Disruptive Innovation Strategy

We're moving away from charging for *access* to software and toward a model of charging for the *work delivered* by a combination of software and AI agents. Let’s dive into what’s happening and what it means for you ⤵️ 1. The rise of disruptive AI pricing models Tech companies are realizing they can't solely rely on seat-based subscriptions in an age of AI, automation and APIs where value is disconnected with how many people are logging in. Perhaps Salesforce going all-in on Agentforce (and charging $2 per conversation) was the push the industry needed. Each product category has its own flavor of disruptive pricing. - Legal AI products might charge for a demand package generated by AI or an AI-generated summary. - Creator AI products might charge for the content that gets produced such as a video generation or amount of video created. - GTM products might charge for specific tasks completed or workflows executed by the AI. 2. Selling work, not necessarily success As a customer, I wish I only had to pay for software when it delivered results. But the reality is that true success-based billing won’t work for the vast majority of today’s products. Most products should charge for work output instead. The issue is attribution. You want the customer to get a fantastic outcome — and you want them to recognize that your product powered that outcome. As soon as you start charging for success, the customer begins to rethink the results. 3. Goodbye ARR as we know it? Shifting to these newer value-based pricing models isn't a simple pricing change you can just announce in a press release. It's a business model evolution that looks a lot like the shift from on-prem to SaaS in the first place. These new AI pricing models might mean greater volatility in both usage and spend. Variable margin profiles across products and customers. Seasonal revenue fluctuations. The potential for project-based, non-recurring use cases. Put simply, annual recurring revenue (ARR) continues to get dethroned. — Full post in today’s Growth Unhinged newsletter: https://lnkd.in/ea5eTrVD Things are about to get interesting 🍿 #ai #pricing #saas

The next wave of marketing innovation isn’t about automation alone — it’s about emotion. Which shoe would you get? AI today can recognize tone, facial expressions, and even micro-emotions in voice and text. This emotional intelligence is turning marketing from mass communication into personal connection. 🧠 Data speaks for itself: + 80% of consumers say they’re more likely to purchase when brands show they understand their emotions. (Capgemini Research) + Emotionally connected customers have a 306% higher lifetime value than those who are merely satisfied. (Motista) + 70% of marketers using AI-driven personalization report double-digit engagement growth. (Salesforce) 💡 Real-world examples: + Coca-Cola uses AI-powered creative tools to adapt campaigns to local culture and sentiment in real time. + Netflix’s recommendation engine reads emotional cues in viewing behavior to tailor what feels just right for each user. + Adidas combines AI sentiment analysis with influencer content to sense trends before they peak — turning feelings into foresight. This isn’t marketing as usual — it’s marketing that feels. When technology understands emotion, brand experience becomes unforgettable. #AI #MarketingInnovation #EmotionalIntelligence #CustomerExperience #DigitalTransformation #MarTech #BrandStrategy

One of the most important applications of GenAI is in foresight. A new report from Paulo Carvalho at IF Insight & Foresight on "How Generative AI Will Transform Strategic Foresight" provides wide-ranging perspectives on the possibilities. Here are some of the most interesting action-oriented frames I found in the report. 🔍 Real-Time Environmental Scanning: Use GenAI to conduct continuous scanning of emerging trends, weak signals, and disruptions across diverse sources. This real-time, dynamic approach allows organizations to stay agile, proactively adjusting strategies as new insights unfold. 🌐 Immersive Scenario Simulations: Utilize GenAI to create interactive VR/AR scenarios that bring potential futures to life. These simulations engage stakeholders deeply, helping them visualize and emotionally connect with complex strategic choices, fostering stronger alignment with future goals. 🔄 Adaptive Scenario Planning: Move from static to adaptive planning by integrating live data into foresight models. Continuous updates based on geopolitical, economic, and technological shifts ensure that scenarios remain relevant and actionable over time. 💬 Enhanced Strategic Conversations: Use GenAI-powered virtual agents to facilitate dynamic "what-if" conversations, helping stakeholders explore a range of possible outcomes. This deepens strategic insights and encourages a proactive approach to complex decision-making. ⚙️ Modeling Complexity and Emergent Behaviors: Use GenAI to simulate complex systems and emergent behaviors, enabling organizations to anticipate interconnected, cascading effects. This prepares them for resilience in the face of unpredictable challenges and non-linear changes. 📊 Multimodal Data Integration for Richer Insights: Leverage GenAI’s capacity to analyze diverse data types (e.g., text, images, audio, video) to gain a nuanced, comprehensive view of trends and risks. This multimodal approach captures intricate patterns that single-source analysis might miss. 🌍 Embrace Multiple Perspectives and Plurality: Design foresight processes that incorporate a wide array of perspectives, blending cross-disciplinary and cultural insights. This inclusive approach creates more robust, innovative scenarios that account for diverse worldviews and challenges assumptions. 🤝 Facilitate Participatory and Co-Creative Approaches: Use GenAI to build interactive platforms that invite diverse stakeholders to co-create and refine scenarios. Real-time collaboration enhances the relevance and inclusivity of strategic models, making them more reflective of shared goals and values. I'll be sharing some of my thoughts on this very important topic in the next little while.

