Straive in the News

After a bunch of calls with one of our interns, Varun (a student at IIT Madras), our CEO, Ankor, messaged me: “This guy is fantastic. How is he doing it?” This is what Varun was doing: he fed transcripts directly into Claude Code (or Codex) and delivered results. That’s the whole process. He doesn’t interpret the content or apply domain knowledge. He simply gets out of the way.

Imagine a shipment getting stuck at a congested port. Traditionally, the system would send an alert to a logistics manager, who would then coordinate with transporters, warehouses, and customers to manage the disruption. But what if the system itself could identify an alternative route, reassign capacity, notify stakeholders, and execute those changes in real time?

While India-based teams are creating frameworks that headquarters signs off on, indicating a function-by-function inversion in decision making, yet responsibility without authority remains a hurdle.

AI adoption in supply chain management is ramping up. It promises to help providers overcome major logistical and supply chain hurdles so they can be more proactive, with key benefits including more accurate demand and delivery forecasting, real-time monitoring, and smart inventory optimization, among others.

Enterprise AI doesn’t have a technology problem. MIT’s headline that 95% of GenAI pilots fail sparked debates on whether AI is ‘ready’ for the enterprise. But recent McKinsey and Atlassian research points to a more uncomfortable truth.

Artificial Intelligence is everywhere, but most companies still struggle to turn AI into a real business impact.

Straive CEO Ankor Rai explains why GenAI pilots stall, arguing that content readiness, governance, and operational discipline, not model capability, determine whether AI scales successfully in regulated enterprises.

Enterprise AI models are improving at unprecedented speed, costs are falling, and access is no longer limited to specialist teams. Yet inside large organizations, AI remains difficult to operationalize.

Enterprises are accelerating the shift of generative AI from pilots into production, but outcomes remain inconsistent. While early experiments often succeed, many initiatives falter once exposed to organisational complexity and real-world data.

As AI becomes more embedded into everyday workflows, drift goes from being a technical concern to an operational and business risk.

Straive CEO Ankor Rai explains why GenAI pilots stall, arguing that content readiness, governance, and operational discipline, not model capability, determine whether AI scales successfully in regulated enterprises.

Enterprise AI models are improving at unprecedented speed, costs are falling, and access is no longer limited to specialist teams.

Enterprises are moving GenAI into production faster than expected, yet results remain mixed.

Technology and data science are at the crux of addressing these hurdles to ensure both safe and speedy delivery.

AI projects often look great in pilot form and then fall apart once they’re added to real workflows.

Digital exhaust is one of the most consequential data streams in modern business strategy, precisely because most organisations generate it in vast quantities and, until recently, treated it as an afterthought.

Enterprises are moving away from a cost-centric lens, drawn instead by India’s strengths in AI, automation, and data-driven innovation

Artificial intelligence (AI) designed for food operations is rapidly evolving, having become razor sharp at forecasting demand and managing inventory, among an expanding menu of capabilities.

This report is from this week’s CNBC’s “Inside India” newsletter, which brings you timely, insightful news and market commentary on the emerging powerhouse.

The expectation to see overnight ROI from AI initiatives is creating high-pressure work environments that will affect how the technology is applied if the situation isn’t addressed.

People and businesses are obsessed with the potential of AI, but 80% of AI projects will fail—and it's not down to want or enthusiasm.

The pressure to adopt AI systems or risk falling behind is growing by the day, with many business leaders believing AI is a one-size-fits-all solution to solving their biggest pain points. However, according to The Rand, over 80% of AI projects are predicted to fail.

Strengthening supply chain resilience must be a top priority for manufacturers in order to literally and figuratively weather the storm that is these environmental disruptions.

The role of artificial intelligence (AI) is only growing throughout the pharmaceutical industry.

All pharmaceutical companies developing new drugs must meet intense regulatory guidelines and submit thousands of pages of clinical study reports and safety summaries to get closer to getting their drug on the market.

Data visualization, where raw data is visually represented, is so much more than creating charts and graphs. It’s about connecting the right dots to map out a clear, logical, and, most importantly, impactful story that a wide range of audiences can understand.

The financial industry is at a turning point where AI has become a double-edged sword. While it’s driving innovation, it’s also introducing new risks to contend with.

Supply chain disruptions are a worst-case scenario that can't be ignored by entrepreneurs. They cause extensive damage, wreaking havoc on operations and logistics, with consequences ranging from unhappy customers to financial calamities.

The concept gained massive popularity with the emergence of ChatGPT in 2023, quickly becoming a hot topic.

In this era of IoT and start-ups, the pioneering spirit is perhaps at its record high.