally discriminate, leading to unfair outcomes and reputation damage. Security Vulnerabilities : Weak AI security practices can make systems susceptible to malicious attacks. Compliance Gaps : If third parties don’t adhere to the same legal standards, you may be held liable for their non-compliance. Steps for Managing Third-Party AI Risks Identify and Assess Third-Party AI Dependencies Start by creating a comprehensive inventory of all third-party partners who use AI or provide AI-enabled services. Understand which business processes depend on their AI systems. Evaluate each partner’s AI practices, focusing on areas like data security, algorithmic fairness, and compliance with regulatory standards. Establish Clear AI Governance Standards Develop governance policies that outline the minimum AI standards your third parties must meet. This includes ethical AI guidelines, data privacy requirements, and security protocols. Incorporate these standards into contracts, making them a binding obligation for partners. Conduct Regular AI Risk Audits Periodically assess your third parties’ compliance with your AI standards. This can include requesting audit reports, conducting on-site evaluations, or leveraging AI assessment tools. Ensure that your partners provide transparency regarding the data sources and algorithms used in their AI systems. Implement Continuous Monitoring Use AI-powered monitoring tools to track the performance and compliance of third-party AI systems in real time. Set up alerts for any anomalies or deviations from expected AI behavior to catch potential risks early. Provide Training and Support for Partners Educate your partners about your AI standards and the importance of responsible AI practices. This could involve training sessions, workshops, or the sharing of best practices. Encourage open dialogue with partners to continuously improve AI governance practices. Next Steps While it’s essential to practice responsible AI internally, managing third-party AI risk is equally important. By following a structured approach and partnering with Lean Compliance, you can better safeguard your business from the risks posed by external AI dependencies. Together, we can help you achieve a safer, more compliant AI ecosystem. How Lean Compliance Can Help At Lean Compliance, we specialize in helping organizations implement effective compliance strategies and programs supporting safety, security, sustainability, quality, ethics, legal, responsible and safe AI, and other sources of obligations.

As a compliance professional, you know that navigating the web of security standards, industry regulations, and business obligations is no easy feat. One common approach organizations take is to try and "map" similar-sounding controls across these different frameworks. But here's the thing - just because two controls use the same terminology doesn't mean they are truly equivalent . In fact, failing to recognize the nuanced differences between compliance requirements in areas like safety, security, sustainability, quality, and ethics can create gaping holes in your overall compliance strategy. The Illusion of Control Overlap Let's look at a concrete example. Consider the common control around "training requirements": Safety Training : Focused on preventing workplace injuries and incidents Security Training : Addressing employee awareness of cyber threats and protective behaviours Sustainability Training : Covering topics like environmental impact, resource conservation, and emissions reduction Quality Training : Targeting process excellence, defect prevention, and continuous improvement Ethics Training: Emphasizing decision-making frameworks, conflicts of interest, and compliance with codes of conduct On the surface, they may all fall under the broad label of "training." But treating them as interchangeable is like saying a chef's knife and a surgeon's scalpel are the same tool just because they both cut. Each of these training requirements has unique: Operational implementation details Underlying security/compliance objectives Key performance indicators and success metrics Stakeholder ownership and review processes Regulatory drivers and audit expectations Fail to recognize these distinctions, and you risk creating blind spots that leave your organization exposed. The Consequences of Misalignment When organizations take a simplistic approach to compliance controls, the ramifications can be severe: Inadequate Domain-Specific Protections : A generic "compliance training" program may fulfill the letter of the law, but leaves gaps in critical areas like workplace safety, cybersecurity hygiene, sustainability practices, quality procedures, and ethical decision-making. Inconsistent Validation and Reporting : Applying the same control verification methods across the board can produce an illusion of overall compliance health, masking deficiencies in specific domains. Redundant Efforts and Wasted Resources : Duplicating control implementation and documentation work across teams leads to inefficiency, potential conflicts, and sub-optimal use of compliance budgets. Ultimately, these oversights create vulnerabilities that can trigger regulatory