Blog - Krazimo

Why Incentives May Be the Missing Piece in AI Adoption

One of the biggest mistakes companies make with AI is assuming rollout alone creates adoption. In reality, even strong tools can sit unused if employees do not feel involved, do not see personal upside, or are unsure how AI fits into their day-to-day work. That is the key takeaway from Fast Company’s coverage of KPMG’s new “AI Spark Innovation” program, which rewards employees for building AI use cases that can improve internal workflows or client work.

According to the article, KPMG’s U.S. advisory division is offering cash prizes for employees who demonstrate standout AI innovation, with payouts described as materially larger than typical end-of-year variable compensation awards. The goal is not just more experimentation, but a shift in culture away from measuring success only through billable hours and toward scalable innovation.

That idea matters well beyond consulting. For businesses investing in AI CRM, AI SDR workflows, AI lead generation, and AI lead conversion, adoption often fails not because the technology is weak, but because the people using it never become active participants in the rollout. If employees view AI as something imposed on them, usage stays shallow. If they help shape the workflows, the odds of long-term success rise sharply. This is an inference based on the article’s discussion of employee input and Krazimo’s core implementation focus.

Why KPMG’s Approach Is Worth Paying Attention To

Fast Company quotes Akhil Verghese calling KPMG’s move “a brilliant move,” arguing that leaders who want employees to embrace AI should actively involve them in generating ideas. His point is that this makes employees part of the company’s AI adoption journey rather than passive recipients of top-down change.

That is a strong framing for enterprise AI. In many organizations, the hardest part is not finding a model or buying software. It is creating real behavioral change across teams. Incentives help because they do two things at once: they surface practical use cases from the people closest to the work, and they reduce fear by making experimentation feel rewarded rather than threatening.

This also aligns with a broader workforce trend mentioned in the article. Fast Company cites a 2025 Lightcast study saying jobs mentioning at least one AI skill offered salaries 28% higher, while jobs mentioning two AI skills offered salaries 43% higher. The article also cites a 2025 Kyndryl report saying 45% of CEOs believe employees are actively resistant to AI. Together, those two points explain why companies are under pressure to build AI-literate teams instead of merely purchasing AI tools.

What This Means for AI CRM and AI SDR Rollouts

For customer-facing systems, the lesson is especially important. A company can deploy an AI CRM, an AI sales assistant, or an automated lead qualification workflow, but if the sales team or operations team does not trust the outputs, they will work around the system instead of through it. That leads to poor data quality, weak follow-up discipline, and disappointing ROI. This application is an inference, but it follows directly from the article’s adoption logic and Krazimo’s existing focus on AI CRM and revenue workflows.

The smarter approach is to treat adoption as part of the product itself. That means identifying real workflow pain points, inviting employees to propose improvements, rewarding practical wins, and using early experiments to build confidence. In that sense, KPMG’s incentive model is not really about prizes. It is about creating the kind of workforce that can actually absorb AI into production.

Verghese makes a related point in the article: many early AI deployments fail because the technology is still maturing, and the most valuable part of these early efforts may be less about immediate results and more about building an AI-literate employee base. That is an especially useful lens for companies deciding whether early experiments are “worth it.” Sometimes the near-term payoff is not just efficiency. It is capability-building inside the organization.

Final Thoughts

KPMG’s program is a useful reminder that successful AI adoption is not purely a technical challenge. It is a people challenge, an incentives challenge, and a workflow design challenge. Businesses that want better outcomes from AI automation, AI CRM, AI SDR, and related systems should think seriously about how they make employees feel ownership over the process, not just compliance with it.

You can read the full original Fast Company article here.

Why Employee Resistance Is Quietly Killing AI CRM and AI SDR Rollouts

A lot of businesses assume that once they buy the right AI tool, adoption will take care of itself. In reality, one of the biggest reasons AI projects underperform is not the model, the workflow, or even the budget. It is employee resistance. In the original Solutions Review article, Akhil Verghese argues that many companies struggle with AI not because the technology lacks promise, but because the people expected to use it do not trust it, do not see how it helps them, or were introduced to it badly in the first place. Readers can see the full original article on Solutions Review.