In a seismic shift for the AI industry, OpenAI co-founder Sam Altman is betting that radical transparency—not proprietary guardrails—will cement his company’s dominance. But will giving away the crown jewels backfire? The Wall Street Journal — This analysis examines OpenAI’s counterintuitive strategy to combat rising competition from Chinese AI firm DeepSeek AI, leveraging unprecedented openness in a field once defined by secrecy. 🔮 Open-Sourcing the Unthinkable OpenAI has begun releasing foundational AI architectures previously considered too dangerous for public access, including advanced reasoning frameworks and multimodal training blueprints. This strategic disarmament aims to undercut DeepSeek’s market position by flooding the sector with state-of-the-art tools—a calculated risk that redefines what “competitive advantage” means in AI. ⚖️ The Ethics Earthquake By open-sourcing models capable of synthesizing complex chemical compounds and analyzing geopolitical scenarios, OpenAI has ignited fierce debate about responsible innovation. Internal documents reveal heated boardroom debates over whether this democratization empowers benevolent researchers or arms bad actors. 🌐 The New AI Cold War The move directly counters DeepSeek’s rapid advances in generative video AI, with leaked emails showing Altman telling staff: “If we don’t break our own monopoly, others will”. Industry analysts note this mirrors geopolitical tech strategies, where controlled proliferation maintains influence over chaotic development. 🧠 Developer Ecosystem Gambit OpenAI’s surprise release of “Model Forge”—a toolkit for building AI assistants with emotional resonance—has already been adopted by 14,000+ developers in its first week. The play: become the indispensable infrastructure layer for AI innovation worldwide, making competitors’ products reliant on OpenAI’s open-source bedrock. 🕳️ The Profitability Paradox While releasing core IP, OpenAI quietly unveiled new premium services for enterprise-scale AI alignment validation—a classic “give away the razor, sell the blades” approach. Early adopters like Pfizer and Airbus are already paying seven figures annually for these certification services, suggesting a blueprint for monetizing openness. This tectonic shift in AI strategy continues to unfold, with regulators scrambling to adapt to an ecosystem where yesterday’s dangerous capabilities are tomorrow’s open-source building blocks. #AIStrategy #OpenSource #TechInnovation #AIEthics #DeepTech #FutureTech #AICompetition #TechDisruption #OpenAI #DeepSeek

When a shipping container becomes a business model. A new multi-functional container design is turning entire trucks into fully deployable shops — and it might change how small businesses think about physical space. With its folding, modular structure, one unit can transform into: 🍽️ a pop-up restaurant 🛒 a mobile supermarket ⛺ a full camping or service station … all at a fraction of the cost of a traditional storefront. Why this matters: ✅ Ultra-low setup costs — no rent, no major construction ✅ Instant deployment — open a new location in hours, not months ✅ High mobility — bring commerce directly to where customers are ✅ Resilience — perfect for rural regions, events, disaster zones, or testing new markets We talk a lot about digital transformation — but physical retail is transforming too. Not by building bigger stores, but by making them move. This is infrastructure innovation at its best: flexible, scalable, and accessible. The future of retail may not be indoors — it may be on wheels. What kind of business would you launch if your store could follow your customers? #Innovation #Mobility #RetailTech #Design #FutureOfWork #Logistics #SmallBusiness Source 🙏 @sutoroveli_news

