AiThority Interview With Arun Subramaniyan, Founder & CEO, Articul8 AI

Arun Subramaniyan, Founder & CEO at Articul8 AI talks about the challenges most AI innovators face when scaling their AI products in this AiThority interview:

_______________ 

Hi Arun, take us through Articul8’s growth journey so far?

Articul8 was founded around a simple but increasingly urgent observation. While interest in generative AI was nascent, enterprises were already struggling to apply it reliably in real-world environments, at scale.

Over the past two years, our growth has been shaped directly by production demands from large enterprises. We’ve worked closely with customers across energy, manufacturing, aerospace, financial services, and other critical infrastructure sectors, industries where accuracy, accountability, and traceability aren’t optional.

That focus has guided how we’ve built the platform, how we measure success, and how we think about AI’s role inside the enterprise. The result has been steady adoption driven by real operational use cases.

Tell us about your recent funding round and the plan ahead for Articul8?

The recent Series B reflects strong investor alignment on where enterprise AI is heading. Rather than viewing AI as a standalone tool, enterprises are increasingly looking for systems that can operate safely and consistently within their existing environments.

Looking ahead, the focus is on scaling what’s already working. This means expanding globally, deepening our reasoning and agentic capabilities, and supporting more complex enterprise deployments. Our goal is to help organizations operationalize AI in ways that deliver sustained business value.

Why do most enterprises today struggle to move GenAI beyond pilots?

Most GenAI pilots are built around small use cases that perform well in demos and general chatbot scenarios, but struggle in production with large-scale datasets and tasks that require domain-specific knowledge. In addition to accuracy, enterprises quickly run into challenges around data governance, security boundaries, reliability, and explainability.

Without addressing how AI fits into broader workflows, systems, and decision-making processes, these efforts remain isolated and difficult to scale.

Ultimately, moving beyond pilots requires treating AI as part of the enterprise system, not as an overlay.

Also Read: AiThority Interview With Claire Southey, Chief AI Officer at Rokt

In 2026 and beyond, based on this, will there be a greater need for full-stack domain-specific platforms? 

Yes, and largely because enterprises are realizing that models alone are not sufficient to get real businesses value. Also, having access to general purpose AI is no longer a differentiator – enterprises are realizing that all their competitors also have access to the same AI technology. Personalizing AI for their own needs is therefore required to differentiate.

Different industries have different data structures, regulatory requirements, and decision-making contexts. Full-stack, domain-specific platforms allow organizations to tailor models, workflows, and evaluation criteria to their specific environments, rather than forcing business processes to adapt to generic AI tools.

As AI becomes embedded in core operations, this level of specialization will be required to drive differentiation.

Why is a reasoning plane that orchestrates LLMs, agents, and enterprise data considered the missing link today?

Large language models are powerful, but on their own they don’t reason across systems, context, and constraints. Enterprises don’t just need responses, they need decisions that account for structured data, domain rules, and operational context.

A reasoning system is needed to determine which models or agents should be used for a given task, how enterprise data is incorporated, and how outputs are evaluated and routed, at runtime. This shifts AI from static responses to dynamic, context-aware decision support, because the rules cannot be completely defined until runtime

Without this layer, enterprises are left stitching together tools manually, which limits scale and reliability.

Five thoughts on AI and the future of AI backed innovations before we wrap up?

1. Organizations using AI effectively will leapfrog their competition. 

Organizations that figure out how to scale AI in their complex environment accurately and reliably will create a disproportional differentiation for themselves and win.

2. AI will move from tools to systems.

The future lies in AI that operates across workflows, not isolated applications.

3. Domain context matters most.

Bigger models won’t automatically mean better outcomes in specialized or regulated environments.

4. Evaluation will become strategic.

Knowing how and when AI fails will be as important as knowing when it succeeds.

5. Responsible AI will be operational and global by design. 

Beyond governance and security, enterprises will expect AI systems to operate with cultural and linguistic awareness, ensuring accountability and trust across markets, not just in Western or English-first contexts.

Also Read: The Death of the Questionnaire: Automating RFP Responses with GenAI

[To share your insights with us, please write to psen@itechseries.com]