Anastasia Pyleva, Senior Supply Chain Expert, Planingo
For decades, the effectiveness of business planning was measured using a straightforward set of metrics: forecast accuracy, service levels, and inventory turnover. The better your forecast, the stronger your competitive advantage.
Today, however, that logic is becoming increasingly unreliable.
Kazakhstan’s economy remains closely linked to global commodity markets. The tenge is highly sensitive to external factors, while supply chains span multiple countries and border crossings. A sudden change in oil prices, disruptions to logistics routes, or new regulatory requirements introduced by trading partners can immediately affect costs, demand, and product availability. At the same time, organizations are dealing with an unprecedented volume of data, making it increasingly difficult to distinguish meaningful signals from background noise.
In this environment, planning is no longer about producing an accurate set of numbers. It has become a discipline for managing business risk and building organizational resilience. Companies need tools that can detect deviations faster, simulate alternative scenarios, and assess the consequences of different decisions. This is precisely why organizations across Kazakhstan are increasingly investing in artificial intelligence.
AI capabilities are already embedded in the world’s leading planning platforms, including Anaplan, o9 Solutions, and Oracle. Machine learning can identify market shifts long before they become obvious to human planners: for example, detecting that consumers have begun switching to more affordable brands following a sharp currency depreciation. Within minutes, AI can evaluate the impact of a delayed shipment: how long existing inventory will last, which SKUs are at risk, where warehouse capacity will come under pressure, and how working capital requirements will change.
Does this mean people are no longer needed?
Not at all. For finance and planning professionals, AI is not replacing the profession, it is redefining it. Algorithms perform calculations, but people remain responsible for the most challenging task of all: making decisions under uncertainty. AI can reveal the consequences of different options, but it cannot determine what is ultimately best for the business—whether inventory should be reallocated, safety stock increased, or promotional campaigns temporarily reduced.
As a result, planners spend less time recalculating spreadsheets and more time interpreting outcomes. Rather than working toward a single “correct” plan, they manage a range of possible scenarios. Their role is to understand how sensitive models are to changes in data, recognize the limitations of algorithms, and translate analytical outputs into sound business decisions. The emphasis is shifting away from computational accuracy and toward the quality of decision-making.
This transformation inevitably changes the skills that planning professionals need.
The ability to produce an accurate forecast is no longer sufficient. Today’s planners must understand how planning decisions affect cash flow, profitability, and supply chain resilience. They need to ask the right questions of AI models rather than accepting their recommendations at face value. Most importantly, they must continuously balance customer service, inventory, and risk—three objectives that often pull the business in different directions.
The planner of the future is not simply an analyst, but an interpreter of complex business systems.
First, data literacy becomes essential not in the sense of building models, but in understanding how they work. What data drives the forecast? Where might distortions occur? Is the model more sensitive to pricing, promotional activity, or historical demand patterns? Without this understanding, working with AI quickly becomes an exercise in blindly trusting the numbers.
Second, scenario thinking is becoming a core capability. Businesses in Kazakhstan operate in an environment defined by probabilities rather than certainty. A single “optimal” plan is no longer enough. Organizations must be able to manage a portfolio of possible outcomes, understand how quickly they can shift from one scenario to another, and identify vulnerabilities before they become critical.
Third, financial acumen is becoming increasingly important. Raising service levels by just a few percentage points is not merely an operational decision, it is also a capital allocation decision. Increasing safety stock may reduce risk, but it also ties up working capital. Today’s planners operate at the intersection of operational performance and financial outcomes, weighing the trade-offs between efficiency, resilience, and profitability.
Risk management and resilience planning have also become essential competencies. The objective is no longer to minimize inventory at any cost, but to determine deliberately where the business should remain agile and where it requires greater buffers. This is no longer optimization in the traditional sense; it is the design of a resilient operating model.
Communication skills are equally critical. Planning is fundamentally about balancing competing priorities. Sales teams seek higher service levels, finance aims to maximize capital efficiency, and operations prioritize stability. The ability to explain trade-offs, communicate the implications of different scenarios, and build alignment across functions has become a defining element of professional expertise.
Finally, adaptability and continuous learning have become indispensable. Technology is evolving faster than job descriptions. The ability to quickly master new tools, challenge established approaches and embrace new ways of working is rapidly becoming a competitive advantage.
AI delivers speed, scale, and analytical depth. But resilience is created through management decisions: through informed trade-offs rather than mathematically optimal answers.
How are planning professionals already using AI in practice? Consider a few examples.
During periods of sharp depreciation in the tenge, many consumers begin switching to lower-priced brands and private-label products. A traditional forecasting model based solely on historical sales data may take time to recognize this shift. A ML model that incorporates price sensitivity and category dynamics can detect changes in demand patterns much earlier. Yet the decision to increase private-label inventory or revise promotional plans still rests with human planners.
Consider another example. A distribution company receives an early warning that shipments may be delayed because of congestion at a border crossing. The planning system immediately recalculates the likely impact: whether current inventory is sufficient, which SKUs are at risk, and where stock should be reallocated across regions. AI provides visibility into the consequences of different scenarios. The planner, however, decides whether to redistribute inventory, increase safety stock, or temporarily scale back promotions.
The paradox is that the more intelligent planning systems become, the higher the expectations are placed on the people who use them. The planner’s role is not disappearing, it is becoming more sophisticated and increasingly strategic. Not because algorithms are inadequate, but because in an environment of uncertainty, the final decision must always remain a human one.
Perhaps the clearest measure of planning maturity in the age of AI is no longer forecast accuracy. It is an organization’s ability to say with confidence: “We understand the risks. We can see the possible scenarios. And we are making this choice deliberately.”
