How to Balance AI Innovation With Caution

Update on AI and Decision-Making from the Harvard Business School: “AI can help leaders work faster, but it can also distort decision-making and lead to overconfidence. If you’re integrating AI tools into forecasting or strategy work, use these safeguards to stay grounded. 1) Watch for built-in biases. AI presents forecasts with impressive detail and confidence and tends to extrapolate from recent trends, which can make you overly optimistic. To counter this, make the system justify its output: Ask it for a confidence interval and an explanation of how the prediction could be wrong. 2) Seek peer input. Don’t replace human discussion with AI. Talk with colleagues before finalizing forecasts. Peer feedback brings emotional caution, diverse perspectives, and healthy skepticism that AI lacks. Use the AI for fast analysis, then pressure-test its take with your team. 3) Think critically about every forecast. No matter where advice comes from, ask: What’s this based on? What might be missing? AI may sound authoritative, but it’s not infallible. Treat it as a starting point, not the final word. 4) Set clear rules for how your team uses AI. Build in safeguards, such as requiring peer review before acting on AI recommendations and structuring decision-making to include both machine input and human insight.”  Posted July 11, 2025, on the Harvard Business Review’s Management Tip Of The Day. For more #ThoughtsAndObservations about #AI and the #Workplace go to https://lnkd.in/gf-d2xXN #ArtificialIntelligence #DecisionMaking

Caught between hype and hesitation? Don’t let FOMO make you cast all the wrong spells. 𝐅𝐎𝐌𝐎'𝐬 𝐏𝐚𝐧𝐢𝐜: This card isn't just any ordinary spell; it taps into the deepest recesses of your mind, exploiting your anxiety about lagging behind in the latest AI advancements. Suddenly, you're forced to cast every spell in your hand, regardless of its usefulness or effectiveness. Every. Single. One. 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐅𝐎𝐌𝐎 𝐚𝐧𝐝 𝐈𝐭𝐬 𝐂𝐨𝐧𝐬𝐞𝐪𝐮𝐞𝐧𝐜𝐞𝐬 FOMO, or the Fear of Missing Out, is a psychological phenomenon that can lead to rash decisions and impulsive actions. In a business context, FOMO can create a sense of urgency and panic, compelling companies to adopt new technologies or trends without thorough evaluation. This reactive approach can lead to wasted resources, ineffective implementations, and ultimately, missed opportunities for genuine innovation. 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐚𝐬 𝐭𝐡𝐞 𝐔𝐥𝐭𝐢𝐦𝐚𝐭𝐞 𝐅𝐎𝐌𝐎 𝐓𝐫𝐢𝐠𝐠𝐞𝐫 Generative AI has taken the world by storm. From creating art to writing poetry, and even composing music, it seems there's nothing this technology can't do. The hype is palpable, and as a manufacturer, you might feel the pressure to jump on the AI bandwagon immediately—or risk being left behind. 𝐀𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐥𝐞 𝐀𝐝𝐯𝐢𝐜𝐞 𝐭𝐨 𝐍𝐚𝐯𝐢𝐠𝐚𝐭𝐞 𝐅𝐎𝐌𝐎 𝐢𝐧 𝐀𝐈 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭𝐬: 𝟏. 𝐀𝐬𝐬𝐞𝐬𝐬 𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐜𝐞 𝐭𝐨 𝐘𝐨𝐮𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬: Not every AI advancement will be relevant to your manufacturing processes. Take a step back and evaluate how generative AI specifically can benefit your operations, whether it's in product design, quality control, or supply chain optimization. 𝟐. 𝐒𝐭𝐚𝐫𝐭 𝐒𝐦𝐚𝐥𝐥, 𝐓𝐡𝐢𝐧𝐤 𝐁𝐢𝐠: Instead of overhauling your entire system, start with small, manageable AI projects. This could be as simple as automating a specific task or implementing AI-driven predictive maintenance. Small successes can pave the way for larger implementations. 𝟑. 𝐈𝐧𝐯𝐞𝐬𝐭 𝐢𝐧 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: The AI landscape is ever-evolving. Encourage your team to stay updated with the latest trends and advancements through courses, webinars, and industry conferences. Knowledge is power, and staying informed can help you make better decisions. 𝟒. 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐞 𝐰𝐢𝐭𝐡 𝐄𝐱𝐩𝐞𝐫𝐭𝐬: You don't have to go it alone. Partner with AI experts and consultants who can provide insights tailored to your specific needs. Their expertise can help you navigate the complexities of AI implementation effectively. 𝟓. 𝐅𝐨𝐜𝐮𝐬 𝐨𝐧 𝐕𝐚𝐥𝐮𝐞: Before diving into any AI project, conduct a thorough cost-benefit analysis. Understand the potential return on investment and prioritize projects that offer the most significant impact on your bottom line. ******************************************* • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

