Scaling Innovative Ideas

At P&G, technology isn't just a supporting function—it's a strategic enabler for growth, innovation, and consumer satisfaction. We're maniacally focused on understanding the wants and needs of the consumer, and technology is how we deliver on that promise. Earlier this summer, I had a fantastic conversation with Peter High on his Technovation podcast. We dove deep into how P&G is leveraging technology like AI, automation and data analytics to improve our operations, elevate consumer experiences, and create greater efficiencies. A few of the key highlights from our conversation: +Data-Driven Innovation: Our AI Factory integrates data across the value chain, enabling faster problem-solving and smarter decision-making. In Brazil, AI improved P&G’s out-of-stock rates by 15 percentage points – a game-changer in our industry! +End-to-End Supply Chain Visibility: With tools like our Pampers Club app, we’re able to ensure that consumers have the essentials they need. +Upskilling for Digital Fluency: We’re investing in workforce training through partnerships with Harvard Business School and Boston Consulting Group (BCG) to ensure everyone can leverage new digital tools like AI effectively. +AI as a Workforce Enabler: We are using AI as a facilitator of productivity to help employees focus on higher value tasks and initiatives. Leveraging AI across the business enhances efficiency and scales operations without burnout. +A Bright Future Ahead: I’m excited about the potential of reasoning models and agentic AI, which have the potential to eliminate dashboards by allowing us to “talk to the data.” Quantum computing offers the potential to optimize supply chains and operations at unprecedented levels.   #PGInnovation is truly transforming how we operate and deliver value, always with the consumer at the center. I'm excited about the continued impact we'll drive in our industry. Ready to hear more about how we're leveraging technology to innovate? Listen to my full conversation with Peter here: https://lnkd.in/gPnKhP3D

🚀 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐆𝐞𝐧𝐀𝐈: 𝐓𝐡𝐞 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐈𝐦𝐩𝐚𝐜𝐭 To make GenAI successful at scale, we need more than just good ideas; we need robust technical setups that can grow with demand. Here are some key lessons we’ve learned in our recent projects: ☁️ 𝐂𝐥𝐨𝐮𝐝 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐟𝐨𝐫 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 We’ve moved to a cloud-native setup, allowing us to adjust resources based on demand. Imagine being able to turn up or down the power needed for GenAI, like adjusting the volume on a speaker, so we’re always prepared without wasting resources. 🔄 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐌𝐨𝐝𝐞𝐥 𝐔𝐩𝐝𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 GenAI models aren’t static; they need regular tuning to stay effective. We’ve set up automatic model retraining so models get updated as new data comes in. It’s like scheduling regular maintenance on a car to keep it running smoothly without manual effort each time. 🔐 𝐁𝐮𝐢𝐥𝐭-𝐢𝐧 𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐟𝐨𝐫 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 For GenAI to work responsibly, data security is key. We’ve embedded data governance controls right into our workflows, so every new GenAI function automatically checks for data quality and compliance with regulations, keeping everything secure as we grow. ⚙️ 𝐑𝐞𝐮𝐬𝐚𝐛𝐥𝐞 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐟𝐨𝐫 𝐅𝐚𝐬𝐭𝐞𝐫 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 We’re building a library of API-based microservices - think of these as plug-and-play GenAI functions that can be used across different projects. This way, instead of building from scratch each time, we simply connect the pieces we need, making deployment faster and more consistent. Scaling GenAI requires more than just strong models; it’s about having the right technology backbone to keep everything running efficiently and securely. 👇 𝐇𝐨𝐰 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐩𝐫𝐞𝐩𝐚𝐫𝐢𝐧𝐠 𝐭𝐨 𝐦𝐞𝐞𝐭 𝐭𝐡𝐞 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐨𝐟 𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐀𝐈? #GenAI #TechInnovation #CloudComputing #ModelMaintenance #DataSecurity #APIMicroservices #AIAtScale ¦ Deloitte

This is how Anthropic decides what to build next—and it's brilliant. Instead of endless spec documents and roadmap debates, the Claude Code team has cracked the code on feature prioritization: prototype first, decide later. Here's their process (shared by Catherine Wu, Product Lead at Anthropic): Step 1: Idea → Prototype Got a feature idea? Skip the spec. Build a working prototype using Claude Code instead. Step 2: Internal Launch Ship that prototype to all Anthropic engineers immediately. No polish required—just functionality. Step 3: Watch & Listen Track usage religiously. Collect feedback actively. Let real behavior, not opinions, guide decisions. Step 4: Data-Driven Prioritization - High usage + positive feedback → roadmap priority - Low engagement or complaints → back to iteration This "prototype-first product shaping" flips traditional product development on its head. Instead of guessing what users want, they're measuring what users actually use. The beauty? They're dogfooding their own tool to build their own tool. The feedback loop is immediate, honest, and impossible to ignore. The takeaway: Your best product decisions come from real user behavior, not theoretical frameworks. Sometimes the fastest way to validate an idea isn't a survey or interview—it's a working prototype.

