Ecosystem Strategy Development

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

𝐎𝐩𝐞𝐧 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 is one of the most impactful paradigms in management research — and also one of the most misunderstood. Since Chesbrough coined the term in 2003, the idea that firms should deliberately open their boundaries to external knowledge has reshaped how companies innovate, how universities engage with industry, and how governments design innovation policy. But how well do we actually know the foundational literature? I have put together a list of 25 𝐜𝐥𝐚𝐬𝐬𝐢𝐜 𝐚𝐫𝐭𝐢𝐜𝐥𝐞𝐬 𝐢𝐧 𝐎𝐩𝐞𝐧 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧— spanning founding theory, empirical evidence, literature reviews, ecosystems, SMEs, users and communities, and practice. Academic papers and practitioner-facing pieces. The articles that shaped the field and continue to shape it. Starting today, I will share one article per day — from #25 down to #1 — with a short explanation of what each paper is about and why it still matters, for researchers and practitioners alike. 📌 #25 — Lichtenthaler & Lichtenthaler (2009), "A capability-based framework for open innovation" — Journal of Management Studies What is it about? This article extends Cohen & Levinthal's absorptive capacity concept into an open innovation framework. It identifies six interconnected knowledge-related capabilities — inventive, absorptive, transformative, connective, innovative, and desorptive — that firms need to manage knowledge flows across boundaries. It bridges dynamic capabilities theory with OI practice in a rigorous and comprehensive way. Why does it matter? For academics, it provided one of the most cited theoretical bridges between dynamic capabilities and open innovation. For practitioners, it offers a diagnostic checklist to assess whether their firm has the organizational capabilities required to actually benefit from openness — not just the strategic intent. Connective capacity has been central in understanding the success of OI, and desorptive capacity is still underexplored in the OI literature, but has been picked up by Rita McGrath and others. 🔗 Read it here: https://lnkd.in/eHWsPJ9z #OpenInnovation #Innovation #InnovationManagement #ResearchMatters #KnowledgeManagement #AbsorptiveCapacity

The strongest automotive groups don’t win by selling one great brand. They win by controlling the entire market spectrum. From entry-level to ultra-luxury, leaders like Volkswagen, BMW or Geely have built structured brand ecosystems that capture every customer segment without losing identity which is a real advantage. A few signals are clear: • Innovation is a long-term investment (GM started the EV journey back in 1996) • Luxury demand remains highly resilient (BMW’s 2.3M premium sales say it all) • Smart acquisitions accelerate global positioning (Geely with Volvo & LEVC) • And today, margins are under pressure and competition are reshaping profitability. => The takeaway is simple: In today’s automotive industry, success is not just about volume, it’s about how well you orchestrate your brands across segments while protecting margins. One clear perspective: Scale built the leaders of yesterday. Strategy will define the winners of tomorrow.

Even while data professionals ‘seem’ to understand the many challenges of building new ML/ AI tools, they often ignore them while implementing They talk about data quality, business needs, engineering, etc. but then forget about it two weeks into the project On the back of yesterday’s post and my article (link in the comments), here is how you should think about implementing a holistic ecosystem approach for your ML/ AI solutions: 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 🎯 - Define the "Why": Identify specific business problems ML/AI will solve with measurable outcomes - Prioritise Use Cases: Focus on highest business value while considering ecosystem readiness - Secure Executive Commitment: Ensure leadership understands potential AND foundational work - Set Realistic Expectations: Be honest about timelines rather than promising overnight transformation 𝟮. 𝗔𝘀𝘀𝗲𝘀𝘀 𝗬𝗼𝘂𝗿 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 𝗥𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 🔍 - Data Foundations & Infrastructure: Evaluate quality/availability of data for priority use cases - Talent and Skills: Map required capabilities against your current team composition - Process Maturity: Can your governance and operational practices support ML/AI deployment? 𝟯. 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀 🏗️ - Target Foundational Improvements: Strengthen specific components enabling priority use cases - Implement in Phases: Break initiatives into smaller chunks delivering incremental value - Establish Feedback Loops: Regularly evaluate both ML/AI outcomes and ecosystem health 𝟰. 𝗘𝗻𝘀𝘂𝗿𝗲 𝗢𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗘𝗻𝗮𝗯𝗹𝗲𝗺𝗲𝗻𝘁 🤝 - Cross-functional Collaboration: Build frameworks for how teams work together - Continue Investing in Skills: Required capabilities will change across the entire organization - Manage Change: Without stakeholder buy-in, even perfect solutions go unused - Evolve Org Structure & Operating Model: Update how the organization works to reflect AI integration Whenever I hire somebody, I look for their ability to think with a holistic perspective. If you nail this and approach things in this way, you will be much more successful in your data projects and your career! Check out the article (link in the comments) and let me know what you think!

