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.  

Navigating the volatility with AI

While it’s refreshing to see some positivity across the industry driven by an increase in demand, the volatility and unpredictability of recent years should still be front of mind for businesses in construction supply chain. We’ve seen many examples of businesses expanding when demand is high only to have to readjust, with firms increasing their capacity and availability, only to struggle with a product surplus further down the line.

Businesses need to think carefully before they make any serious moves in terms of increasing production, with artificial intelligence (AI) now playing a key role in helping to get a better handle on forecasting, providing better long-term visibility. While volatility will always be prevalent in the sector, AI is a great way to sense check what’s happening now, what will likely happen in the future and — crucially — allows you to be agile enough to change and pivot should the picture change.

Let’s take a look at five ways AI is making a difference across different areas of construction supply chain.

What to make

Production planning is a difficult balancing act. Manufacturers need to produce the right materials at the right time without overcommitting resources which get wasted or underproducing and missing sales. Traditional planning systems only work at a broad level, making it hard to respond to real demand shifts in an agile way. 

However, AI uses demand forecasts, capacity constraints and inventory data to create smarter, more efficient schedules that removes the guesswork and provides real-time insights on what to produce and when. AI generates tailored weekly or daily production plans, ensuring resources are used effectively across all sites. It also helps businesses plan months ahead, aligning raw material procurement with demand. 

By balancing priorities across sales, procurement and logistics, AI streamlines workflows and improves service levels. This avoids costly overproduction and leads to reduced inefficiencies, smoother operations and the ability to pivot quickly when market conditions change.

What to hold

Holding too much finished inventory ties up valuable working capital, while too little leads to missed sales, supply bottlenecks and delays. Striking the right balance is difficult, especially when demand fluctuates in a volatile market. AI forecasting helps businesses determine the ideal stock levels by factoring in factors like seasonality, sales trends and real-time market conditions — instead of relying on static models, businesses can now adjust in real time to stay efficient.

By optimizing stock levels, businesses avoid unnecessary storage costs and reduce excess stock that eats into cash flow. AI helps ensure inventory is held only where and when it’s needed, keeping operations agile and responsive to market changes.

What to allocate

Once stock is produced, it needs to be in the right place at the right time. AI has the ability to distribute inventory dynamically across depots, branches and distribution centers based on demand forecasts, historical data and location-specific needs.

Instead of reacting to shortages, AI anticipates demand surges and shifts inventory proactively. This prevents regional stockouts, improves overall service levels and keeps supply chains running smoothly without unnecessary delays. Plus, it plays a key role in delivering an optimal customer experience, with customers able to get hold of the products they need, when they need them, from the most convenient location — which keeps them from drifting towards your competitors.

What to charge

As confidence returns, demand increases, with many businesses looking to adjust pricing strategies accordingly. However, pricing in the construction supply chain is often inconsistent, leading to lost margins and missed opportunities. 

AI can analyze customer demand, competitor pricing and historical sales to recommend the right price, first time, ensuring businesses stay competitive without undercutting their own value. It’s particularly useful in areas like quote pricing.

It helps by analyzing past sales data and market conditions to generate fast, accurate quotes that are designed to find that ‘sweet spot’ between maximizing margin whilst having the highest likelihood of winning the deal. 

This reduces the time sales teams spend on manual pricing decisions (which can be laborious), improving conversion rates and protecting margins. By automating and optimizing pricing, businesses can respond quickly to market shifts while maintaining profitability.

What next?

The rise of agentic AI

Increases in national insurance means that labor costs are rising and workforce expansion is becoming less viable. Businesses need to find smarter ways to do more with the teams they already have — and this is where a new era of agentic AI will come into its own. Agentic AI deploys ‘agents’ to handle tasks, automating workflows, reducing inefficiencies and increasing productivity without adding headcount.

However, unlike traditional automation, agentic AI doesn’t just follow preset rules. It proactively learns from data, makes intelligent decisions and continuously optimizes processes. Whether it’s managing stock movements, adjusting production schedules or fine-tuning pricing strategies, agentic AI helps businesses stay agile, reduce operational costs and improve service levels with fewer manual interventions required from staff.

AI for construction supply chain: why now?

The building materials industry is evolving rapidly, with economic shifts and rising competition driving the need for smarter decision making. AI empowers businesses to work smarter, not harder — reducing costs, improving margins and enhancing the overall customer experience. Peak customers are seeing some incredible results with AI, including…

• Speedy Hire: 4% inventory savings, 8% more demand
• Eurocell: £1.86m in inventory released, 6.7% increase in availability
• Heidelberg Materials: 2% quote price conversion increase, 10,000+ hours saved

Explore these success stories in more detail via the links below.

EVs are on the up, but there’s more to consider

Despite EV adoption increasing in the UK, the overall automotive market remains in a difficult position compared to years gone by. Total vehicle demand is still yet to recover to the levels seen before the COVID-19 pandemic, reflecting a changed world with more economic pressures and different consumer preferences.

While increased EV sales drives important progress towards sustainability goals, it currently doesn’t do enough to offset the decline in overall sales. This imbalance intensifies financial strain, forcing manufacturers to shift gear and pivot their strategies.

To add to this complexity, businesses are being held accountable to government-mandated sustainability targets. Failing to meet these targets would carry significant penalties, pushing manufacturers to innovate while managing tighter profit margins. Achieving this balance between profitability and sustainability requires new ways of thinking and operating.

Economic pressures and workforce implications

Reduced overall demand for vehicles triggers economic pressures beyond financial metrics, with an impact on workforce and labor. With fewer cars being sold, manufacturers face tough decisions around staffing and operational capacities, with redundancies sadly often unavoidable. This emphasizes the need for businesses to take proactive steps to sustain jobs and achieve growth.

