TL:DR
- Enterprises are struggling with AI complexity as only 5% of projects reach production, driving the need for multiple specialized partners across cloud, models and hardware.
- Distributed AI success requires vendor-neutral platforms that connect data sources with compute across clouds, regions and partners at high speed.
- Organizations achieve up to 70% lower TCO and unlock AI’s full potential through vendor-neutral ecosystems that avoid lock-in and ensure compliance.
As enterprises race to implement AI at scale, a critical truth is emerging: Success isn’t just about having the right technology—it’s about having the right partners. The most successful AI deployments aren’t built in isolation but thrive within expansive partner ecosystems that bring together cloud providers, model builders, hardware innovators and specialized service providers on neutral, interconnected infrastructure.
The complexity of modern AI workloads demands capabilities that no single vendor can provide. From GPU optimization and model selection to data sovereignty and real-time processing, enterprises need access to best-of-breed solutions that can work together seamlessly. As we explored in our blog on distributed AI infrastructure, the ability to distribute AI workloads across multiple locations and partners is essential for innovation at scale. This is where the power of an expansive partner ecosystem becomes clear: It transforms infrastructure from a limiting factor into a multiplier for innovation.
According to IDC, “By 2028, more than 90% of newly developed applications will be multicloud enabled, having been architected to leverage platform-delivered capabilities and deliver more innovative solutions.”[1] This shift toward distributed, multi-partner approaches isn’t just a trend—it’s becoming the foundation for competitive advantage in the AI era. Organizations that can effectively orchestrate diverse partners within their ecosystem will be the ones that unlock AI’s full potential while maintaining the flexibility to evolve as the technology landscape changes.
Industry leaders weigh in on partner ecosystem success
Lisa Miller, SVP Platform Alliances, Equinix – Expansive Partner Ecosystems
The Modern AI Lakehouse imperative
Vinay Samuel, CEO and Founder, Zetaris, highlights the challenges businesses face when it comes to managing their AI datasets:
“Businesses are still trying to run AI on cloud data platforms built for BI, and that’s why we’re seeing 40% overspend and only 5% of projects make it to production. Success demands a Modern AI Lakehouse that delivers real-time access to all available data across hybrid, multicloud environments, with governed, high-performance processing and data center resiliency. Combined with Equinix’s global ecosystem of clouds, networks and partners, sovereign AI can run securely at the edge, lowering total cost of ownership by up to 70%.”
Vinay’s insight underscores how the right ecosystem partnerships can dramatically improve both the economics and success rates of AI initiatives. When organizations can seamlessly connect specialized data platforms with distributed compute resources, they move from experimental AI to production-ready solutions.
The power of open, modular architectures
Mindy Cancila, Vice President of Corporate Strategy at Dell Technologies, emphasizes the importance of flexibility and choice:
“Dell has long believed in the importance of open, modular architectures that give customers choice. This is why we strategically partner across all layers of the stack—from silicon diversity to model providers and data center partners like Equinix. As organizations deploy inference across distributed datasets, each AI use case brings unique requirements for model selection, accuracy, performance and cost. To meet evolving demands, solutions must remain adaptable, ensuring security, ROI and the business outcomes customers need.”
This perspective reveals how leading technology providers are thinking beyond their own solutions to enable entire ecosystems. The ability to mix and match components—from chips to models to infrastructure—gives enterprises the agility to optimize for specific use cases rather than being locked into one-size-fits-all approaches.
Matching global capacity with demand
Patrick Kennedy, Founder of ServeTheHome, looks at the infrastructure challenges ahead:
“The next generation of infrastructure will need to seamlessly connect data sources with AI compute located near where insights are required. As AI proliferates into new industries and roles, a key challenge is being able to match this capacity and demand globally while maintaining sovereignty and security around the data and insights generated. This process requires flexible, neutral infrastructure that allows individuals and companies access to trusted networks and providers at scale and in real time.”
Patrick’s observation points to a fundamental shift in how we think about infrastructure. It’s not just about raw compute power or storage capacity—it’s about creating intelligent networks that can dynamically connect resources wherever they’re needed, whenever they’re needed.
The ecosystem advantage in action
These expert perspectives converge on a central theme: The organizations that will win with AI are those that can effectively orchestrate diverse partnerships within a unified infrastructure platform. This isn’t just about technical integration—it’s about creating environments where innovation can flourish through collaboration.
As explored in our recent post on AI Ecosystems 101, choosing the right mix of data, model and infrastructure providers is becoming a core competency for enterprise AI success. The expansive partner ecosystem approach enables organizations to leverage specialized expertise at every layer of the stack while maintaining the flexibility to adapt as requirements evolve.
This collaborative approach is particularly critical for emerging AI patterns like federated learning, where multiple organizations train models on their local data and share insights without exposing raw datasets. These advanced use cases require not just technical infrastructure but trusted relationships and secure interconnection between partners—exactly what neutral, interconnected platforms enable.
To dive deeper into how expansive partner ecosystems are reshaping enterprise AI strategies and hear more insights from industry leaders, visit Volume 3 of the Equinix Indicator. Discover how leading organizations are building the collaborative foundations for AI success and learn practical strategies for orchestrating your own partner ecosystem to accelerate AI innovation.
[1] IDC, IDC FutureScape: Worldwide Cloud 2025 Predictions, #US52640724, October 2024.
Subscribe to the Equinix Blog