The article explains that resistance usually comes from three places. The first is simple resistance to change. Many teams would rather stay with a process they already know than risk disruption from a new system. The second is bad implementation: employees quickly lose confidence when the tool does not fit the real workflow or creates more cleanup work than value. The third is fear of replacement, especially in roles that are heavily task-based. That framework is especially relevant for companies exploring AI CRM, AI SDR, AI lead generation, and AI lead conversion systems, because these tools are often introduced directly into revenue workflows where trust, speed, and clarity matter most.

One of the most practical insights from the article is that AI adoption should not start with abstract demos. It should start with real workflows. The recommended approach is to identify a few early adopters, have them document a specific task AI improves, and run live training sessions around that concrete use case. That matters in sales and customer operations because teams rarely buy into AI from vision alone. They buy in when they can see that an AI assistant saves time on CRM updates, improves lead qualification, drafts better follow-ups, or helps them respond faster without sacrificing judgment. For an AI SDR workflow, that could mean showing reps exactly how AI reduces manual research and prepares better outreach. For an AI CRM workflow, it could mean demonstrating how AI keeps records cleaner, follow-ups tighter, and pipeline actions more consistent.

The article also makes an important business point: leaders need to define success before rollout. It gives an example using outbound sales metrics, emphasizing that managers should know current performance, current cost, what level of performance drop would be unacceptable, and what success would actually look like before deploying AI. That is the right lens for any company investing in AI lead generation or AI lead conversion. If you do not know your current close rate, lead response time, cost per booked meeting, or cost per qualified opportunity, then you cannot tell whether the AI is helping or simply creating the illusion of progress. This is where many AI sales rollouts go wrong: they optimize activity instead of revenue outcomes.

Another strong takeaway is the warning against buying into vague “AI” promises. The article notes that many products are marketed as intelligent systems without being genuinely adapted to a company’s specific workflow, tools, or guardrail requirements. That is highly relevant in the market for AI CRM and AI SDR tools, where businesses are often sold generic automation that does not integrate cleanly, does not reflect internal sales logic, and cannot be trusted in production. Krazimo’s positioning fits naturally here: reliable AI for sales and lead workflows is not just about adding a model. It is about designing the workflow, enforcing controls, measuring outcomes, and making sure the system actually supports how teams work.

The article further argues that useful AI systems should be launched in phases, not dumped into production all at once. The recommended pattern is to first run the AI in parallel with human staff, compare outputs, and only expand responsibility once the system proves it can reproduce competent work safely. It also stresses strong guardrails, such as limiting retries, escalating edge cases to humans, and requiring permission before any expensive or legally sensitive action. That phased-launch approach is especially important for AI lead conversion systems, where an agent might otherwise send the wrong message, mishandle a discount, or create inconsistent customer communication. In other words, the path to successful automation is closer to training a junior teammate than flipping on a piece of software.

The piece also highlights something many companies underestimate: AI systems require maintenance. Prompts drift, policies change, source data changes, and workflows evolve. That is why monitoring is not optional. In a sales environment, a once-effective AI workflow can become harmful if the CRM schema changes, qualification logic shifts, or messaging standards move. This is one reason high-performing AI lead generation systems are usually tied to ongoing iteration rather than one-time deployment. The companies that see lasting value are the ones that keep tuning, auditing, and improving the system after launch.

A final point from the article is that AI adoption can create opportunities for reskilling rather than simple replacement. It gives the example of customer service staff moving into sales-oriented roles. That is a useful framing for businesses worried about internal pushback. The most effective AI rollouts are not sold as “headcount elimination software.” They are introduced as a way to remove repetitive busywork so people can focus on higher-value work. In the context of AI CRM, AI SDR, and AI lead conversion, that means fewer hours lost to manual data entry, repetitive prospect research, scattered follow-ups, and inconsistent handoffs — and more time spent on closing, relationship management, and judgment-heavy work.

The broader lesson is simple: businesses do not get value from AI just because they buy a product. They get value when they deploy the right workflow, prove it against real business metrics, train teams around practical use cases, and roll it out in a way that builds trust instead of fear. That is true across the board, but it is especially true for customer-facing systems. If a company wants AI CRM, AI SDR, AI lead generation, or AI lead conversion to work, it has to treat adoption as both a systems problem and a people problem. The technology matters, but so does the rollout.

Read the article at Solutions Review.