Banking-as-a-Service (BaaS) is a divisive concept. Instrumental in the rise of #fintech, but also behind some notable failures. However, not only is BaaS going nowhere, but it’s connected to a huge opportunity. Let’s take a look. BaaS is essentially the SaaS model in #banking jargon. It has revolutionized financial services by enabling them to move away from the traditional infrastructure, on-premises play to an agile software distribution model based on the cloud. BaaS is to a large extent the powerhouse behind the rise of fintech, as the majority of fintechs were originally built on the back of a BaaS model. There are two main reasons, why this proved so successful: —    it allowed small start-ups to focus their limited resources on the front-end (essentially on solving problems and on the customer relationship) by using the infrastructure and licensing of partners —    it did so on the basis of a flexible, pay-as-you pricing model they could afford However, #financialservices is a highly regulated industry with strict risk and compliance requirements, which are easy to fail. US-based Synapse is perhaps the most notable example: originally one of the first and leading BaaS players, it followed a full-stack model, meaning an end-to-end play that would take care of all the back-end stuff on a modular basis and rely on partner banks for the licensing piece. The model seemed to work well until Synapse collapsed under increased regulatory scrutiny, unveiling a chaos between the roles and responsibilities of the multi-layered model, that has not yet been sorted out. More than 10 million accounts were affected. Many voices have been quick to announce the extinction of BaaS as a whole, as a result. But it’s not the model that failed, rather than the implementation. Which is why the industry has been moving from a multi-layer, indirect model to a bank-first model where clients (fintechs) have a direct relationship with the banks (and not vice-versa) in order to mitigate the risk of their BaaS provider going out of business. The model is being transformed into a more efficient, more scalable, integrated end-to end set-up. At the same time BaaS is powering one the biggest revolutions that are disrupting financial services from the ground up: embedded #finance. There is a fundamental change in the way that we consume financial services: customers are no longer going to their bank but are increasingly making finance decisions in non-financial contexts: platforms, marketplaces, digital wallets, SuperApps or even directly at brands themselves. BaaS is the big enabler in this model: it is the bottom, infrastructure layer that feeds into the various embedded finance offerings on the outcome, front-end side. In this changing landscape, BaaS is one of banks’ biggest opportunities to ride the wave of #innovation. The how is a topic for a next discussion. Opinions my own, Graphic sources: Brankas & WhiteSight

It’s time for CX to wake up! Too many Customer Experience (CX) leaders have lost their way, stuck in a cycle of endless surveys while the future of CX is passing them by, along with their business partners. The current reality is clear: Advanced Analytics, Predictive & Prescriptive Models, and AI should be at the core of every CX strategy today. But instead of pushing forward, many are clinging to outdated methods that still prioritize surveys over action. If we’re truly serious about elevating the customer experience, we need to shift our focus. The most forward-thinking organizations are already leveraging AI to predict customer behavior, personalize interactions, and prescribe the best actions for delighting customers before issues even arise. But this doesn’t come without a fight. It’s time to organize for and request the budget and resources needed to build a modern CX strategy that moves beyond the basics. Here’s what you should be doing: 1. Integrate AI into your CX programs to move from reactive to proactive. 2. Harness Advanced Analytics to uncover deep insights from customer data. 3. Develop Predictive Models that anticipate customer needs and prevent churn. 4. Implement Prescriptive Solutions to guide your teams toward the best actions. Don’t let your organization fall behind because you’re too focused on yesterday’s tools. Fight for the budget, readjust your strategy, and lead your team and your business! #customerexperience #AI #analytics #cxstrategy #futureofcx