Clinicians don’t trust your HealthTech product. And they’re right not to. You think you’re selling innovation. But they’re seeing liability. When a doctor uses your product, they’re not just clicking a button. They’re staking their license, reputation, and someone’s life on a tool they didn’t build… Made by someone who’s never stepped inside an operating theatre. This is the Clinical Trust Chasm. Most HealthTech companies never cross it. They win pilots, not trust. Investors, not integration. Press, not protocols. Trust in medicine isn’t earned with features. It’s earned with consequences. Ask any surgeon why they use a specific tool. It’s not because it’s cutting-edge. It’s because it’s predictable under pressure. They’ve seen it fail, and seen what happens next. They know it's blind spots. They know when not to use it. You can’t shortcut that with UI polish and a few endorsements. If you want your HealthTech product to be adopted, not just trialled: You have to reverse the trust equation. Here’s how I’ve seen it work: - Put the clinician in control - Stop “automating decisions”. Start augmenting judgement. - Build fail-safes, override paths, audit trails. Trust starts when you acknowledge what you don’t know. Design for blame Assume someone will get hurt using your product. Will they say: “We knew this tool. We trusted it. We stood by it.” Or: “They promised it would work.” Over-communicate uncertainty No one’s ever said, “That medical device was too transparent.” Show the confidence intervals. Flag the edge cases. Clinicians are trained to work with ambiguity, just not surprise. Many HealthTech founders think clinicians are “resistant to change”. IMO they’re not. They’re allergic to risk they didn’t consent to. They don’t need to understand your model. They need to understand how it breaks, and what happens when it does. Build for that moment. That’s where real adoption begins.

Digital transformation success relies on thoughtful planning, aligning technology upgrades with business priorities, and fostering a culture that embraces innovation as a strategic tool for growth and adaptability in an ever-changing market. Digital transformation can be effectively approached through four distinct strategies. The first focuses on building a streamlined tech and data infrastructure tailored to one or a few targeted use cases, laying the groundwork for scalable improvements. The second emphasizes prioritizing front-end improvements, such as enhancing user experiences and interfaces, while progressively modernizing the back-end systems to ensure cohesive functionality. The third approach addresses the challenges of scattered and disjointed technology landscapes by rationalizing and integrating systems, enabling businesses to scale and operate efficiently. Lastly, organizations can adopt a bold approach by creating entirely new business models centered on cutting-edge technology platforms, driving innovation and unlocking untapped market opportunities. #DigitalTransformation #Innovation #TechStrategy #DigitalEconomy #FutureOfWork

If I had to start a GenAI company from scratch today— I wouldn’t begin with code. I’d begin with a conversation. Not with a VC. Not with an engineer. But with someone stuck in a broken process—frustrated, overworked, and just trying to move faster. That’s how real AI products are born. Here’s the exact framework I’d follow: 1. Find the wedge. Not the biggest problem—just the one that hurts most. The unscalable workflow. The 3 AM fire drill. Forget broad solutions. Start with one thing you can solve 10x better than anyone else. 2. Sell clarity, not capabilities. No one buys “transformative GenAI.” They buy time saved. Risk reduced. Outcomes they can count on. Frame the impact clearly—and make it feel inevitable. 3. Prototype in 10 days—or move on. If your idea can’t be expressed in a working prototype fast, it’s probably too complex or too vague. Speed isn’t just execution—it’s conviction. 4. Treat your first 3 customers like co-founders. Don’t just sell to them. Build with them. Your first few implementations are more than logos—they’re your proof points. Overdeliver, refine, and repeat. If I had to do it again, I wouldn’t chase the flashiest demo. I’d chase the quietest bottleneck — and build from there. Because in GenAI, it’s not about who builds fastest. It’s about who builds what truly lasts. The future won’t be shaped by code alone. It’ll be shaped by the conversations we choose to have — and the problems we choose to solve. #GenAI #AgenticAI #FluidAI #StartupLessons #AIForBusiness #EnterpriseAI #GoToMarket #ProductStrategy #FoundersJourney