A lot of companies think they’re “safe” from AI compliance risks simply because they haven’t formally adopted AI. But that’s a dangerous assumption—and it’s already backfiring for some organizations. Here’s what’s really happening— Employees are quietly using ChatGPT, Claude, Gemini, and other tools to summarize customer data, rewrite client emails, or draft policy documents. In some cases, they’re even uploading sensitive files or legal content to get a “better” response. The organization may not have visibility into any of it. This is what’s called Shadow AI—unauthorized or unsanctioned use of AI tools by employees. Now, here’s what a #GRC professional needs to do about it: 1. Start with Discovery: Use internal surveys, browser activity logs (if available), or device-level monitoring to identify which teams are already using AI tools and for what purposes. No blame—just visibility. 2. Risk Categorization: Document the type of data being processed and match it to its sensitivity. Are they uploading PII? Legal content? Proprietary product info? If so, flag it. 3. Policy Design or Update: Draft an internal AI Use Policy. It doesn’t need to ban tools outright—but it should define: • What tools are approved • What types of data are prohibited • What employees need to do to request new tools 4. Communicate and Train: Employees need to understand not just what they can’t do, but why. Use plain examples to show how uploading files to a public AI model could violate privacy law, leak IP, or introduce bias into decisions. 5. Monitor and Adjust: Once you’ve rolled out your first version of the policy, revisit it every 60–90 days. This field is moving fast—and so should your governance. This can happen anywhere: in education, real estate, logistics, fintech, or nonprofits. You don’t need a team of AI engineers to start building good governance. You just need visibility, structure, and accountability. Let’s stop thinking of AI risk as something “only tech companies” deal with. Shadow AI is already in your workplace—you just haven’t looked yet.

Generative AI: A Reality Check for Fraud and Risk Management. As we continue to explore the transformative potential of generative AI, Gartner’s recent study provides crucial insights into the current landscape and its evolving challenges. Amid all of the hype, we’re now deep in the “Trough of Disillusionment”. This leads us to question, where has the ROI been on the $16 billion dollar investment in AI in 2023? One area that is readily apparent is in the market caps of the Magnificent Seven. But, for most commercial entities, ROI questions remain. Where should the focus be for generating the desired returns? 1. Strategic Implementation: The current phase emphasizes the need for a strategic approach. We should focus on integrating AI tools that offer proven benefits for fraud detection and risk management while continuously assessing their effectiveness and security implications. 2. Balance and Governance: It’s crucial to balance innovation with caution. Investing in AI-driven solutions must be accompanied by rigorous testing and validation. Governance and model risk management is where many AI initiatives are falling down. A well-defined and consistent approach to model governance is crucial for success. 3. Enhanced Detection and Prevention: While we navigate the hype and disillusionment, it’s clear that generative AI can enhance our fraud detection systems. Leveraging AI to analyze patterns and identify anomalies in real-time can significantly improve our ability to combat sophisticated fraud attempts, while co-pilots and AI based automation can drive needed efficiencies across programs. As we move forward, let’s use this period of recalibration to refine our strategies and strengthen our governance models. By adopting a thoughtful and trustworthy approach to generative AI, we can turn potential risks into opportunities for advancing our fraud prevention and risk management capabilities. http://2.sas.com/6041WYECF #BankingIndustry #GenAI #Hype #Fraud #Risk

