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.