Category: Blog

Krazimo CEO Akhil Verghese writes for Finopotamus on why enterprise AI adoption stalled for many companies in 2025 and what business leaders need to do differently to achieve measurable AI ROI in 2026. The editorial examines the gap between AI demos and production-ready enterprise AI solutions — a recurring theme in failed AI agent deployments across industries including financial services, insurance, and healthcare.

The piece draws on Gartner’s prediction that over 40% of agentic AI projects will be canceled by 2027, and argues that the root cause is not the technology itself but a lack of governance, testing, and clearly defined success metrics before deployment. Verghese outlines a practical AI implementation framework built on three principles: fencing AI agents into narrow, well-defined workflows; tying agent performance to explicit quantitative benchmarks; and defining clear escalation paths for human-in-the-loop oversight.

The article also offers a forward-looking estimate that 15–20% of enterprises will demonstrate real ROI from AI agents by the end of 2026, with enterprise-scale AI adoption reaching near-100% before 2030. For CTOs, VPs of Engineering, and operations leaders evaluating AI consulting partners, the editorial provides a vendor evaluation checklist: structure payments around measurable outcomes, baseline current human performance before onboarding any AI solution, and adopt phased launch strategies — from shadow launches to supervised automation to full deployment.

This is essential reading for any enterprise leader developing an AI strategy, evaluating AI consulting firms, or building a business case for deploying multi-agent systems and intelligent automation within their organization.

Read the full editorial on Finopotamus →

Krazimo CEO Akhil Verghese sits down with TMCnet to discuss one of the most pressing challenges facing enterprise technology leaders today: how to rigorously evaluate AI agents before trusting them with business-critical workflows. The conversation addresses the fundamental trust deficit that exists between the promise of agentic AI and the reality of deploying autonomous systems in production environments.

Verghese explains why traditional software evaluation methods fall short when applied to AI agents. Because large language models produce non-deterministic outputs, enterprises need new testing frameworks that go beyond standard QA. Krazimo’s approach — grounded in the same engineering rigor Verghese practiced during six years as a senior software engineer at Google — centers on deterministic workflow design, modular agent architecture, and robust evaluation pipelines that measure accuracy, consistency, and edge-case handling before any agent touches live data.

The interview covers Krazimo’s phased deployment methodology: starting with shadow launches where the AI operates in parallel with human workers, progressing to human-in-the-loop validation where the agent performs the task but a human approves the output, and only moving to full automation once performance matches or exceeds human baselines over a sustained period. This approach applies across use cases — from AI-powered CRM automation and customer service bots to intelligent document processing and multi-agent orchestration systems.

For enterprise buyers evaluating AI development agencies, AI consulting firms, or building internal AI capabilities, Verghese provides a clear framework: demand outcome-based contracts, insist on phased rollouts with measurable checkpoints, and treat any vendor who skips testing and governance as a red flag.

Read the full interview on TMCnet →

With Gartner predicting that 40% of AI agent projects may be abandoned by 2027, the stakes for getting enterprise AI right have never been higher. In an authored piece on The New Stack — one of the most respected publications in the developer and DevOps community — Krazimo CEO Akhil Verghese breaks down why so many AI agent projects fail and provides a practical engineering framework for building ones that don’t.

The article draws on Verghese’s experience at Google and his work at Krazimo helping enterprises deploy reliable generative AI systems. He argues that most AI agent failures aren’t caused by limitations in the underlying models — they stem from poor engineering practices: lack of proper testing, over-reliance on non-deterministic one-shot approaches, and premature deployment without adequate validation.

Verghese’s prescription centers on three principles: building deterministic, modular workflows where each step can be tested independently; implementing rigorous evaluation frameworks that go beyond traditional unit tests; and adopting phased deployment strategies that include shadow launches and human-in-the-loop validation before full automation.

For engineering leaders evaluating AI agent projects, this article serves as both a diagnostic tool (identifying where your current approach may be vulnerable) and a playbook (providing specific techniques for building more reliable systems). The message is clear: with the right engineering discipline, AI agents can deliver transformative value — but cutting corners on reliability will likely land you in that 40% failure bucket.

