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Planning
Predict , discover , test , respond
Algorithmic retailing ’ s insights are as granular as they need to be . They can offer anything from a sweeping oversight to minute-byminute analysis of an individual product . Since we cannot assume that the effects of inflation will be the same across every product line and on every supplier , an algorithmic approach allows product verticals to be separated , analyzed , and acted upon individually . Changing market dynamics , however slight , need not lead to panic ; with all the data available and an algorithm ready to respond , an effective response is readily available .
AR can build predictive models based on market data . It backs up proposed changes by generating detailed simulations , taking past data into account . Every strategic decision is then ready to be confidently put in place , having already been fully tested . Those areas where the fluctuating market clashes with existing strategy can be quickly identified , and the solutions found . ONS statistics show that inflation has been highly unstable over the past few years , yet the agility offered by AR creates the opportunity to pivot on a moment ’ s notice and make the most effective response to changes as they happen .
Retail AI is not equivalent to the AI currently being popularized by text-generating neural networks ; AR ’ s live calculations are based on rigid data and sharp calculations , not on educated guesswork . Its automations must follow strict guard rails , ensuring that prices and orders stay within defined limits . Caution and calculation are the tools that will fight inflation , and the extra advantages of AR make it a critical piece of the puzzle .
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