Consulting

70% of change initiatives fail. (And it's rarely because the idea was bad.) Here's what actually kills transformation: You picked the wrong change model for the job. It's like performing surgery with a hammer. Sure, you're using a tool. But it's the wrong one. I've watched brilliant CEOs tank their companies this way: Using individual coaching (ADKAR) for company-wide transformation. Result: 200 people change. 2,000 don't. Running a massive 8-step program for a simple process fix. Result: 6 months wasted. Team exhausted. Nothing changes. Forcing top-down mandates when they needed subtle nudges. Result: Rebellion. Resentment. Resignation letters. Here's what nobody tells you about change: The size of your change determines your approach. Real examples from the field: 💡 Startup pivoting product: → Used Lewin's 3-stage (unfreeze old way, change, refreeze) → 3 months. Clean transition. Team aligned. 💡 Enterprise going digital: → Used Kotter's 8-step process → Created urgency first. Built coalition. Enabled action. → 18 months later: $50M in new revenue. 💡 Sales team adopting new CRM: → Used Nudge Theory → Made old system harder to access → Put new system as browser homepage → 95% adoption in 2 weeks. Zero complaints. The expensive truth: Wrong model = wasted months + burned budgets + broken trust Right model = faster adoption + sustained results + energized teams Warning signs you're using the wrong model: • High activity, low progress • People comply but don't commit • Changes revert within weeks • Energy drops as you push harder • "This too shall pass" becomes the motto Match your medicine to your ailment: Small behavior change? Nudge it. Individual performance? ADKAR it. Cultural shift? Influence it. Full transformation? Kotter it. Enterprise overhaul? BCG it. Stop treating every change like a nail. Start choosing the right tool for the job. Your next change initiative depends on it. Your team's trust demands it. Your company's future requires it. Save this. Share it with your leadership team. Because the next time someone says "people resist change," you'll know the truth: People don't resist change. They resist the wrong approach to change. P.S. Want a PDF of my Change Management cheat sheet? Get it free: https://lnkd.in/dv7biXUs ♻️ Repost to help a leader in your network. Follow Eric Partaker for more operational insights. — 📢 Want to lead like a world-class CEO? Join my FREE TRAINING: "The 8 Qualities That Separate World-Class CEOs From Everyone Else" Thu Jul 3rd, 12 noon Eastern / 5pm UK time https://lnkd.in/dy-6w_rx 📌 The CEO Accelerator starts July 23rd. 20+ Founders & CEOs have already enrolled. Learn more and apply: https://lnkd.in/dwndXMAk

𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝗮𝗻 𝗔𝗜 𝗦𝗧𝗥𝗔𝗧𝗘𝗚𝗬 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆? This is one of the clearest roadmap you’ll ever get to build your own: ⬇️ 1. 𝗔𝗜 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗚𝗼𝗮𝗹 𝗦𝗲𝘁𝘁𝗶𝗻𝗴 (𝗧𝗵𝗲 𝗖𝗼𝗿𝗲): This is your strategic north star — where you define your ambition and guide every downstream decision. • Drivers → Why are you doing this? Clarifies the business/tech forces pushing AI forward.   • Value → What are you aiming to achieve? Links AI directly to measurable outcomes.   • Vision → Where is this going long-term? Provides inspiration and direction across teams.   • Alignment → Is everyone rowing in the same direction? Ensures synergy. • Risks → What could go wrong? Sets the baseline for governance and responsible AI.   • Adoption → Who will actually use it? Anticipates friction and enables change management. 📍 This is the master blueprint — Without this, you’re just building disconnected POCs. No clear target = no impact. 2. 𝗔𝗹𝗶𝗴𝗻𝗲𝗱 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 (𝗠𝗮𝗸𝗲 𝗜𝘁 𝗙𝗶𝘁 𝗬𝗼𝘂𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀): This is where your AI ambition meets the reality of your broader enterprise. • Business Strategy → AI must serve the core business goals — not exist as a side project.   • IT Strategy → Ensures your infrastructure can support scalable AI.   • R&D Strategy → Aligns innovation with AI capabilities and funding priorities.   • D&A Strategy → Without data strategy, no AI strategy will scale. • (...) Strategy → ... 📍 Connect AI to the real levers of power in your organization — so it doesn’t get siloed or shut down. 3. 𝗔𝗜 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 (𝗠𝗮𝗸𝗲 𝗜𝘁 𝗥𝗲𝗮𝗹):   Once you know what you want to do, this defines how you’ll deliver it at scale. • Governance → Sets up ethical, legal, and operational oversight from day one.   • Data → Builds the pipelines and quality foundations for smart AI.   • Engineering → Equips you with the technical backbone for deployment.   • Technology → Selects the right tools, platforms, and architecture.   • Organization → Assigns ownership and accountability.   • Literacy → Ensures the workforce can actually work with AI. 📍 This is your AI engine room — without it, strategy stays theoretical. 4. 𝗔𝗜 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 (𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝘁𝗵𝗲 𝗩𝗮𝗹𝘂𝗲):   Now it’s time to build — but with structure and intent. • Ideation/Prioritization** → Surfaces the best use cases, aligned with strategy.   • Use Cases → Translates goals into concrete applications and MVPs.   • Buy-Build → Decides how to deliver: in-house, outsourced, or hybrid.   • Change Management → Drives real adoption beyond pilots.   • Value/Cost Management → Measures success and ensures scalability. 📍 This is where value is realized — where strategy finally touches the customer and the business. 𝗬𝗼𝘂𝗿 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗼𝗿𝗸 𝗹𝗶𝗸𝗲 𝘆𝗼𝘂𝗿 𝘁𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸: 𝗙𝘂𝗹𝗹𝘆 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱, 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗮𝗻𝗱 𝗯𝘂𝗶𝗹𝘁 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲! Graphic source: Gartner

