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Tech entrepreneur Furkat Kasimov distills business productivity in the age of artificial intelligence into a striking equation. He argues that artificial intelligence combined with human intelligence delivers productivity improvements (AI + HI = PI). “Use AI for speed, scale and consistency; use people for judgment, creativity and accountability,” he says. This blend, he believes, creates a lasting edge that competitors cannot easily replicate.
For Kasimov, AI is neither magic nor marketing hype. It serves as a tool that rewards disciplined execution over big promises. He stresses that productivity breakthroughs do not come from throwing AI at every problem but from careful design and relentless measurement.
Mapping workflows before automating
The foundation, Kasimov says, is understanding the work itself. He begins every AI discussion by mapping out processes in full detail. Companies often operate with four overlapping but misaligned views of the same workflow: the official procedures in handbooks, management’s interpretation of how things work, the actual practices of frontline staff and the version designed for software.
“When you map those four views side by side, the gaps jump out,” he explains. If a process relies on someone’s memory, it will not scale until that knowledge is stored in a system. He argues that process redesign is a prerequisite for automation. The sequence is deliberate: Clarify the tasks, capture the steps and then apply AI where it removes friction most effectively.
Building a data wheel that learns faster
Once a process is mapped and instrumented, Kasimov focuses on speed and learning. His model for competitive advantage is what he calls the Data Wheel, a feedback loop where workflows collect context and results, and those results continuously refine the next cycle.
“Spin the wheel faster with shorter feedback cycles and consistent data capture,” he says. “The faster your organization learns, the harder it is to catch you.”
He redesigned meetings around this concept. Presenters prepare materials in advance, conversations are recorded and transcribed, and AI models review the discussions with a blunt prompt: What was missed? What other options exist? Humans review the insights before any decision is made.
One surprising finding came out of this system. “Less background produced more inventive outputs,” Kasimov says, noting that providing AI models with just enough context sometimes resulted in more original suggestions.
Measuring value in financial terms
Kasimov’s view of AI’s worth is as direct as any CFO’s. He says if AI cannot be tied to measurable gains like time saved, lower costs, better conversion or reduced fraud, then it is not worth the investment.
In his businesses, developers using AI coding assistants have reduced their time spent on common tasks by 50%. A lead marketplace he manages uses AI to determine which buyer sees a lead first, increasing conversions and margins. Fraud detection systems that once depended on rigid rules now adjust dynamically to new behaviors, cutting refunds and bad leads.
“Across time, revenue, margin and risk, the improvements are measurable,” Kasimov says. “That’s how you know AI is working.”
Knowing when to buy and when to build
On sourcing AI solutions, Kasimov is blunt, saying most companies should not build from scratch. “Buying works more often than building when you’re getting started,” he says.
He avoids being tied to a single vendor by testing models on real workloads, designing modular systems and negotiating contracts that protect data ownership. For him, flexibility is essential. “Vendors know we can move if performance or cost shifts,” he says.
Choosing the right AI techniques
His technical strategy is grounded in practicality: General-purpose large language models handle simple tasks like summaries and drafts. Retrieval-augmented generation supports tasks requiring company-specific knowledge. Fine-tuned models are used sparingly, reserved for situations with strong datasets and high accuracy demands.
For Kasimov, AI enhances rather than replaces human talent. “AI doesn’t replace humans; it gives humans superpowers,” he says. “Choose the lightest method that achieves the outcome, and keep people in control.”
The edge that competitors cannot buy
Kasimov’s competitive doctrine condenses into a simple formula: AI plus human intelligence equals a lasting edge. But the strength of that edge is not in the tools but in how rigorously they are used. Companies must map real processes, start where data is plentiful, track measurable improvements and keep systems open to better options.
“Competitors can buy the same tools tomorrow,” Kasimov says. “What they can’t buy is your learning velocity, how fast your Data Wheel spins, how well your teams combine AI with human judgment and how consistently you turn that into outcomes. That’s the edge.”