penalties, reputation damage, operational disruptions, and other costly incidents. No compliance program should ever risk these consequences. A Holistic, Nuanced Approach Rather than taking a simplistic approach to compliance control mapping, the key is to adopt a more holistic, nuanced perspective. This means deeply understanding how each requirement functions within the unique context of different business domains and regulatory frameworks. At Lean Compliance, our experts work closely with you to: Identify the distinct properties, dependencies, and risk implications of controls across safety, security, sustainability, quality, ethics, and other key compliance areas Align controls thoughtfully to maximize synergies without compromising the integrity of individual requirements Streamline implementation, validation, and reporting across your entire compliance ecosystem Continually optimize your program as regulations, standards, and business needs evolve The result is a compliance program that is not only efficient, but also truly effective at mitigating risk and ensuring comprehensive protection for your organization. Ready to discuss how Lean Compliance can transform your approach to managing controls? Book a discovery call with our experts today:

While recently listening to a podcast about leveraging AI to extract insights from complaints, it highlighted something that's long bothered me. Despite manufacturing's strong embrace of proactive quality assurance, most corporate compliance systems still operate in firefighting mode - reacting to issues after they emerge. This reactive approach not only wastes resources but poses serious risks to companies and everyone connected to them. Instead of transitioning to proactive strategies, many are investing more and doubling down on reactive processes. The Problem with Complaint-Driven Compliance Think about this: Would you trust a car manufacturer that relied solely on customer complaints to identify defects? Of course not. Yet many organizations effectively do just that with their compliance programs, waiting for whistleblowers, customer complaints, or regulatory findings to identify issues. When we depend on complaints to drive compliance improvements, we're essentially outsourcing our quality control to stakeholders who never signed up for the job. This approach is problematic for several reasons: Late-Stage Detection : By the time a complaint surfaces, the compliance failure has already occurred, potentially causing harm to individuals, damaging trust, and exposing the organization to liability. Incomplete Coverage : Not all compliance issues result in complaints. Many stakeholders stay silent, leading to blind spots in our compliance programs. Resource Drain: Investigating and resolving complaints is far more expensive and time-consuming than preventing issues in the first place. Reputation Risk: Each complaint represents a stakeholder who has had a negative experience with your organization—something that could have been prevented. Learning from Quality Management The manufacturing sector learned decades ago that quality control alone isn't enough. This led to the development of Total Quality Management (TQM) and other frameworks that embed quality throughout the entire production process. The same principles should apply to compliance: Quality Control vs. Quality Assurance in Compliance Traditional Approach (Quality Control): Audit findings Customer complaints Regulatory investigations Internal reports of violations Modern Approach (Quality Assurance): Process-integrated controls Predictive analytics Continuous monitoring Design-stage compliance considerations Continuous risk & performance assessments The Path Forward: Building Quality into Compliance To truly advance corporate compliance, organizations need to shift from reactive to proactive approaches. Here's how: 1. Design-Stage Integration Compliance considerations should be built into new processes, products, services, and organizational functions from the beginning. This means: Include compliance expertise in design meetings Conduct compliance impact assessments during planning Build automated controls into workflows 2. Continuous Monitoring Instead of waiting for complaints: Implement real-time monitoring systems ( measures of adherence, conformance, performance, and effectiveness) Use data analytics to identify potential issues before they escalate Regularly assess control performance and effectiveness 3. Process-Oriented Thinking Move beyond checkbox compliance to: Map compliance requirements to business processes Identify essential compliance capabilities Build in preventive controls to detect and prevent issues 4. Proactive Thinking Make proactive thinking part of organizational culture: Train employees to recognize risks including those associated with compliance Encourage proactive reporting of potential issues Reward proactive behaviour: anticipate, plan, and act The Bottom Line Organizations that continue to rely on complaints as their primary compliance feedback mechanism are operating on borrowed time. In today's complex regulatory environment, we need to move beyond reactive approaches and embrace proactive compliance management. Just as manufacturing evolved from quality control to quality assurance, compliance must evolve from complaint resolution to managed obligations. The cost of not making this transition—in terms of regulatory penalties, reputational damage, and lost opportunities—far outweighs the investment required to build a proactive compliance program. The question isn't whether to make this transition, but how quickly we can implement it. Our stakeholders deserve better than being our unpaid quality control team. Lean Compliance offers an advanced program design specifically to help organizations transition from reactive to proactive compliance. This program is called, "The Proactive Certainty Program™". You can learn more here:

As a compliance engineer who's spent years helping organizations streamline their governance, risk and compliance programs, I've noticed one common source of confusion: the distinction between Corporate and Operational governance. Let me break this down in a way that will hopefully make sense to everyone. The Corporate Governance Perspective: Foresight & Oversight Think of corporate governance as standing at the helm of a ship with a telescope. From this vantage point, leadership has two critical responsibilities: Foresight: Scanning the horizon for opportunities and threats Oversight : Monitoring the overall direction and health of the organization This level of governance is all about the big picture. It's where the board and executive leadership ask crucial questions like: Where are we headed as an organization? What risks lie ahead in our industry? How do we ensure long-term sustainability? Do we have what we need to succeed? The Operational Governance Perspective: Steering & Regulation Now, let's shift to operational governance – this is where the rubber meets the road. If corporate governance is about looking through the telescope, operational governance is about having your hands on the wheel. This involves: Steering : Implementing strategies and making tactical decisions Regulation: Adjusting and maintaining operations to stay within acceptable boundaries and away from uncertainty Operational governance focuses on questions like: How do we implement our strategic decisions? What regulatory mechanisms need to be in place? How do we measure and monitor performance? What processes ensure we stay on course and make progress? Why the Distinction Matters Understanding these two levels of governance isn't just academic – it's practical. When organizations blur these lines, they often end up with: Confusion of Accountability : Without clear separation between corporate and operational governance, responsibility becomes murky. Who owns which decisions? Who's accountable for what outcomes? This confusion leads to either excessive finger-pointing when things go wrong or, worse, critical responsibilities falling through the cracks because everyone assumes someone else is handling them. Loss of Agency: When governance layers become tangled, decision-making power gets stuck in organizational limbo. Teams lose their ability to act decisively within their domains. Corporate governance becomes hesitant to make bold strategic moves, while operational teams become overly cautious about taking necessary tactical actions. This paralysis affects everything from innovation to daily operations. Failure to Regulate : Perhaps most critically, blurred governance lines compromise an organization's ability to stay on mission, operate within acceptable boundaries, and manage emerging risks. Corporate governance loses its ability to provide effective oversight, while operational governance struggles to implement proper steering mechanisms. The result? Organizations drift off course, cross compliance boundaries, and face unforeseen risks without adequate preparation. The key is to ensure both levels work in harmony while maintaining their distinct roles. Corporate governance sets the destination and watches for icebergs, while operational governance keeps the engine running and regulates the ship's course and progress through various conditions. Remember, good governance isn't about creating bureaucracy – it's about enabling your organization to move forward confidently and safely. Get these two aspects right, and you've got a powerful framework for sustainable success. Looking to strengthen your governance framework? Let's chat – that's what we're here for at Lean Compliance!

