The Governance Advantage: Ecosystem Leadership in a Regulated Age
Executive Thesis
Ecosystem leadership refers to the ability of a company (often a large platform provider) to orchestrate a network of partners, developers, and users around its core products and services. In practice, hyperscale digital platforms (think of leading mobile OS, search, or social media firms) achieve outsized success by mastering three pillars: architecture, governance, and trust. They design open-but-controlled technical architectures, enforce governance rules that align the ecosystem’s incentives, and cultivate digital trust among participants. This paper argues that the sources of ecosystem leadership are evolving. Historically, platform giants won by keeping tight control over their ecosystems while leveraging network effects. Now, a wave of regulations and standards is forcing greater openness, interoperability, and formal assurance. Compliance-driven requirements – from opening app marketplaces to adhering to AI risk frameworks – are reshaping how platforms assert leadership. The thesis: those who adapt by blending architectural excellence with proactive governance and demonstrated trustworthiness will maintain their competitive advantage in this regulated age.
The Economics of Orchestration
Digital platforms thrive on the economics of networks and orchestration. Fundamentally, a platform is an open architecture with governance rules designed to facilitate interactions[1]. The architecture enables third parties (complementors) to participate, while governance motivates them to create value[2]. Each interaction – a search query, a ride hail, an app install – is a source of value that the platform orchestrator scales up via network effects. Platform economics thus center on magnifying these interactions: the more participants and complementary offerings, the more valuable the ecosystem becomes (demand-side economies of scale). Classic research by Parker, Van Alstyne, and others emphasizes that interactions scale best when friction is low and value is shared[3][4]. In other words, orchestrators succeed by making it easy for users and partners to find each other and by ensuring partners have incentives (profit, data, reach) to join.
Michael Jacobides defines digital ecosystems as “interacting organizations with modular architectures, not governed by hierarchical structures, and connected to digital networks”[5]. This highlights that ecosystems are not traditional supply chains; instead of one firm vertically integrating everything, the orchestrator creates a modular system where independent actors plug in components (apps, services, content) via defined interfaces. Jacobides and colleagues note that orchestrators shape “industry architecture” – deciding which roles capture value and how tightly the parts fit together[5][6].
Orchestration types. Not all ecosystems have a single all-powerful leader. Research by Lingens et al. identifies single-, double-, and multi-orchestration patterns[7]. In a single-orchestrator model, one firm (e.g., a dominant platform) handles all key coordination tasks. A double-orchestrator ecosystem might have two anchor firms sharing leadership (for instance, a partnership where two platforms interconnect and jointly set standards). Multi-orchestrator ecosystems distribute orchestration across several players or a consortium. The allocation depends on knowledge and roles: if production and consumption knowledge are concentrated, a single firm can lead; if not, tasks might be shared[7]. Effective leaders discern the right model – sometimes even enabling co-orchestration with partners to achieve scale.
Network dynamics further underscore why a few big platforms often dominate. Network effects mean value grows as user bases and complementary offerings grow, leading to winner-take-most outcomes in many markets. “Each multi-trillion-dollar ecosystem is ruled by unforgiving mathematics: a few large-scale platforms are likely to win a disproportionately large portion of the value because they will own the customer,” as one strategist notes[8][9]. Owning customer relationships (and data) is the holy grail; it lets the orchestrator set the terms of interaction. This is why platform orchestration is often compared to being a ‘hub’ in a hub-and-spoke network[10] – the hub firm intermediates all transactions and can leverage data and scale to stay ahead. However, with great power comes the challenge of keeping the network healthy: if the hub over-extracts value or stifles complements, it can trigger disintermediation or regulatory scrutiny.
In summary, the economics of orchestration require balancing openness and control. As the World Economic Forum succinctly put it, “opening too little means third parties cannot add value; opening too much means loss of control and inability to steer the community”[11]. Masterful ecosystem leaders find the sweet spot – open enough to attract a critical mass of partners, yet guided enough to ensure coherence, quality, and monetization. In the next sections, we explore how that balance is achieved through architectural design and governance choices, and how it’s now being tested by new health and trust imperatives.
Designing for Health: Architecture Meets Governance
A healthy ecosystem is one where innovation thrives, participants profit, and the overall system proves resilient. Achieving this health is largely a design question: how the platform’s technical architecture and its governance policies reinforce each other. As Tiwana (2013) observed, modules and interfaces allow third parties to contribute, but governance determines if that innovation potential is fully leveraged[12][13]. In other words, architecture provides the “bones” of the ecosystem, while governance is the “immune system” that keeps it functioning smoothly.