If we were sitting down for a coffee and you asked me where I’d start before talking about growth, I’d probably say this: Most HealthTech companies don’t stall because the product is bad. They stall because the evidence can’t keep up with the ambition. Before partnerships, before scaling plans, before the shiny stuff, I always come back to one simple question: Does this solve a real health or system problem, and can you prove it? Traction doesn’t really mean downloads or pilots. It means trust. Trust from clinicians. Trust from employers or systems. Trust from partners who know that once something is adopted, it has to work, not just once, but over time. This is what I mean by product–market integrity. It’s not about being perfect. It’s about being honest, evidence-led, and willing to look at the data even when it’s uncomfortable. The founders I see scale well tend to: – fall in love with the data as much as the idea – build feedback loops early – treat quality and compliance as part of growth, not admin – understand that credibility compounds faster than capital In my role at Well Purposed, I sit alongside clinical and technical experts translating evidence into commercial clarity. The old “move fast and break things” mindset doesn’t hold up. What the market rewards now is proof, not promises. If you’re building in preventative health, wellbeing, metabolic health, wearables, or B2B2C health, and you’re post-MVP or post-raise, this lever matters more than most people realise. Because if integrity cracks early, everything downstream gets harder. Next week, I’ll share Lever 2 - why so many HealthTech companies think they’re market-ready… and aren’t. ----- ⭐I’m Sara - a HealthTech strategic advisor, fractional operator, and 4x founder who’s scaled ventures to £10M+ ARR across the UK, Europe and Africa. I help founders navigate complexity, rebuild strategic clarity, and scale sustainably. Founder of Well Purposed.

Common trap after raising a big round: thinking you need to immediately accelerate hiring and "become a real company." The pressure from big expansions often kills the culture that made you successful in the first place. Here's how we maintain our experimental DNA while adding the necessary structure to scale: 1. Don't raise money without clear intent Raising because you can (vs should) is a recipe for premature scaling. Know exactly what you'll do with the capital before taking it. 2. Hire painfully slowly—even with a full bank account This isn't about being conservative. It's about being deliberate. Slow hiring forces you to: → Deeply understand each function before delegating it → Truly prioritize which roles you actually need → Create proper onboarding and context for each hire → Build systems that set people up for success 3. Think like a future-focused leader Your job isn't just to fill roles—it's to create an environment where future leaders can thrive. You can't do this if you're just throwing bodies at problems, hoping they'll "figure it out." 4. Focus on integration over speed When you do hire, prioritize: → Proper onboarding time (don't rush this) → Clear context about their role and objectives → Gradual immersion vs throwing them in the fire → Cultural alignment and team cohesion The reality is that maintaining an innovative culture isn't about moving fast—it's about moving deliberately. Money can accelerate your growth, but only if deployed with patience and intention. Otherwise, you risk building a “bigger” company at the expense of building a better one.

The most desired AI PM qualification in 2025 is shipping production-ready B2B agents. Here’s my 3-step playbook to go from idea to production: Step 1: Build a Scrappy Prototype - Forget complex front-ends. Start with no-code tools (n8n/ MSFT Co-pilot Studio) and focus on speed. - Describe your goal in English: Use tools like Microsoft Copilot Studio to build an agent by simply describing what you want it to do. - Use existing apps: Integrate with Mail or Slack for your front-end. Meet your users where they already are. Start with a one-pager: Your goal is a working prototype based on a simple requirements doc, not a 50-page PRD. Step 2: Evaluate Ruthlessly - Building is easy. Building a reliable agent is hard. This is where most people fail. - Acknowledge the limits: The tech for full human replacement isn't there yet. Reasoning is still hacked into models, and accuracy on hard benchmarks is low. The cost of stabilizing a reliable agent can be 10-100x the cost of the initial build. - Use the HHH Framework: Evaluate your agent on three simple questions: Is it Helpful? Is it Honest? Is it Harmless? Set Clear Launch Criteria: Work with experts to define what "good" looks like and set objective scores (e.g., "70% helpfulness") before you ship to a wider audience. Step 3: Iterate Relentlessly - Use your evaluation data to guide your roadmap. - Focus on Assisting, Not Replacing: The winning strategy is building tools that assist people and deliver tangible artifacts. Think of a tool like Loveable(now with cloud+AI support) that builds a functional website, not just code snippets. - Let the Data Guide You: Use the feedback and evaluation scores from your early users to set your next targets and features. This data loop is what turns a prototype into a scalable product. Very few AI PMs have actually done this, and you’ll immediately stand out if you do. I’ve seen it myself: This is the exact process that members of my cohort on @Maven have used to automate complex workflows and save their companies millions.

🌍 Reporting from New York during the UN General Assembly — where conversations about innovation, technology, and humanitarian response give us real hope for the future. At the World Food Programme, we’re not just talking about innovation — we’re scaling it. Three examples: 🔹 Building Blocks: Our blockchain-based system, now used by 55 agencies in Ukraine, has saved $270M in the last three years while reducing unintended assistance overlaps. 🔹 SCOUT: An AI solution for supply chain optimization — already saving $2M in its very first pilot. 🔹 Food Security Forecasting: Using AI, we can now forecast food security up to 90 days ahead, helping us prepare and respond faster to crises. This is exactly what humanitarian innovation should do: make us more efficient, effective — ensuring that every donated dollar reaches the people who need it most. #Innovation #Humanitarian #AI #Blockchain #UNGA WFP Innovation Accelerator