Join Neysa AI & AIM Innovator Series to Learn What Breaks When AI Moves to Production

AI has moved well beyond experimentation. Enterprises are now deploying AI across customer support, software development, internal knowledge management, cybersecurity, and business operations. But for many organisations, the real challenge begins after deployment.

In India, AI rarely breaks in the lab. It breaks when it tries to scale. Data has to stay local. Regulators expect visibility rather than explanations. And costs surface only once workloads reach production volume.

[Register now]

Running AI in production introduces a set of problems that look nothing like the ones teams solved during the pilot. Infrastructure costs rise faster than usage. Model quality can shift without warning, and teams often cannot see why or stop it. Infrastructure availability and integration work slow deployments down, with engineering time going into managing infrastructure rather than building AI.

Underneath all three sits the same issue. Most of what determines whether AI works day to day is controlled by the cloud provider, not by the enterprise running it.

Traditionally, organisations have had two ways to respond. 

• Public cloud platforms offer flexibility and quick access to computing resources, but make long-term costs harder to predict and offer limited infrastructure control. 
• Building on-premises AI infrastructure provides greater control, but requires significant upfront investment and longer deployment timelines.

Neither delivers all three. 

That is the trilemma at the centre of this session: cost, control, and speed. Optimise for two, and the third gives.

As enterprise AI adoption accelerates, a third category of AI infrastructure, often described as a Neocloud, is emerging between these two approaches, promising a different balance between cost, operational control, and deployment speed. Understanding these options has become an important decision for technology leaders planning AI at scale.

To explore these challenges, Analytics India Magazine, in partnership with Neysa, is hosting the next edition of the AIM Innovator Series.

AIM Innovator Series | The AI Trilemma: What Breaks in Production

Date: 3 September 2026 Time: 5.00 to 6.00 pm IST

[Register now]

The session will examine what changes when AI moves from proof of concept to production, and why infrastructure decisions increasingly determine the success of enterprise AI initiatives.

The session will cover:

• Why AI costs often grow disproportionately as workloads scale.
• What causes quality degradation in production AI systems, and why it is difficult to detect and manage.
• Why infrastructure availability and integration remain major bottlenecks for enterprise AI deployments.
• Why data residency and regulatory visibility make production AI harder in India than elsewhere.
• The trade-offs between public cloud, on-premises infrastructure, and emerging AI infrastructure models.
• How organisations running AI at scale balance cost, governance, and deployment speed.

The session is designed for:

• CTOs, CIOs, and Heads of AI responsible for production AI deployments.
• Cloud architects, platform leaders, and engineering managers evaluating where AI workloads should run.
• Enterprise AI programme leaders and digital transformation executives managing AI cost, risk, and business outcomes.

The featured speaker is Sujit Janardanan, Chief Marketing Officer at Neysa.

With more than two decades of experience in enterprise technology, Janardhan has led go-to-market strategies across organisations including Google Cloud India, AWS, and Cropin Technology. At Neysa AI, he focuses on helping enterprises adopt AI infrastructure that enables scalable and practical AI transformation.

As more organisations move AI into production, infrastructure choices are becoming business decisions rather than purely technical ones. Whether enterprises prioritise faster deployment, predictable costs, or greater operational control, understanding the trade-offs is critical to building sustainable AI systems.

If you are responsible for deploying, managing, or scaling AI within your organisation, this session offers practical perspectives on the infrastructure decisions shaping enterprise AI today.

[Register now] and join us on 3 September, 5.00 to 6.00 pm IST, to work through the AI trilemma of cost, control, and speed.