Modularity and APIs. Platform architecture typically follows a modular design: a stable core with plug-in modules. Each module (an app, a device driver, a service) may have little value standalone, but when connected through standard interfaces, they together deliver the final user experience[14]. For example, a smartphone OS provides core functions and APIs, while thousands of third-party apps provide specialized features – none of those apps is useful without the OS, and the OS is far less useful without apps. A key design principle is clear, stable APIs and policies on how they can be used. By documenting interfaces, the platform enables external innovation, while by controlling and versioning those APIs, it maintains stability. Gawer and Cusumano highlighted that “modular architectures are particularly useful when the interfaces are open — that is, when the platform leader specifies publicly how to connect”[15]. Openness here means any developer can build to the interface, but importantly, the platform leader still defines that interface. This ensures compatibility and quality control across a diverse ecosystem.
Governance policies are then layered on the architecture. They answer: Who may participate? What can they do? How are rewards and responsibilities allocated? Effective platform governance “provides the rules of who may participate, how they create and divide value, and how to resolve conflict among partners.”[16] These rules are often encoded in developer guidelines, app store policies, data sharing terms, and compliance tests. For instance, Android, an open-source mobile OS, is open to device manufacturers and app developers – but to keep the ecosystem healthy, Google employs the Compatibility Definition Document (CDD) and Compatibility Test Suite (CTS). Only devices that meet the CDD’s detailed requirements and pass CTS are deemed “Android compatible” (and get access to Google’s ecosystem)[17][18]. The CDD/CTS governance ensures that regardless of the myriad manufacturers, an Android app will “run properly” on any certified device by disallowing alterations that would break APIs or fragment the experience[19][18]. This combination of modular architecture (Android’s open-source code) with governance (compatibility policy) has enabled Android’s ecosystem to flourish with diverse players yet maintain sufficient uniformity to attract millions of apps.
A case comparison in mobile operating systems illustrates architecture-governance balance. Android’s model is often characterized as “open, but with guardrails.” Many OEMs can use and modify Android, but compliance tests enforce a baseline consistency and security. This approach traded off some control for massive scale – Android captured global market share by enabling an ecosystem of manufacturers, carriers, and developers, all coordinated by Google’s governance (and sweetened by Google’s proprietary apps and services). Apple’s iOS, by contrast, historically took a “closed, curated” approach: tightly integrated hardware and software, no external device licensees, and a single App Store with strict review. This produced a more uniform, arguably more secure user experience (and lucrative app monetization for Apple), but at the cost of less partner autonomy. Over time, even Apple adopted some principles of openness in architecture (e.g., publishing many APIs for developers), while maintaining strict governance (e.g., app review guidelines, private API restrictions). The net result for both ecosystems has been robust health, but via different balances: Android maximized reach and variety, Apple maximized consistency and control.
Architecture meets governance in the design of interface policies. Tiwana and others stress that technical openness must be coupled with rule-setting to prevent chaos. For example, consider API change management – a healthy ecosystem avoids “breaking” its developers too often. Platforms track metrics like API change velocity, trying to minimize disruptive changes or at least document them with deprecation timelines. If an API must evolve, good governance provides support (tools, guides, backward compatibility) to help partners adapt, thereby maintaining trust. Conversely, if a platform neglects governance – say it allows unvetted integrations or frequent arbitrary changes – the ecosystem can become fragile (prone to security breaches, inconsistent user experience) and lose developer confidence.
Case: Android vs. Apple under new regulations. The interplay of architecture and governance is being tested by regulatory demands for openness. Android’s governance model already mandated a form of openness-compliance via CDD/CTS. Now Apple is being pushed in that direction by the EU Digital Markets Act (DMA). Historically, Apple tightly controlled app distribution (only via its App Store) and interfaces (e.g., requiring WebKit for browsers on iOS). Under the DMA, Apple has had to design new interfaces and policies to open up its platform: for instance, Apple’s forthcoming iOS changes will allow alternative app marketplaces and web downloads of apps in the EU, via new APIs and a “notarization” process to ensure security[20][21]. Apple is effectively creating an “API” for third-party app stores – a dramatic shift in architecture – while layering on governance like requiring those stores to register with Apple and scan apps for malware (notarization)[22][23]. In parallel, Apple must open other interfaces (allowing third-party browser engines, opening NFC access to payment apps, etc.)[24][25], again illustrating how architectural openness is paired with new governance measures (authorization processes, alternative business terms for developers, etc.). This anonymized case of “Platform X” (historically closed) versus “Platform Y” (historically open) shows that healthy ecosystems can be achieved via different paths, but the future favors those who can blend openness with compliance. The “Openness–Control Matrix” (a hypothetical Exhibit 1) would position such platforms: Android historically in the high-openness/high-control quadrant (open source core with Google’s control through compatibility and Play services), and Apple in low-openness/high-control (proprietary integrated model). Now regulations nudge Apple toward the middle: higher openness (alternative stores, interoperability) while seeking to maintain high control (notarization, app review standards applied to third parties). Managing this shift is an architectural and governance challenge – effectively a redesign of the ecosystem contract.