As AI systems are increasingly embedded in critical functions across industries, ensuring their reliability, security, and performance is paramount. Currently, the AI field lacks established frameworks for comprehensive assurance, but several existing models from other domains may offer useful guidance. This exploration considers how approaches in asset management, cybersecurity, quality management, and medical device life-cycle management could be adapted to create an effective AI assurance model. Each approach brings a distinct perspective, which, if adapted, could support the evolving needs of responsible and safe AI. 1. Asset Management Approach – Life-cycle Management Adapting an asset management framework to AI would involve treating AI systems as valuable organizational assets that need structured life-cycle management. This would mean managing AI systems from acquisition through deployment, operation, monitoring, and ultimately decommissioning. By applying a lifestyle management approach, organizations would focus on maintaining the value, managing risks, and ensuring the performance of AI systems over time. This model could involve practices like identifying assets, assessing risks, optimizing usage, and planning for system retirement, creating a comprehensive end-to-end view of each AI asset. By implementing a lifecycle-based framework, organizations could proactively monitor performance, identify shifts or deviations, and address potential risks of obsolescence or system degradation. This approach could offer a robust foundation for ongoing AI performance management. 2. Cybersecurity Approach – Threats and Controls A cybersecurity approach to AI assurance would focus on identifying and addressing potential security threats that could compromise AI system confidentiality, integrity, and availability. While traditional cybersecurity frameworks address general IT vulnerabilities, an AI-focused approach would need to account for specific threats such as data poisoning, adversarial attacks, and model inversion. If adapted for AI, this model could include threat modelling, attack surface analysis, and security control frameworks tailored to AI’s unique vulnerabilities. Additional focus would be needed on ongoing monitoring and rapid response to emerging threats. With AI-specific threat detection and control mechanisms, this model could serve as a proactive defence layer, safeguarding AI systems against intentional and unintentional security risks. 3. Quality Management Approach – Quality Control (QC) and Quality Assurance (QA) The quality management framework emphasizes consistency, reliability, and accuracy in outputs, and could be repurposed to support AI assurance. This approach would involve a combination of quality control (QC) to inspect outputs and quality assurance (QA) to enforce systematic processes that reduce the risk of errors. Applied to AI, QC would involve rigorous testing and validation of data, models, and algorithms to detect potential errors or inconsistencies, while QA would provide structured processes—such as documentation, audits, and process checks—to ensure model reliability. Together, these QC and QA elements could establish an assurance framework for identifying and addressing bias, error propagation, and output inaccuracies. Adopting a Quality Management approach could help mitigate many of the risks associated with model performance and data integrity. 4. Medical Device Approach – Life-cycle Management with End-to-End Verification and Validation (V&V) The medical device life-cycle model, known for its stringent focus on safety and compliance, offers a compelling foundation for high-stakes AI systems in sectors such as healthcare and finance. If adapted for AI, this model would incorporate end-to-end life-cycle management alongside robust verification and validation (V&V) procedures to ensure that AI systems are reliable and safe across all phases, from development to deployment. Such a framework would involve a series of verification and validation checkpoints, ensuring that the AI system performs as designed and meets regulatory standards. After deployment, continuous monitoring would allow organizations to respond to new challenges in real-time. This structured V&V approach would align well with the requirements of high-risk, regulated AI applications. Comparing and Contrasting the Proposed Assurance Models Life-cycle Management Emphasis : The Asset Management and Medical Device models both emphasize life-cycle management. However, while Asset Management would focus on maximizing the asset’s value and performance, the Medical Device approach would prioritize safety and compliance, especially in regulated contexts. Security Focus: The Cybersecurity model is unique in its focus on threats and controls, making it particularly suited for mitigating risks from adversarial attacks and other AI-specific security vulnerabilities. Consistency and Reliability : The Quality Management model would provide a framework for minimizing errors and ensuring reliable AI outputs. Unlike the other approaches, it would emphasize both ongoing quality control (QC) and quality assurance (QA), providing dual layers of checks to prevent bias and inaccuracy. End-to-End Validation : The Medical Device model, with its rigorous V&V processes, offers a comprehensive approach for ensuring that AI systems perform reliably and safely throughout their life-cycle. It would be particularly suited to high-stakes or regulatory-sensitive applications. While these models have not yet been formally adapted to AI, they each offer valuable principles that could form the basis of a future AI assurance framework. Leveraging insights from asset management, cybersecurity, quality management, and medical device life-cycle models could help organizations create a robust, multi-faceted approach to managing AI risk, reliability, performance, and safety.

