Business Strategy

Most companies are bleeding cash in 4 places. (And don't even know which wound to fix first.) Here's the brutal reality: Every dollar leaving your business falls into one of these buckets: OPERATIONS (OpEx) - The Daily Bleed ↳ Your rent, salaries, software subscriptions ↳ The money that vanishes every month ↳ Cut too deep? Your business stops tomorrow ASSETS (CapEx) - The Big Bets ↳ That $50K machine, new warehouse, custom software ↳ Spend once, use for years ↳ Get it wrong? You're stuck with expensive mistakes SALES (RevEx) - The Revenue Tax ↳ Every sale costs you something ↳ Materials, commissions, shipping, processing fees ↳ Ignore this? Profit margins turn into losses MONEY (FinEx) - The Hidden Killer ↳ Interest on loans, bank fees, credit charges ↳ The cost of using other people's money ↳ Let it grow? It eats your profits alive Let's look at 2 examples: Coffee Shop Reality: • OpEx: $15K/month (rent, barista wages, utilities) • CapEx: $80K once (espresso machine, renovation) • RevEx: $3 per cup sold (beans, cup, lid) • FinEx: $500/month (equipment loan interest) SaaS Startup Truth: • OpEx: $50K/month (team, AWS, office) • CapEx: $200K once (custom platform build) • RevEx: $20 per customer (payment fees, onboarding) • FinEx: $2K/month (venture debt interest) Most founders lump all expenses together. Then wonder why they can't scale. But when you separate them? OpEx → Find your true burn rate CapEx → Time investments perfectly RevEx → Price products profitably FinEx → Optimize capital structure Crucial insights: ✓ High OpEx? You're not scalable yet ✓ No CapEx? You're not building moats ✓ Rising RevEx? Your unit economics are broken ✓ Climbing FinEx? You're overleveraged Common traps: ❌ Treating CapEx like OpEx (and vice versa) ❌ Ignoring RevEx when setting prices ❌ Letting FinEx compound silently ❌ Not tracking any of them separately The uncomfortable truth: Your competitor who's winning? They know these numbers cold. While you're guessing, they're optimizing. While you're hoping, they're measuring. Watch what happens when you finally see where your money really goes. Your future self will thank you. Your investors will respect you. Your business will actually scale. Stop managing "expenses." Start managing OpEx, CapEx, RevEx, and FinEx. That's how you build something that lasts. ♻️ Repost to help a founder in your network. Follow Eric Partaker for more financial insights. — 📌 Do you know how scale-ready your business is? Take my free 5-minute assessment to benchmark your company, your leadership, and your own performance as CEO — and identify your biggest growth blockers. https://lnkd.in/gu-m6yQj

𝗧𝗵𝗶𝘀 𝘁𝗲𝗮𝗺 𝗴𝘂𝗲𝘀𝘀𝗲𝗱 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗣𝗼𝗽𝗲 𝗿𝗶𝗴𝗵𝘁 — 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝘀𝗰𝗶𝗲𝗻𝗰𝗲. ⬇️ No divine intervention. No prophecy. Just data, relationships, and a multilayered model of power inside one of the world’s most opaque systems: the Papal Conclave. Researchers from Università Bocconi (Giuseppe Soda, Leonardo Rizzo, Alessandro Iorio) mapped the "Vatican’s cardinal network" by combining: ➜ Official co-memberships (commissions, councils, curia) ➜ Lines of episcopal consecration ➜ Informal ties (ideology, mentorship, patronage) 𝗧𝗵𝗲𝗻, 𝘁𝗵𝗲𝘆 𝘂𝘀𝗲𝗱 𝘁𝗵𝗿𝗲𝗲 𝗰𝗼𝗿𝗲 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 𝘁𝗼 𝗿𝗮𝗻𝗸 𝗰𝗮𝗿𝗱𝗶𝗻𝗮𝗹𝘀: 1. Status — via eigenvector centrality (influence among the influential) 2. Information Control — via betweenness centrality (bridge across factions) 3. Coalition Building — via clustering, reach, and strategic brokerage The twist? Without trying to predict the outcome…Their top-ranking cardinal — Robert Prevost — was elected Pope Leo XIV this week. Let that sink in. Network science didn’t just describe the structure. It surfaced the name. 