Why Access to Great Models Is Not Enough to Win in AI

One of the most common mistakes in AI strategy is assuming that success comes mainly from model quality. In this The Deep View piece, Krazimo CEO Akhil Verghese explains why that view is incomplete. The companies that lead in AI are rarely the ones that simply have access to strong models. They are the ones with the right combination of product direction, organizational urgency, technical talent, data strategy, and execution discipline. Without those pieces in place, even the most well-resourced companies can struggle to turn AI into meaningful product progress.

That lesson matters well beyond Big Tech. For enterprise leaders, the article is a reminder that AI transformation depends on far more than plugging a model into an existing workflow. Businesses need clear use cases, well-defined ownership, access to the right data, internal alignment on priorities, and the engineering maturity to turn experiments into dependable systems. AI strategy is ultimately a question of execution: how quickly an organization can move, how well it integrates AI into real workflows, and whether it can build systems people actually trust and use.

This is especially relevant for companies evaluating enterprise AI strategy, AI product execution, AI architecture decisions, and how to create long-term business value from AI investments. The real moat is rarely just raw model access. It is the ability to operationalize AI effectively inside a real product or business environment. That is why the article is such a strong match for Krazimo’s positioning around reliable AI systems, thoughtful deployment, and real-world business outcomes.

Read the full article on deepview.

Why AI Literacy and Governance Matter More Than Ever

As artificial intelligence becomes part of everyday work, many organizations are discovering that successful AI adoption depends on much more than choosing the right model or software. In this Education Week article, Krazimo CEO Akhil Verghese highlights a core issue that applies far beyond schools: employees are often already experimenting with AI tools, but leadership has not always provided the policy, guardrails, and structured support needed to use those tools safely and effectively. That gap creates risk. It can lead to inconsistent usage, weak oversight, unclear accountability, and avoidable compliance problems.

The broader lesson for businesses is clear. AI readiness is not just a technical problem. It is an organizational capability. Companies need teams that understand the basics of large language models, prompting, privacy, appropriate use, and human review. They also need leadership-level decisions about where AI should be used, what data it can access, when outputs require approval, and how success should be measured over time. In other words, real AI adoption depends on AI literacy, governance, training, and policy as much as it depends on software.

This is one of the most important shifts happening in enterprise AI right now. The companies that succeed will not just be the ones that buy tools first. They will be the ones that build an AI-literate workforce, define responsible usage clearly, and create repeatable systems for deploying AI in day-to-day operations. For any organization thinking seriously about responsible AI implementation, AI upskilling, enterprise AI governance, or workforce training for AI adoption, this article is a useful reminder that strong leadership and clear policy are becoming essential.

Read the full article here.

The Fundamentals of AI for Business: What to Automate, What to Protect, and How to Scale

Every week, a business owner somewhere hears that AI can automate their customer service, supercharge their sales pipeline, and transform their operations. And every week, some of those business owners spend tens of thousands of dollars on a solution that doesn’t actually work — because nobody told them the things that matter before you sign a contract.

Our CEO, Akhil Verghese, recently joined Tristan Harris on The Crawl podcast for an in-depth conversation about the fundamentals and ethics of AI in business. The discussion covers a lot of ground — from why Akhil left Google after six years to build Krazimo, to how companies should evaluate automation candidates, to the uncomfortable question of what happens to average performers in an AI-powered economy.

Here’s what business leaders need to know.

Why Akhil Left Google to Build Krazimo

The short version: at Google, the standards for AI reliability are extraordinarily high because any mistake ends up in the news. Akhil spent his final years there working within the Workspace organization on applying AI to specific problems, where the team developed strict techniques for reducing hallucinations, keeping AI on-topic, and preventing it from saying anything it shouldn’t.

When he started talking to people at other companies, he realized most of these techniques weren’t widely known — and they produced significant improvements in AI reliability for any enterprise willing to implement them. Companies started reaching out, asking how to get the same results. Google, to their credit, allowed him to consult on his own time. Within a year, the side business was making more than his Google salary. By July 2025, Krazimo was full-time.

The founding principle hasn’t changed: building AI solutions that are useful, deployable, repeatable, predictable, and reliable. Not demos. Not prototypes. Production systems that actually work.

The Scaling Problem Nobody Talks About

When software engineers think about scaling, they think about resources — servers, parallelization, infrastructure costs. AI introduces an entirely different dimension that most people miss: behavioral scaling.

