Why Technology Investments in Operations Don’t Deliver Expected Value

In many organisations, analytics and execution still operate as two parallel layers. Systems generate forecasts, scenarios and optimisation outputs, but actual decisions are made elsewhere, based on local constraints, team agreements and partially outdated information.

Decisions can look reasonable within each function, while across the supply chain they follow different timelines and assumptions. This disconnect becomes visible when looking at how decisions move through the system.

A typical supply chain decision flows across several stages: planning, procurement, production and logistics. In theory, these steps are connected. In reality, they often operate with delays and partial misalignment.

Planning defines one version of demand. Procurement reacts later and relies partly on earlier inputs. Production adjusts schedules based on current capacity constraints rather than the latest demand signals.

Because of this, decisions are formed step by step instead of being coordinated at the same time. Each function works with a slightly different picture of reality. This leads to repeated adjustments, excess inventory in some areas and shortages in others.

‍McKinsey highlights that alignment of decisions over time is a key driver of productivity. When this alignment is missing, improvements in individual parts of the process do not translate into overall performance.

AI expands the ability to analyse data and model scenarios, but it does not automatically change how decisions are made. In many organisations, AI remains at the level of recommendations. It can suggest an optimal option, while execution still depends on existing processes and habits.

In some cases, this increases misalignment. Faster and more frequent decisions amplify existing inconsistencies between functions, especially when they are based on different assumptions.

AI does not change how decisions are structured across the organisation. In most cases, three issues remain:

1. Decisions are distributed across functions
Planning, procurement, production and logistics each make their own decisions, often using different assumptions and timelines.
2. There is no single point where decisions come together
No shared process exists where trade-offs are evaluated across cost, service and risk before actions are taken.
3. Ownership of the final outcome is unclear
Responsibility is split between functions, which makes it difficult to coordinate decisions and act consistently. 

As a result, the system may identify a better course of action, but continue operating in the same way.

‍BCG highlights that model accuracy is improving, while the main constraint lies in how these models are embedded into planning and execution processes. Without that connection, even strong analytical tools remain detached from daily operations.

Companies that see real impact change how decisions are organised across the business. They connect data, analysis and execution into a single process. The difference comes from how decisions are structured and executed across the organisation.

This shift becomes visible in how decisions are handled day to day. Instead of being formed separately within each function, they are shaped within a shared process that brings planning, procurement, production and logistics together.

This typically involves several practical changes:

Together, these changes allow companies to move from isolated improvements to coordinated decision-making across the supply chain.

‍Bain describes this as a shift from execution-focused systems toward systems that support decision-making.

The gap between analytics and execution is becoming one of the main constraints in supply chain performance. Most organisations already have access to data, forecasting models and optimisation tools, yet these elements often remain disconnected from how decisions are made across planning, procurement, production and logistics. As a result, decisions are formed at different points in time and based on partially misaligned assumptions, which leads to delays, rework and inconsistent outcomes.

Closing this gap requires changes in how decisions are structured and coordinated. Teams need to work with the same data and update decisions simultaneously, with clearly defined ownership so that trade-offs between cost, service levels and risk are resolved within a single decision. AI and optimisation tools play a role only when they are embedded into these workflows, directly influencing how orders are prioritised, suppliers are selected and capacity is allocated.

Organisations that align decisions in this way achieve more stable and coordinated performance across the supply chain. Others continue to expand their technology stack while facing the same coordination challenges.