We analyzed over 13,600 investor portfolios and ranked the largest 250 PE investors in Europe (300+ hours of research) Congratulations to all the leaders: 🥇 CVC (managing a total enterprise value of €70bn across Europe) 🥈 KKR (€66bn) 🥉 EQT Group (€61bn) Other investors in the top 10 include Blackstone (€58bn), Cinven (€45bn), Ardian (€41bn), The Carlyle Group (€33bn), TDR Capital (€32bn), Advent (€32bn) and Bain Capital (€31bn). Collectively, the top 250 private equity firms manage an EV of €1.7tn in Europe. A few other insights from the data: 1. Investors established in the 1990s or before manage 77% of the total EV 2. The top 25 investors manage roughly the same EV as the next 225 combined 3. Europe 250 investors have an avg. EBITDA of €94m and manage 26 companies each 4. German HQ’d investors are underrepresented in the ranking with just 3% of total EV 5. London is home to 50 of the top 250 investors, followed by Paris (32) and New York (21) 𝗦𝗲𝗰𝘁𝗼𝗿 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 - Hg (TMT) - CVC (Services and Industrials) - EQT Group (Science & Health) - KKR (Energy & Materials)  - Cinven (Financial Services) - TDR Capital (Consumer) Services, Consumer, and TMT are the largest PE markets by sector. Notably, Hg in TMT and TDR Capital in Consumer predominantly target those sectors, representing 71% and 69% of their portfolio, respectively. Compared to European investors, North American investors overweight TMT, Financial Services and Energy & Materials. They underallocate to Services, Industrials and Healthcare. 𝗚𝗿𝗼𝘄𝘁𝗵 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 Hg, Cinven and Astorg stand out with high-growth, high-margin portfolios. CD&R, TDR Capital and PAI Partners rank among the largest employers in Europe given their large retail/consumer portfolio. Waterland Private Equity stands out as a big buyer of family-owned businesses. ________ 𝗙𝘂𝗹𝗹 𝗥𝗲𝗽𝗼𝗿𝘁 Tons of more insights and charts in the full analysis: 💡List of top 250 investors 💡Sector and Regional rankings 💡Portfolio insights (Growth, holding periods, and more) 💡Detailed methodology Get it here ➡️ https://lnkd.in/ezekm4MJ #investors #pe #europe #insights