In my 30+ years working with organizations on their transformation journeys, I've noticed a concerning trend that keeps me up at night. While companies rush to embrace Lean methodologies - and they absolutely should - many are inadvertently creating serious vulnerabilities in their pursuit of efficiency. Let me explain why this matters to you. The Efficiency Trap Picture this: Your team is deep into a value stream mapping exercise. Everyone's excited about identifying "waste" and streamlining operations. Someone points to a series of checks and approvals in your process. "Look at all these non-value-adding steps!" they exclaim. The room nods in agreement. But here's the thing - not all "inefficiencies" are created equal. The Hidden Value of Controls Those seemingly redundant checks? That "bureaucratic" approval process? They might actually be critical controls put in place after hard-learned lessons. The problem is, institutional memory fades. What was once a crucial safeguard becomes "just the way we've always done it" - until it's not. Think of it like removing what looks like redundant code from a critical system. Sure, it might make your code cleaner, but what if that "redundancy" was actually a crucial fail-safe? We would also never remove code just because we don't understand what it does? However, this happens more often than many care to admit in the name of "cost reductions." Real-World Consequences In my work across highly regulated, high-risk sectors, I've observed a concerning trend where the enthusiasm for Lean methodologies sometimes overshadows critical safety, security, quality, and regulatory considerations. Incidents of LEAN Teams value streaming management processes have eliminated what they viewed as "redundant" inspection steps, documentation requirements, and more. While this appeared efficient on paper, these were actually crucial safeguards developed from hard-learned lessons of the past. Here's what's at stake: in industries where a single oversight can trigger catastrophic consequences, labelling such things as safety controls, "waste" is a dangerous gamble. The potential for environmental disasters, safety incidents, and regulatory penalties demands a more nuanced approach. That's why I strongly advocate for including Lean and Compliance experts during improvement initiatives - professionals who understand both operational efficiency and managed compliance. Remember, true operational excellence in high-risk industries isn't just about removing steps - it's about optimizing processes while preserving the controls that keep us safe. The Solution: Lean & Compliance Expertise Here's where I see a massive opportunity: bringing together Lean methodology and compliance expertise. It's not an either/or situation. You can have both efficiency AND effective controls. The key? Having the right experts at the table. What Lean and Compliance Experts bring to the Table: Deep understanding of regulatory requirements Experience in optimizing control frameworks Ability to spot critical vs. redundant processes Knowledge of emerging risks and compliance trends Expertise in designing efficient, compliant processes Your Action Plan Ready to get this right? Here's what you need to do: Audit Your Lean Initiatives Who's on your transformation team? Are risk and compliance experts involved? How are you evaluating control removal decisions? Engage the Right Expertise Bring in risk & compliance specialists Document control rationales Create risk-aware improvement processes Measure What Matters Track both efficiency gains AND risk metrics Monitor compliance effectiveness Document the impact of process changes Yes, Lean methodologies can transform your organization. Yes, you should be looking for ways to eliminate waste. But remember - not everything that looks like waste actually is. The key is knowing the difference. Moving Forward Don't let your Lean journey become a cautionary tale. Invest in the right expertise. Create processes that are both efficient AND secure. Your future self (and stakeholders) will thank you. As you embark on your next process improvement initiative, ask yourself: "Do we really understand what these controls are protecting us from?" If you can't answer with certainty, it's time to bring in someone who does. Remember: True operational excellence isn't just about speed - it's about sustainable, secure, and safe processes that protect your organization while delivering value to your customers.

When it comes to developing capabilities that need to perform, that are reliable and that you can trust, within targeted budgets and time constraints, there is much to be learned from Defense programs. The document "Best Practices for Using Systems Engineering Standards (ISO/IEC/IEEE 15288, IEEE 15288.1, and IEEE 15288.2) on Contracts for Department of Defense Acquisition Programs" gets right to the point: "The Department of Defense (DoD) and the defense industry have found that applying systems engineering (SE) processes and practices throughout the system life cycle improves project performance, as measured by the project's ability to satisfy technical requirements within cost and schedule constraints." In other words, "projects that use effective SE processes perform better than those that do not." Given this knowledge, it is in the best interest of both acquirers and suppliers to ensure that defense acquisition projects use effective SE processes as the core of the technical management effort. Systems engineering is the primary means for determining whether and how the challenge posed by a program’s requirements can be met with available resources. It is a disciplined learning process that translates capability requirements into specific design features and thus identifies key risks to be resolved. Our prior best practices work has indicated that if programs apply detailed SE before the start of product development, the program can resolve these risks through trade-offs and additional investments, ensuring that risks have been sufficiently retired or that they are clearly understood and adequately resourced if they are being carried forward. The same principle applies to compliance systems, whether they are for safety, security, sustainability, quality, regulatory, responsible AI, or other outcomes. We have observed that effective systems engineering processes and practices are essential for compliance to deliver its purpose, protect value creation, and earn the trust of stakeholders. If mission success depends on compliance success, make sure you incorporate systems engineering as a key part of your team and approach. Lean Compliance offers an advanced program based on the principles of systems engineering along with other necessary domains . This program is called, "The Proactive Certainty Program™". You can learn more here:

Getting the most value from your ISO Management System requires more than just maintaining certification. By taking a strategic approach, organizations can transform their ISO standards from conformance requirements into powerful tools for business excellence. This guide outlines essential practices that help managers leverage their ISO Management System to drive operational improvements, enhance risk management, and achieve strategic objectives. Whether you're implementing a single standard or managing multiple ISO frameworks, these insights will help you maximize the return on your ISO investment. Maximizing ISO Management System Benefits Managers can maximize the benefits of their ISO management program by understanding its strategic value and focusing on continuous improvement, integration, and alignment with business objectives. Here’s what managers need to know to get the most out of their ISO management system: 1. Understand the Strategic Value of ISO Standards ISO standards, such as ISO 9001 (Quality Management), ISO 14001 (Environmental Management), ISO 27001 (Information Security), and ISO 45001 (Occupational Health and Safety), provide a structured framework for improving processes and achieving organizational goals. Action: Managers should view ISO standards not just as check-box requirements but as tools to drive operational excellence, enhance customer satisfaction, and improve risk management. Use ISO management systems to align processes with strategic goals, leveraging them to identify opportunities for growth, innovation, and competitive advantage. 2. Focus on Continuous Improvement ISO management programs are designed to support continuous improvement through the Plan-Do-Check-Act (PDCA) cycle, which emphasizes planning improvements, implementing changes, monitoring performance, and taking corrective action. Action: Regularly review and update processes based on performance data, audit results, and stakeholder obligations. Foster a culture of continuous improvement by encouraging teams to identify areas of improvement and risk. Utilize internal audits, performance metrics, and stakeholder expectations to drive the improvement process. 3. Integrate Multiple ISO Standards Many organizations adopt more than one ISO standard to cover different aspects of their operations, such as quality, environmental management, and information security. Integrating these standards can reduce duplication and streamline processes. Integrated management reduces complexity, saves time, and ensures consistency across various compliance areas. Action: Develop an Integrated Management System (IMS) that combines requirements from multiple ISO standards into a single, cohesive framework (e.g., ISO 37301) Train staff to understand how different standards overlap (e.g., risk management in ISO 9001 and ISO 27001) and leverage common requirements for efficiency. 4. Align ISO Programs with Business Objectives An ISO management system is most effective when it supports the organization’s strategic goals, such as customer satisfaction, cybersecurity, operational efficiency, or stakeholder trust. Aligning ISO programs with business objectives ensures that the management system adds value and supports the overall mission. Action: Set measurable objectives that align with the organization’s goals (e.g., reducing waste in line with ISO 14001 to support sustainability targets). Use performance indicators from ISO programs to track progress toward strategic objectives and adjust plans as needed. 5. Engage Leadership and Drive a Culture of Ownership Leadership commitment is crucial for the successful implementation of ISO standards, as it sets the tone for the entire organization. Engaged leadership fosters a culture of accountability and promise-keeping, making ISO principles part of the everyday mindset. Action: Managers should actively participate in ISO initiatives, set clear expectations, and communicate the benefits of the management system to all employees. Encourage staff at all levels to take ownership of their obligations and establish processes to keep all their commitments. 6. Leverage Data for Informed Decision-Making ISO management systems emphasize the use of data to monitor performance and make informed decisions. Action: Implement software solutions for data collection, analysis, and reporting to support real-time decision-making. Collect relevant data from key processes (e.g., incident reports for ISO 45001, audit findings for ISO 9001) and analyze it to identify trends, risks, and opportunities. Use data-driven insights to prioritize initiatives, allocate resources effectively, and justify investments in improvements. 7. Optimize Resource Allocation Efficiently managing resources (time, budget, personnel) is essential for maximizing the return on investment in ISO programs. Optimizing resource allocation ensures that ISO programs deliver maximum value without overburdening staff. Action: Identify key areas where improvements will have the most significant impact and allocate resources accordingly. Streamline processes and eliminate redundancies to make the best use of available resources. 