In conclusion, designing for ecosystem health means architecting modular systems with well-defined touchpoints and enforcing policies that align the participants’ behavior toward common goals. The best platforms are neither laissez-faire nor overly restrictive; they act as “gardeners” of the ecosystem, providing fertile soil and fences against weeds, but allowing diversity and innovation to bloom within. Next, we examine how digital trust becomes the new competitive moat that underpins this ecosystem governance – the trust that makes all participants comfortable engaging under the platform’s rules.
Digital Trust as a Competitive Moat
In the digital ecosystem context, trust is not just a value – it is a measurable asset and a competitive moat. If developers, users, and partners do not trust a platform’s fairness, security, or resilience, they will eventually gravitate elsewhere (or be pried away by regulators). Ecosystem leaders therefore invest heavily in operationalizing trust: building processes, standards, and cultural norms that ensure the platform is trustworthy by design. This section explores how frameworks like NIST’s AI Risk Management Framework and global standards are being used to turn digital trust into a systematic advantage.
Firstly, what do we mean by digital trust? The World Economic Forum defines it as “individuals’ expectation that digital technologies and services – and the organizations providing them – will protect all stakeholders’ interests and uphold societal values.”[26] In practical terms, digital trust is the confidence that the platform will do what it says (reliability), guard against harm (security & safety), treat participants fairly (ethics, lack of bias), and be accountable if things go wrong (transparency & redress). WEF’s Digital Trust Initiative identifies eight dimensions of trust for technology providers: security, safety, transparency, interoperability, auditability, redressability, fairness, and privacy[27]. Leading companies treat these like performance areas, akin to cost or quality – key performance indicators (KPIs) for trust can include metrics like incidents prevented/responded (security), time to patch vulnerabilities, privacy compliance rates, algorithmic bias measures, user satisfaction and confidence scores, etc.
Frameworks and standards are emerging as the lingua franca of digital trust. For example, the NIST AI Risk Management Framework (AI RMF) provides a structured approach for organizations to identify, measure, and mitigate risks in AI systems, thereby improving trustworthiness[28]. It’s a voluntary framework (as of 2023) that guides companies to incorporate considerations like transparency, fairness, and safety into AI design and deployment. In essence, NIST’s guidance “seeks to cultivate trust in AI technologies and promote innovation while mitigating risk.”[29] Ecosystem leaders have begun mapping their internal governance to such frameworks – e.g., a cloud AI service platform ensuring its models go through bias testing and have documentation consistent with the AI RMF’s recommendations. This not only reduces regulatory risk but also signals to enterprise customers that the platform’s AI services are trustworthy (a selling point).
Similarly, the new ISO/IEC 42001:2023 standard – effectively an “AI Management System” standard – institutionalizes trust practices. It requires organizations to implement policies and controls around AI transparency, accountability, bias mitigation, safety, and privacy[30]. Think of it as ISO 9001 (quality management) but for AI governance. A platform that achieves ISO 42001 certification can demonstrate to partners and regulators that it has a robust, audited process to govern AI development (covering everything from leadership commitment and risk assessment to ongoing monitoring and improvement)[31][32]. This can be a competitive moat in sectors like finance or healthcare, where clients will favor platforms that meet high assurance standards for AI.
Sector-specific trust standards also play a role. In healthcare, for instance, the British Standard BS 30440 provides a “validation framework for the use of AI within healthcare – Specification”, which sets criteria for evaluating AI products’ clinical benefit, performance, safety, and ethics[33][34]. An ecosystem leader operating in digital health might require that apps or algorithms in its marketplace adhere to BS 30440, or even facilitate audit/certification against it. The benefit is twofold: it protects end-users (patients, clinicians) and elevates the platform’s reputation as a safe, regulated environment. As one BSI director noted, such auditable standards “help build digital trust in cutting-edge tools” by giving all stakeholders – doctors, patients, regulators – confidence that the AI products are “safe, effective, and ethically produced.”[35][36]
In practice, ecosystem leaders operationalize trust through both technology and culture. Technologically, they embed trust-by-design: encryption everywhere, privacy-enhancing tech (differential privacy, federated learning), bias testing toolkits integrated into developer APIs, transparent algorithmic summaries, robust uptime and incident response processes. Culturally, they set norms – for example, Mozilla, with its open-source Firefox and other projects, has a long-standing public-interest ethos. Mozilla’s governance includes community participation and transparency at levels unheard of in most Big Tech firms. This public-interest governance ensures decisions consider user privacy and open web principles first. A platform can emulate some of this by establishing internal “digital trust councils” or external advisory boards to oversee ethical issues, by training employees in responsible innovation, and by tying bonuses to trust metrics (just as they do to growth metrics).