We often treat cyberattacks as isolated technical incidents. But in reality, a single ransomware event can trigger disruptions that span industries, economies, & even national security. This isn’t hypothetical. As Jess Burn explores in her recent Forrester piece (“Too Big To Fail, Cyber Edition”), we’re operating in a deeply interdependent environment where one breach can cascade across sectors and borders. The impact is no longer confined to a single company, it’s systemic. At Palo Alto Networks and through our work at Palo Alto Networks Unit 42, we’ve seen firsthand how the most resilient organizations are the ones preparing beyond their own walls. That means: ⭐ Testing crisis scenarios that include third parties, not just your internal team ⭐ Bringing suppliers to the table during tabletop exercises and resilience planning ⭐ Moving from static risk assessments to real-time monitoring ⭐ Micro segmenting your environment to reduce blast radius ⭐ And reinforcing vigilance against social engineering at every level This is a boardroom concern, a national imperative, and a shared responsibility. If you haven’t tested your response across your full ecosystem, now is the time to start.

𝗪𝗵𝗮𝘁'𝘀 𝗵𝗶𝗱𝗱𝗲𝗻 𝗯𝗲𝗹𝗼𝘄 𝘁𝗵𝗲 𝘀𝘂𝗿𝗳𝗮𝗰𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗜𝗰𝗲𝗯𝗲𝗿𝗴? 𝟳𝟬% of all the knowledge which you accumulate in any organisation. The implicit and tacit knowledge, the why and how which drives actions and decisions and is lost when employees leave. Only 𝟯𝟬% of what we truly know is captured in documents, data, facts and figures. I recently wrote about the challenge of retaining knowledge in Procurement teams, particularly in times of high fluctuation. Left unaddressed, it's causing productivity leakage and lowering employee morale. But not all knowledge is of the same kind. And not all can be harvested the same way. 𝗙𝗶𝗻𝗱 𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝟯 𝗺𝗮𝗶𝗻 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗲𝘀: 𝗘𝘅𝗽𝗹𝗶𝗰𝗶𝘁: The "What" is known and documented (e.g., SOPs, processes, spend reports). 𝗜𝗺𝗽𝗹𝗶𝗰𝗶𝘁: The "How" of actionable insights, often unspoken but transferable (e.g., negotiation tactics, best practices). 𝗧𝗮𝗰𝗶𝘁: The "Why," deeply embedded in experience and values, hard to express but crucial (e.g., personal insights about market trends or suppliers). 𝗦𝘂𝗿𝗳𝗮𝗰𝗶𝗻𝗴 𝘁𝗮𝗰𝗶𝘁 𝗮𝗻𝗱 𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗶𝘀 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗶𝗻𝗴. Turning experience into tangible, documented information and data points is like reverse engineering and is often resisted by knowledge owners when it comes to sharing. Technologies, such as Knowledge Graphs, Ontologies, and AI assistants, can collaborate with employees to harvest knowledge at the source, whether from structured data (files, tables, logs) or unstructured data (voice, audio, video, documents). This can help to reduce the burden of knowledge capture, centralise its management and make it accessible for everyone. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁, 𝗹𝗶𝗸𝗲 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁, 𝗶𝘀 𝗮 𝗰𝘂𝗹𝘁𝘂𝗿𝗮𝗹 𝗵𝗮𝗯𝗶𝘁 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻𝗻𝗼𝘁 𝗯𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗱 𝘄𝗶𝘁𝗵 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗼𝗻𝗹𝘆. 𝗜𝘁 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘀𝗺𝗮𝗹𝗹𝗲𝗿 𝘀𝘁𝗲𝗽𝘀. Here are some practical tips to kickstart knowledge sharing to surface tacit and implicit knowledge: ▪️𝗕𝗿𝗼𝘄𝗻 𝗯𝗮𝗴 𝗹𝘂𝗻𝗰𝗵𝗲𝘀 where category teams share use cases and insights ▪️𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗻𝘂𝗴𝗴𝗲𝘁𝘀 captured in documents and support tickets ▪️𝗟𝗲𝘀𝘀𝗼𝗻𝘀 𝗟𝗲𝗮𝗿𝗻𝘁 sessions to review project outcomes post-mortem ▪️𝗖𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 & 𝗪𝗲𝗯𝗶𝗻𝗮𝗿𝘀 used for knowledge dissemination ▪️𝗪𝗼𝗿𝗸 𝘀𝗵𝗮𝗱𝗼𝘄𝗶𝗻𝗴 & 𝗿𝗼𝘁𝗮𝘁𝗶𝗼𝗻𝘀 to skill-up new people in a role ❓What kind of knowledge assets are most valuable in Procurement? ❓How is your company tapping into your submerged knowledge #knowledgemanagement #procurement #lessonslearnt #artificialintelligence

Modern corporations are creating innovation ecosystems where internal teams work directly with portfolio companies, sharing resources, expertise, and market access. This integration goes far beyond traditional corporate-startup partnerships: ➡️ Shared Technology Platforms: Portfolio companies gain access to proprietary corporate platforms and APIs, while corporations benefit from rapid external innovation cycles. ➡️ Cross-Pollination of Talent: Employees move between corporate R&D teams and portfolio companies, creating knowledge transfer and cultural bridges. ➡️ Collaborative Product Development: Joint development projects between corporate teams and startups are becoming more common, leading to products that neither could create independently.

