Blog - Krazimo

Why Faster AI Isn’t Reducing Customer Service Workloads

Most customer service teams now have AI somewhere in the stack. Tickets get classified the moment they come in, reply drafts appear before a rep finishes reading the email, and knowledge-base summaries show up on screen without anyone asking. And yet, as CRM Buyer reports in a recent feature, the human workload isn’t really shrinking. That gap — between AI getting faster and teams getting freed — is the core of the article, and it’s a useful lens for any company currently evaluating the ROI of its AI CRM, AI SDR, or customer service automation investments.

Krazimo CEO Akhil Verghese, quoted throughout the piece, frames the issue plainly: the model is getting faster, but the workflow around it hasn’t caught up. For leaders wondering why their AI projects aren’t moving the right metrics, that sentence is worth sitting with.

The Efficiency Illusion

The article’s core argument is that “AI usage” has quietly become a poor proxy for customer outcomes. Support teams can dramatically increase the number of AI-assisted actions they run per day and still see the same handle times, the same escalation rates, and the same backlog. Verghese’s phrasing in the piece — “efficiency without orchestration is just speed without throughput” — captures it sharply.

That distinction matters because it’s easy to mistake velocity for value. A model that drafts replies 40% faster looks like a clear win on a slide. If the rep still has to open three other systems, verify what was retrieved, check permissions, and manually close out the ticket in the CRM, the time saved on drafting has just been spent somewhere else. This is an inference drawn from the article’s description of how support reps work today, but it mirrors what Krazimo consistently sees inside AI CRM engagements.

Why AI Gains Get Stuck at the Workflow Layer

The CRM Buyer piece points to a structural reason for the stall: customer issues rarely live inside a single system. Resolving a typical case means pulling data from the CRM, billing, the product back-end, and an order-management tool — then taking action in at least one of them. When the AI is wired into only one of those surfaces, it can summarize and suggest, but it can’t finish the job. The rep ends up as the integration layer between AI output and the systems where action actually happens. Throughput doesn’t move, because the bottleneck was never drafting — it was coordination.

That pattern isn’t unique to customer service. It’s visible in AI SDR programs where an agent drafts a beautifully personalized outbound message but can’t log the touch in the CRM, enrich the contact, or schedule the follow-up without a human handoff. It shows up in AI lead generation workflows where scoring is instant but routing, ownership, and next-step logic still rely on someone manually dragging records between tabs. This broader application is an inference from the article’s framing, but it’s a direct one for anyone running revenue operations in 2026.

What Orchestration Actually Looks Like

“Orchestration” is a word that gets overused, so it’s worth being specific about what a mature AI workflow actually requires. A real orchestrated system has a clear task graph — each issue broken down into the actual steps needed to resolve it, with explicit handoffs between AI and human steps rather than implicit ones. It gives the AI layer the permissions and plumbing to execute changes across connected systems under clear guardrails, instead of producing text a human has to copy, paste, and defend. And it treats every AI-initiated action as something to be logged, evaluated against an expected outcome, and reversed if needed. That last property is what makes it safe to expand autonomy over time without introducing new categories of risk.

Without those properties in place, adding more AI tools tends to produce more fragments, not fewer. Each tool solves its slice; the human is still responsible for reassembling the whole.

The Measurement Problem

A quieter but equally important point in the article is that companies are often measuring the wrong things. AI dashboards typically report usage — how many queries, how many summaries, how many assisted responses. Those numbers rise reliably once a tool is deployed, but they don’t tell leadership whether customer problems are being resolved faster, whether satisfaction is improving, or whether agents are actually getting their time back.

For an AI CRM or AI customer service program to justify its spend, the real operating metrics should look more like first-contact resolution, net handle time including the minutes reps spend switching between systems, escalation rate, and verified CSAT specifically on AI-touched tickets. Verghese argues in the article that objective, third-party evaluation is often the cleanest way to distinguish real outcomes from more activity. That’s a useful discipline for any team trying to separate AI that works from AI that merely runs.

What This Means for AI CRM, AI SDR, and AI Lead Generation

The CRM Buyer article is about customer service, but the implication carries across AI CRM, AI SDR, and AI lead generation programs. If the AI layer isn’t orchestrated across the systems where work actually happens — sales, support, billing, operations — then the CRM becomes a tool for capturing AI output rather than a platform for reducing the cost of running the business. This application to the broader revenue stack is an inference, but it follows directly from the article’s logic and from Krazimo’s ongoing work in AI CRM design.

