Streamlining Daily Tasks

AI is powerful - your business expertise indispensable - and empathy irreplaceable. How are you integrating these three to secure your position and lead in the future of work? The question isn’t whether AI will change how we work anymore - it’s how you, as a leader, will adapt to lead. Integrating AI, Expertise, and Empathy: A Winning Formula 1️⃣ AI’s Power: Imagine leading a global sales team. AI can analyze market trends, predict customer needs, and optimize pricing strategies in seconds. But while AI provides the what, it’s up to you to determine the why and how. 2️⃣ Your Expertise: Your business acumen turns AI’s data into actionable insights. From deciding which market to prioritize to tailoring customer solutions or navigating tough negotiations, your strategic lens transforms data into impactful decisions. 3️⃣ Empathy’s Irreplaceability: AI might suggest the best time to send an email, but it can’t sense when a team member is disengaged or when a stakeholder needs reassurance. Your ability to connect, inspire, and build trust is the differentiator that transforms AI-driven strategies into real results. 🚀 Client Success Story: Merging AI with Leadership A senior operations leader at a multinational logistics firm sought to integrate AI into her team’s workflows to optimize delivery routes, reduce costs, and improve customer satisfaction. While AI provided precise data, her team resisted the changes, fearing redundancy and feeling overwhelmed. In coaching, we prioritized on 3 areas: 1️⃣ Building Empathy: She led the way to open and vulnerable communication, shared her fears too, addressed concerns, and created psychological safety, transforming resistance into collaboration - as a team. 2️⃣Leveraging AI: Together they reframed AI as a tool to enhance capabilities, expanding the team’s perspective to focus on it as strategic, high-value partner. 3️⃣ Showcasing Expertise: Now that the resistance was being replaced by openness, and even enthusiasm, the team was in flow, blending their deep industry knowledge enhanced with their AI readiness. The results? 🚀 A 25% boost in operational efficiency, 18% fewer delivery delays, and a 30% rise in customer satisfaction. 🚀 Her leadership also secured her and members of her team promotions and broader responsibilities. The future isn’t just about what technology can do - it’s about what you can achieve with it. Let’s ensure you’re not just adapting to the future of work - you’re shaping it. DM me to discuss your game plan.

Every duplicate data point in your dataset magnifies existing biases and can derail your model's learning path. Particularly alarming is that traditional quality metrics can easily miss this problem. As your model repeatedly ingests the same patterns, you might develop a misleading sense of security about its performance. This overconfidence stems from creating artificial spikes in precision-recall curves due to duplicates. These distortions can lead to inflated measurements like mean average precision (mAP). They also reduce visual diversity, skewing confidence thresholds and affecting the robustness of your model. Strategically prune your dataset to combat this issue. By thoughtfully removing redundancies, you can maintain the diversity of viewing angles while eliminating the harmful effects of duplicates. Here's what you can do 👇🏼 Start with Selective Pruning: Begin the process by eliminating exact duplicates and then evaluate your model's performance. This step will give you insights into how these removals impact the model. Gradual Adjustment for Near Duplicates: After addressing exact duplicates, carefully target near duplicates. Monitor your evaluation metrics after each removal phase to ensure the model’s performance remains on track. Preserve and Validate: Within duplicate clusters, retain images representing rare viewing angles or unique lighting conditions. Following these adjustments, validate your dataset choices by assessing your model’s performance across different classes.  My Coursera course explores these techniques and a lot more. You can audit it for free, check it out 👇🏼! #deeplearning #computervision #artificialintelligence

This is the second follow-up in my ongoing series on The Future of IT, expanding on my original post where I argued that GenAI will become the new operating layer between humans, systems, and data. These posts reflect 𝗺𝘆 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝘃𝗶𝗲𝘄 on how enterprise technology will evolve — and how radically different day-to-day IT operations could look. Integration has always been one of the most expensive and time-consuming parts of enterprise IT. Decades of harmonisation programmes promised efficiency — but at the cost of years and billions. In an AI-native architecture, integration will no longer be a human-led engineering exercise. It will be 𝗔𝗜-𝘁𝗼-𝗔𝗜 𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻: 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 — AI agents will detect new systems, map their interfaces, and identify functional overlaps without a single line of manual documentation. 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 — Instead of brittle field-by-field mapping, AI will exchange meaning, reconciling differences through shared ontologies and learned business logic. 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗮𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻 — As processes or data structures change, integrations will self-adjust, renegotiating their contracts instantly without triggering a new “project.” No tickets. No manual data mapping. No multi-month workshops. Integration becomes a persistent background conversation between systems, with changes executed in milliseconds. The human role will shift from 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 integrations to 𝘀𝗲𝘁𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝘂𝗹𝗲𝘀 — defining the policies, priorities, and trust boundaries within which AI agents negotiate and act. The real challenge will be governance at this speed: ensuring that when integration is instantaneous, it is also correct, compliant, and safe. The organisations that master this won’t just integrate faster — they will make integration disappear as a visible process entirely.

