Winning in AI Ecosystems: Why Dynamic Capabilities Matter More Than Ever

With Praveen Tanguturi, Ph.D. and Shalabh Kumar Singh

AI strategy needs more than just investment

When it comes to AI, the Resource-Based View of ensuring competitive advantage by controlling proprietary datasets, computing models and elite AI talent has been the dominant guiding principle of business strategy for at least a decade if not more. But AI is not a static resource. It is evolving continuously. Datasets, patents, or value chains considered most important today will need to adapt to changing regulatory structures, technological shifts and evolving market needs. The best AI models may need to be updated constantly, integrated with other AI models or be completely replaced. In other words, AI needs to be built into a dynamic capability of the firm with continuous upgradation and upskilling of talent rather than being viewed as a static resource like patents, assets, etc.

 Given this, the question facing companies trying to gain leadership with and in AI is no longer “Should we invest in AI?” or “What resources do we need to protect?” but rather “How do we build AI into a sustained, long-term competitive advantage?”

 With AI emerging as the foundation of modern business ecosystems, the biggest players in AI are not merely developing new foundation models; they are shaping ecosystems, defining interoperability, and influencing industry-wide adoption.  However, many organizations across industries struggle to scale AI despite large investments due to platform lock-in, fragmented AI adoption, and rapid obsolescence. Why? AI success is no longer about what resources a firm owns, but how well it adapts to continuous change.

 This is where Dynamic Capabilities Theory (Eisenhardt & Martin; Teece) becomes critical. Applying the framework from Teece (2018), dynamic capabilities go beyond static resource advantages to suggest that winning in AI ecosystems requires firms to:

• Sense emerging AI opportunities before competitors (e.g., identifying next-generation AI architectures or regulatory shifts).
• Seize AI investments effectively and scale them across business functions (e.g., transitioning from AI pilots to full-scale AI-enabled enterprises).
• Transform continuously by reconfiguring AI models, governance structures, and strategies to remain competitive.

 This article explores why dynamic capabilities—combined with the key insights from our previous blogs on platform leadership (blog #2), interoperability (blog #3) and real options (blog #1) investment strategies—offers a path forward not only for AI adoption but also for mastering the future of AI success. 

 AI Ecosystems: Why leadership is about ecosystem control, not just adoption 

 The AI landscape is rapidly shifting from isolated tools to integrated ecosystems. Historically, firms gained an edge by owning scarce resources—data, computing power, or proprietary models. But AI’s future will be dictated by platform control, not just ownership.  Companies that own AI platforms, orchestrate interoperability, and manage AI investments strategically will shape the future.   

• Microsoft’s OpenAI investment grants it direct influence over AI platform adoption across enterprises. 
• Google’s Gemini strategy integrates AI across its entire search, cloud, and software ecosystem. 
• Nvidia dominates AI hardware by shaping the infrastructure on which AI models run. 

These firms not only control ownership of AI models but also exercise control over how AI is accessed, deployed, and monetized, and under what terms and constraints. AI leaders must create and control business ecosystems, not just optimize internal AI models. AI strategy is not static, it is continuously evolving and winning firms develop systems that enable ongoing resource reallocation.

 This aligns with Teece’s (2007) concept of ecosystem dynamics and leadership, where firms shape market rules rather than merely participating in them. For businesses, the question is not just "How do we use AI?" but "How do we shape AI ecosystems so that we can shape the rules of market engagement?"

 Interoperability is the Next Competitive Battleground

 The AI industry is split between open vs. closed models, and the ability to integrate seamlessly across ecosystems will define success.   

• Meta’s Llama models are open source, allowing enterprises to fine-tune their own AI stacks.
• Anthropic’s Claude AI is positioning itself as a privacy-first alternative with flexible integrations. 
• Amazon’s Bedrock service lets enterprises mix and match AI models within AWS, avoiding vendor lock-in.   

Drawing on modularity theory (Baldwin & Clark, 2000), AI success will require flexible, modular architectures that allow businesses to leverage multiple models rather than betting on a single vendor or technology stack. More than model performance, it requires ability to integrate AI across vendors and platforms. We believe the future of AI competition will be won by those who embrace openness and adaptability, rather than those who create walled gardens.  