Additionally, the rise of second-hand car markets demonstrates a continued shift in consumer behavior. As more people steer away from purchasing brand new vehicles, manufacturers need to adapt their strategies to remain relevant. Strategies around flexible pricing, extended warranties or alternative leasing models are some examples of approaches that some are taking.

Dealing with supply chain disruption

Supply chain challenges continue to give automotive manufacturers sleepless nights. Sourcing materials like lithium and cobalt — materials essential for EV batteries — is becoming increasingly competitive and costly. Disruptions in global logistics adds further delays and expense, with a knock-on effect that ripples through production schedules, inventory management and the ability to meet consumer demand and achieve sales targets.

Tackling these challenges will require innovation and adaptability, with artificial intelligence (AI) set to play a crucial role in the near future. By leveraging supply chain data, manufacturers can use AI to identify inefficiencies, predict potential bottlenecks and optimize logistics.

AI: Driving the future of automotive

With the above challenges considered, AI solutions are rapidly becoming a key component of modern automotive manufacturing. Areas like machine learning optimizations and agentic AI are being used across a number of use cases to empower manufacturers to overcome these hurdles. For example, AI can help manufacturers to:

• Optimize production: AI can balance production levels with fluctuating demand, minimizing wastage and reducing excess inventory
• Enhance material sourcing: AI can identify the most cost-effective and sustainable material suppliers, reducing the impact of scarce resources
• Streamline logistics: AI algorithms can improve processes in areas like route planning and inventory distribution, reducing costs and delivery times

For example, Peak is currently working with a leading global automotive manufacturer that is leveraging AI to improve its overall operational performance. By enhancing supply chain modeling and improving last-mile delivery efficiency, AI is empowering this business to operate more effectively in a challenging market.

A new era for automotive manufacturing

Sustainability can no longer be treated as a nice-to-have; it’s a cornerstone of all modern automotive strategies. However, in pursuit of sustainability businesses must face up to the economic realities. Balancing these priorities involves leveraging technology to reduce costs, improve efficiency and minimize environmental impact.

This new era of EVs and a more sustainable automotive sector brings with it an opportunity for the sector to redefine itself. Success will require a blend of innovation, resilience and adaptability, with investment in AI and digital transformation to streamline operations of the utmost importance.

By embracing new strategies, the industry can overcome its current challenges and position itself for long-term growth — navigating the complexities of today’s market and accelerating towards a brighter, more sustainable future.

Prefer to listen?

The digitisation of the construction industry is forcing builders’ merchants and suppliers to up their game in terms of how they’re managing their stock and the service they’re offering their customers. Those companies taking the leap of faith and investing in new technologies to help them do this are starting to reap the rewards – and it’s artificial intelligence (AI) that is powering their success.

By AI, we don’t mean robots replacing your workforce or drones handling your deliveries – we’re talking about using clever machine learning techniques to put the vast amount of data every merchant has at their disposal to work.

AI transforms your data into powerful insights that can help you keep your stock levels optimised, improve the way you target and communicate with existing and potential customers, and ultimately drive profit and revenue growth.

What’s happening in merchanting?

With profit margins shrinking and material costs on the rise, there’s no room for error in the increasingly-competitive builders’ merchant space. The current market is clouded by economic and political uncertainty, and the time is now for companies to be taking steps to help future-proof and protect their businesses.

Disruptive and agile businesses are keeping the market’s leading players on their toes, with same-day delivery of parts becoming more commonplace and smaller players turning to e-commerce enhancements in a bid to remain competitive.

Customer loyalty is also less apparent than it is in retail sub-sectors such as fashion or food. If your customer wants a particular product and you’re out of stock, they won’t wait around – they’ll simply go straight to your competitor.

Space in the warehouse is at a premium, with merchants typically handling items with a low space-to-value ratio, and demand is often difficult to predict – particularly for those larger orders associated with big construction jobs

What does this all mean? It means that businesses need to be taking action and striking a balance between predicting demand accurately, optimising space in the warehouse and keeping their customers happy. This is where AI can play a crucial role, and leads directly to business and profit growth, cost reductions and a higher return on capital employed (ROCE). For an example of this in practice, look no further than our case study with hire firm Speedy.

What can AI do?

AI can help you tackle these issues by providing you with a holistic view of your data from across the entire business, from marketing and customer data to stock and asset information.

It quickly becomes a new ‘brain’ at the centre of your business, taking and enhancing data from your existing business systems and turning it into actionable insights tied to business outcomes and objectives, all whilst getting smarter and more effective over time.

AI for business growth

Using AI, marketing and sales teams can gain a clearer view of who to target, when they’re likely to buy, what products they’re interested in and how to reach them. This is where B2B retailers can follow in the footsteps of those high-performing, fast-growing fashion retailers – they’re all using AI to enhance their e-commerce offering and improve the customer experience dramatically. For example, AI could help your builders’ merchants business to accurately predict when your customers are in-market to purchase, what they’re likely to buy and how they’d prefer to be contacted.

AI for supply chain efficiency

AI offers a sophisticated approach to stockholding and asset management. It leverages data from across the business to improve forecasting accuracy, allowing you to have more of what you need when you need it, whilst also freeing up space in your warehouse and on your logistics fleet. Unlike most supply chain management systems, AI doesn’t rely on a “one size fits all” basis – it uses real data to automatically adjust and set stock safety levels, and can track stock levels and space across multiple depots and branches simultaneously.

The results? Well, imagine if you could ensure that you never run out of the products your customers want, but at the same time reduce your inventories and hold optimal assets in order to free up cash. This is all achievable with AI – and the time is now to embrace this powerful technology in order to reap the rewards for your business.