Are you grappling with how to integrate #GenAI into your daily work? The AI Integration Framework might just be the keys you need to unlock this new technology. When I developed this framework, my goal was to empower individuals, teams, and organizations to intentionally incorporate AI by starting with their own tasks. Whether you’re an AI novice or a seasoned professional, the AI Integration Framework helps identify the tasks only you can do, the ones AI can handle for you, and - most excitingly - the work where you and AI can collaborate to create exceptional results. Effectively integrating AI into our days means working at higher quality, in less time, with less mental strain, and with more enjoyment. This model helps you spot where and how. In this article, I dive into the three categories of work: 🙋♂️ Human Exclusive Tasks: The uniquely human, high-touch work AI can’t replicate. This is My Work. 🤖 AI Automation Potential: Tasks AI can do independently, freeing you for higher-value work. This is “For Me” Work. 🤝 AI Collaboration Opportunities: The "sweet spot" where AI becomes a true partner, enhancing quality, efficiency, and enjoyment. This is “With Me” Work. The AI Integration Framework is not just about understanding AI; it’s about owning your AI journey. It’s about reshaping the narrative from "What will AI do to me?" to "What will I do with AI?" I’ve included practical steps, reflective questions, and real-world examples to help you start applying this framework today. Whether you’re exploring AI’s role in change management, team leadership, or strategic decision-making, this framework offers a foundation for thoughtful integration. Enjoy! And remember, sharing is caring, especially with articles that help you unlock AI 😉

The Cybersecurity and Infrastructure Security Agency (CISA), together with other organizations, published "Principles for the Secure Integration of Artificial Intelligence in Operational Technology (OT)," providing a comprehensive framework for critical infrastructure operators evaluating or deploying AI within industrial environments. This guidance outlines four key principles to leverage the benefits of AI in OT systems while reducing risk: 1. Understand the unique risks and potential impacts of AI integration into OT environments, the importance of educating personnel on these risks, and the secure AI development lifecycle.  2. Assess the specific business case for AI use in OT environments and manage OT data security risks, the role of vendors, and the immediate and long-term challenges of AI integration 3. Implement robust governance mechanisms, integrate AI into existing security frameworks, continuously test and evaluate AI models, and consider regulatory compliance.  4. Implement oversight mechanisms to ensure the safe operation and cybersecurity of AI-enabled OT systems, maintain transparency, and integrate AI into incident response plans. The guidance recommends addressing AI-related risks in OT environments by: • Conducting a rigorous pre-deployment assessment. • Applying AI-aware threat modeling that includes adversarial attacks, model manipulation, data poisoning, and exploitation of AI-enabled features. • Strengthening data governance by protecting training and operational data, controlling access, validating data quality, and preventing exposure of sensitive engineering information. • Testing AI systems in non-production environments using hardware-in-the-loop setups, realistic scenarios, and safety-critical edge cases before deployment. • Implementing continuous monitoring of AI performance, outputs, anomalies, and model drift, with the ability to trace decisions and audit system behavior. • Maintaining human oversight through defined operator roles, escalation paths, and controls to verify AI outputs and override automated actions when needed. • Establishing safe-failure and fallback mechanisms that allow systems to revert to manual control or conventional automation during errors, abnormal behavior, or cyber incidents. • Integrating AI into existing cybersecurity and functional safety processes, ensuring alignment with risk assessments, change management, and incident response procedures. • Requiring vendor transparency on embedded AI components, data usage, model behavior, update cycles, cybersecurity protections, and conditions for disabling AI capabilities. • Implementing lifecycle management practices such as periodic risk reviews, model re-evaluation, patching, retraining, and re-testing as systems evolve or operating environments change.

