How startups can implement AI products and build processes to ensure they work effectively and securely

This is an excerpt from a podcast about technology, startups, and the real-world application of AI. It contains the main ideas and conclusions; you can listen to the full version of the podcast via the link.

Zhanar Sabazova, city — Astana, MBA in AI, PR in Tech&IT, LinkedIn

About Me

I previously worked at TransTeleCom and hold an MBA in Artificial Intelligence. For over 20 years, I have been operating at the intersection of technology and communications, helping to make complex DeepTech solutions understandable to the market and in demand by businesses.

AI Startups

In AI startups, it is crucial to manage expectations from the very beginning. There is often a myth that AI understands everything, never makes mistakes, and can replace all specialists. Such misconceptions distort the perception of the product, create pressure on the team, and can lead to incorrect decisions.

The product must demonstrate real value, not "magic." For example, AI can accelerate data processing or analyze resumes, but the final decision always remains with a human. This approach helps avoid legal and reputational risks.

When demonstrating the product, it is important to clearly explain which processes are automated, where human control is required, and what limitations exist. A transparent process structure and clear escalation logic build customer trust.

For instance, a product might speed up the initial response in customer support by 40%, and in cases where the AI is uncertain, the request is automatically transferred to an operator. This is the formula for value: "we accelerate or reduce the cost of X in process Y at stage Z, while maintaining control."

In HR scenarios, AI can review thousands of resumes and conduct preliminary candidate filtering, while the final decision remains with the specialist. This approach reduces the team's workload and makes the process more transparent.

It is important for a startup to consider all levels of cost: the price of one request, one action, verification, and error correction. Defining these metrics allows for more accurate forecasting of budgets, resources, and scaling.

For example, in projects to automate social media, attempting to do everything independently often leads to difficulties. Involving experts simplifies the process and makes the result manageable.

In the unit economics of LLMs, it is necessary to monitor the cost of input and output tokens, control generation expenses, and optimize the use of models of varying complexity.

A transparent pricing system helps the customer immediately understand what they are paying for and reduces the risk of inflated expectations. A notable example is Intercom, where the cost per action is $0.99. This directly links the customer's result to the product's economics and allows the startup to manage costs effectively.

How to Implement AI Products and Build Processes

AI is a tool that accelerates and simplifies processes, but it is not magic nor a replacement for humans. In all sensitive cases—medicine, finance, aviation, social services—it is necessary to include stages for verification and escalation to a human. This reduces the risk of errors and lawsuits and preserves customer trust.

In HR, AI can review thousands of resumes and perform preliminary filtering, but the final decision remains with the specialist. In aviation services, AI assists with customer support and tariffs, but complex cases are immediately transferred to a human. Ignoring this approach leads to errors and legal consequences, as demonstrated by the Canada Air case.

In medical services, AI is used for preliminary symptom diagnosis, but a doctor must always verify the results, as any error can have serious consequences. In social services, AI sorts content, automatically identifies spam and unwanted messages, and transfers questionable cases to a moderator. This increases accuracy and reduces legal risks.

All these cases show one thing: AI accelerates processes, but human control remains key. It is important for a startup to explain to the customer from the very beginning that AI works as a tool, that there are limitations, and that a human is responsible for critical decisions.

Startups often encounter myths about AI: that it understands context like a human, that it is always right, or that it will replace all employees. These expectations raise requirements for the product and lead to disappointment. The real value of AI lies in automating routine tasks, accelerating processes, and reducing the number of errors, not in completely replacing humans.

When building an AI product, it is important to document processes: where it helps, where a human is involved, what limitations exist, and how escalation occurs. The customer needs to be informed about the cost of each action, the result, and the model's limitations.

This forms a GTM (Go-To-Market) strategy without hype: a product with real value, transparent processes, clear expectations, manageable risks, and transparent economics. An AI product is a tool. The main value for the customer is the result and reliability. A startup that builds processes correctly and ensures human control gains trust, stability, and the ability to scale safely.

Recommendations for Beginner Startups

A product is a process, not magic. It is important to clearly explain which tasks AI automates and at which stages human intervention is required. It is necessary to manage customer expectations: do not promise 100% accuracy, but instead discuss the model's limitations and escalation stages in advance.

Special attention should be paid to unit economics and token costs. It is important for a startup to monitor generation expenses, understand the difference between input and output tokens, account for hidden "thinking tokens," and optimize the use of models.

Verification and control must be built into the product by default. Human control is necessary in any sensitive area. Transparent pricing allows the customer to understand the cost of their request or action, and allows the startup to control expenses and plan for scaling.

Customer education is another critically important element. It is necessary to form an understanding of the product at all levels, avoiding hype and the illusion of "magic." These rules allow a startup to be resilient, build customer trust, minimize legal and reputational risks, and scale effectively.