AI ambitions are rising quickly across enterprises, with organizations testing new models, use cases and infrastructure across functions. Turning that activity into repeatable business value requires a production foundation that connects data, infrastructure, governance, applications and industry expertise.
As AI workloads spread across cloud, on-premises and edge environments, those dependencies become harder for any organization or technology provider to address in isolation. Ecosystem partnerships can bring together complementary capabilities and create a clearer route from experimentation to enterprise scale.
At AI Infra Summit 2026 in Santa Clara, Trusha Pandya, Chief Partner Strategist at Lenovo, discussed what enterprises need as they move from experimentation to production and why complementary capabilities across the ecosystem can make that transition easier to scale.
Our The AI Impact Imperatives, 2026 research reinforces the importance of collaboration. Among organizations already using external AI expertise, 90% say partners accelerate ramp-up and time to value, while 89% say partners increase the business impact of AI initiatives that reach production.
From pilot to production
Many enterprises have identified use cases, secured investment and tested AI across functions. Greater complexity emerges when a successful pilot must operate reliably across a much larger business.
“There’s no shortage of ambition right now,” said Pandya. “The question now is, how do you take it from a POC environment to a production environment, and how do you take it to scale?”
Production brings a wider set of considerations into the picture.
- Data must be accessible and governed.
- Infrastructure needs to support the performance and economics of the workload.
- Ownership must be clear, while security, operations and ongoing optimization need to be considered from the outset.
Our 2026 research, The Blueprint for AI Leadership, illustrates the importance of the underlying foundation. AI Leaders are eight times more likely than Followers to express confidence in their data foundations for GenAI initiatives, at 74% compared with 9%.
Pandya sees the same challenge from an infrastructure perspective.
“There’s no shortage of AI projects. There’s no shortage of funding for those AI projects. But how do you take those projects and scale it across different segments of a business, and how do you keep that momentum going?”
Sustaining that momentum requires infrastructure and operational capabilities that can support AI as demand increases and use cases expand across the enterprise.
Complementary capabilities create a stronger foundation
Ecosystem partnerships can help connect the different layers required for AI scale.
Our strategic alliance with Lenovo brings together capabilities across Edge Computing, Hybrid AI at the Edge, Hybrid Cloud, GPU-as-a-Service and Digital Workplace. Through a dedicated Center of Excellence, we co-innovate and scale integrated solutions that address real-world transformation use cases, from intelligent edge deployments to AI-optimized hybrid cloud architectures.
Pandya described the partnership in practical terms. Lenovo brings infrastructure capabilities spanning data centers to the edge, while we bring industry, integration and operational expertise that can help apply those technologies in complex enterprise environments.
“You’re the experts. You’re boots on the ground. You know the industry,” she said. “What better way to tap into your expertise to help provide a holistic solution for our customers together?”
Connecting those capabilities can help enterprises align infrastructure decisions with the business process being changed, the data required and the operating model needed to sustain the solution.
Start with the business outcome
The economics of AI make that alignment increasingly important.
As enterprises scale inference and deploy more agents, infrastructure choices have a direct bearing on the business case. The right architecture depends on the value of the workload, where the data resides, how quickly decisions need to be made and what level of control the organization requires.
“The conversation shouldn’t be what is the infrastructure cost,” said Pandya. “It should be going back to the basics of what is the business outcome, and how do we incorporate all these different KPIs?”
Productivity, operational efficiency and growth can each require different combinations of models, infrastructure and services. Starting with the desired outcome helps enterprises shape the technology environment around the economics and requirements of each use case.
This becomes particularly relevant in hybrid AI environments. Workloads may span private infrastructure, cloud and the edge depending on data requirements, performance, security and cost. Our alliance with Lenovo is designed to support that diversity, including hybrid cloud and Hybrid AI at the Edge.
Physical AI expands the ecosystem challenge
The infrastructure and partnership requirements will continue to evolve as AI moves further into physical environments.
Agentic AI is extending automation across digital workflows, while Physical AI brings intelligence into machines, devices and real-world operating environments. That creates greater demand for edge infrastructure, connectivity, operational data and integration with physical systems.
“As we are moving from Agentic AI to Physical AI, intelligence is going to be much deeper,” said Pandya. “It’s going to rely on more information at the edge.”
Lenovo positions its AI infrastructure around a hybrid approach designed to bring AI to where enterprise data resides, combining AI solutions, Hybrid AI Platforms and AI expertise and services to support the journey from pilot to production.
As these environments become more distributed, enterprises will increasingly need partners that can combine infrastructure, engineering, industry knowledge, operations and governance around individual business requirements.
Pandya expects demand for more integrated support to grow.
“They’re not going to want to rely on 10 different vendors or partners coming in,” she said.
Strong ecosystem partnerships can simplify that environment by bringing the right capabilities together around a shared outcome. For enterprises moving from pilots toward production, that coordination can help create the foundations, operating model and flexibility required to scale AI sustainably.