Framework for Integrating AI into Daily Workflows for Non-Technical Employees   1 Establish a Digital Mindset Objective: Create a culture of AI readiness and openness to technological integration.   Key Actions: -AI Awareness Campaigns -AI-Driven Communication Tools -Gamified Learning   2 Establish AI Change Management Practices Objective: Ensure a smooth transition by addressing resistance, adapting workflows, and providing continuous support during AI adoption.   Key Actions: -Stakeholder Engagement -AI Adoption Champions -Iterative Pilots   3 Design Role-Based AI Enablement Objective: Align AI capabilities with specific roles and responsibilities to ensure direct impact.   Key Actions: -AI Co-Pilot Models -Generative AI for Productivity -Data Democratization Tools   4 Seamless Workflow Integration Objective: Embed AI technologies intuitively into existing processes to ensure non-disruptive adoption.   Key Actions: -AI-Powered Workflow Automation -AI Assistant Widgets -Contextual Recommendations   5 Leverage Generative and Adaptive AI for Training Objective: Use AI’s adaptive capabilities to create personalized and contextual learning experiences.   Key Actions: -AI-Generated Learning Modules -Digital Twins for Training -Interactive Chatbots   6 Introduce AI Governance and Ethical Practices Objective: Ensure responsible AI usage, emphasizing trust and transparency.   Key Actions: -Transparent AI Outputs -AI Ethics Training -Feedback Mechanisms   7 Create AI Risk Management Protocols Objective: Proactively identify and mitigate risks related to AI deployment, including ethical concerns, technical failures, and compliance issues.   Key Actions: -AI Risk Assessment Framework -Scenario Simulations -Bias Monitoring and Incident Response Plans     8 Foster AI Confidence with Collaborative Tools Objective: Ensure employees feel empowered to collaborate with AI tools.   Key Actions: -Human-in-the-Loop (HITL) -AI-Powered Collaboration Suites -Knowledge Graphs   9 Measure Adoption and Performance with AI Analytics Objective: Continuously refine AI integration through data-driven insights.   Key Actions: -Behavioral Analytics -Sentiment Analysis -Performance Dashboards   10 Continuous Evolution and Support Objective: Ensure the AI tools and processes evolve alongside advancements in technology and employee needs.   Key Actions: -Adaptive AI Upgrades -Community of Practice -Proactive Support   Key Success Metrics 1.  Adoption Rate: Percentage of employees actively using AI tools in their workflows. 2.  Task Efficiency Gains: Reduction in time taken to complete tasks post-AI integration. 3.  Error Reduction: Decrease in manual errors in AI-supported tasks. 4.  Employee Confidence: Improvement in employee confidence scores regarding AI use. 5.  Innovation Contributions: Increase in employee-initiated ideas leveraging AI. Transform Partner – Your Digital Transformation Consultancy

Principles for the Secure Integration of Artificial Intelligence in Operational Technology Since the public release of ChatGPT in November 2022, artificial intelligence (AI) has been integrated into many facets of human society. For critical infrastructure owners and operators, AI can potentially be used to increase efficiency and productivity, enhance decision-making, save costs, and improve customer experience. Despite the many benefits, integrating AI into operational technology (OT) environments that manage essential public services also introduces significant risks—such as OT process models drifting over time or safety-process bypasses—that owners and operators must carefully manage to ensure the availability and reliability of critical infrastructure. This guidance—co-authored by the Cybersecurity and Infrastructure Security Agency (CISA) and Australian Signals Directorate’s Australian Cyber Security Centre (ASD’s ACSC) in collaboration with the National Security Agency’s Artificial Intelligence Security Center (NSA AISC), the Federal Bureau of Investigation (FBI), the Canadian Centre for Cyber Security (Cyber Centre), the German Federal Office for Information Security (BSI), the Netherlands National Cyber Security Centre (NCSC-NL), the New Zealand National Cyber Security Centre (NCSC-NZ), and the United Kingdom National Cyber Security Centre (NCSC-UK), hereafter referred to as the “authoring agencies”—provides critical infrastructure owners and operators with practical information for integrating AI into OT environments. This guidance outlines four key principles critical infrastructure owners and operators can follow to leverage the benefits of AI in OT systems while reducing risk: 1. Understand AI. Understand the unique risks and potential impacts of AI integration into OT environments, the importance of educating personnel on these risks, and the secure AI development lifecycle. 2. Consider AI Use in the OT Domain. Assess the specific business case for AI use in OT environments and manage OT data security risks, the role of vendors, and the immediate and long-term challenges of AI integration. 3. Establish AI Governance and Assurance Frameworks. Implement robust governance mechanisms, integrate AI into existing security frameworks, continuously test and evaluate AI models, and consider regulatory compliance. 4. Embed Safety and Security Practices Into AI and AI-Enabled OT Systems. Implement oversight mechanisms to ensure the safe operation and cybersecurity of AI-enabled OT systems, maintain transparency, and integrate AI into incident response plans. The authoring agencies encourage critical infrastructure owners and operators to review this guidance and action the principles so they can safely and securely integrate AI into OT systems. https://lnkd.in/gVtgEWMM