How does your AI model behave as it encounters new edge cases? How does it respond to new data flowing in over time? Almost every useful deployed AI model involves feedback loops — the system learns and adjusts based on what happens. But what happens when policies change? When refund rules get updated? When a new product launches?

Akhil argues that people dramatically overemphasize the scaling costs of raw intelligence (which are dropping fast and will continue to drop) and dramatically underemphasize the real scaling challenge: ensuring your AI solution adapts gracefully to new data, new environments, and new feedback over time without breaking.

If you’re evaluating an AI vendor, ask them how their solution handles change. If they don’t have a clear answer, that’s a red flag.

Don’t Start with Solutions. Start with Problems.

This is the core operational insight of the entire conversation, and it’s worth reading twice.

The biggest mistake Akhil sees companies make when adopting AI is working backwards. They hear about an exciting AI capability — customer service automation, sales intelligence, lead scoring — and they try to bolt it onto their business without first asking whether it solves a problem that actually matters to them.

He gives a pointed example. A company doing a few million in annual revenue, converting 30% of their inbound leads with 30-40 leads per week, comes to him wanting to automate inbound sales. His response: why? The absolute best-case scenario is that an AI agent reduces that 30% conversion to 25% — because some people will always be annoyed by talking to a machine. The team is handling the volume fine. There’s no bottleneck here. The ROI is negative.

Compare that to an accounting firm getting 30 leads per week, where each lead requires significant manual research — looking up the company, checking revenue thresholds, verifying legitimacy, entering data into the CRM, sending follow-up emails, managing intake forms. That’s a perfect automation candidate: repeatable, well-defined, low-stakes per individual action, and genuinely time-consuming for humans. The AI does it at least as well as a human (probably better for routine research), it scales instantly, and freeing up human time for the high-value work of actually serving clients is a clear win.

The framework: Before you automate anything, define what success means in measurable terms. Calculate whether the math actually works. Identify whether this is a real bottleneck or just something that sounds cool to automate. Then act.

The 95% Trap: Why “Pretty Good” AI Is Often Useless

This might be the most counterintuitive point in the entire conversation, and it’s one that separates people who understand AI from people who’ve just seen demos.

Getting 95% accuracy on an AI task is relatively easy. Getting from 95% to 99% is where the real engineering lives. And in many business contexts, the difference between 95% and 99% is the difference between useful and worthless.

But here’s the key insight: whether 95% accuracy is useful depends entirely on what you’re automating.

If AI misqualifies 5% of your leads, nobody dies. The value of each individual lead is low. As the system improves from 95% to 99%, you proportionally benefit the whole way. The improvement curve is linear — every percentage point of improvement delivers incremental value.

If an AI radiologist is wrong 3% of the time, telling people they have cancer when they don’t (or worse, missing it when they do), it’s useless. There is no middle ground. The value curve is binary — it either meets the threshold for clinical reliability or it doesn’t.

The practical filter: When evaluating any automation candidate, ask yourself — is this a task where “pretty good” still provides real value? Or is it a task where anything less than near-perfect accuracy creates more problems than it solves? Automate the first category first.

Data Hygiene Is Not Optional — It’s the Foundation

Before any AI agent touches your business systems, you need to label everything clearly:

Is this data sensitive? Customer credit card information, medical records, personally identifying information — AI should never have unsupervised access to any of it. Full stop. Human-in-the-loop is mandatory.

Does this setting require human approval to change? Issuing refunds, modifying account details, accessing customer records — the guardrails here cannot be based on AI judgment. They must be deterministic, rule-based restrictions. If the only thing stopping your AI from doing something catastrophic is that nobody told it to, you’ve already lost.

What’s the blast radius if something goes wrong? For low-stakes actions (qualifying a lead, sending a follow-up email), full automation makes sense. For high-stakes actions (legal compliance, financial transactions, customer data access), human oversight is non-negotiable.

Akhil puts it memorably: a client once asked him, “What questions should I never ask my agent?” His response: “If you’re asking that question, you’ve already lost. The architecture should make it impossible for the agent to do anything harmful, regardless of what it’s asked.”

The Illusion of Competence: AI’s Most Dangerous Failure Mode

Here’s something that doesn’t get enough attention. When a human employee writes four paragraphs of marketing copy and the first three are excellent, you reasonably assume the fourth will be good too. That’s how human competence works — it’s generally consistent.

