To develop a robust logistics platform, transport cannot be understood as a mere cost centre. After taking over a distribution company during the pandemic, Mamiev inherited trucks, 55,000 retail points, and a general distribution contract with Nestlé.

After paying off debts and settling with suppliers, several brands tried to rewrite terms pushing for unreasonable dollar pricing and prepayment. He told these companies “I will clear your products from every shelf across the region and leave the stock to sit. Your brands belong to you, but everything else belongs to me: the cold storage, the trucks, and the relationships with 55,000 shops.” This, he says, is where the value of a supply chain lies.

This instance reveals the strategic divide between owning a product brand and owning an operational pipeline. Mamiev stresses that data longevity outlasts individual commercial agreements – the information generated while moving goods is what compounds. Shops reordering the same items at the same time intervals provide more valuable information than a forecast. When the number of shops being tracked is in the thousands and over a period of years, a reading of demand across a whole region can be generated. A company that holds this information can plan, price, and raise finance against it. When those suppliers wanted to change their deals, Mamiev knew he held leverage in the form of understanding everyday product demand.

Kairat Mamiev began working in distribution with pagers and handwritten order sheets, packing ice cream in cold storage by himself. Later, he ran forecasts in spreadsheets and checked them against accounting software at the end of each month. Each step helped him to understand what patterns were developing and allowed him to move proactively.

Today, failing to track this data will only create additional hardships. Data from Boston Consulting Group shows that only around a tenth of logistics companies have fully adopted generative AI, and those early adopters are already creating positions that will be difficult to displace. The cost of waiting reveals itself in the lag between changing demand and a company’s response. One 2026 study estimated this delay at roughly 5 cents lost per dollar – $55 million for a billion dollar operation. Mamiev experienced that delay for years, ‘My performance was booked the moment I sold stock into our service centers, meaning revenue was recorded on my side before the goods reached the end customer. The sale might land a month later, in a different period entirely. The information that would have resolved that sat in ordering records the whole time, and nobody was reading it.”

An advanced logistics platform is entirely dependent on the culture of the workforce feeding it. Mamiev mentions that a model can produce a clean answer, but that answer is unimportant if the people do not trust AI. He experienced this resistance firsthand with mechanics who declined to share their service records, making his forecasting attempts little more than educated guesses. In other words, AI can only be helpful if human users give it the right information to be helpful.

For businesses looking to scale or secure backing, data integrity is the primary indicator of long-term viability. When looking operations to back over the coming decade, Mamiev prioritises teams that treat operational records as their primary asset. “An operator who has spent years watching where goods move notices things that a purely technical team walking the same warehouse will miss. That pairing takes longer to build than a funding round, and it holds its value when conditions turn.”

​The future of logistics belongs to companies that understand capturing and owning demand data is incredibly valuable. Kairat Mamiev’s experience in the sector proves that a company who views itself as a box-carrier will always be at the mercy of suppliers. Those who understand records and can accurately read demand, on the other hand, can dictate their own terms. The technologies that allow companies to effectively read and store data, however, are only as good as the input and care they are given. Predictive AI models should be companions to real-world experience. No matter the specific sector, data is always being produced. Those who succeed will be the ones to take that data and act on it.

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