How Ziroh Labs sees the next phase of enterprise AI beyond GPU infrastructure

Enterprise AI is moving into a new phase where success is increasingly determined by business outcomes rather than infrastructure scale. As organisations look beyond AI pilots, they are becoming more conscious of infrastructure costs, data sovereignty and measurable return on investment. This shift is creating fresh opportunities for system integrators, managed service providers and channel partners that can help enterprises deploy AI efficiently without significantly increasing capital expenditure.

According to Vineet Mittal, Senior Vice President, Ziroh Labs, the next wave of enterprise AI will not be driven solely by larger GPU clusters. Instead, it will focus on making AI economically viable by allowing organisations to leverage their existing infrastructure while delivering production-ready AI solutions that solve real business problems.

"Enterprise AI adoption will be driven not just by better models but by making AI economically viable at scale."

CPU-based AI is changing enterprise AI economics

Mittal believes many enterprise AI workloads do not necessarily require expensive GPU infrastructure. Instead, a large number of business applications can run efficiently on existing x86 server environments through CPU-based AI, allowing organisations to accelerate adoption without undertaking major hardware refresh programmes.

He points to enterprise use cases such as data classification, data labelling, sentiment analysis, optical character recognition, object detection and machine learning workloads that can be integrated directly into existing business processes.

"Many enterprise challenges can be addressed by models specifically suited for enterprise problems rather than relying on expensive GPU infrastructure."

This approach allows organisations to deploy production AI using infrastructure they already own, significantly lowering entry barriers for enterprise AI initiatives.

AI outcomes are creating new revenue opportunities for channel partners

Mittal believes the IT channel's biggest opportunity lies not in selling AI infrastructure but in delivering complete enterprise solutions that generate measurable business outcomes.

By modernising existing customer infrastructure, partners can accelerate AI adoption while helping customers avoid costly GPU refresh cycles. This also opens opportunities for managed AI services, particularly where unused CPU resources within enterprise data centres can be repurposed for AI workloads.

"The market is shifting from selling AI infrastructure to delivering AI outcomes."

He also sees significant potential in the mid-market, where organisations often want AI-powered business applications but cannot justify investing in dedicated GPU clusters. Rather than focusing solely on model deployment, channel partners can differentiate themselves by integrating AI into enterprise workflows, proprietary data and existing applications.

Enterprise AI is moving beyond experimentation

According to Mittal, enterprise customers are increasingly looking for production-ready AI solutions rather than standalone models. Organisations are carefully evaluating AI investments as infrastructure costs continue to rise, placing greater emphasis on tangible return on investment.

He notes that many businesses accelerated AI infrastructure spending during the initial wave of enterprise AI, only to realise that hardware alone does not generate business value.

"The focus has shifted from experimentation to demonstrating tangible ROI, with AI budgets increasingly tied to the business value delivered rather than the scale of the underlying infrastructure."

To address this challenge, Ziroh Labs has built an ecosystem of system integrators, independent software vendors and technology partners that develop industry-specific AI solutions. These partners help organisations modernise legacy applications, automate business processes and build AI-native solutions with governance, security and human oversight built into enterprise workflows.

Private AI is addressing data sovereignty and compliance

Data sovereignty has become an increasingly important consideration for enterprises operating in regulated sectors such as banking, healthcare, government and manufacturing. Many organisations want to benefit from AI while ensuring sensitive information never leaves their own controlled environments.

Mittal explains that the platform has been designed to support fully private deployments inside customer data centres or private cloud environments.

"Customers should have complete control over their AI infrastructure, models and data."

Beyond keeping enterprise data within customer environments, the platform incorporates capabilities such as governance, secure model deployment, auditability, role-based access control and integration with existing enterprise identity and security systems. This allows partners to offer AI platforms that satisfy stringent compliance requirements while supporting future regulatory changes.

Rather than delivering individual AI models, partners are able to position comprehensive sovereign AI environments that integrate with existing enterprise infrastructure and support multiple AI models as business requirements evolve.

Partners are becoming enterprise AI advisors

Looking ahead, Mittal expects enterprise AI platforms to evolve well beyond model execution. Efficient CPU inference will remain important, but customers increasingly require integrated capabilities such as model orchestration, retrieval-augmented generation, agentic AI workflows, governance, observability and enterprise security.

He believes these broader platform capabilities create stronger opportunities for channel partners than infrastructure sales alone.

Over the next 12 to 18 months, Ziroh Labs plans to expand enterprise adoption, strengthen its partner ecosystem and continue investing in AI innovation across governance, infrastructure optimisation and enterprise AI capabilities. The company is also developing a structured partner programme that supports technology startups, independent software vendors, system integrators and managed service providers at different stages of their AI journey.

Mittal also highlighted investments in AI education, including partnerships with higher education institutions to develop AI courses for developers, alongside the introduction of industry-specific AI solutions for sectors such as education, healthcare, manufacturing and enterprise services.

"Partners that invest now in AI skills, industry expertise and solution development will be best positioned to lead this next wave of enterprise transformation."

Building enterprise AI around business value

As enterprise AI matures, organisations are becoming less focused on infrastructure size and more concerned with measurable business outcomes, governance and long-term operational efficiency. Vineet Mittal's perspective reflects a broader transition towards cost-efficient, private AI environments that leverage existing enterprise infrastructure while addressing security and compliance requirements. For system integrators, managed service providers and channel partners, this shift presents an opportunity to move beyond infrastructure deployment and establish themselves as long-term advisors delivering practical AI solutions that align technology investment with business value.

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