𝗔 𝘀𝘁𝗿𝗶𝗸𝗶𝗻𝗴 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝗼𝗳 𝗵𝗼𝘄 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗰𝘂𝘁𝘀 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗵𝗲 𝗳𝗼𝗴 — 𝗲𝘃𝗲𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘀𝗮𝗰𝗿𝗲𝗱 𝗮𝗻𝗱 𝘀𝗲𝗰𝗿𝗲𝘁𝗶𝘃𝗲 𝘀𝗽𝗮𝗰𝗲𝘀. 𝗜𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲 𝗶𝘀 𝗻𝗲𝘃𝗲𝗿 𝘁𝗿𝘂𝗹𝘆 𝗶𝗻𝘃𝗶𝘀𝗶𝗯𝗹𝗲. 𝗬𝗼𝘂 𝗷𝘂𝘀𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘄𝗵𝗲𝗿𝗲 — 𝗮𝗻𝗱 𝗵𝗼𝘄 — 𝘁𝗼 𝗹𝗼𝗼𝗸. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗱𝗮𝘁𝗮 𝗮𝘁 𝗶𝘁𝘀 𝗯𝗲𝘀𝘁: 𝘀𝘂𝗿𝗳𝗮𝗰𝗶𝗻𝗴 𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲, 𝗲𝘅𝗽𝗼𝘀𝗶𝗻𝗴 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗱𝗲𝗰𝗼𝗱𝗶𝗻𝗴 𝗽𝗼𝘄𝗲𝗿. Full story in the comments. ⬇️

Sustainability Services Ecosystem Map 🌎 This diagram, developed by Giki, offers a structured view of the growing ecosystem of organizations and platforms supporting sustainability. Its relevance today is undeniable, particularly as regulatory pressure, investor scrutiny, and stakeholder expectations accelerate. The sustainability landscape is growing increasingly complex. Companies are no longer relying on a single advisor or platform but are engaging with a wide range of actors, from disclosure bodies to emissions software providers, capacity-building networks, and global initiatives. This map organizes the ecosystem into five service categories: Measurement and Disclosure, Capacity Building and Engagement, Strategy and Net Zero Transition, and External Stakeholder Relationships. Each plays a distinct role in supporting the design, implementation, and tracking of sustainability strategies. In the measurement space, frameworks, standards, rating systems, and software tools coexist to support robust disclosure practices. Understanding their scope and interconnections is critical for building consistent and reliable reporting processes. In the consulting and advisory realm, various firms provide strategy development and transition planning, often acting as integrators across tools, frameworks, and data systems. Their role is central in operationalizing sustainability commitments. The capacity-building and engagement segment includes platforms focused on employee activation, public education, and behavioral change. These initiatives help embed sustainability into organizational culture and broader stakeholder engagement. Global initiatives and offset providers help align ambition across sectors while offering access to shared methodologies, benchmarks, and mechanisms for emissions reduction or removal. Their influence extends across policy, market signaling, and credibility. As sustainability becomes a core business function, it is essential to map out the ecosystem of support available. Knowing the distinct role of each actor allows organizations to build the right partnerships and infrastructure to deliver credible, impactful outcomes. #sustainability #sustainable #business #esg