To all Executives looking to build AI systems responsibly, Yoshua Bengio and a team of 100+ of AI Advisory Experts from more than 30 countries recently published the International AI Safety Report 2025, consisting of ~300 pages of insights. Below is a TLDR (with the help of AI) of the content of the document that you should pay attention to, including risks and mitigation strategies, as you continuously deploy new AI-powered experiences for your customers. 🔸AI Capabilities Are Advancing Rapidly: • AI is improving at an unprecedented pace, especially in programming, scientific reasoning, and automation • AI agents that can act autonomously with little human oversight are in development • Expect continuous breakthroughs, but also new risks as AI becomes more powerful 🔸Key Risks for Businesses and Society: • Malicious Use: AI is being used for deepfake scams, cybersecurity attacks, and disinformation campaigns • Bias & Unreliability: AI models still hallucinate, reinforce biases, and make incorrect recommendations, which could damage trust and credibility • Systemic Risks: AI will most likely impact labor markets while creating new job categories, but will increase privacy violations, and escalate environmental concerns • Loss of Control: Some experts worry that AI systems may become difficult to control, though opinions differ on how soon this could happen 🔸Risk Management & Mitigation Strategies: • Regulatory Uncertainty: AI laws and policies are not yet standardized, making compliance challenging • Transparency Issues: Many companies keep AI details secret, making it hard to assess risks • Defensive AI Measures: Companies must implement robust monitoring, safety protocols, and legal safeguards • AI Literacy Matters: Executives should ensure that teams understand AI risks and governance best practices 🔸Business Implications: • AI Deployment Requires Caution. Companies must weigh efficiency gains against potential legal, ethical, and reputational risks • AI Policy is Evolving. Companies must stay ahead of regulatory changes to avoid compliance headaches • Invest in AI Safety. Companies leading in ethical AI use will have a competitive advantage • AI Can Enhance Security. AI can also help detect fraud, prevent cyber threats, and improve decision-making when used responsibly 🔸The Bottom Line • AI’s potential is massive, but poor implementation can lead to serious risks • Companies must proactively manage AI risks, monitor developments, and engage in AI governance discussions • AI will not “just happen.” Human decisions will shape its impact. Download the report below, and share your thoughts on the future of AI safety! Thanks to all the researchers around the world who took created this report and took the time to not only surface the risks, but provided actionable recommendations on how to address them. #genai #technology #artificialintelligence

Over the past few months, I’ve been working behind the scenes on an initiative that’s shaping how we approach AI security at scale, the CSA AI Controls Matrix. If I’ve been quieter than usual, it’s because I’ve been focused on defining practical security controls that help organizations secure AI-driven technologies, third-party AI integrations, and enterprise AI adoption. AI is fundamentally shifting how businesses operate, but with that comes new security challenges: 🔹 How do we evaluate AI supply chain risks as third-party AI services become more embedded in SaaS and enterprise environments? 🔹 What baseline security controls should exist for AI models, training data, and operational workflows? 🔹 How do we balance risk management with the speed of AI innovation? The CSA AI Controls Matrix provides a structured, risk-based framework to help security teams navigate these challenges. It’s designed to be practical and adaptable, giving organizations clear guidance on how to integrate security, governance, and risk management into their AI strategies. 📝 The Peer Review is Still Open This is a collaborative effort, and industry input is critical. If you work in AI security, governance, compliance, or risk, I encourage you to review the matrix and provide feedback. The more perspectives we gather, the stronger the framework will be. https://lnkd.in/gCgNhxAi I’d love to hear your thoughts: What security gaps do you see in AI adoption today? #AI #Security #ThirdPartyRisk #CloudSecurity #AICompliance #SecurityArchitecture #Cybersecurity #SaaS

