Straive in the News

AI adoption is moving beyond testing models to making them useful in everyday business operations. That shift is at the centre of Straive’s appointment as an OpenAI Select Partner within the OpenAI Partner Network, a global programme that helps partners build, sell and deliver AI solutions.

A string of layoffs and closures at India’s GCCs in recent months is highlighting a shift in the country’s GCC story, with parent-company restructuring, and AI-led productivity gains putting some centres under pressure even as India continues to attract new investments.

Enterprise AI is entering a more demanding phase. The early question was whether organisations could use AI to solve a particular problem. Today, as models become more capable and AI moves into everyday business processes, the harder question is whether organisations can make those systems work consistently, responsibly and economically at scale.

Enterprise AI adoption has reached a critical inflection point, where long-term competitive advantage is determined not by model availability, but by the operational discipline applied to real-world deployment. Moving past pilot projects requires bridging the gap between rapid prototyping and production-grade execution by embedding AI directly into complex business workflows, enterprise data architectures, and cross-system dependencies. Rather than waiting for perfect data environments, organizations achieve faster, measurable returns by granting intelligent agents safe, read-only access to existing repositories, using real-time workflows to expose and fix data quality issues as they arise.

An agentic AI system recently processed 400 insurance claims overnight. The committee responsible for overseeing it meets once a week, on Thursdays.

Most enterprise AI spend today is being measured the wrong way. Companies are grading their AI on accuracy and adoption, the same scorecard used three years ago, while the technology itself has quietly moved from assisting decisions to executing them. The result is a growing number of organizations that can point to impressive pilots and rising budgets, but not to a business process that actually works differently.

Traditional publishing models are rapidly reforming as mounting support for open access continues to soar. Last year, submissions soared by 30%, according to Springer Nature. But this demand is proving to be a double-edged sword as retracted articles have hit an all-time high in the past decade, and journals are flooded with AI-generated articles and fabricated research.

For the past few years, enterprises have ploughed ahead with an “AI everywhere” approach. Much of this was driven by performance anxiety as every organization wanted to demonstrate successful adoption that was promised to be a fast track to innovation.

India’s AI sovereignty debate may have been looking at the wrong thing. The question is not simply whether India can build its own AI models. It is whether India can remain in control of the technology those models depend on.

Enterprise AI adoption is accelerating, but the majority of AI pilots never reach full production. Industry research consistently finds that more than 80% of enterprise AI projects stall between proof-of-concept and production, a trap practitioners call "pilot purgatory.

These days, AI is reducing the number of entry-level jobs that we have. The trouble is, these are the jobs that are actually training tomorrow’s experts. How do we solve this?

Next phase of enterprise AI not about deploying models, but managing their evolution: Report

As AI moves from experimentation to institution-wide adoption, the focus is shifting towards connected data systems, scalable content frameworks and responsible governance. Abhijeet Sethi, Strategic Business Head, LearningMate a straive company in an exclusive interaction with Kaanchi Chawla of Elets News Network, discusses the barriers to scaling AI, the future of multilingual learning and what fully AI-enabled institutions could look like over the next three to five years.

At a time when the United States and China are locked in an intense race for AI leadership, the choice between open models and proprietary closed models is increasingly viewed as a matter of national strategy.

Straive has been named a Leader in AIM Research’s Top AI Service Providers for Life Sciences and Healthcare 2026 Penetration Maturity (PeMa) Quadrant.

Straive has been named a Leader in AIM Research’s Top AI Service Providers for Life Sciences and Healthcare 2026 Penetration Maturity (PeMa) Quadrant.

As home to the lion’s share of Global Capability Centers (GCCs), India is fast establishing itself as a hub of AI-driven innovation on the world stage in the pharmaceutical arena. Some of the world’s leading pharmaceutical firms—AstraZeneca, Pfizer, Novartis, to name but a few—have their largest GCCs established in India, with plans to expand these centers.

As AI automates many entry-level technical tasks, organisations risk weakening the pipeline that develops future experts. Rather than abandoning traditional career paths, businesses must redesign learning, redefine entry-level work and determine where AI should automate, augment or accelerate human capability.

The cybersecurity world has been thrown into chaos after Hugging Face announced on July 16 that it was hacked by an autonomous agent from an OpenAI model.

As AI automates entry-level tech work, organisations face a growing challenge in sustaining the talent pipeline for future experts. Rather than replacing traditional career ladders, firms must redesign learning and development by identifying where to delegate work to AI, where to preserve human expertise, and where AI can accelerate skill-building.

The core value of agentic AI to create faster, smarter search experiences hinges on quality, accessibility, and accuracy. AI-powered search delivers the best results when it understands user intent, context, and nuance.

Healthcare leaders in India are increasingly emphasizing that the next phase of AI adoption should focus on measurable impact rather than isolated pilot projects. While many hospitals and healthcare organizations have experimented with AI for tasks such as medical imaging, clinical decision support, administrative automation, and patient engagement, experts argue that lasting value will come from integrating AI into routine clinical workflows and public health systems.

As the FIFA World Cup plays out across the United States, Canada, and Mexico, AI is not just narrating the game anymore. It is inside it. Every player competing was digitally scanned before the tournament, and their 3D avatars now feed the offside decision system in real time, with the renderings broadcast to stadium screens. All 48 national teams have equal access to Football AI Pro, a generative AI assistant built on decades of FIFA’s own match data. The smallest federation now has the same tactical brain as the largest.

Artificial intelligence has already become embedded across financial services. Institutions use it to detect fraud, monitor transactions, automate compliance reviews, improve customer service, accelerate investigations, and support decision-making at scale. Most discussions over the past several years have focused on a familiar question: can AI help organizations operate faster, more efficiently, and with greater accuracy?

A pattern plays out with remarkable consistency across enterprise AI programs. An organization identifies a meaningful use case, assembles a capable team, selects a credible model, and runs a pilot that produces results senior leadership finds genuinely compelling. Budget follows and production begins and then, gradually, the program loses its footing. Timelines get extended, output quality drops in ways that are difficult to diagnose and finally adoption stalls. The initiative that once looked like a proof point becomes a cautionary tale.

European founders need to stop chasing the same model that their American counterparts follow. Why? Because artificial intelligence (AI) operationalization in Europe means tackling region-specific barriers beyond technical ones.

Software testing has always been a critical pillar in ensuring application quality and reliability. Traditionally, testing relied heavily on manual processes and later on automation scripts, which brought speed but lacked adaptability. Today, applications are far more complex, involving micro-services, APIs, cloud-native architectures, and multi-platform integrations. This complexity makes traditional automation insufficient because it cannot keep up with dynamic changes and frequent releases. AI-driven testing introduces intelligence, adaptability, and predictive capabilities, transforming testing from a reactive process into a proactive strategy.

The promise of AI was that it would let firms operate at a speed and scale they could not before. A few years into the buildout, however, most enterprise AIs are still running at human speed. In fact, a recent study showed that 95% of AI output is still verified by human end-users rather than automated systems.

Emerging research reveals that AI and data-driven systems are redefining customer service by prioritising real-time, personalised, and transparent interactions, moving beyond basic automation towards integrated contextual intelligence.

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.