A helpful mental model is to think of a “Digital Trust KPI Wheel” (Exhibit 2) – a circular dashboard with slices for Security, Privacy, Fairness, Transparency, etc., each with one or two quantifiable KPIs. For example: security might track incident rate and mean time to recovery; privacy might track percentage of data flows covered by consent or encryption; fairness might track disparity in service outcomes across demographic groups; compliance might track coverage of systems under NIST RMF or ISO 42001. Leading platforms present such metrics to their boards and even in public reports, treating them as core to their value proposition. In fact, the World Economic Forum suggests organizations use both perception measures (e.g., user trust surveys, NPS related to trust) and objective measures (like number of bias issues corrected, adherence to standards) to gauge digital trust progress[37][38]. By measuring and improving these, a company creates a moat that is hard for less trustworthy rivals to cross.
In conclusion, digital trust is becoming a defining competitive differentiator for ecosystems. A platform that consistently demonstrates high trustworthiness – through compliance with frameworks (e.g., NIST, ISO), sector standards (e.g., health AI validations), and strong internal governance – builds a reservoir of goodwill and reduced risk. Customers and partners will prefer an ecosystem they trust not to mishandle data or expose them to harm. As the next section explores, impending regulations are about to make trust mandatory. Those already investing in it will be ahead of the game, turning what could be a compliance burden into a strategic advantage.
Regulatory Realities Reshaping Strategy
Regulation has caught up with the power of digital ecosystems. In the past few years, landmark laws like the EU’s Digital Markets Act (DMA) and AI Act have introduced new obligations that fundamentally reshape platform strategies. No longer can gatekeeper platforms unilaterally decide how open or closed to be – the law is now dictating minimum openness, data portability, and fairness requirements. This section outlines key regulatory mandates and examines how ecosystem leaders are redesigning their policies, APIs, and interfaces in response.
EU Digital Markets Act (DMA). The DMA targets large “gatekeeper” platforms offering core services (social networks, app stores, search, operating systems, etc.) that meet size and user thresholds. As of 2024, six companies (including Apple, Alphabet/Google, Meta, Amazon, Microsoft, and ByteDance) have been designated as gatekeepers and must comply with a host of pro-competitive obligations[39][40]. Some of the key requirements include: allowing end-users to uninstall preloaded apps and change defaults, permitting the installation of third-party apps and app stores (as long as security isn’t compromised), and ensuring interoperability for messaging services so they can work with competitors[41][42]. Gatekeepers also must open up data access – e.g., provide business users with access to data they generate on the platform, ensure data portability for users, and be transparent about advertising metrics[43][44]. Self-preferencing and tying practices are banned: a gatekeeper cannot rank its own services higher or force developers to use its payment system or browser engine exclusively[45][46]. In short, the DMA is pushing these ecosystems from closed gardens toward more open, fair marketplaces.
The strategic implications are enormous. Consider Apple and Google as exemplars (without focusing on Amazon per instructions): Both have announced broad changes to comply. Apple, as noted, is introducing the ability for users in the EU to install alternative app marketplaces and use third-party payment systems on iOS[47]. It’s also opening up interoperability – for instance, it must allow iMessage to connect with other messaging apps if requested (to comply with DMA’s messaging interoperability mandate). Apple’s Safari browser on iOS will have to tolerate other browser engines, ending the WebKit-only rule[24][48]. These changes require Apple to redesign parts of its OS architecture and business terms (it has created an “Alternative Distribution” program with an addendum to developer contracts, new APIs for web downloads, and a process to authorize third-party stores)[49][23]. This proactive response – albeit one Apple accompanies with new safeguards like app notarization and optional fees for third-party stores – shows ecosystem leaders retooling their governance.
Google, similarly, has rolled out updates to Android and Google Play policies: it’s making it easier to sidestep exclusivities by downloading apps from third-party sources or using alternative app stores on Android, and allowing alternative billing options within Google Play apps[50]. It’s also giving users more choice in default services (search engines, voice assistants, etc.), and sharing more data with advertisers and competitors (for example, providing more transparency in its ad platforms)[51]. Interestingly, some of Google’s compliance changes (such as easier sideloading) were areas where Android was already more open than iOS – yet the DMA forces even clearer rules and user empowerment, reducing subtle frictions that remained.