The energy transition is more than just a shift to renewables; it’s a total reinvention of our infrastructure, with electricity distribution networks acting as vital enablers of this change. Electricity is the best vector for decarbonization, and the world increasingly relies on it. Effectively these networks expand, must be capable of supporting renewable integration, but they must also be optimized for digital innovation, efficiency, and sustainability. This is where Electricity 4.0 plays a transformational role. The concept of Electricity 4.0 assumes massive electrification in tandem with deployment of digital intelligence within electric systems, turning traditional distribution networks into smart, responsive systems. These networks don’t just distribute power—they actively manage, monitor, and adapt in real-time, creating an energy ecosystem that is reliable, efficient, and more sustainable. One compelling example of making progress is the adoption of SF6-free medium-voltage (MV) switchgear. In our case it’s AirSeT. Let me recap how it fits into the bigger picture: 1. Integrating renewables at scale: Distributed renewables need robust networks to balance power flows dynamically and manage fluctuating demands. AirSeT is equipped with CompoDrive, 10x stronger than its predecessor to accommodate massively increasing switching requirements. 2. Optimizing energy management through digitalization: By embedding IoT and AI, we can achieve real-time monitoring and predictive maintenance, minimizing losses and boosting efficiency. Switchgear needs powerful digital capabilities to gather intelligence from the field. 3. Sustainable infrastructure with sustainable MV solutions: SF6-free minimizes CO2e footprints while ensuring network reliability. Each kilogram avoided means 24,300 kg of CO2e less in the networks. Operational life extended by up to 30% and no toxic byproducts of breaking support circularity. The journey toward a low-carbon economy demands more than just clean power generation; it requires revolutionary approaches to how energy is managed, distributed, and optimized. Electric distribution networks aren’t just supporting the transition—they’re driving it, like Drakenstein Municipality in South Africa. Let’s continue to lead this transformation, ensuring every step forward brings us closer to a resilient, sustainable energy future. Read this eBook to discover how SF6-free and digital solutions enable decarbonization and efficiency: https://lnkd.in/dGThND2Q #SF6Free #LifeIsOn #AirSeT

Your Digital Twin isn't a file. It's a nervous system. We often get asked, "So, a Digital Twin is just a really detailed 3D model, right?" It's a fair question, but it's like asking if a smartphone is just a pocket calculator. It misses the big picture. The image attached shows the reality: a Digital Twin isn't one thing. It's the central hub connecting every critical technology in your ecosystem. It’s where: - BIM & 3D models provide the anatomical "bones." - IoT sensors act as the "nerves," feeding it real-time feelings and data. - AI becomes the "brain," analyzing data and making predictions. - VR are the "eyes," allowing you to interact with this data in immersive ways. It is not visualization. It’s about interrogation. You can ask it questions: "What's the energy consumption impact if we have a heatwave next Tuesday?" "Which components are most likely to fail in the next 6 months?" "Simulate the evacuation route with the current occupancy data." A static model can't answer those questions. A living Digital Twin can. This is the shift from passive documentation to active intelligence. What's the most exciting question you would ask your asset if it could talk back?😂 Share your thoughts. #SmartCity ------- Follow me for #digitaltwins Links in my profile Florian Huemer

Building scalable IoT systems isn’t just about connecting devices - it’s about connecting teams, tools, and data into one intelligent ecosystem. I've seen projects stall because the left hand didn't know what the right was doing. Siloed expertise is the enemy of scalable IoT. Here's how high-performing IoT teams break down those silos: ➞ Hardware Fundamentals: Teams collaborate on microcontroller choices, shared circuit designs, and power-efficient hardware setups for reliable long-term deployments. ➞ Sensor & Actuator Expertise: Engineers work together to calibrate, standardize, and optimize sensor data accuracy, ensuring consistent automation and response precision. ➞ IoT Protocols (MQTT, CoAP, HTTP): Collaboratively manage pub/sub patterns, REST APIs, and protocol throughput while aligning security and payload efficiency as a team. ➞ Edge AI & TinyML: Teams deploy lightweight machine learning models on edge devices to enable intelligent, real-time decisions and optimize AI workloads jointly. ➞ Cloud IoT Platforms: Build shared IoT dashboards, digital twins, and data pipelines using platforms like AWS IoT or Azure IoT Hub for seamless collaboration. ➞ Networking & Antennas: Evaluate connectivity options together, optimize range–power trade-offs, and maintain robust device-to-cloud communication pipelines. ➞ IoT Security: Unify authentication, encryption, and OTA updates across devices - building a shared security-first mindset for all team components. ➞ Embedded Programming: Collaborate on firmware coding in C, C++, or MicroPython. Ensure code consistency, memory safety, and optimized control logic across modules. ➞ DevOps for IoT (IoTOps): Automate firmware CI/CD, version control, and alerting pipelines to manage devices at scale with coordinated rollout strategies. ➞ Data Analytics & Visualization: Work as a team to clean, preprocess, and visualize IoT data - transforming collective insights into smarter decisions and predictive intelligence. In the connected world of IoT, collaboration is the new engineering superpower. Build together. Learn together. Scale smarter. 🔁 Repost if you're building for the real world, not just connected demos. ➕ Follow Nick Tudor for more insights on AI + IoT that actually ship.