Why AI is becoming a non-negotiable for manufacturers

The economic volatility of recent years has reached new heights altogether. In particular, the new wave of tariffs just announced by Trump could totally transform how companies have to operate — demand could change radically. Manufacturers are likely to be hit hard, with some businesses potentially losing hundreds of millions of pounds in sales.

Whatever happens, understanding how demand will be impacted in the coming months depends on data analysis and AI. Without it, companies are essentially playing a game of ‘guess how much?’

I’ve seen many examples of companies increasing output when demand is high, only to swiftly pivot strategy when demand changes and end up with a product surplus. Therefore, careful calculation is required to manage production levels. Knowing what to make (or not), what to hold, what to allocate and what to charge are all becoming even more important metrics to understand and predict.

AI can help navigate current volatility by analyzing a range of data points to aid forecasting and long-term visibility; it allows manufacturers to understand the current climate, what is likely to take place in the future and quickly adapt when changes arise.

To make or not to make?

The balancing act of producing the right amount of materials at the right time is a difficult one to master. Producing too much means wasted products and higher operational/storage costs, but underproducing means missed sales opportunities and delays — and both aspects harm revenue. Many traditional planning systems only work at a broad level, limiting manufacturers’ ability to dynamically react to sudden demand shifts. And when this happens, strategy turns into guesswork.

Yet the latest inventory platforms use AI to analyze metrics like inventory data, capacity constraints and demand forecasts to produce real-time insights on what to produce, how much and when. This allows manufacturers to access bespoke production plans that enable more precise and efficient scheduling and ensure the procurement of raw materials falls in line with demand.

Such plans mitigate expensive overproduction and improve efficiencies across the production pipeline, allowing for streamlined operations and better service levels. Crucially, manufacturers can pivot quickly to any sudden changes in demand.

The inventory balancing act

The same balancing act occurs when making strategic decisions around holding and shifting ‘finished’ products. If manufacturers end up holding on to too much inventory, they tie up working capital, but if they don’t have enough, this again leads to missed sales and delays. Fluctuating inflation and stagnant economic growth, coupled with factors like unforeseen weather events disrupting deliveries, can all impact demand and make striking this balance complex.

With so many variables, AI is perfect for optimizing inventory levels as it can account for live market conditions, seasonality and sales trends to recommend the ideal amount of stock for a site. This can reduce excess stock and the unnecessary waste and storage costs that comes with it.

Getting products where they need to be

It’s not only about how much stock sites hold, but also delivering it to the right place at the right time. There might be one manufacturing site that is well-stocked but another location nearby that is short on supply to meet orders. Without AI providing insights from across the network, the manufacturer may end up buying more raw materials instead of spotting the opportunity to transfer goods between the two locations.

AI can assess demand, historical sales and trends data — alongside location-specific needs — to distribute inventory dynamically across production sites. And not only can it react to shortages but also anticipate when demand may surge at a location and thereby allocate inventory accordingly.

These insights help form a process that ensures inventory is only stored where it’s needed and allows manufacturers to adapt operations quickly to market changes.

Securing the optimal deal

Pricing lists in manufacturing can remain static for months, despite the cost of materials, services and demand changing in that timeframe. Many teams are reliant on spreadsheets and so they can’t adapt quick enough to these shifts, leading to lost margins and revenue.

There’s also the added complexity of working out the optimal price to shift unsold stock or to maximize margins for products selling well. And in a fast-moving market, clients want tailored quotes for products and services delivered promptly.

By analyzing demand and market conditions alongside past sales data and specific business KPIs, AI can generate optimal recommendations for list pricing and quotes that best preserve margins while driving revenue: these are the prices most likely to land the deal without undercutting the manufacturer’s value.

Uncertainty needs AI predictability

Alongside macroeconomic headwinds, the new national insurance increases for employers are limiting manufacturers’ ability to expand their workforces. This is giving AI another level of importance. The predicted rise in agentic AI this year, where AI agents independently handle tasks like ordering inventory, can become a smart way for teams to do more with what they already have. 

These agents don’t just follow rules but proactively learn from data and make intelligent decisions, meaning activities like stock allocation and pricing strategies are continuously refined. Humans are still crucial to the process, but it means fewer manual interventions and better service levels.

But in today’s context, AI tools can give manufacturers a dynamism and agility in optimising their inventory levels and pricing – qualities which are pivotal for navigating such an unpredictable landscape. They can maximise their margins while avoiding stockouts and lost business. And in a market where you just don’t know what’s coming next, AI provides a predictability needed now more than ever.