These adaptations reflect a broader point: regulatory compliance is now a catalyst for platform design innovation. Rather than simply doing the bare minimum, some leaders are turning compliance into a feature. For example, Meta (Facebook) under DMA has enabled third-party messaging apps to interoperate with WhatsApp and Messenger (an engineering feat to open up APIs securely)[52]. It also started offering an ad-free paid tier for Facebook/Instagram in Europe to address regulatory concerns around data usage[53] – a policy change unthinkable a few years ago. Microsoft is unbundling some services (e.g., opening LinkedIn data or loosening integration in Windows)[54]. These moves show platforms experimenting with new business models and technical integrations under regulatory pressure.
EU AI Act. Meanwhile, the EU’s AI Act, expected to fully apply by 2025-2026, introduces obligations for AI systems, especially those deemed “high-risk” or “general purpose”. Platform leaders who deploy AI (which is virtually all of them) are preparing by implementing the Act’s requirements. For high-risk AI systems (e.g., algorithms for credit scoring, recruitment, medical diagnostics), providers will have to conduct risk assessments, ensure human oversight, provide detailed technical documentation, and register these systems in an EU database. They must also implement quality management (similar to ISO 42001) and continuous monitoring for compliance. Penalties for non-compliance are hefty (up to 6-7% of global turnover)[55][56]. Thus, any platform offering AI-driven services in regulated areas must bake these controls into their development lifecycle.
One particularly salient part of the AI Act for hyperscalers is the section on General Purpose AI (foundation models). Companies providing large AI models (like GPT-style models or cloud AI services) will be required to publish transparency documentation about training data, detail how they mitigate biases and risks, and in some cases perform third-party audits[57]. By August 2025, even models with “systemic risks” (very advanced models affecting many people) must comply with additional obligations: “model evaluations, adversarial testing, risk mitigation, and incident reporting”[58]. Major platform players (Google, Meta, OpenAI, etc.) are already adjusting their practices: releasing “system cards” or model cards that describe how their AI works, setting up red-team testing for AI releases, and creating mechanisms to rapidly address user reports of AI failures. For example, a large cloud provider might introduce a new AI service dashboard for customers that includes all the information needed by the AI Act – from the intended use and limitations of the model, to an API for users to report issues which the provider must track and possibly relay to regulators. These are governance interfaces akin to an app store policy, but for AI: the rules by which AI operates in the ecosystem.
In effect, the AI Act pushes ecosystem leaders to redesign their AI governance and transparency. A practical example: if Platform X runs a social network with AI-curated feeds, the Act might classify that algorithm as high-risk (due to impact on information access or mental health). Platform X would then need to provide users some explanations of how the AI ranks content (transparency), allow opt-outs or human alternatives, and rigorously monitor the algorithm for harmful outcomes (like spread of disinformation or discrimination). We’re seeing early signs – some social media firms now publish information on their recommender algorithms and give EU users options to see an unpersonalized feed, anticipating compliance with EU rules (the DSA – Digital Services Act – also plays a role here). Though not explicitly part of the AI Act, it’s part of the same trust regulation trend.
Gatekeeper designations and strategy shifts. One noteworthy result of the DMA gatekeeper designations is that it validated which firms truly hold ecosystem power – and those firms are now under a magnifying glass. The designated gatekeepers have six months to implement changes (the initial deadline was March 2024 for the first cohort)[59][40]. This has accelerated strategic decisions that might have taken years. For instance, Apple reportedly began internal projects to enable sideloading and alternate payments well ahead of the deadline, effectively creating an “EU Edition” of iOS policies. Such forethought indicates savvy strategy: embrace the inevitable and potentially shape it to your advantage. Apple’s approach, as seen in its developer notice, is to comply in a way that still emphasizes user safety – by introducing Notarization and requiring alternative store developers to go through Apple’s authorization[22][60]. This could set a new industry standard for how to open walled gardens without letting in malware. Similarly, Google’s changes are framed as user-friendly improvements (even as travel competitors argue Google’s compliance choices favor itself[61]). Meta’s move to open messaging while launching a paid no-ads option might preempt harsher regulatory moves by giving users choice voluntarily.
In summary, regulations like the DMA and AI Act are not just box-ticking exercises; they are reshaping the competitive landscape. Platforms that quickly adapt – redesigning APIs (e.g., for interoperability), rewriting policies (for fair access), and building compliance capabilities (risk offices, documentation pipelines) – will not only avoid penalties but could win users’ favor. The playing field is also leveling: smaller competitors long complained about closed ecosystems; now they have legal hooks to participate (e.g., a rival messenger integrating with WhatsApp via newly opened APIs, or independent app stores appealing to iOS users). Ecosystem leaders must therefore play a new game: ecosystem leadership under constraint. The next section outlines a playbook for thriving in this era – how to be an ecosystem leader when you can’t unilaterally dictate all the terms.