Originally published on The New Stack. Krazimo specializes in building reliable, enterprise-grade AI agents and generative AI solutions.

Read the full article at The New Stack.

Intellectual property is often the most valuable asset a small business has — yet it’s also one of the most commonly overlooked. In a comprehensive guide published by the U.S. Chamber of Commerce (CO-), Krazimo CEO Akhil Verghese shares insights from his experience running a technology company on how small businesses can better protect their IP.

Verghese highlights a key blind spot: while large companies typically run training courses explaining what’s proprietary when employees join, small companies tend to get straight to work — leaving employees unclear on what is and isn’t privileged information. This cultural gap creates real risk, especially for tech and AI companies where intellectual property is the core of the business.

He also addresses the power dynamics that small businesses face when negotiating contracts with larger clients. When you’re a small business, it can be difficult to insist on particular contract terms, especially if the client is a large company. This pressure can lead small businesses to sign away IP rights they should be protecting.

The article covers the fundamentals of IP protection — from patents and trademarks to trade secrets and copyrights — and provides actionable steps for businesses at any stage. For AI and technology companies in particular, where proprietary algorithms, training data, and code represent significant competitive advantages, getting IP protection right from the start is essential.

Originally published on CO- by the U.S. Chamber of Commerce. Krazimo is an enterprise AI consulting firm founded by former Google engineers, specializing in reliable generative AI solutions.

Read the full article on the U.S. Chamber of Commerce Website.

Gartner recently issued a stark warning: enterprises should block AI browsers due to the security risks they pose. These agentic browsing tools can expose sensitive data, undermine long-standing browser protections, and create organization-wide vulnerabilities. But is a blanket ban realistic?

In a feature on TechNewsWorld, Krazimo CEO Akhil Verghese offered a candid assessment. While he agrees the security concerns are legitimate, he questions the practicality of Gartner’s advice. AI browsers provide little visibility into what happens to data before it reaches the underlying AI provider, and terms of service can change over time. But expecting individuals or organizations to continuously monitor these shifting policies isn’t realistic either.

The article explores the tension between the productivity benefits of AI-enhanced browsing and the genuine enterprise security risks it introduces. As AI browsers become more capable and more common, organizations face a growing challenge: how to capture the benefits of AI-assisted workflows without exposing sensitive data to unknown backend processing.

For businesses evaluating AI tools, the takeaway is clear — due diligence on data handling and security practices is essential, but blanket bans may not be the answer. A thoughtful, risk-based approach that includes employee education and clear usage policies is likely more effective.

Originally published on TechNewsWorld. Krazimo helps enterprises adopt AI responsibly with a focus on security, reliability, and production-grade engineering.

Read the full article at TechNewsWorld.

As India proposes a blanket licensing system that would require AI companies to pay creators when their content is used for model training, the debate over AI training data compensation has reached a critical inflection point. TechRound assembled a panel of tech leaders to weigh in — including Krazimo CEO Akhil Verghese.

Verghese’s take is nuanced and thoughtful. He argues that while it may be feasible to compensate large content generators like the New York Times or Reddit, creating a fair system for every blog author whose work contributed to training a state-of-the-art model would be extraordinarily difficult. He identifies three key areas of debate: whether the transformative way AI reuses content constitutes fair use, whether the practical difficulty of compensating everyone fairly means the issue can’t be addressed, and whether AI dominance is so strategically important that legal concerns become secondary.

On the fair use question, Verghese is direct: based on how transformers actually work, he finds it difficult to classify AI training data usage as fair use in the traditional sense. He also pushes back on the idea that difficulty justifies inaction — arguing that the brilliant minds who built these models could develop workable compensation structures if they dedicated effort to the problem.

The article features perspectives from six industry experts, making it a comprehensive look at one of the most important policy questions in AI today.

Originally published on TechRound. Krazimo is an AI consulting firm that builds reliable enterprise AI solutions with a focus on engineering excellence.