💡 A Practical Guide to Climate Scenarios! Really pleased to have written the forward to this valuable report on the types and applications of climate scenarios by MSCI Inc. and my former United Nations Environment Programme Finance Initiative (UNEP FI) FI colleagues Looking for a handy summary of the types of scenarios from qualitative to quantitative? Here it is: 1. 𝗙𝘂𝗹𝗹𝘆 𝗡𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 These scenarios are qualitative descriptions of potential climate futures. ✅ Strengths: - Easily customizable - Useful for high-level strategic discussions - Can capture complex risks that are difficult to quantify ⚠️ Limitations: - Subjective and vulnerable to bias - Lack of numerical outputs makes them hard to integrate into risk models 2. 𝗤𝘂𝗮𝗻𝘁𝗶𝗳𝗶𝗲𝗱 𝗡𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 This type builds on fully narrative scenarios by adding expert-driven quantitative estimates (macroeconomic forecasts, asset class returns, regional physical risks). ✅ Strengths: - Balances qualitative storytelling with numerical data - Allows for scenario comparisons without requiring sophisticated models - Easier to communicate results with clear quantitative insights ⚠️ Limitations: - Can give a false sense of precision if assumptions are weak - Still dependent on subjective expert input, leading to potential biases 3. 𝗠𝗼𝗱𝗲𝗹-𝗗𝗿𝗶𝘃𝗲𝗻 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 These scenarios rely on integrated quantitative models to project how climate change and transition risks might evolve under different policy and economic conditions, using macroeconomic models, IAMs, and energy system models. ✅ Strengths: Highly structured and data-driven, reducing subjectivity. Can produce detailed, sector-specific outputs useful for investment decisions. Widely used by regulators and financial institutions for stress testing. ⚠️ Limitations: - Expensive and time-consuming to develop and maintain - “Black box” nature of complex models makes interpretation difficult - Results are only as good as underlying assumptions and data inputs 4. 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘀𝘁𝗶𝗰 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 Probabilistic models go beyond single-scenario forecasting by assigning probabilities, variance, and uncertainty estimates to different climate outcomes. ✅ Strengths: - Models uncertainty, improving risk management - Enables sophisticated stress testing for asset prices, portfolios, and corporate exposure - Valuable for insurance, catastrophe modeling, and financial risk assessments ⚠️ Limitations: - Highly complex and computationally demanding - Requires strong assumptions about uncertainty - Limited research on how climate change affects probability distributions #ClimateFinance #ClimateScenarios #SustainableInvesting #RiskManagement #ScenarioAnalysis #Risk #Finance

🌟 𝐒𝐭𝐨𝐩 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐁𝐢𝐠 - 𝐒𝐭𝐚𝐫𝐭 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐖𝐢𝐝𝐞! The biggest breakthroughs don’t happen by digging deeper into one area - they happen when ideas, industries, and technologies collide. Think about it: AI combined with IoT has transformed healthcare. Sustainability powered by cloud solutions is opening new markets. The magic lies at the 𝐢𝐧𝐭𝐞𝐫𝐬𝐞𝐜𝐭𝐢𝐨𝐧𝐬 - where fresh opportunities emerge. 🚀 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 1️⃣ 𝐅𝐚𝐬𝐭𝐞𝐫 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: Combining technologies like AI and cloud accelerates growth. 2️⃣ 𝐍𝐞𝐰 𝐌𝐚𝐫𝐤𝐞𝐭 𝐑𝐞𝐚𝐜𝐡: Partnerships across industries unlock untapped customers. 