As organizations increasingly adopt hybrid-cloud architectures, understanding the right path and tools is crucial for professionals aiming to deliver resilient, scalable, and efficient applications. Here’s a Cloud Native roadmap breaking down the skills and tools to master across critical domains. Dive in and explore the ecosystem that powers modern applications! 🔴 𝟭. 𝗟𝗶𝗻𝘂𝘅 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀   Linux remains at the heart of cloud-native systems. Get comfortable with terminal commands, bash scripting, and distributions like Ubuntu and Red Hat for a solid start. 🟢 𝟮. 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝘀   Protocols like HTTP, SSL, and SSH form the backbone of connectivity. Tools like Wireshark are invaluable for monitoring and securing network traffic. 🔵 𝟯. 𝗖𝗹𝗼𝘂𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀   The cloud is non-negotiable! Whether AWS, Azure, or Google Cloud, understanding SaaS, PaaS, and IaaS is key to harnessing the cloud's potential. 🟣 𝟰. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆   Security is foundational in cloud-native environments. Tools like Open Policy Agent and Prisma provide the framework for enforcing policies and securing applications. 🟡 𝟱. 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀 & 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻   Containers revolutionized app deployment! Master Docker, Kubernetes, and service meshes like Istio to orchestrate, scale, and manage applications seamlessly. 🟠 𝟲. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝘀 𝗖𝗼𝗱𝗲 (𝗜𝗮𝗖)   IaC tools like Terraform, Chef, and Puppet automate infrastructure, ensuring consistency and efficiency across deployments. IaC is a must for scalable cloud-native applications. 🟢 𝟳. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆   With tools like Prometheus, Grafana, and Elastic Stack, observability gives you the visibility needed to monitor, troubleshoot, and optimize performance in real time. 🔵 𝟴. 𝗖𝗜/𝗖𝗗   Continuous Integration and Delivery streamline deployments. GitLab, Jenkins, and GitOps practices (Argo) enable rapid, reliable application delivery. This roadmap covers essential areas for cloud-native development, from Linux fundamentals to CI/CD and observability. But, the cloud-native landscape is vast and rapidly evolving! Did I miss any critical tools or concepts? Whether it's a tool you swear by or an emerging trend you're excited about, drop it in the comments! 👇

Introducing the web's first market map of the Product Analytics Market: I was floored when I couldn't find one of these online. Surely, Gartner or CBInsights or A16Z would have created one? It turns out not. So I spent the past 3 months: • Talking with 25 buyers • Researching the space myself • Interviewing 5 product leaders at key players This is what I learned about the most significant players in each space: (that PMs and product people need to know) 1. Core Product Analytics Platforms     The foundational tools for tracking user behavior and product performance Amplitude : The leader, an all-in-one platform for PMs to master their data Mixpanel : The leader in easy UX and pioneer in event-based analytics Heap | by Contentsquare: The automatic event tracking and real-time insights leader 2. A/B Testing & Experimentation     Platforms for analysis Optimizely : The premier tool for sophisticated A/B and multivariate testing VWO : The best for combining A/B testing with heatmaps and session recordings AB Tasty: The all-in-one solution for testing, personalization, and AI-driven insights 3. Feedback & Session Recording     Capture qualitative insights and visualize user interactions Medallia: The top choice for comprehensive experience management Hotjar | by Contentsquare: The go-to for visual feedback and user behavior insights Fullstory: The best for detailed session replay and user interaction analysis 4. Open-Source Solutions     Customizable, free analytics platforms for data sovereignty Matomo: The robust, privacy-focused open-source analytics platform Plausible Analytics: The lightweight, privacy-first analytics solution PostHog: The versatile, open source product analytics tool 5. Mobile & App Analytics     Specialized tools for mobile and app performance analysis UXCam: The best for in-depth mobile user interaction insights Localytics: The leader in user engagement and lifecycle management Flurry Analytics: The comprehensive, free mobile analytics platform 6. Data Collection & Integration     Gather and unify data across platforms Segment: The top choice for effortless customer data unification Informatica: The enterprise-grade solution for data integration and governance Talend: The flexible, open-source data integration tool 7. General BI & Data Viz     Non-product specific tools for data analysis and visualization Tableau: The leader in interactive, rich data visualization Power BI: The best for deep integration with Microsoft tools Looker: The modern BI tool for customizable, real-time insights 8. Decision Automation & AI     Systems for automated insights and decisions Databricks: The unified platform for data and AI collaboration DataRobot: The leader in automated machine learning and AI Alteryx: The comprehensive solution for analytics automation Check out the full infographic to see where your favorite tools fit and discover new platforms to enhance your product analytics stack.