Read the whole story on TechRound.

What does it take to leave a senior engineering role at Google and start an AI consulting company from scratch? In an in-depth interview with Tech Startup Network, Krazimo founder Akhil Verghese tells the full story.

Verghese’s journey began at BITS Pilani in India, where he studied physics and civil engineering before pivoting to software. After starting at Fiberlink (later acquired by IBM), he spent years as a machine learning consultant and served as the founding Head of AI at Butter.ai, a startup backed by General Catalyst. In 2019, he joined Google, where he spent six years — ultimately leading reporting projects for Gemini within Google Workspace and advising teams on optimizing LLMs for reliability.

That advisory work is what sparked Krazimo. Verghese saw firsthand how even sophisticated companies struggled to deploy AI reliably in high-stakes environments. The gap between a compelling demo and a production-ready system was vast, and most organizations lacked the engineering discipline to bridge it.

The interview covers Krazimo’s philosophy of enterprise-grade AI: systems that are creative and intelligent yet remain predictable, testable, and auditable. Verghese explains the company’s signature phased launch strategy — shadow launches, human-in-the-loop validation, and only then full automation — and discusses why engineering rigor matters more than ever in the age of generative AI.

Originally published on Tech Startup Network. Krazimo specializes in reliable, enterprise-grade generative AI solutions built by former Google engineers.

Read more on the Tech Startup Network.

The promise of agentic AI — autonomous systems that make decisions and execute workflows with minimal human oversight — is enormous. But there’s a catch: if business leaders can’t trust these systems, the technology becomes worthless.

In a feature on Geek Insider, Krazimo CEO Akhil Verghese breaks down exactly why trust in enterprise AI is so often lacking, and what companies can do about it. The core problem? A massive gap between flashy AI demos and production-ready agents. As Verghese puts it, many companies are rushing to market with agents that simply aren’t ready for enterprise environments.

The article outlines three pillars that businesses should demand from any AI agent provider: Determinism (breaking complex workflows into individually testable steps rather than relying on unpredictable one-shot LLM calls), rigorous Testing (using techniques like LLM-on-LLM reflection and outcome-oriented unit tests), and Phased Launches (progressing from shadow launches to human-in-the-loop validation before full automation).

Verghese also shares his outlook on the future: while LLMs will continue to improve and hallucinate less, the biggest growth opportunity lies in better agent-building best practices and tools. For any enterprise considering AI adoption, this article is a roadmap for doing it responsibly and effectively.

Originally published on Geek Insider. Krazimo is an enterprise AI solutions provider helping companies leverage generative AI with engineering rigor and reliability.

Read more on the GeekInsider.

2025 was supposed to be the year AI agents went mainstream. So did it live up to the hype? In a year-end analysis by SDxCentral, Krazimo CEO Akhil Verghese provides one of the most grounded assessments of where agentic AI actually stands.

Verghese’s perspective is both ambitious and pragmatic. He believes 40-70% of all white-collar work will be automatable within three years — but is quick to distinguish between automatable and automated. The gap between what’s technically possible and what’s actually deployed in production is significant, and Verghese suggests a 10-year timeline is more realistic for seeing widespread automation of white-collar work as it exists today.

Looking back at 2025, Verghese characterizes it as primarily a testing and experimental phase — and a year of painful lessons for companies that adopted AI solutions without adequate guardrails, success criteria, and maintenance plans. He expects 2026 to continue this pattern of experimentation, with enterprises becoming more sophisticated about how they evaluate and deploy AI.

The article draws on perspectives from multiple industry leaders and provides a comprehensive view of the current state of agentic AI adoption. For business leaders planning their AI strategy, the takeaway is clear: the technology is advancing rapidly, but success depends on engineering discipline, realistic expectations, and a willingness to learn from early failures.

Originally published on SDxCentral. Krazimo is an enterprise AI consulting firm that helps businesses adopt AI with the rigor and reliability needed for production environments.

Read the whole story at SDxCentral.