AI doesn’t work that way. Three perfect paragraphs tell you nothing about the fourth. Each output is an independent prediction. The confidence and fluency of AI writing creates what Akhil calls an “illusion of competence” — and it’s especially dangerous when businesses delegate review tasks to people who develop unwarranted trust based on a track record that doesn’t actually exist.

This is an ethics issue, not just a quality issue. If your clients trust your firm’s expertise, and you’re delegating work to AI without adequate review, you’re trading on a reputation your AI didn’t earn. The solution isn’t to avoid AI — it’s to build review processes that account for how AI actually fails.

What the Next Three Years Look Like

Akhil’s outlook is both optimistic and grounded. He expects models to continue getting incrementally better — cheaper intelligence, fewer hallucinations, better self-correction through reflection loops. He points to Claude Code as an example of what happens when brilliant engineering is layered on top of already-good models: the coding tool works not because the underlying model is perfect, but because the verification and correction loops around it are excellent.

He expects that pattern to expand into other fields — law, medicine, accounting — as similar effort gets invested in domain-specific reflection and correction systems.

The human impact is harder to predict. Akhil is direct about this: the age of AI will disproportionately reward excellence. If your work is genuinely exceptional — the best writing, the best strategic thinking, the deepest expertise — your job is safe for the foreseeable future. If your work is average and entirely task-based, the economics are moving against you. The advice isn’t to fear AI — it’s to invest in becoming genuinely great at something you care about, and to use AI as the tool that amplifies that excellence rather than replaces it.

Where to Start

If you’re a business owner who’s been hearing about AI for months but hasn’t taken the first step, here’s the simplest possible action plan:

Talk to your team. Find out who’s already using AI tools. Their use cases are your best candidates for formalized automation.
Pick one workflow that’s high-volume, well-defined, and low-stakes per individual action. Lead qualification is usually the best starting point for service businesses.
Define success numerically before you build or buy anything. Conversion rate, response time, error rate — whatever matters for that specific workflow.
Label your data and settings. Mark what’s sensitive, what needs human approval, and what can be fully automated.
Deploy in phases. Shadow launch first, human-in-the-loop second, full automation only after the system has proven itself over a meaningful period.

The companies seeing real ROI from AI right now all followed some version of this path. The ones still waiting are watching the gap widen.

Watch the whole interview at https://www.youtube.com/watch?v=9bVZAxMljn8

Ethical AI Automation: Where Human Judgment Still Matters (And Where It Doesn’t)

If you run a business right now, you feel it. AI is everywhere. Automation promises are everywhere. And you’re asking yourself the same question every other business owner is asking: am I behind — or am I about to make an expensive mistake?

Our CEO, Akhil Verghese, recently sat down with Stacy on The Authority Business Show to answer exactly that question. The conversation covered the practical reality of AI automation for business owners — not the hype, not the theoretical possibilities, but the actual steps you should take this week if you want to use AI without losing control of what matters most.

Here are the key takeaways.

AI Is Making Businesses Faster — Not Necessarily Smarter (Yet)

One of the first distinctions Akhil draws is between speed and intelligence. Right now, most productive AI solutions in the real world are focused on automating existing workflows — doing what already works, but doing it faster and more consistently. Very few businesses are using AI to generate genuinely new ideas or creative strategies. That’s still firmly in the domain of human leadership.

This matters because it shapes how you should think about your first AI investment. You’re not buying a replacement for your best strategic thinker. You’re buying a way to handle the repetitive, high-volume work that’s eating up your team’s time.

Before You Automate Anything: Two Steps You Can’t Skip

Akhil’s number one piece of advice for any business owner considering AI is deceptively simple: before you automate, evaluate and structure.

Step 1: Define your metrics. Take the specific workflow you want to automate — say, responding to leads from Instagram ads — and look at how it’s performing right now. What’s your conversion rate? What’s your average response time? What does success actually look like in numbers? Without this baseline, you’ll never know whether your AI is helping or hurting.

Step 2: Label your data and settings. Go through everything the AI would need access to and clearly mark what’s sensitive, what requires human permission to change, and what can be fully automated. You don’t want an AI agent issuing $1,000 refunds to angry customers or using your business credit card without oversight. These boundaries need to be hard-coded, not left to the AI’s judgment.