This is the design thesis behind Krazimo’s Custom AI CRM. AI sits inside the CRM as a set of specialized agents wired into real workflows — lead response, account management, service orchestration, analytics — rather than as a bolted-on assistant that hands work back to the human at the first ambiguous moment. Rollouts are staged through shadow launches and human-in-the-middle approvals, so autonomy expands only when the data supports it. That approach is built specifically to avoid the illusion of productivity the article warns about.

RAG-as-a-Service plays a similar role on the knowledge side. If an AI answer is pulled from scattered documents but can’t be trusted enough to act on, the rep is still doing the verification work. When retrieval is grounded, auditable, and tied to permissions, it becomes a workflow input rather than a draft someone has to defend before using it.

Final Thoughts

The CRM Buyer piece is a useful reminder that the next wave of AI ROI won’t come from buying another point tool. It will come from connecting what’s already deployed into workflows that can actually finish a task — and from measuring outcomes instead of activity. For businesses running AI CRM, AI SDR, or AI lead generation programs, the quieter question behind every rollout is whether the AI is genuinely moving work off the team’s plate, or just moving faster while the team stays just as busy.

You can read the full original CRM Buyer article here.

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 →

How to Evaluate AI Agents for Enterprise Use

Most enterprise AI agents fail in the same place: they look impressive in a demo and fall apart the first week they touch real data. The gap between “it worked in the sandbox” and “we can trust it with a business-critical workflow” is where evaluation lives — and it’s the step most teams rush. This is a practical framework for evaluating an AI agent before you let it run in production, drawn from how we build and ship agents for enterprise clients.

The core problem is that AI agents aren’t traditional software. A normal program is deterministic: same input, same output, every time, so you can test it exhaustively. A large language model produces non-deterministic outputs — the same prompt can yield different results — so standard QA simply doesn’t catch the failure modes that matter. Evaluating an agent means measuring behavior across many runs and many edge cases, not confirming a single correct answer.

Why traditional QA falls short for AI agents

Conventional testing assumes you can enumerate the cases and assert the expected result. With an agent, three things break that assumption:

Non-determinism — output varies run to run, so a single passing test proves almost nothing. You need to measure consistency across repeated runs.
Open-ended inputs — users (and other systems) send things you never scripted. The agent has to degrade gracefully on inputs no test suite anticipated.
Compounding errors — in multi-step or multi-agent workflows, a small early mistake cascades. A 95%-accurate step run five times in sequence is not 95% reliable end to end.

So evaluation isn’t a pass/fail gate at the end. It’s a measurement system that runs continuously and tells you, in numbers, whether the agent is good enough to trust — and keeps telling you after it’s live.

What to evaluate: the criteria that actually matter

Before you can score an agent, define what “good” means for your use case. The dimensions that decide whether an enterprise agent is deployable:

Accuracy — how often the output is correct against a defined ground truth or marking standard, not a vibe.
Consistency — how stable the output is across repeated runs of the same input. High variance is a deployment blocker on its own.
Edge-case handling — what the agent does with malformed, adversarial, or out-of-scope inputs. Does it fail safe, or confidently do the wrong thing?
Safety and governance — whether it respects guardrails, avoids leaking sensitive data, and stays inside policy. For regulated workflows this is non-negotiable.
Latency and cost — response time and per-task cost at real volume, because an accurate agent that’s too slow or too expensive still doesn’t ship.
Explainability — can the agent show why it produced a result? A decision you can’t defend to an auditor or a customer isn’t usable in most enterprise contexts.

The mistake is grading only on accuracy. A 90%-accurate agent that’s wildly inconsistent, can’t explain itself, and breaks on edge cases is not 90% ready — it’s not ready.

How to evaluate AI agents before deployment: the framework

This is the phased methodology we use. It treats full automation as something an agent earns by demonstrating performance, not something you switch on at launch.