AI Investment Needs More Flexibility: Enter Real Options Theory  

One of the biggest AI strategy mistakes is treating AI investments as fixed, long-term bets rather than as a series of flexible, staged investments. 

 AI leaders should use real options thinking, where AI investment is treated as a portfolio of real options—allowing firms to scale, pivot, or abandon AI initiatives as needed. AI strategy should integrate multi-stage decision-making, continuously reassessing the value, risk, and potential of AI investments. Examples include:  

• Amazon’s AI-first logistics strategy: Small-scale AI adoption in warehouses before expanding globally.
• Microsoft’s cloud-based AI strategy: Integrating multiple AI models into Azure rather than committing to one.   

AI should not be viewed as a one-time investment but as a living portfolio of adaptive AI strategies, allowing firms to assess the value, risks and competitive implications of their AI investments.

 The Dynamic Capabilities Framework for AI Success

 While AI platform control, interoperability, and strategic investment matter, firms need the ability to continuously adapt—which is where Dynamic Capabilities Theory (Eisenhardt & Martin; Teece) becomes crucial. 

1. Sensing: Identifying AI Shifts Before Competitors  

Firms must develop AI foresight capabilities to anticipate: 

• Emerging AI architectures (e.g., multimodal models, agentic AI). 
• Shifts in AI regulation (e.g., EU AI Act & responsible AI mandates). 
• Ecosystem power plays (e.g., OpenAI’s API monetization vs. Meta’s open-source approach). 

Best Practices for Sensing AI Trends:

• AI research labs within firms (e.g., Google DeepMind's forward-looking AGI research). 
• AI ecosystem partnerships to track evolving standards. 
• AI experimentation teams that rapidly test new models in controlled environments. 

2. Seizing: Capturing AI Opportunities at Scale

 Once firms sense an AI opportunity, they must mobilize quickly to capitalize on it.

Best Practices for Seizing AI Investments:

• AI-first decision-making structures → Cross-functional AI adoption teams. 
• AI infrastructure flexibility → Cloud-agnostic AI deployment strategies. 
• AI talent pipelines → Investing in AI-specialized hiring & upskilling. 

 Examples:

• JPMorgan rapidly scaled AI for financial modeling, integrating AI across trading, risk, and fraud detection systems. 
• Microsoft built AI-first business units, embedding AI into every cloud product rather than treating it as a separate initiative. 

3. Transforming: Reinventing Business Models with AI

 Transformation isn’t a one-time shift to AI—it’s an ongoing process of reconfiguring business models to stay competitive. 

 Best Practices for AI Transformation:

• AI-first culture → AI upskilling across the entire enterprise. 
• AI governance evolution → Adopting responsible AI frameworks proactively. 
• AI-driven business reinvention → Moving from automation to **AI-enabled decision-making.

 Examples:

• Tesla’s AI-driven supply chain continuously refines itself using real-time data from its fleet.
• BloombergGPT transformed financial research by embedding AI insights into core financial products. 

Conclusion: Winning in AI Requires More Than Just Technology

 AI is no longer a technology arms race—it is a strategic imperative that requires: 

• Platform leadership: Controlling AI’s access and distribution. 
• Interoperability & ecosystem adaptability: Avoiding vendor lock-in. 
• Strategic AI investment decisions: Using real options to stay flexible. 
• Dynamic capabilities: Continuously sensing, seizing, and transforming AI opportunities. 

 Call to Action for AI Leaders: 

• Stop thinking of AI as a one-time investment—start treating it as a dynamic capability. 
• Move beyond adopting AI models—start shaping AI ecosystems. 
• Build AI-first cultures that continuously adapt—because AI’s competitive landscape will only accelerate. 

 Let’s continue the conversation—because AI strategy is evolving, and so should we.

 References:

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Disclaimer: The views expressed in this article are solely those of the authors and do not necessarily reflect the opinions or positions of any affiliated organizations.