Playbook: Ecosystem Leadership Under Constraint
Even under tighter constraints, savvy leaders can guide their ecosystems to success. Below is a seven-step playbook for ecosystem leadership in a heavily regulated, trust-focused environment. This playbook draws on insights from McKinsey’s ecosystem strategy frameworks, Mozilla’s public-interest governance principles, and real-world metrics used by leading firms:
- Articulate a Joint Value Vision: Start with a clear ecosystem value proposition – define the end-to-end need your ecosystem meets and rally partners around it. As McKinsey research advises, think expansively about customer journeys and where an ecosystem can “expand the pie”[62][63]. For example, if your ecosystem is about “smart homes,” envision a platform where devices, services, and data integrate seamlessly to improve living – beyond what your firm alone could do. This vision anchors all participants to a common goal and justifies the collaboration. Communicate how compliance and openness serve this vision (e.g., “Opening our platform to third-party devices will help us deliver a truly smart home experience to customers”). A shared vision helps overcome internal resistance to change and encourages partners to invest.
- Design the Openness–Control Balance (Architecture Strategy): Using an Openness–Control Matrix approach, decide which interfaces to open via APIs and standards and where to retain tight control. Map your ecosystem’s components and categorize: which could be modules for others to build (open APIs), and which are core services you must tightly govern? Strive for “open architecture, guarded governance.” For instance, open up data access APIs as required by law, but implement audit and rate-limit controls to prevent misuse. Develop an interface policy for each: who can access, with what permission, under what SLAs. This step is where you update your technical architecture to comply (adding new APIs for interoperability) and to maintain integrity (securing those APIs). The goal is to maximize innovation surface area while preserving quality and security. Document these choices – transparency with partners builds trust that you’re opening where it counts and controlling where it’s prudent.
- Embed Digital Trust Mechanisms: Treat trust as a design parameter, not an afterthought. Implement a “trust-by-design” program that aligns with frameworks like NIST AI RMF and relevant ISO standards. Concretely, set up internal processes to assess risks (privacy, bias, security) at every product iteration. For AI features, integrate bias detection and user disclosure from the beginning (so you aren’t scrambling to add it later). Build trust metrics dashboards (the Digital Trust KPI Wheel) that track things like time since last major incident, percentage of systems covered by risk assessments, compliance training completion, etc. By making trust measurable, you signal its importance. Culturally, champion an ethos like Mozilla’s: put users first, be transparent, encourage ethical debates internally. Mozilla’s public-interest governance teaches that having an independent oversight or community voice can keep you honest – consider an external advisory panel or open forums for feedback on your policies. Ultimately, a reputation for trustworthiness will differentiate your ecosystem, turning compliance into a competitive edge.
- Govern through Shared Value and Fair Rules: Revamp your governance model to emphasize fairness and benefit-sharing. This means updating partner terms and community guidelines in light of new laws and trust goals. Ensure your revenue-sharing, data-sharing, and conflict resolution policies are perceived as just and clear – remember, “Shared value is the essence of motivating third parties”[4]. In practice, consider more flexible business terms: for instance, under DMA you might offer a lower commission or alternative fee structure for apps using their own payment, to comply and also show goodwill. Adopt FRAND (Fair, Reasonable, and Non-Discriminatory) principles voluntarily in areas beyond what’s mandated – e.g., provide equal access to key APIs for all partners, large or small. Also, set up governance forums (like developer councils or partner advisory boards) to give ecosystem members a voice in policy changes. Mozilla’s model of community input can inspire here. When participants feel the rules are fair and they had input, they’re more likely to stick around even when regulations force changes.
- Leverage Metrics and Transparency for Continuous Improvement: In a constrained environment, you must excel at execution. Use specific operational metrics to keep the ecosystem humming. Two critical ones: Time to First Transaction (TTFT) – how quickly a new user or developer achieves value on your platform – and API Change Velocity – how frequently you update interfaces in ways that require partner changes. Aim to minimize TTFT (through better onboarding, sandbox environments, tutorials) because a short TTFT means partners see value quickly, offsetting any compliance frictions during onboarding[64]. Monitor API change velocity; if regulations force an API overhaul (say for data portability), support developers by clustering changes into predictable release cycles and offering long deprecation periods. Additionally, track regulatory compliance KPIs: e.g., DMA compliance scorecard (percentage of obligations met ahead of deadline, number of third-party app stores onboarded successfully, etc.) and Risk Management Framework coverage (which products have completed AI risk assessments or have ISO 42001 processes in place). Publicize progress on these metrics – transparency builds credibility with regulators and partners. Think of it like a “trust scoreboard” where you show you’re winning.