3️⃣ 𝐒𝐡𝐚𝐫𝐞𝐝 𝐕𝐚𝐥𝐮𝐞: Cross-industry collaboration lowers costs and drives new value. At Deloitte, I’ve seen the power of collaboration. By partnering with organizations like #Celonis, #Schaeffler, #HumboldtInnovation, and #GermanEntrepreneurship, we’ve established the European non-profit AI ecosystem, #KIPark. This initiative brings together players from different industries to unlock innovation. For example, we’ve developed an ESG platform, marking a significant step toward sustainable solutions that are robust and business-relevant. 🛠️ 𝐓𝐡𝐫𝐞𝐞 𝐖𝐚𝐲𝐬 𝐭𝐨 𝐒𝐭𝐚𝐲 𝐀𝐡𝐞𝐚𝐝 1️⃣ 𝐋𝐨𝐨𝐤 𝐎𝐮𝐭𝐬𝐢𝐝𝐞 𝐘𝐨𝐮𝐫 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲: Who could you partner with to create something new? 2️⃣ 𝐁𝐮𝐢𝐥𝐝 𝐌𝐢𝐱𝐞𝐝 𝐓𝐞𝐚𝐦𝐬: Pair data scientists with operations or customer-facing teams. 3️⃣ 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭 𝐁𝐨𝐥𝐝𝐥𝐲: Start small pilots that combine tech and business ideas. 🌍 𝐓𝐡𝐞 𝐁𝐨𝐭𝐭𝐨𝐦 𝐋𝐢𝐧𝐞 The future belongs to businesses that connect the dots others don’t see. Breadth - not just depth - is the key to growth and resilience. 💬 𝐘𝐨𝐮𝐫 𝐓𝐮𝐫𝐧 What’s one unexpected partnership or idea you’ve seen recently that sparked innovation? Let’s exchange ideas. Who knows what new intersections we might uncover together? #Deloitte #AI #Innovation #Leadership #BusinessStrategy #Partnerships 𝐴𝑟𝑡𝐵𝑎𝑠𝑒𝑙. 𝐶ℎ𝑎𝑛𝑔𝑒𝑂𝑓𝑃𝑒𝑟𝑠𝑝𝑒𝑐𝑡𝑖𝑣𝑒. 𝐹𝑜𝑢𝑛𝑑 𝑎𝑡 @𝑔𝑎𝑏𝑟𝑖𝑒𝑙𝑙𝑒𝑒𝑒𝑟𝑢𝑡ℎ

As Industry 4.0 reshapes our business landscape, it’s critical to grasp its diverse aspects. The "Periodic Table of Industry 4.0 Elements" serves as a high-level guide, focusing on crucial areas for companies to consider in their strategic planning. 𝐁𝐫𝐞𝐚𝐤𝐢𝐧𝐠 𝐃𝐨𝐰𝐧 𝐭𝐡𝐞 𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐞𝐬: 𝟏. 𝐄𝐧𝐚𝐛𝐥𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬: The major technological innovations that serve as the engine for Industry 4.0. Invest in upskilling your workforce with the proper digital literacy to effectively leverage these technologies for innovation and competitive advantage. 𝟐. 𝐕𝐚𝐥𝐮𝐞 𝐃𝐫𝐢𝐯𝐞𝐫𝐬: The primary benefits and objectives that Industry 4.0 aims to deliver. Prioritize and measure your initiatives based on their potential to drive value in these specific areas. 𝟑. 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬: The most common use cases where enabling Industry 4.0 technologies change how companies operate and how employees work. 𝟒. 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬: The most common use cases where products are being reimaged and improved with enabling Industry 4.0 technologies. Products are not only being designed differently, but designed with intelligence built in and customer experiences as part of the sale. 𝟓. 𝐍𝐞𝐰 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐌𝐨𝐝𝐞𝐥𝐬: Industry 4.0 introduces new ways of conducting business and providing value. Explore partnerships and platforms that could enable you to adopt new business models and expand your market reach. 𝟔. 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬: The foundational guidelines that enable the seamless integration and efficient functioning of advanced digital technologies and processes within the industrial ecosystem. This table is not exhaustive, but rather a focused lens to view the critical elements that can help drive strategic decisions during these transformative times. And just like the elements in nature, the elements of Industry 4.0 are vast and continuously expanding as we learn and discover new things! 💬 I'd love to hear your thoughts: How are you leveraging these elements in your Industry 4.0 strategy? 𝐅𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/eCFwNzJg ******************************************* • 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!