💡Anyone in AI or Data building solutions? You need to read this. 🚨 Advancing AGI Safety: Bridging Technical Solutions and Governance Google DeepMind’s latest paper, "An Approach to Technical AGI Safety and Security," offers valuable insights into mitigating risks from Artificial General Intelligence (AGI). While its focus is on technical solutions, the paper also highlights the critical need for governance frameworks to complement these efforts. The paper explores two major risk categories—misuse (deliberate harm) and misalignment (unintended behaviors)—and proposes technical mitigations such as:   - Amplified oversight to improve human understanding of AI actions   - Robust training methodologies to align AI systems with intended goals   - System-level safeguards like monitoring and access controls, borrowing principles from computer security  However, technical solutions alone cannot address all risks. The authors emphasize that governance—through policies, standards, and regulatory frameworks—is essential for comprehensive risk reduction. This is where emerging regulations like the EU AI Act come into play, offering a structured approach to ensure AI systems are developed and deployed responsibly.  Connecting Technical Research to Governance:   1. Risk Categorization: The paper’s focus on misuse and misalignment aligns with regulatory frameworks that classify AI systems based on their risk levels. This shared language between researchers and policymakers can help harmonize technical and legal approaches to safety.   2. Technical Safeguards: The proposed mitigations (e.g., access controls, monitoring) provide actionable insights for implementing regulatory requirements for high-risk AI systems.   3. Safety Cases: The concept of “safety cases” for demonstrating reliability mirrors the need for developers to provide evidence of compliance under regulatory scrutiny.   4. Collaborative Standards: Both technical research and governance rely on broad consensus-building—whether in defining safety practices or establishing legal standards—to ensure AGI development benefits society while minimizing risks. Why This Matters:   As AGI capabilities advance, integrating technical solutions with governance frameworks is not just a necessity—it’s an opportunity to shape the future of AI responsibly. I'll put links to the paper below. Was this helpful for you? Let me know in the comments. Would this help a colleague? Share it. Want to discuss this with me? Yes! DM me. #AGISafety #AIAlignment #AIRegulations #ResponsibleAI #GoogleDeepMind #TechPolicy #AIEthics #3StandardDeviations

🎙️ Transcending AI Fear and Hype: How to Think About the Human-AI Relationship People and Projects Podcast with Andy Kaufman In this episode, Andy talks with FAISAL HOQUE, author of "Transcend: Unlocking Humanity in the Age of AI". Faisal brings a unique blend of deep philosophical insight, entrepreneurial experience, and technological expertise to the conversation. They explore how leaders can navigate the fast-evolving landscape of artificial intelligence while staying grounded in what makes us human. The discussion explores how to think about AI not just as a tool or collaborator, but as a mirror that reflects our biases and decisions. Faisal introduces the OPEN and CARE frameworks as practical ways to innovate while managing risk, making this conversation highly actionable for project managers and leaders. From detaching from digital noise to preparing the next generation for an AI-shaped world, Faisal offers a thoughtful roadmap for embracing technology without losing our agency. If you’re looking for insights on how to thrive in the age of AI with both optimism and responsibility, this episode is for you! Sound Bites: “You can be optimistic, but you can also mitigate risk at the same time. One doesn’t really work without the other.” “Catastrophizing is not doomism, but it’s really a risk management practice.” “AI is not just a tool or collaborator, it’s a mirror.” “Don’t outsource your agency. It’s easy to let AI nudge your decisions without even realizing it.”' “Just like you don’t put a 10-year-old in a car to drive… technology can do a lot of good, but it can also be disastrous.” Full Episode @ https://lnkd.in/epzr4Np3 #humanity #ethics #business #AI #leadership