- Innovate within Constraints (Turn Compliance into Opportunity): Rather than grudgingly comply, use constraints as a catalyst for innovation. Encourage teams to ask, “How can we delight users while meeting these new rules?” For example, opening your platform? Develop an App Store Trust Seal program for third-party stores – if they meet your security standards, they get a badge. This not only meets DMA requirements but also differentiates your ecosystem as curated yet open (a competitive twist). Another example: to comply with AI transparency, create a user-friendly AI facts label (like a nutrition label for AI decisions) that improves user experience. By innovating in response to regulation, you may create new value. McKinsey notes that ecosystem strategies should be holistic and bold[62][65] – so, reimagine your business model if needed. If payments unbundling threatens revenue, perhaps launch a premium trust-based subscription (e.g., ad-free paid options, which Meta did, or value-added services around privacy). The ability to pivot creatively will set leaders apart from those who simply cut features or complain.
- Adapt and Iterate Governance Continuously: Finally, recognize that ecosystem leadership is an ongoing journey, especially under evolving laws. Build an agile governance capability: a cross-functional team that scans the horizon for regulatory changes, gathers ecosystem feedback, and iterates policies and technical measures accordingly. In practice, this might mean quarterly ecosystem health reviews that include compliance status and partner sentiment. Be ready to adjust – perhaps your initial implementation of interoperability APIs needs refinement based on developer input or an update in standards. Maintain a living handbook for ecosystem participation that is frequently updated (and versioned, so changes are transparent). Continuously educate your ecosystem on these changes – offer webinars, documentation, and support lines for partners adapting to, say, a new AI documentation requirement or a new data API. In essence, make regulatory agility a core competency. The ecosystems that thrive will be those that can bend without breaking – staying true to their core value while fluidly integrating new rules.
By following this playbook, leaders can remain the orchestrators of their ecosystems even as the playing field is leveled. In fact, those who do this well will shape how regulations are ultimately interpreted – setting industry best practices (much like Mozilla helped set norms for privacy, or how certain firms’ compliance programs become benchmarks). Ecosystem leadership under constraint is still leadership: it requires vision, design, trust, fairness, measurement, innovation, and agility. Get those right, and you not only comply – you prosper.
Appendix: Key Standards and Principles Landscape
In navigating the regulated, trust-centric era, executives encounter a landscape of frameworks and standards. Here is a brief, practical summary of the key ones referenced in this paper:
- NIST AI Risk Management Framework (AI RMF 1.0): A voluntary U.S. framework released in 2023, providing guidelines to incorporate trustworthiness into AI design and use. It outlines core functions – Govern, Map, Measure, Manage – for handling AI risks. In essence, it helps organizations “improve the ability to incorporate trustworthiness considerations into AI products”[28]. For an executive, think of it as a checklist ensuring your AI is secure, interpretable, fair, and robust. Adopting it can demonstrate proactive risk management and is often a first step toward regulatory readiness.
- ISO/IEC 42001:2023 (AI Management System Standard): The world’s first international standard for AI management systems. It specifies requirements for establishing an organizational process to develop and deploy AI responsibly and safely. Key themes include leadership commitment, risk assessment, transparency, bias mitigation, and continuous improvement[31][66]. Achieving ISO 42001 certification signals that your company runs AI with a “quality management” approach – akin to ISO 9001 for AI governance. It’s executive-friendly in that it provides a structured framework to ensure AI projects meet trust and compliance goals (improving accountability to boards and regulators alike).
- IEEE 7000-series (Ethical Technology Standards): A collection of IEEE standards addressing ethics in autonomous and intelligent systems. It spans topics like transparent system design (IEEE 7001), algorithmic bias reduction, data privacy, and even specific areas like child data governance. The series “addresses issues at the intersection of technology and ethics”, aiming to embed values like transparency, accountability, and privacy into engineering practice[67][68]. For example, IEEE 7001-2021 defines measurable levels of transparency for autonomous systems. Executives should see the 7000-series as providing practical guidelines to “build ethics in”, which can augment internal policies or inform compliance with broader laws (like AI Act expectations on transparency and bias).
- BS 30440 (British Standard for AI in Healthcare): A sector-specific specification titled “Validation framework for the use of AI within healthcare – Specification”. It provides criteria for evaluating healthcare AI products – covering clinical effectiveness, safety integration, ethical considerations, and even equity in outcomes[34]. It is auditable, meaning healthcare organizations or vendors can be certified against it. Executives in healthcare or life sciences should view BS 30440 as a way to demonstrate that an AI tool meets a high bar of trust (useful for procurement and regulatory acceptance). Even outside healthcare, it exemplifies how to structure validation of AI – focusing on performance and ethics to build confidence among users and regulators[69][35].