When AI moves from labs into production systems, a new set of questions need to be asked. I explored some of these questions in a recent conversation with Le Monde and Amir Banifatemi at our Cognizant AI Lab.  The discussion focused on the gap between headline narratives and measurable outcomes. Individual incidents often attract attention, but the broader data show that well‑designed AI systems improve safety, reliability, and productivity at scale. Recent advances in model efficiency and long‑horizon reasoning are also making these systems more practical and dependable. The pace of change is notable. AI will reshape work faster than previous technology shifts, but in practice, we’re already seeing how accessible these tools have become. When AI is integrated into everyday workflows, people are able to expand their capabilities quickly, including taking on more complex technical tasks without traditional barriers. The path forward is grounded in pragmatic integration, continuous learning, and human-centered deployment. When progress is evaluated through outcomes and evidence, AI’s role becomes clear: amplifying human capability and enabling better systems across industries. https://lnkd.in/e7Hx-nxZ 

📊 Struggling with project delays, fragmented data, and manual reviews? Imagine a world where real estate project management isn’t hindered by siloed teams and disconnected workflows, but propelled by intelligence and integration. In today’s high-stakes real estate and infrastructure sector, the proliferation of point solutions and a patchwork of spreadsheets has created a digital labyrinth, slowing down coordination and eroding margins. Teams are forced to juggle disconnected systems for design, cost, procurement, and reporting—where data is trapped in silos and critical decisions are delayed by manual reviews and version control chaos. What if we leveraged AI to unite these disparate tools into one consolidated delivery platform? AI-powered project management can automate repetitive tasks, instantly review and flag anomalies in design documents, perform commercial benchmarks against global best practices, and generate adaptive workflows tailored to project realities. As McKinsey notes in their 2024 study, “Reimagining Construction Productivity with AI,” the potential for AI-driven platforms to transform productivity is immense, suggesting efficiency gains of up to 20%—including faster design reviews and more accurate forecasting. Deloitte’s 2023 publication, “AI and the Built Environment,” identifies process automation and predictive analytics as major levers for reducing errors and accelerating decision-making in construction and real estate. And as PwC highlights in their 2024 report, “The Future of Real Estate Technology,” integrating automation with human expertise leads to cost optimization, data-driven design, and more adaptive governance across complex portfolios. Real transformation happens when data isn’t just connected—it’s harmonized by intelligent engines that automate reviews, uncover trends, and create actionable insights. The result: streamlined approvals, better resource allocation, and projects that truly deliver on cost, quality, and speed. 🏗️ 🤖 The future of real estate isn’t about managing myriad workflows—it's about orchestrating intelligence. Industry leaders must invest in platforms that harness, not hinder, decision-making. Let’s shape the next era together: where every project is powered by insight, not just oversight. #ArtificialIntelligence #PropTech #RealEstateInnovation #ProjectManagement #ConstructionTechnology #DigitalTransformation #SmartInfrastructure #FutureOfWork

AI Integration and Safeguards How Intelligence Is Applied Responsibly Preo Communications integrates AI as an operational layer that enhances judgment, speed, and accuracy without introducing unnecessary risk. The objective is controlled leverage, not automation for its own sake. Where AI Is Applied AI is used in areas where it meaningfully improves outcomes. Common applications include: Pattern detection in analytics and attribution Forecasting and scenario modeling Audience segmentation and personalization Content optimization and performance analysis Workflow automation and efficiency gains AI supports teams by surfacing insight faster and reducing manual overhead. Human Led Decision Making AI informs decisions, it does not make them. Strategic direction, prioritization, and brand judgment remain human-led. AI outputs are treated as inputs to evaluation rather than instructions to follow without context. This prevents over-optimization and protects brand integrity. Data Quality and Input Control AI performance depends on data discipline. Inputs are carefully selected, cleaned, and structured to avoid bias, leakage, or misleading conclusions. Models are adjusted as data sources change to maintain reliability over time. Guardrails and Testing AI systems are introduced incrementally. Each application is tested in controlled environments before being expanded. Performance thresholds, review checkpoints, and rollback options are defined in advance to limit downside risk. Transparency and Traceability Outputs must be explainable. AI-driven insights are documented and traceable so teams understand why a recommendation exists and how it was generated. This maintains trust and supports better decision-making. Why AI Governance Matters Unstructured AI adoption increases volatility and risk. Governance ensures that efficiency gains do not come at the cost of accuracy, compliance, or strategic clarity. AI becomes valuable when it is embedded into well-designed systems with clear ownership and oversight. By applying AI deliberately and responsibly, Preo Communications enhances performance while preserving control, consistency, and long-term resilience.