1. Define success metrics first. Before building or scoring anything, write down the numbers that mean “deployable” — target accuracy, acceptable variance, latency ceiling, cost per task. If you can’t define success, you can’t evaluate it. 2. Build an evaluation pipeline. Assemble a representative dataset of real inputs (including the messy and adversarial ones) and run the agent against it repeatedly, scoring every dimension above. This is the instrument you’ll reuse for every change to the agent. 3. Benchmark against the human baseline. Measure how well a capable human performs the same task, then hold the agent to matching or beating it. “Better than nothing” is not the bar; “as good as or better than the person doing it today” is. 4. Shadow launch. Run the agent in parallel with human workers on live data, with its output captured but not acted on. This surfaces the real-world edge cases no test set contains, with zero production risk. 5. Human-in-the-loop validation. Promote the agent to doing the task for real — but a human reviews and approves each output before it takes effect. You collect accuracy data on live work while a person remains the backstop. 6. Graduate to full automation — conditionally. Only remove the human once performance matches or exceeds the baseline over a sustained period, and keep monitoring in production. Evaluation doesn’t end at launch; agents drift, and the pipeline that qualified the agent is the same one that catches regressions later.

This is grounded in the same engineering rigor practiced over six years as a senior software engineer at Google: deterministic workflow design around the non-deterministic core, modular agent architecture, and measurement before trust. It applies across use cases — from AI-powered CRM and customer-service automation to intelligent document processing and multi-agent orchestration.

An enterprise AI agent evaluation checklist

Before any agent touches a business-critical workflow, you should be able to check every box:

Success metrics are defined in numbers (accuracy, consistency, latency, cost).
A representative evaluation dataset exists — including edge and adversarial cases.
Accuracy and consistency are measured across many runs, not one.
The agent fails safe on out-of-scope input rather than guessing confidently.
Safety, data-handling, and governance constraints are tested, not assumed.
Performance is benchmarked against a real human baseline.
A shadow-launch and human-in-the-loop period happened on live data.
Production monitoring is in place to catch drift after go-live.

What enterprise and finance leaders should demand from a vendor

If you’re buying agentic AI rather than building it, the evaluation discipline above is exactly what separates a serious partner from a risky one. Three things to insist on:

Outcome-based contracts tied to the metrics that matter to your business, not hours billed.
Phased rollouts with measurable checkpoints — shadow, human-in-the-loop, then automation — never a big-bang launch.
Testing and governance as a default. Any vendor who skips evaluation and governance, or can’t show you their evaluation pipeline, is a red flag.

The bottom line

Evaluating an AI agent for enterprise use isn’t one test — it’s a measurement system: define what “good” means in numbers, measure accuracy and consistency across real and adversarial inputs, prove it against a human baseline, then earn full automation through shadow and human-in-the-loop stages while monitoring for drift. Do that, and you deploy agents you can defend to an auditor, a customer, and your own board. Skip it, and you ship the demo that breaks in week one.

Frequently asked questions

How do you evaluate an AI agent before deploying it?

Define success metrics in numbers first, then run the agent against a representative dataset — including edge and adversarial cases — scoring accuracy, consistency, safety, latency, and cost across many runs. Benchmark it against a human baseline, then validate on live data through a shadow launch and a human-in-the-loop stage before allowing full automation, with monitoring continuing in production.

Why can’t I just use normal software QA for AI agents?

Because agents are non-deterministic — the same input can produce different outputs — so a single passing test proves little. You have to measure behavior across repeated runs and unscripted inputs, and account for errors compounding across multi-step workflows, which traditional pass/fail QA doesn’t capture.

What metrics matter most when evaluating an AI agent?

Accuracy against a defined ground truth, consistency across repeated runs, edge-case and adversarial handling, safety and governance compliance, latency and cost at real volume, and explainability. Grading on accuracy alone is the most common and costly mistake.

How long should you test an AI agent before full automation?

Long enough to prove it matches or beats the human baseline over a sustained period on live data — through shadow and human-in-the-loop stages — not a fixed number of days. The agent earns automation by sustained measured performance, and monitoring continues after launch because models drift.

What should enterprise leaders ask an AI vendor about evaluation?

Ask to see their evaluation pipeline and metrics, insist on outcome-based contracts tied to your business numbers, and require phased rollouts with measurable checkpoints. A vendor who can’t show how they test and govern agents — or who proposes a big-bang launch — is a red flag.

Krazimo is a team of former Google engineers who build reliable, evaluated AI agents for enterprise workflows. If you’re assessing agentic AI for a business-critical process, talk to us about your evaluation criteria →

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.