Strategy is all about anticipating and creating a desired future. To prepare for this, it is essential to understand the Futures Cone, outlining five types of future. There is no such thing as “the future.” It all depends on what we mean, how far we look ahead and on whether we are trying to predict or create the future. One of the most helpful tools to understand this is Hancock and Bezolt’s (1994) “Futures Cone.” It describes five different types of future. They are PROJECTED FUTURE The future we tend to get when we simply stick to business as usual and extrapolate the current baseline strategy. It’s more of the same and contains the least uncertainty. PROBABLE FUTURE The future that most likely is going to happen, taking into account trends and developments within and outside the organization. It’s a bit more uncertain, but still quite predictable. PLAUSIBLE FUTURE The future that could happen according to our current knowledge. This is broader than just the probable future and includes futures that we could foresee rather than just expect. POSSIBLE FUTURE The broadest type of future, including everything that might happen. This is the realm of our imagination and extends beyond our current knowledge, tools and technologies. PREFERABLE FUTURE The future that we want to happen. This is different from the four above as it reflects our desires, preferences and intentions rather than what we cognitively can anticipate. As the image illustrates, the Preferable Future often deviates from the Projected Future (business as usual) or Probable Future (following the trends). This means it requires active imagination and bringing in our desires and intentions to imagine a future that is different. At the same time, it also shows that the Preferable Future should mostly reside within the boundaries of the Plausible Future with perhaps a touch of the Possible Future. Otherwise the gap between where you are today and how you want your future to look is too big. This is where the distinction is made between organizations that make smaller, incremental changes, and those that create breakthrough innovations. The further you can stretch your Preferred Future away from the Projected Future towards the Plausible and Possible Futures, the more visionary you need to be, and the more you will be an industry leader. Here’s the question for you: where is your Preferred Future targeted—more of the same (Projected or Probable) or at creating something new (Plausible and Possible)? — For more useful strategy and leadership content, join my Soulful Strategy newsletter: https://lnkd.in/eKjb8Uss #forecast #futureinsight #impactleaders

🍱 How To Design Effective Dashboard UX (+ Figma Kits). With practical techniques to drive accurate decisions with the right data. 🤔 Business decisions need reliable insights to support them. ✅ Good dashboards deliver relevant and unbiased insights. ✅ They require clean, well-organized, well-formatted data. ✅ Often packed in a tight grid, with little whitespace (if any). 🚫 Scrolling is inefficient in dashboards: makes comparing hard. ✅ Start with the audience and decisions they need to make. ✅ Study where, when and how the dashboard will be used. ✅ Study what metrics/data would support user’s decisions. ✅ Explore how to aggregate, organize and filter this data.  ✅ More data → more filters/views, less data → single values. 🚫 Simpler ≠ better: match user expertise when choosing charts. ✅ Prioritize metrics: key insights → top left, rest → bottom right. ✅ Then set layout density: open, table, grouped or schematic. ✅ Add customizable presets, layouts, views + guides, videos. ✅ Next, sketch dashboards on paper, get feedback, iterate. When designing dashboards, the most damaging thing we can do is to oversimplify a complex domain, or mislead the audience. Our data must be complete and unbiased, our insights accurate and up-to-date, and our UI must match users’ varying levels of data literacy. Dashboard value is measured by useful actions it prompts. So invest most of the design time scrutinizing metrics needed to drive relevant insights. Bring data owners and developers early in the process. You will need their support to find sources, but also clean, verify, aggregate, organize and filter data. Good questions to ask: 🧭 What decisions do you want to be more informed on? (Purpose) 😤 What’s the hardest thing about these decisions? (Frustrations) 📊 Describe how you are making these decisions? (Sources) 🗃️ What data helps you make these decisions? (Metrics) 🧠 How much detail is needed for each metric? (Data literacy) 🚀 How often will you be using this dashboard? (Value) 🎲 What constraints should we know about? (Risks) And, most importantly, test dashboards repeatedly with actual users. Choose representative tasks and see how successful users are. It won’t be right the first time, but once you get beyond 80% success rate, your users might never leave your dashboard again. ✤ Dashboard Patterns + Figma Kits: Data Dashboards UX: https://lnkd.in/eticxU-N 👍 dYdX: https://lnkd.in/d6yvKS6G 👍 Ethr: https://lnkd.in/eSTzcN7V Orange: https://lnkd.in/ewBJZcgC 👍 Semrush Charts + Tables: https://lnkd.in/dnDRtG32 👍 UI Charts: https://lnkd.in/eJkyB6zS UKO: https://lnkd.in/ehvcSnuV 👍 Wireframes: https://lnkd.in/e-m3VQqs 👍 [continues in comments]