All valuable work will increasingly be done by Human-AI hybrids. An insightful research paper identifies both challenges and good practices from multiple case studies to propose an overall framework. The authors propose that generating effective human-AI hybrids is divided into two phases: Construction - in which Technical implementers design the architecture of the hybrid - and Execution - where Organizational implementers facilitate how participants engage and interact. They suggest 3 primary success factors: 🔧 Interface and Technical Design focuses on making AI systems accessible and reliable through code-free interfaces. The technical architecture should allow rapid testing of different approaches while being supported by effective data curation strategies. 🧠 Human Capability Development prepares people to work effectively with AI systems through training, in critical assessment and prompting techniques. Employees must understand AI's capabilities and limitations, and develop skills to integrate AI into existing workflows. 🤝 The Collaboration Framework structures successful human-AI interaction through aligned mental models and clear role definitions. It emphasizes improving underperforming areas rather than disrupting successful processes, while ensuring both human and AI agents contribute their unique strengths to achieve optimal outcomes.

Ecosystems are built on relevance, not reach. Companies often fall into the trap of building broad, scattered partner lists, hoping that quantity will somehow convert into pipeline. That is when partnerships become a numbers game, and hope becomes the strategy. But a wider ecosystem without shared customers, shared motion or shared outcomes just creates noise. What drives results is relevance. And that is where we focus. Regardless of maturity, the same rule applies: impact comes from going narrow and deep, not broad and hopeful. The partnerships that actually move the needle are embedded across the customer journey. They show up where your customers already are. They offer complementary value that makes the product or experience better. Think about Zeller launching inside Officeworks because that is where small business owners already shop. Or Uber and Spotify's integration that still drives millions of new users monthly. Or Canva and HubSpot, with ecosystem-level workflow built in. These are not just brand alignments. They are functional ecosystems, driving measurable outcomes from acquisition through to retention. The mistake is thinking partnerships are just about co-marketing or shared logos. In reality, the best ecosystems solve real problems across the full customer journey, from discovery and decision, all the way through to delivery and retention. And that is why more CROs and CFOs are leaning into partnerships. Because when executed well, an ecosystem strategy reduces sales hiring pressure, protects CAC and increases lead quality. Many of the PE-backed and founder-led teams we work with are not looking to add ten more sales reps. They are looking to go deeper with partners who influence, deliver and expand customer impact, with fewer resources. So the question is not “How many partners do we have?” It is “Who has our customer’s attention, and how do we build something meaningful with them?” Curious how you are thinking about relevance versus reach in your own ecosystem? Send a DM. Always up for the chat. #growth #ecosystem #partnerships

In 20 months, we launched 6 forges (products) Not because we lacked focus but because we wanted to build a $100M ARR company. Everyone tells you to focus. One product. One ICP. One clean roadmap. “Don’t expand until you’ve nailed the first one.” And honestly? That advice makes sense... Until you’re in a Red Ocean where every player is just another point solution solving a narrow problem. So we chose a different path. Instead of trying to fight for a single wedge in the market, we built an ecosystem. This wasn’t a reckless sprint toward feature bloat. It was a strategic shift toward software compounding — the same approach companies like Salesforce, HubSpot, and Apple have used to scale from one-product winners to category-defining platforms. Each product we built was deeply intentional. Every tool in the Forge ecosystem targets the same buyer, solves a connected problem, and increases the stickiness and lifetime value of the entire stack. The bet was simple in theory, but hard in execution: Could we build multiple products simultaneously without sacrificing speed and quality? It was a high-risk move. We had to hire more engineers. We had to manage multiple roadmaps. We had to think beyond shipping code and start thinking like ecosystem architects. Every product needed to function on its own but also amplify the value of the others. And yet… it worked. The payoff was clear and fast. → Customers started buying more than one product from Day 1 → Our ACV shot up → Churn went down, as switching costs increased and workflows became interconnected → We were able to amortize our infrastructure and acquisition costs across the entire stack → Our unit economics improved, even while our product velocity accelerated So reflecting back, here’s my thesis: When your TAM is limited, you can’t scale by selling the same thing to more people. You scale by selling more things to the same people with higher utility, stronger retention, and more embedded workflows. You stop thinking like a product company and start thinking like a platform business. So no — launching 6 products in 20 months wasn’t a distraction. It was the only way we could build something defensible, scalable, and fundamentally differentiated in a sea of point tools. That being said, this only works if the products actually make sense together. It only works if the same buyer benefits from all of them. And it only works if you can execute fast enough that your ecosystem feels coherent not like a Frankenstein of disconnected apps. That’s where most multi-product strategies fail. That’s also where we’ve won. Thank you Daniel & Dovydas for ABS 👏🏼 #AlwaysBeShipping