- Contract for the Web: An initiative by the Web Foundation (Tim Berners-Lee) outlining broad principles for governments, companies, and citizens to protect the open internet and its users. Companies that endorse it commit to principles like “making the internet affordable and accessible to everyone,” “respecting and protecting people’s privacy and personal data to build online trust,” and “developing technologies that support the best in humanity and challenge the worst.”[70]. While not a formal standard or law, it’s a high-level moral compass. Executives can use it as a quick checklist of digital responsibility: are we increasing access? safeguarding privacy? ensuring our tech is not amplifying harm? Many big tech firms have signed on, so it also reflects peer commitment. Aligning corporate digital responsibility programs with Contract for the Web principles can bolster reputation and ensure you’re on the right side of societal expectations.
Together, these standards and principles form an ecosystem of governance tools. Leading in the regulated age means leveraging them – to not only comply, but to differentiate. An executive can use the above as a roadmap: NIST and ISO 42001 to systematize trustworthy AI, IEEE 7000-series to dive deep on ethical design practices, sector standards like BS 30440 to get domain-specific assurance, and overarching pledges like the Contract for the Web to keep the organization anchored to its public duty. By speaking the language of these standards, one also navigates the conversations with regulators, clients, and partners more effectively – demonstrating The Governance Advantage in action.
Sources: The insights and data in this whitepaper are drawn from a mix of academic research, industry reports, and emerging regulatory texts. Key references include works by Jacobides et al. on platform strategy[5], Parker & Van Alstyne on platform network effects[1][2], Tiwana on platform architecture and governance alignment[14], Gawer & Cusumano on platform leadership, as well as recent policy documents (EU DMA[46], EU AI Act[57]), standards (NIST[29], ISO/IEC 42001[30], BS 30440[34]), and case examples from Apple, Google, and Mozilla’s practices. These are cited throughout to provide a trail for further reading and evidence of the concepts discussed.
[1] [2] [3] [4] [6] [11] [16] WEF Digital Platforms and Ecosystems Pages 1-2 and 8-15 - Briefing Paper Platforms and Ecosystems: - Studocu
[5] International experience of adaptation and application of digital ecosystems by industrial enterprises and clusters
https://www.bio-conferences.org/articles/bioconf/pdf/2024/64/bioconf_ForestryForum2024_04048.pdf
[7] Publikationen | Lingens Innovation
https://lingens-innovation.com/publikationen
[8] [9] Strategies to win in the new ecosystem economy | McKinsey
[10] Towards a theory of ecosystems - Jacobides - 2018 - SMS - Wiley
https://sms.onlinelibrary.wiley.com/doi/full/10.1002/smj.2904
[12] [13] [14] journals.vilniustech.lt
https://journals.vilniustech.lt/index.php/JBEM/article/download/15047/10675/55228
[15] The Elements of Platform Leadership
https://sloanreview.mit.edu/article/the-elements-of-platform-leadership/
[17] [18] [19] Android Developers Blog: On Android Compatibility
https://android-developers.googleblog.com/2010/05/on-android-compatibility.html
[20] [21] [22] [23] [24] [25] [48] [49] [60] Update on apps distributed in the European Union - Support - Apple Developer
https://developer.apple.com/support/dma-and-apps-in-the-eu/
[26] [27] [37] [38] To stop the erosion of digital trust, measure it - Tech For Good Institute
https://techforgoodinstitute.org/blog/perspectives/to-stop-the-erosion-of-digital-trust-measure-it/
[28] [29] AI Risk Management Framework | NIST
https://www.nist.gov/itl/ai-risk-management-framework
[30] [31] [32] [66] Understanding ISO 42001
https://www.a-lign.com/articles/understanding-iso-42001
[33] [34] [35] [36] [69] BSI publishes guidance to boost trust in AI for healthcare
https://www.artificialintelligence-news.com/news/bsi-publishes-guidance-boost-trust-ai-healthcare/
[39] [40] [47] [50] [51] [52] [53] [54] [59] [61] Apple, Meta, Google, and Others Must Now Comply With New Law as EU Takes Aim at Big Tech
[41] [42] [43] [44] [45] [46] EU Digital Markets Act Enters Into Force on November 1, Creating New Regulatory Regime for Large Tech Platforms | Insights | Skadden, Arps, Slate, Meagher & Flom LLP
https://www.skadden.com/insights/publications/2022/10/eu-digital-markets-act-enters-into-force
[55] [56] [57] [58] AI models with systemic risks given pointers on how to comply with EU AI rules | Reuters
[64] Web3 Product Metrics Every Founder Should Monitor - Rock'n'Block
https://rocknblock.medium.com/web3-product-metrics-every-founder-should-monitor-5f9ee6e3b96a
[67] [68] IEEE SA - How To Make Autonomous Systems More Transparent and Trustworthy
[70] Contract for the Web - Wikipedia
