How AMD's AI strategy is opening new growth avenues for channel partners

The AI conversation is often dominated by model sizes, GPUs and benchmark scores. However, AMD executives believe the next phase of AI adoption will be shaped just as much by software maturity, local AI execution and open ecosystems as by hardware innovation.

In separate interactions, Rahul Tikoo, Senior Vice President & General Manager, Client Business Unit, AMD, and Andrej Zdravkovic, Senior Vice President of GPU Technologies & Engineering Software and Chief Software Officer, AMD, outlined how the company is approaching AI PCs, developer platforms, enterprise AI deployments and partner-led opportunities. Their comments reveal a broader shift taking place across the AI ecosystem, where software enablement, local AI processing and open standards are becoming increasingly important.

AI PCs are evolving beyond performance-driven conversations

For AMD, the discussion around AI PCs is no longer limited to raw compute power. According to Rahul Tikoo, the growing interest around AI-enabled systems is increasingly tied to productivity, workflow acceleration and local AI execution.

He explained that AMD's Ryzen AI platforms are designed around a combination of CPU, GPU, NPU and large shared memory architectures that allow more sophisticated AI workloads to run locally.

“The superpower of that box is the large memory footprint. That enables very large models and even multi-agent environments where several AI models can run simultaneously,” said Rahul Tikoo, Senior Vice President & General Manager, Client Business Unit, AMD.

The company sees local AI as a growing requirement across multiple user segments. While gamers and content creators remain important audiences, AI developers, engineers, architects and data scientists are emerging as key adopters of these platforms.

Tikoo noted that many workstation users are increasingly integrating AI into their existing workflows rather than treating AI as a separate function. This convergence is creating demand for systems capable of handling traditional workloads and AI workloads simultaneously.

Software maturity is becoming a critical differentiator

While hardware remains important, AMD acknowledges that software has become a decisive factor in enterprise AI adoption.

According to Andrej Zdravkovic, AMD has invested heavily in strengthening the ROCm ecosystem, which serves as the software foundation for its AI platforms.

“Software becomes a different challenge. We are fighting an extremely established ecosystem, but we have made major progress with ROCm. It has reached a maturity point where customers can move applications and achieve very good performance,” said Andrej Zdravkovic, Senior Vice President of GPU Technologies & Engineering Software and Chief Software Officer, AMD.

He argued that the industry is increasingly moving towards higher software abstraction layers where developers focus on frameworks and models rather than underlying hardware architectures.

As AI development environments continue to evolve rapidly, AMD's focus is on ensuring that developers can deploy workloads across cloud, data centre, and client environments using a common software foundation.

Open ecosystems are becoming central to enterprise AI strategies

One of the strongest themes emerging from AMD's discussions is the growing importance of open ecosystems.

Zdravkovic believes enterprises and governments are increasingly looking for AI platforms that reduce dependency on a single vendor while offering greater control over data, infrastructure and software environments.

“If you really want to be truly sovereign, you do not want to be dependent on a single source. We are very much about open ecosystems and open standards,” he said.

The conversation around sovereign AI is gaining relevance as organisations seek greater control over where data resides and how it is processed. According to AMD, open software frameworks and open standards can play an important role in helping customers maintain that control.

Zdravkovic also linked open-source development with stronger security outcomes.

“By having open-source security, you are exposing your algorithms to the world and benefiting from the collective expertise of the industry. That is the core idea of openness,” he explained.

India is emerging as a strategic market for AI adoption

AMD views India as a significant growth market not only for AI PCs but also for edge AI and workstation deployments.

Rahul Tikoo highlighted the country's expanding technology adoption, infrastructure development and growing demand for advanced computing solutions.

“India is a very important market for us. We have significant investments in channel partnerships and programmes that provide training, incentives and go-to-market support to local partners,” he said.

The company believes AI adoption in India will not be limited to premium devices. AMD is positioning AI-enabled products across multiple price points to support broader accessibility.

At the same time, local AI deployment is becoming increasingly relevant in India due to connectivity constraints, regulatory requirements, data gravity concerns and growing AI token consumption costs.

Tikoo noted that AMD's India teams are actively working with local partners and ISVs to address these opportunities through edge AI deployments and industry-specific solutions.

AI is creating new opportunities for developers and gaming ecosystems

Beyond enterprise deployments, AMD is seeing growing interest from game developers and content creators.

The company highlighted how AI capabilities are being used not only within games but also throughout game development workflows.

According to Tikoo, technologies such as AI-powered game assistance, NPC enhancement and AI-assisted game creation are gaining traction.

“Game studios are using AI to actually develop games. We are seeing a lot of innovation happening around AI, NPUs and machine learning capabilities within gaming environments,” he said.

AMD's workstation platforms are also attracting developer communities that need local AI capabilities without depending entirely on cloud infrastructure.

The company's focus on larger memory footprints is aimed at enabling increasingly sophisticated local AI workloads, including large language models and multi-agent systems.

The partner opportunity lies in solutions, not hardware alone

For channel partners, system integrators and developers, AMD's strategy points towards a broader opportunity than simply selling hardware.

The company sees partners playing a crucial role in helping customers deploy AI solutions, optimise workflows and address industry-specific requirements.

Tikoo emphasised that customers are increasingly purchasing outcomes rather than individual products.

“Customers do not buy workstations. They buy solutions to their most complex problems,” he said.

As AI adoption expands across enterprises, government organisations and developer communities, channel partners that combine AI expertise, software enablement and domain-specific solutions may find themselves at the centre of the next phase of growth.

Conclusion

AMD's AI strategy reflects a broader shift taking place across the industry. While hardware innovation remains important, software maturity, open ecosystems, sovereign AI considerations and local AI execution are becoming equally significant. For channel partners and enterprise customers, the opportunity increasingly lies in building practical AI solutions that deliver measurable outcomes rather than focusing solely on infrastructure specifications.

Read More:

Synology Computex 2026: How Enterprise Storage and AI Readiness Are Reshaping Channel Opportunities

How Hybrid Cloud Complexity Is Driving Demand for Managed Services

Cybersecurity managed services are reshaping enterprise security models

Why Business Continuity Services Are Becoming the Next Growth Engine for MSPs