The traditional strategy consulting process is linear, analytical and top-down. But what is the true nature of the strategy process in modern, 21st century organizations? It has these nine features. 1. Co-Constructive Both the client and the consultant have key expertise that they bring together in the process. None of them is the ultimate expert and both learn from the other. 2. Intersubjective There are no objective truths in strategy. The best that can be achieved is agreement between a diverse group of people that bring in their expertise and judgment. 3. Iterative Because any idea about the future is speculative and most likely wrong, strategy consulting needs to rely on a short-cycle approach in which ideas are constantly put to the test. 4. Participative To benefit from all people’s expertise and foster engagement, alignment and learning, strategy consulting needs to be a participative process involving many or even all employees. 5. Appreciative Problems need of course attention, but people are energized by focusing on what’s good. Therefore, the emphasis is on leveraging the best in people and organizations. 6. Integrative A key purpose of strategy is that it creates alignment to channel all energy in the same direction. This implies the consultant keeps the whole picture in mind and brings people closer together. 7. Social Because strategy consulting induces changes in people’s roles and relationships, the process is inherently social. This applies to strategy generation as well as execution. 8. Continuous While the consultant may be with the organization for just a while, strategy and organizations themselves are continuous. There is no start or end point, only ongoing development. 9. Non-sequential While there is a logical sequence in which things are theoretically done, strategy is inherently non-linear. Not only by its iterative nature, but also because its parts or steps run in parallel. Time to reflect. If you are a strategy consultant, do you work like this? If you work with strategy consultants, do they work like this? If not (or even if so), you may be interested in the Certified Strategy & Implementation Consultant (CSIC) program that I developed together with Timothy Timur Tiryaki. Registrations for our second cohort open soon. #changemanagement #strategyconsulting #managementdevelopment

Modern IIoT systems demand a balance of safety, security, reliability, resilience, and privacy. This isn't just a tech challenge; it's a cultural one, bridging IT's obsession with privacy and OT's focus on safety. The 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐈𝐨𝐓 𝐂𝐨𝐧𝐬𝐨𝐫𝐭𝐢𝐮𝐦’𝐬 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 (𝐈𝐈𝐒𝐅), first released in 𝟐𝟎𝟏𝟔, is now on 𝐕𝐞𝐫𝐬𝐢𝐨𝐧 𝟐.𝟎, with its latest update in 𝟐𝟎𝟐𝟑. Over the years, it has evolved into a robust guide for securing IIoT systems, addressing the unique challenges of integrating IT and OT. The IISF is designed to help manufacturers build trustworthiness across systems by aligning safety, security, reliability, resilience, and privacy in a single framework. The 𝐈𝐨𝐓 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐌𝐚𝐭𝐮𝐫𝐢𝐭𝐲 𝐌𝐨𝐝𝐞𝐥 (𝐒𝐌𝐌), first released in 𝟐𝟎𝟏𝟖, is a structured framework that builds on the IISF’s principles by helping organizations assess and improve their security practices. 𝐖𝐡𝐚𝐭 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬 𝐝𝐨 𝐭𝐡𝐞𝐲 𝐬𝐨𝐥𝐯𝐞? • Securing legacy (brownfield) environments alongside modern, cloud-integrated systems. • Bridging the gap between IT (focused on data security) and OT (focused on operational safety). • Equipping manufacturers with tools to assess risks, address gaps, and build actionable security roadmaps. 𝐇𝐨𝐰 𝐓𝐡𝐞𝐲 𝐖𝐨𝐫𝐤 𝐓𝐨𝐠𝐞𝐭𝐡𝐞𝐫 • 𝐈𝐈𝐒𝐅 𝐏𝐫𝐨𝐯𝐢𝐝𝐞𝐬 𝐭𝐡𝐞 "𝐖𝐡𝐚𝐭" 𝐚𝐧𝐝 "𝐖𝐡𝐲": It explains what security goals organizations should aim for and why they matter in an IIoT context. • 𝐒𝐌𝐌 𝐏𝐫𝐨𝐯𝐢𝐝𝐞𝐬 𝐭𝐡𝐞 "𝐇𝐨𝐰": It helps organizations evaluate their current security maturity, define targets based on IISF principles, and create actionable roadmaps to achieve those targets. 𝐖𝐡𝐲 𝐔𝐬𝐞 𝐁𝐨𝐭𝐡? Together, the IISF and SMM offer a top-down and bottom-up approach: • Start with the IISF to understand the overarching security needs for your IIoT systems. • Use the SMM to assess where you stand and implement practical improvements to achieve those needs. 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐈𝐈𝐒𝐅:  https://lnkd.in/eypinq3G 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐒𝐒𝐌: https://lnkd.in/e398Y9TU ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Financial Value of Climate Risks and Opportunities 🌍 Companies are under increasing pressure to reflect climate risks and opportunities in financial decision making. This is essential for embedding sustainability into strategy and unlocking measurable business value. ERM highlights that financial valuation of environmental and social factors enables companies to align investment decisions with long term performance. Value is created through energy efficiency, circular models, responsible sourcing, and workforce inclusion. These actions contribute to resilience, innovation, and cost efficiency. Sustainable products are experiencing significantly higher growth rates than conventional alternatives. Efficiency measures can reduce operating costs by up to 30 percent, while green finance instruments can lower the cost of capital. These gains can be captured directly in financial models and forecasts. At the same time, climate related risks are increasing in scale and frequency. Physical risks already account for over 270 billion dollars in annual damages. Transition risks may result in stranded assets worth hundreds of billions. The broader economic cost of unmitigated climate change could reduce global GDP by up to 18 percent by mid century. ERM presents two complementary approaches. Value creation focuses on capturing upside through efficiency, innovation, and market expansion. Risk mitigation addresses downside exposure by incorporating climate risks into business planning and decision processes. Both require integration of ESG into financial structures. This means applying standard financial tools such as internal rate of return and discounted cash flow to evaluate climate related actions. It also involves including environmental risks in sensitivity testing, pricing models, and capital planning frameworks. Translating these impacts into financial terms enables clearer comparison and stronger governance. Capital markets are moving toward companies that manage climate exposure effectively. Lower financing costs, stronger investor confidence, and increased access to sustainability linked capital are all benefits of a robust ESG integration strategy. Quantifying the financial value of climate related risks and opportunities enables companies to move from qualitative ambition to strategic execution. Those that lead in this area are better prepared to compete, attract capital, and deliver long term results. Source: ERM #sustainability #sustainable #esg #business