8. Proactively Enhance System Performance Regular monitoring and analysis help keep your ISO management system dynamic, forward-looking, and aligned with future business needs. Action: Develop a comprehensive monitoring program that integrates leading indicators, process metrics, and future-focused assessments. Establish systematic monitoring to identify enhancement opportunities and address potential issues before they emerge Use performance data to guide improvement initiatives and system optimization, ensuring continuous advancement and capability building 9. Promote Risk-Based Thinking ISO standards emphasize a proactive approach to identifying and managing risks and opportunities. Focusing on risk management helps prevent problems before they occur, reducing disruptions and improving resilience. Action: Embed risk-based thinking into all levels of the organization, integrating it with decision-making processes. Use risk assessments to prioritize areas for improvement and develop contingency plans. 10. Stay Informed About Changes in ISO Standards ISO standards are periodically revised to reflect new best practices, regulatory changes, and industry developments. Action: Keep up to date with the latest revisions to ISO standards and understand how they impact your organization’s management system. Plan for transition periods and ensure training is provided to adapt to new requirements. Leverage resources such as ISO certification bodies, industry groups, and consultants to stay informed about changes. By following these practices, managers can ensure that their ISO management programs are not only compliant but also drive meaningful improvements across safety, security, sustainability, quality, reliability, and ethics. Lean Compliance offers an advanced program design specifically to help organizations teach better outcomes from their compliance programs. This program is called, "The Proactive Certainty Program™". You can learn more here:

As a compliance engineer with 30+ years of experience, I've learned that not all metrics are created equal. Today, I want to share a framework that has transformed how organizations approach compliance measurement. Here's the thing: We often get caught up in measuring everything we can, but what truly matters are the metrics that drive real compliance outcomes. I'm talking about tangible improvements in safety, security, sustainability, quality, and profitability—all of which build that precious stakeholder trust we're aiming for. Let me break down the five essential categories of metrics that I've seen make a real difference: 📈 Adherence Metrics: These show you're walking the walk. They measure how well you're meeting your rule-based obligations—think regulatory requirements, internal policies, and mandatory procedures. It's about having concrete evidence that you're doing what you say you're doing. 📈 Conformance Metrics: These demonstrate alignment with industry best practices and standard operating procedures. They're your proof that you're not just meeting the minimum requirements but following established practices that work. 📈 Performance Metrics : This is where we track progress against specific targets. Are we hitting our compliance KPIs? Are we meeting our performance obligations consistently? These metrics show if we're delivering on our promises. 📈 Effectiveness Metrics: These are the "so what" metrics—they measure the actual impact of our compliance efforts. Are we seeing fewer incidents? Better risk management? Improved outcomes? This is where we prove our program is making a difference. 📈 I ntegrity Metrics :Perhaps the most crucial of all—these metrics measure the confidence level in our ability to meet obligations and keep promises. They're about trust, reliability, and the strength of our compliance culture. Why does this framework work? Because it helps you focus on what truly matters. It's not about drowning in data—it's about measuring the right things that keep you: ✅ True to your mission ✅ Operating within boundaries ✅ Ahead of potential risks Here are some screenshots (below) from our Elevate Compliance Webinar we recently held. We talked about how to use metrics that really matter to compliance. If you’re interested, book a call with us to learn how you can use this framework to make compliance a success for your business.

LEAN teaches that it is important to go to the Gemba – the scene of the crime, so to speak, before we decide on what to change. This is the place were value is created and where we can best understand how to improve. Taiichi Ohno used the phrase: Don’t look with your eyes, look with your feet. Don’t think with your head, think with your hands. The principle behind these words is that in order to solve real problems we need to get as close to reality as we can. We need to go beyond what we perceive and what we might think. We should not rely on data and reports alone to know what is really going on. That is why he encouraged us to go to the factory floor (use your feet) then interact with people (think with your hands) to truly understand what is happening. By using “Andon” signalling and “Kanban” material handling line managers could see directly if a manufacturing process was performing well or not. There was a time when factory managers could meet customer demand without the use of an ERP system. Gemba walks have proven extremely useful for physical factories. However, how is this done for today's Information Factories ? Information Factories Information Factories are a category of business were data (raw material) is processed to create insights – the product of an information factory. The machinery includes data intake streams, data processing (removal of waste), data lakes, machine learning, and other forms of artificial intelligence (AI) to create insights that customers desire and willing to pay for. Here as with physical factories there are performance targets to reach, standards to conform to, quality to achieve, safety (people, equipment and data to protect), and environmental impacts and other risks to address. The challenge for LEAN practitioners is that Gemba for these factories is not something you can directly observe. When the place where value is created is hidden and unseen we need another way for us to "Go and See." Gemba Walks for Information Factories For information factories we don’t look with our eyes, we look with our algorithms. We don’t think we our heads, we think with AI. What Taiicho Ohno reminds us is that improvement requires people. And for that we need algorithms and AI where the rules are transparent and explainable for people to "go and see." I wonder if Taiicho Ohno might say to us today: Don’t only look with your algorithms, look with your eyes. Don’t only think with your AI, think with your head. We need to re-imagine what Gemba walks looks like so we can better observe the information factory floor. Perhaps, walking the physical Gemba will be replaced by walking digital threads that provide transparency and explainability so we can better understand and interpret what is really going on. This "Gemba" Thread could help reconstruct the "scene of the crime" so people can observe, interact, and take steps to improve the place where value is created. 1. "Digital Threads: The Future of Compliance: https://www.leancompliance.ca/post/digital-threads-the-future-of-compliance

The use of Artificial Intelligence (AI) to drive autonomous automobiles otherwise known as "self-driving cars" has in recent months become an area of much interest and discussion. The use of self-driving cars while offering benefits also poses some challenging problems. Some of these are technical while others are more of a moral and ethical nature. One of the key questions has to do with what happens if an accident occurs and particularly if the self-driving car caused the accident. How does the car decide if it should sacrifice its own safety to save a bus load of children? Can it deal with unexpected issues or only mimic behavior based on the data it learned from? Can we even talk about AI deciding for itself or having its own moral framework? Before we get much further, it is important to understand that in many ways, the use of computers and algorithms to control machinery already exists and has for some time. There is already technology of all sorts used to monitor, control, and make decisions. What is different now is the degree of autonomy and specifically in how machine learning is done to support artificial intelligence. In 2016, authors from Google Brain, Standford University, UC Berkley and OpenAI, published a paper entitled, " Concrete Problems in AI Safety. " In this paper, the authors discuss a number of areas of research that could help to address the possibility of accidents caused by using artificial intelligence. Their approach does not look at extreme cases but rather looks through the lens of a day in the "life" of a cleaning robot. The paper defines accidents as, " unintended and harmful behavior that may emerge from machine learning systems when we specify the wrong objective function, are not careful about the learning process, or commit other machine learning-related implementation errors ." It further goes on to outline several safety-related problems: Avoiding Negative Side Effects : How can we ensure that our cleaning robot will not disturb the environment in negative ways while pursuing its goals, e.g. by knocking over a vase because it can clean faster by doing so? Can we do this without manually specifying everything the robot should not disturb? Avoiding Reward Hacking: How can we ensure that the cleaning robot won’t game its reward function? For example, if we reward the robot for achieving an environment free of messes, it might disable its vision so that it won’t find any messes, or cover over messes with materials it can’t see through, or simply hide when humans are around so they can’t tell it about new types of messes. Scalable Oversight: How can we efficiently ensure that the cleaning robot respects aspects of the objective that are too expensive to be frequently evaluated during training? For instance, it should throw out things that are unlikely to belong to anyone, but put aside things that might belong to someone (it should handle stray candy wrappers differently from stray cellphones). Asking the humans involved whether they lost anything can serve as a check on this, but this check might have to be relatively infrequent—can the robot find a way to do the right thing despite limited information? Safe Exploration: How do we ensure that the cleaning robot doesn’t make exploratory moves with very bad repercussions? For example, the robot should experiment with mopping strategies, but putting a wet mop in an electrical outlet is a very bad idea. Robustness to Distributional Shift: How do we ensure that the cleaning robot recognizes, and behaves robustly, when in an environment different from its training environment? For example, strategies it learned for cleaning an office might be dangerous on a factory work floor. These problems, while instructive and helpful to explore AI safety, also offer a glimpse of similar issues observed in actual workplace settings. This is not to say that people behave like robots; far from it. However, seeing things from a different vantage point can provide new insights. Solving AI safety may also improve overall workplace safety. The use of artificial intelligence to drive autonomous machinery will no doubt increase in the months and years ahead. This will continue to raise many questions including how process and occupational safety will be impacted by the increase in machine autonomy. At the same time, research into AI safety may offer fresh perspectives on how we currently address overall safety. "Just when you think you know something, you have to look at in another way. Even though it may seem silly or wrong, you must try." From the movie, "Dead Poets Society"