The Real-World Math: When AI Lead Conversion Makes Sense

Here’s where the conversation gets specific — and directly relevant if you’re running a service business.

Akhil shares a concrete example from a cosmetology practice (think med spas, Botox, aesthetic services). When someone clicks an Instagram ad for Botox and an AI agent responds within 60 seconds instead of the typical 30 minutes to 2 hours, the results are dramatic. Studies show response rates can increase by 20x to 50x when contact happens within a minute. For a business like a med spa in a competitive market, where a potential client has 20 other options within a few minutes, that speed difference translates directly into booked appointments and revenue.

But here’s the nuance: the same approach applied to a real estate company produced very different results. Why? Because someone looking at a multi-million dollar property is willing to wait two hours for a response. Speed matters enormously for low-consideration, high-competition services. It matters much less when the purchase decision is inherently slow.

The takeaway for service businesses: If you’re in an industry where response time is the competitive battleground — home services, med spas, legal consultations, any appointment-driven business — AI lead conversion is likely your highest-ROI first automation. If you’re selling something where customers naturally take their time, look elsewhere first.

The Biggest Red Flag: Falling for a Cool Demo

Akhil is blunt about the most common mistake he sees: businesses falling for impressive demonstrations that bear no resemblance to production-ready solutions.

The problem is structural. It’s incredibly easy to get 85-90% of the way to a working AI solution. But in many business contexts, 85% accuracy is effectively useless — because if you’re correcting things one in ten times, you need to be just as vigilant as if you were doing everything manually. And the consequences of confidently wrong AI output are often worse than no output at all.

The gap between a cool demo and a reliable, deployable agent is typically tens of thousands of dollars and months of careful work. On day one, you look 80% of the way there. Then it takes five months to reach the 96% accuracy threshold you actually need for production.

What AI Can’t Replace: Agency, Creativity, and Accountability

The conversation turns to something many business owners quietly worry about: what can’t AI do?

Akhil’s answer is clear. AI is exceptional once you know what needs to be done. It makes the process of getting there dramatically more efficient. But figuring out what to do — the strategic vision, the creative spark, the leadership decisions — that’s still entirely human territory. He has never had an AI, even with significant autonomy, independently identify a problem worth solving that he wasn’t already working on.

And on the accountability front: no computer can be held accountable for its decisions. Someone in your organization needs to own the outcomes of any automated process, and Akhil recommends that person be the manager of whoever was doing the task before — they’re the most incentivized to get it right, and they’re already accountable for results in that area.

The Three-Step Rule for Adopting AI

For business owners who want a simple framework, Akhil offers three steps:

1. Talk to your employees. The best automation ideas almost always come from the people doing the work. They’re already using AI in ways that might surprise you. Listen to them, involve them in the process, and let ideas bubble up from the bottom.

2. Evaluate before you deploy. Define what success looks like. Understand the current workflow in detail. Identify every point where things could go wrong. Then decide whether to build internally or hire external expertise.

3. Set guardrails, monitor continuously. Every AI deployment needs hard limits on what it can access and do. And those limits need to be monitored — not just for a few days after launch, but permanently. If your conversion rate drops below a threshold for three consecutive days, you need an automatic alert.

What Should You Do This Week?

If you’re a business owner listening to all of this and feeling overwhelmed, Akhil’s advice is simple: start small, but start now.

The companies that have already adopted AI and worked through the early mistakes are now seeing real, measurable upside — real revenue increases from real agents deployed in real workflows. The gap between them and companies that haven’t started is widening. The biggest mistake you can make right now isn’t deploying AI badly. It’s keeping your workforce AI-illiterate.

Pick one simple, repeatable workflow. Define what success looks like. Set clear guardrails. Deploy it. Monitor it. Learn from it. Everything else will follow.

Watch the full interview at: https://www.youtube.com/watch?v=pwcSPE0Rwz8

Why Most Enterprise AI Projects Fail — And How to Ensure Yours Doesn’t in 2026

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 →

A Practical Guide to Evaluating AI Agents for Enterprise Deployment

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 →

Why 40% of AI Agents Might Fail — And How to Save Yours

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.

Protecting Your Intellectual Property: What Every Small Business Needs to Know

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.