AI in the Chain

Why growth and profitability increasingly diverge

Many supply chains appear successful on the surface. Volumes grow, revenue increases, service levels remain acceptable, and operations teams stay busy. Yet margins stagnate, working capital rises, and every planning cycle feels harder than the last.

This contradiction is not accidental. It is structural.

Across industries, companies have learned how to grow demand faster than they learn how to understand its true cost. As a result, supply chains become more complex each year while profitability quietly erodes.

At the center of this challenge lies one concept that remains poorly understood and rarely operationalized: cost-to-serve.

Cost-to-serve is not a finance exercise. It is a decision framework. When ignored, it allows value destruction to hide behind averages. When applied correctly, it becomes one of the most powerful levers for restoring profitable growth.

Why averages distort reality

Most supply chain metrics are built on averages. Average transport cost per unit. Average warehouse cost per order. Average service cost per customer.

Averages feel safe and objective, but they hide structural imbalances.

In reality, supply chain costs are highly uneven. A small share of customers, products, or delivery patterns typically generates a disproportionate share of complexity and cost. Other flows are stable, predictable, and highly profitable.

When leaders manage by averages, profitable flows subsidize unprofitable ones. The business appears healthy while margin leakage accelerates beneath the surface.

This is why many companies experience revenue growth without corresponding profit growth.

Why traditional cost-to-serve initiatives fail

Cost-to-serve is not a new idea. Many organizations have tried to implement it before and walked away frustrated.

The failure pattern is consistent.

Traditional cost-to-serve models rely on static activity-based costing. They require extensive manual allocations, dozens of assumptions, and long modeling cycles. By the time results are available, the operating environment has already changed.

Teams lose confidence in the numbers. Leaders struggle to translate insights into decisions. Cost-to-serve becomes a report rather than a capability.

The problem is not the concept. The problem is treating cost-to-serve as a periodic calculation instead of a continuous decision input.

Why cost-to-serve is a leadership issue

True cost-to-serve transparency forces difficult conversations.

It reveals that some large customers are unprofitable. That some popular service features destroy margin. That internal policies drive cost without improving customer outcomes.

These insights challenge long-standing assumptions across sales, supply chain, and finance.

As a result, cost-to-serve initiatives often stall not because of technical limitations, but because organizations are unwilling to act on what the data reveals.

Making cost-to-serve actionable requires leadership alignment and clear decision ownership.

How AI changes the cost-to-serve equation

AI fundamentally changes what is possible.

Instead of allocating costs manually, AI learns cost drivers directly from operational data. Order frequency, delivery distance, handling complexity, demand volatility, service level requirements, and exception rates are modeled dynamically.

Costs are inferred continuously rather than calculated annually.

This allows cost-to-serve to move from a static analysis to a living input into daily decisions.

How AI-enabled cost-to-serve works in practice

Granular cost visibility

AI assigns cost at the level where decisions are actually made: customer, product, order, lane, or service promise. This reveals which combinations create value and which destroy it.

Economic segmentation

Instead of segmenting customers by volume alone, AI enables segmentation based on economic behavior. Customers are grouped by cost-to-serve, variability, and margin contribution rather than size.

This supports differentiated strategies that protect both service and profitability.

Service differentiation with intent

AI-powered cost-to-serve makes it possible to align service levels with economic reality. Not every customer requires the same delivery speed, order frequency, or flexibility.

Service differentiation becomes intentional rather than reactive.

Scenario-based decision-making

AI allows leaders to test decisions before implementing them. Teams can simulate the impact of changing delivery frequency, lead times, minimum order quantities, or network design.

Instead of debating assumptions, decisions are based on expected outcomes.

Commercial and supply chain alignment

Cost-to-serve transparency creates a shared fact base across supply chain, sales, and finance.

Growth discussions begin to include profitability. Service promises include operational consequences. Trade-offs become explicit.

This alignment is impossible when cost remains opaque.

Common mistakes to avoid

Organizations often overcomplicate cost-to-serve models, chasing precision instead of relevance. Others treat cost-to-serve as a one-time project rather than a capability.

The most damaging mistake is failing to link cost-to-serve insights to decision ownership.

AI amplifies value only when decisions are clearly owned and acted upon.

Implications for supply chain leadership

For supply chain leaders, managing by averages is no longer viable.

Rising volatility, service complexity, and margin pressure make cost-to-serve transparency a strategic necessity.

Leaders must ensure cost-to-serve insights are embedded into daily planning, customer strategy, and network design.

AI is not optional in this journey. It is the enabler that makes cost-to-serve timely, scalable, and actionable.

Practical prompts to unlock value

Which customers generate the highest operational complexity relative to margin?

Which service promises cost the most to deliver?

Where do we see margin erosion despite volume growth?

Which internal policies drive cost without improving outcomes?

These questions shift the focus from efficiency to value creation.

The deeper lesson

Cost-to-serve is not about cutting service or pushing cost onto customers.

It is about making trade-offs visible and intentional.

Supply chains that understand their true cost-to-serve can grow profitably, serve customers intelligently, and invest where it matters.

Those that do not will continue to chase efficiency while value quietly leaks away.

AI turns cost-to-serve from a painful analytical exercise into a sustainable competitive advantage.

References

Harvard Business Review – When Customers Destroy Value
https://hbr.org/2004/10/when-customers-destroy-value

McKinsey – Why Supply Chain Transparency Is Critical
https://www.mckinsey.com/capabilities/operations/our-insights/why-supply-chain-transparency-is-critical

Boston Consulting Group – Managing Complexity in Supply Chains
https://www.bcg.com/publications/2016/lean-manufacturing-managing-complexity

KPMG – Cost-to-Serve Analysis in Supply Chains
https://kpmg.com/xx/en/home/insights/2018/03/cost-to-serve.html