On REPEAT ♻️ Being easy to work with does not make you a “Push Over”. It's a strategic advantage helps you increase efficiency, profitability, and drive long-term success. Here’s why it makes business sense: 🚀 Efficiency: When you're easy to work with, you streamline processes. You communicate clearly, make decisions promptly, and collaborate smoothly. This efficiency translates to saved time and resources, boosting productivity 🌟 Positive Reputation: People prefer doing business with those they enjoy working with. Being pleasant and cooperative builds a positive reputation. Clients, partners, and colleagues are more likely to recommend and return for future collaborations 🗣️ Problem Solving: Approachability and openness create an environment where issues can be openly discussed. This facilitates faster problem-solving, leading to better solutions and ultimately more successful outcomes 🎨 Innovation: A relaxed, open atmosphere encourages creativity. Employees are more likely to share ideas when they feel comfortable. Being easy to work with fosters innovation, which is often the key to staying competitive 🤝 Reduced Conflict: A harmonious working relationship minimises conflicts. Fewer disputes mean less time and effort spent on resolving issues, allowing the focus to remain on business objectives 😊 Employee Satisfaction: When leaders and colleagues are easy to work with, it leads to higher job satisfaction. Happy employees are more engaged, loyal, and productive, reducing turnover cost 🛍️📞 Customer Experience: Customers notice when a company is easy to work with. From seamless transactions to responsive customer service, it all contributes to a positive customer experience, which is essential for repeat business 🔄💼 Adaptability: In today's fast-paced business world, adaptability is crucial. Those who are easy to work with are often more open to change and can quickly adjust to new market demands and technologies 💼🤝 Long-Term Relationships: Building lasting business relationships is a key to success. Being easy to work with fosters trust and loyalty, which can lead to long-term partnerships and sustained revenue streams 💰📈 Bottom Line: All these factors ultimately impact the bottom line. Businesses that prioritise being easy to work with tend to be more profitable due to increased efficiency, customer satisfaction, and a positive reputation

Beside core #banking transformation the next wave of the banking evolution is increasingly driven from a focus on BaaS (Banking as a Service) models. Let’s take a look.   Core banking systems are back-end software that banks use to manage their most critical processes, from transactions to accounts and to all kinds of day-to-day operational needs. Originally developed in the 1960s and 1970s they are instrumental for a bank’s make-or-break: they influence costs, time-to-market, operational efficiency, and product sophistication. In essence they shape the bank's ability for #innovation.   Initially seen as the banks’ competitive edge, they have gradually transformed into their biggest handicap as their performance and sophistication could not match the needs of an increasingly digital world and competition from new, agile challenger players (the so-called fintechs).   Today core banking transformation is the jargon used by banks all over the globe to modernize their infrastructure with two things standing out: the move of in-house to the cloud and the adoption of flexible, open microservices.   But it’s a very expensive and uncertain game, with 70% failing according to Mckinsey research.   In parallel the need for open, scalable, flexible, efficient and fast set-ups as well as the decoupling of the back-end from the front-end, have brought to the fore the SaaS principle, which in banking we call BaaS (having banking replacing the software element).   Initially BaaS focused on servicing the outcome of the decoupling, meaning providing the #fintech challengers with the necessary infrastructure and licensing (often under one bundle) needed to compete on the interface, user experience side.   And it’s of course to be found behind the embedded #finance revolution, where BaaS is the bottom, infrastructure layer feeding into the various embedded finance offerings on the outcome, front-end side.   As of late we increasingly see BaaS main principles taking over core banking as well. The next generation of core banking transformation will be modular, cloud-native and fully API capable.   Opinions: my own, Graphic source: Royal Park Partners