Unlocking Success in Enterprise AI with SUSE and Katonic

Escape the Infrastructure Trap

Guest blog by Prem Naraindas, Founder and CEO of Katonic AI

Author Bio

Prem Naraindas is the Founder and CEO of Katonic AI, bringing over 20 years of technology leadership experience to democratizing enterprise AI. Prior to founding Katonic AI, he held leadership positions at DXC Technology, Luxoft, and Tata Consultancy Services. A Forbes Technology Council member and LinkedIn Top Voice, Prem is a recognized thought leader in enterprise AI implementation and speaks regularly at international technology conferences

Twenty years of building enterprise technology solutions taught me that the most elegant innovations often die in the complexity of enterprise infrastructure.  Today, this pattern is playing out on a massive scale in artificial intelligence.

Industry research suggests that generative AI could add between $2.6 to $4.4 trillion annually to the global economy. However, studies indicate that up to 85% of enterprise AI projects fail to reach production. (McKinsey: Economic potential ($2.6-$4.4 trillion) & Gartner: Enterprise AI project failure rate (85%)) Having founded Katonic AI after leadership roles at DXC Technology, Luxoft, and Tata Consultancy Services, I’ve witnessed this paradox firsthand across hundreds of enterprise implementations.

The problem isn’t the technology.

It’s how we’re thinking about enterprise AI infrastructure.

The Root Cause: Infrastructure Before Innovation

Most enterprise AI failures stem from a fundamental misalignment. Organizations approach AI deployment like traditional software projects—starting with infrastructure, then trying to layer innovation on top. This backwards approach creates three critical bottlenecks.

• First, the skills trap. Data scientists spend 80% of their time wrestling with GPU management, model serving infrastructure, and security compliance instead of solving business problems. Meanwhile, business users who best understand these problems remain locked out by technical barriers.
• Second, the security paradox. Enterprises need AI to be both innovative and compliant. Traditional approaches force a choice: either compromise on security to move fast, or get trapped in compliance discussions that kill momentum.
• Third, the scaling wall. Even successful AI pilots struggle to scale because they’re built on fragmented infrastructure that can’t handle multi-cloud, edge, and hybrid deployments simultaneously.

These aren’t implementation challenges. They’re architectural problems that require architectural solutions.

Rethinking the Stack: Infrastructure as an Enabler

At Katonic AI, we’ve taken a different approach. Instead of building AI platforms that require specific infrastructure, we built AI capabilities that leverage enterprise-grade Kubernetes infrastructure as a foundation.

This is where our partnership with SUSE becomes transformative. SUSE AI is a secure, modular, extensible, and enterprise-grade infrastructure solution, empowering you with choice and sovereignty over your AI environment. When you layer Katonic’s AI Platform on this foundation, something remarkable happens: infrastructure becomes invisible.

The Katonic and SUSE AI stack features:

• SUSE Rancher Prime provides multi-cluster Kubernetes management across any distribution
• SUSE Security delivers zero-trust security and FIPS 140-2 compliance
• SUSE Observability enables unified monitoring across infrastructure and AI workloads
• Katonic AI delivers an enterprise-grade, Kubernetes-native Gen AI platform enabling both no-code and code-first AI development
• Multi-tenant architecture provides secure resource isolation with centralized governance across distributed teams and business units
• ISO 27001 certified with complete data sovereignty across any deployment model — cloud, on-premises, or hybrid

The business impact is profound:

• A telecommunications provider’s sales teams now save 2-3 hours daily with AI-generated proposals that increase win rates
• A research university empowered hundreds of researchers and academics to deploy dozens of innovative AI use cases in their first year
• A consulting firm reduced data analysis time by 60% through conversational analytics

These weren’t months-long implementations.  They were 30-day deployments that achieved break-even 25 times faster than traditional approaches.

The Democratization Imperative

The most significant shift isn’t technical—it’s organizational. Successful AI implementations democratize AI development while maintaining enterprise control.

Traditional AI platforms require specialized teams to build, deploy, and maintain AI applications.  This creates bottlenecks that scale poorly and exclude the business users who understand problems best.

Katonic AI’s no-code platform inverts this model. Business analysts can build sophisticated AI agents and virtual assistants through visual workflows.  Domain experts can create conversational interfaces for their data without writing code.  IT teams maintain governance and security without becoming deployment bottlenecks.

This isn’t about replacing technical teams.  It’s about enabling them to focus on innovation while business users solve their own problems with AI tools.

Enterprise Requirements Without Enterprise Complexity

The partnership with SUSE addresses a fundamental challenge in enterprise AI: how do you deliver consumer-grade simplicity with enterprise-grade requirements?

Air-gapped deployments,  multi-tenant isolation, FIPS compliance, SOC 2 certification, GPU resource management: These aren’t optional features for enterprise AI — they’re table stakes.

Traditional approaches require organizations to choose between simplicity and enterprise requirements.  SUSE’s infrastructure platform provides enterprise capabilities as foundational services, allowing Katonic to deliver simplicity on top of enterprise-grade infrastructure.

The result is AI that works how enterprises actually operate: across multiple clouds, in regulated environments, with complete data sovereignty when required.

Looking Forward: The Open Infrastructure Advantage

The AI landscape will continue evolving rapidly – new models, new capabilities, new deployment patterns will emerge regularly.  Organizations that lock themselves into proprietary AI stacks will struggle to adapt.

Our approach with SUSE creates optionality:

• any large language model
• any  GPU
• any cloud provider
• any deployment model

This isn’t vendor neutrality for its own sake — it’s strategic flexibility that lets organizations adapt to changing requirements without rebuilding their AI infrastructure.  The enterprises succeeding with AI today are those that treat AI as a capability to be enabled, not a project to be managed.  They’re building on open, standards-based infrastructure that can evolve with their needs.

The Path Forward

Enterprise AI success requires rethinking the relationship between infrastructure and innovation.   Infrastructure should enable innovation, not constrain it.  Security should be foundational, not an afterthought.  Business users should be empowered, not excluded.

The 15% of organizations succeeding with enterprise AI aren’t necessarily smarter or better funded.  They’re approaching the problem differently.  They’re starting with business outcomes and working backward to technology choices.  They’re choosing platforms that democratize AI development while maintaining enterprise control.

Most importantly, they’re building on infrastructure that scales with their ambitions rather than limiting them.

The choice for enterprises isn’t whether to adopt AI — it’s whether to join the 15% that succeed or the 85% that struggle with infrastructure complexity.  The technology to succeed is available today.  The question is whether organizations are ready to think differently about how they deploy it.

So what are some next steps you can take?

• Read more about the joint solution here
• Become a SUSE partner by sending us an email: certifications@suse.com

Ready to explore enterprise AI that actually works?  Visit www.katonic.ai or contact SUSE to discuss how SUSE Rancher Prime can deliver the flexible, scalable foundation for your AI workloads.