I’ve been running my own econometric model of the U.S. economy for almost 30 years now. The basic structure is simple. You start by forecasting the components of demand, that is to say, consumption, investment, trade and government spending. This gives you an initial projection of real GDP growth. You then feed this into labor market equations, along with some demographic assumptions, to forecast the growth in jobs, the unemployment rate and wage growth. All of this, along with assumptions about energy prices and the dollar, then drive forecasts of inflation. Given this outlook for growth and inflation, you make an assumption about the path for the federal funds rate and then run forecasts of other interest rates. With all of this in hand, you can forecast productivity, corporate profits, the federal budget deficit and household net worth. And then you go back to the start to see how all these changes impact your original demand forecast. You repeat the process until you arrive at a reasonably consistent solution. #markets #economy #investing

BREAKING! The FDA just released this draft guidance, titled Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations, that aims to provide industry and FDA staff with a Total Product Life Cycle (TPLC) approach for developing, validating, and maintaining AI-enabled medical devices. The guidance is important even in its draft stage in providing more detailed, AI-specific instructions on what regulators expect in marketing submissions; and how developers can control AI bias. What’s new in it? 1) It requests clear explanations of how and why AI is used within the device. 2) It requires sponsors to provide adequate instructions, warnings, and limitations so that users understand the model’s outputs and scope (e.g., whether further tests or clinical judgment are needed). 3) Encourages sponsors to follow standard risk-management procedures; and stresses that misunderstanding or incorrect interpretation of the AI’s output is a major risk factor. 4) Recommends analyzing performance across subgroups to detect potential AI bias (e.g., different performance in underrepresented demographics). 5) Recommends robust testing (e.g., sensitivity, specificity, AUC, PPV/NPV) on datasets that match the intended clinical conditions. 6) Recognizes that AI performance may drift (e.g., as clinical practice changes), therefore sponsors are advised to maintain ongoing monitoring, identify performance deterioration, and enact timely mitigations. 7) Discusses AI-specific security threats (e.g., data poisoning, model inversion/stealing, adversarial inputs) and encourages sponsors to adopt threat modeling and testing (fuzz testing, penetration testing). 8) And proposed for public-facing FDA summaries (e.g., 510(k) Summaries, De Novo decision summaries) to foster user trust and better understanding of the model’s capabilities and limits.