𝐀𝐈 𝐫𝐢𝐬𝐤 𝐢𝐬𝐧’𝐭 𝐨𝐧𝐞 𝐭𝐡𝐢𝐧𝐠. 𝐈𝐭’𝐬 𝟏,𝟔𝟎𝟎 𝐭𝐡𝐢𝐧𝐠𝐬. That’s not hyperbole. A new meta-review compiled over 1,600 distinct AI risks from 65 frameworks and surfaced a tough truth: most organizations are underestimating both the scope and structure of AI risk. It’s not just about bias, fairness, or hallucination. Risks emerge at different stages, from different actors, with different incentives: • Pre-deployment design decisions • Post-deployment human misuse • Model failure, misalignment, drift • Unclear accountability across teams The taxonomy distinguishes between human and AI causes, intentional and unintentional behaviors, and domain-specific vs. systemic risks. But here’s the real insight: Most AI risks don’t stem from malicious design. They emerge from fragmented ownership and unmanaged complexity. No single team sees the whole picture. Governance lives in compliance. Development lives in product. Monitoring lives in infra. And no one owns the handoffs. → Strategic takeaway: You don’t need another checklist. You need a cross-functional risk architecture. One that maps responsibility, observability, and escalation paths, before the headlines do it for you. AI systems won’t fail in one place. They’ll fail at the intersections. 𝐓𝐫𝐞𝐚𝐭 𝐀𝐈 𝐫𝐢𝐬𝐤 𝐚𝐬 𝐚 𝐜𝐡𝐞𝐜𝐤𝐛𝐨𝐱, 𝐚𝐧𝐝 𝐢𝐭 𝐰𝐢𝐥𝐥 𝐬𝐡𝐨𝐰 𝐮𝐩 𝐥𝐚𝐭𝐞𝐫 𝐚𝐬 𝐚 𝐡𝐞𝐚𝐝𝐥𝐢𝐧𝐞.

💡 Are Compliance Standards Killing Innovation, or Are We Framing Them Wrong?💡 Compliance standards are often viewed as barriers to creativity, especially in fields like artificial intelligence (AI). But frameworks like ISO42001 are not obstacles as much as they are enablers. They provide the structure needed to innovate responsibly, ensuring organizations can offer accountability, trust, and scalability. For leaders implementing an Artificial Intelligence Management System (AIMS), conformance to the standard can help establish a foundation for trustworthy AI systems, reducing risks and enabling sustainable innovation that also aligns with the OECD.AI’s Principles. ➡️ How ISO42001 Drives AI Innovation 1. Clarity Creates Confidence 🔹 Challenge: Teams hesitate to deploy AI when risks like bias or privacy breaches remain unresolved. 🔹ISO42001 Solution: Establishes clear processes for risk management, documentation, and decision traceability. 🔸Impact: Developers can innovate confidently within a framework that reduces uncertainty. 2. Risk Management Enables Bold Ideas 🔹Challenge: AI development involves unpredictable outcomes and operational risks. 🔹ISO42001 Solution: Provides structured tools to identify, mitigate, and monitor risks throughout the AI lifecycle. 🔸Impact: Teams can pursue ambitious ideas with safeguards in place, balancing creativity with accountability. 3. Accountability Builds Trust 🔹Challenge: Stakeholders demand transparency and fairness in AI decision-making. 🔹ISO42001 Solution: Embeds accountability mechanisms, ensuring decisions are traceable and ethical. 🔸Impact: Encourages collaboration and risk-taking, knowing ethical considerations are part of the process. 4. Collaboration Fuels Innovation 🔹Challenge: Innovation often stalls when teams operate in silos. 🔹ISO42001 Solution: Defines clear roles and responsibilities, enabling cross-functional alignment. 🔸Impact: Teams work together more effectively, addressing risks early and accelerating progress. ➡️ AIMS as a Platform for Innovation ISO42001 creates the environment where AI innovation thrives. By integrating ethical considerations, risk management, and lifecycle monitoring, you can scale your AI solutions responsibly while fostering creativity. 🔹Example: AIMS ensures challenges like bias or transparency are proactively addressed, allowing developers to focus on building impactful AI systems. 🔸Long-term Value: Innovations are not just scalable but also aligned with societal and organizational goals. ➡️ Rethinking Compliance Governance/Management frameworks like ISO42001 are not roadblocks, they are opportunities. They establish trust, reduce uncertainty, and provide the structure you need to innovate responsibly. 🔸Key Takeaway: Success in AI isn’t defined by how quickly systems are built, but by how effectively they deliver ethical, sustainable value. A-LIGN #TheBusinessofCompliance #ComplianceAlignedtoYou ISO/IEC Artificial Intelligence (AI)