Helium AI and GTT DATA target enterprise AI execution gap with scale-first partnership
As enterprises move rapidly from experimenting with AI to embedding it into core operations, a familiar challenge is emerging: while AI capabilities are advancing quickly, execution at scale remains elusive. Many organisations remain stuck between pilots and production, constrained by fragmented ownership, governance concerns and integration complexity.
Against this backdrop, Helium AI, developed by Neural Arc, has entered into a strategic partnership with GTT DATA to help enterprises deploy AI across business workflows in a governed, execution-led manner.
The partnership, announced on January 7, 2026, is aimed at global enterprises seeking to move beyond proof-of-concept deployments and operationalise AI across teams, systems and decision-making processes.
From AI pilots to enterprise execution
While generative and agentic AI technologies have matured significantly, operationalising them remains a bottleneck for many organisations—particularly in regulated industries. According to both companies, the gap lies less in intelligence and more in execution: aligning AI systems with enterprise data foundations, governance frameworks and real-world workflows.
The collaboration brings together Helium AI’s execution-first AI operating platform with GTT DATA’s experience in enterprise data architecture, AI implementation and governance. Together, they aim to support production-ready AI deployments that prioritise security, compliance and accountability alongside speed and scale.
At the centre of the partnership is Helium AI, which is designed to autonomously reason, plan and act within enterprise workflows. GTT DATA will complement this with strategy, integration and governance capabilities to ensure that AI systems are deployed responsibly and aligned with organisational priorities.
Balancing autonomy with oversight
A key focus of the collaboration is addressing one of the most persistent enterprise concerns around AI adoption: how to balance autonomy with human oversight. As AI systems take on more responsibility for operational decisions and execution, enterprises are under pressure to ensure transparency, control and auditability.
According to the companies, the combined approach—autonomous AI supported by enterprise-grade governance—aims to reduce execution friction while maintaining trust. This is particularly relevant for organisations operating in complex, regulated environments where AI adoption must align with compliance and risk management requirements.
Commenting on the partnership, Aniket Tapre, Founder and CEO of Neural Arc, said:
“The enterprise AI challenge today is not intelligence, it’s execution. Most organisations are stuck between pilots and impact. This partnership brings together autonomous AI that can execute with human expertise that understands enterprise complexity, enabling organisations to move from intent to outcomes with control, accountability, and trust.”
From GTT DATA’s perspective, the collaboration is positioned as a way to help enterprises operationalise generative AI in a structured and scalable way. Srikumar Kumar, President at GTT DATA, said:
“By working with Helium AI, we are enabling enterprises to operationalise Generative AI in a scalable and practical way, while maintaining strong governance and alignment with business needs.”
Embedding AI into everyday operations
Rather than treating AI as a standalone initiative, the partnership is focused on embedding AI into day-to-day operations and decision-making. By combining execution-focused AI capabilities with enterprise data and governance frameworks, the two companies aim to help organisations turn AI into a core, trusted business capability.
The broader objective, according to the announcement, is to enable consistent execution, measurable outcomes and responsible scaling of AI across the enterprise. For organisations struggling to bridge the gap between experimentation and impact, the partnership reflects a growing recognition that successful AI adoption depends as much on governance and execution discipline as on model sophistication.
As enterprises enter the next phase of AI adoption, collaborations that address operational complexity, accountability and scale are likely to play a defining role in determining which AI initiatives deliver lasting business value.