Why Mathematical Optimization Is Critical to Digital Transformation Success

Digital transformation has long been a strategic priority for organizations across every industry. However, many companies still struggle to translate data and AI investments into real operational impact. This is where mathematical optimization in digital transformation plays a critical role, turning insights into actionable outcomes and measurable business impact.

Recent industry data reinforces this disconnect. McKinsey reports that while 88% of organizations now use AI in at least one function, only 6% see more than 5% EBIT impact from it.¹ Similarly, BCG finds that 60% of companies generate no material value from AI investments.²

They’ve moved their data to the cloud, deployed IoT across operations, and implemented modern ERP systems. On paper, they have everything they need to operate in a data-driven way. And yet, many of these same companies are still making critical operational decisions, such as planning routes, scheduling production lines, and allocating resources using spreadsheets and manual intuition.

They have data. But not always impact.

This is the optimization gap, and it’s often where digital transformation efforts stall.

From Data to Decisions: The Role of Mathematical Optimization in Digital Transformation

Digital transformation is often reduced to a technology upgrade. In reality, its goal is much more ambitious: to improve how decisions are made and executed.

Most organizations progress through three stages:

The challenge is that many companies stop at the second stage. Dashboards and predictive models provide insight, but they don’t answer a critical question:

What is the best decision, given all constraints?

This is the gap that optimization fills.

Mathematical optimization transforms insights into recommended actions using prescriptive analytics and operations research (OR). It evaluates thousands, or even millions, of possible scenarios and identifies the best course of action based on business constraints, objectives, and real-world complexity.

From Predictive to Prescriptive 

To unlock the full potential of digital transformation, organizations must move beyond predictive analytics to prescriptive analytics.

• Descriptive: What happened?
• Predictive: What will happen?
• Prescriptive (Optimization): What should we do?

Prescriptive capabilities allow companies to act with confidence, speed, and precision. McKinsey identifies workflow redesign as the single strongest predictor of AI-driven EBIT impact.¹ Optimization is the engine of that redesign by embedding constraint-aware decision logic into day-to-day workflows.

Why Optimization Matters for Business Performance

Operational decisions are rarely simple. The number of possible plans quickly becomes enormous, and in practice, most tools settle for a solution that “works.”

But “works” is not the same as optimal. And that difference matters. Take a routing and outbound logistics problem: when you have hundreds of vehicles and thousands of delivery points, the number of possible sequences is huge. Even the most advanced spreadsheet cannot evaluate more than a fraction of these, leaving significant value on the table. In a high-stakes environment where vehicles move from plants to hubs and then to dealers, the constraints are massive, from truck capacities and delivery windows to complex lead times. Optimizing these routes across hundreds of vehicles requires solving complex combinatorial problems that cannot be handled manually or through standard software.

In Toyota’s outbound logistics operations, moving from manual planning to optimization reduced transportation costs by around 10% while cutting planning time from several hours to just 20 minutes, and enabling near real-time plan updates throughout the day.

In many real-world cases, optimized plans outperform feasible ones by 10 to 30% in cost, service level, or resource utilization, particularly in logistics, supply chain, and production planning. This becomes even more critical as organizations adopt AI agents. An agent’s performance depends entirely on the quality of its underlying decision logic. Optimization ensures those agents make globally optimal decisions, not just « good enough » solutions.

By replacing manual planning with automated, constraint-aware decision systems, industrial organizations can achieve faster planning cycles and:

• Allocate resources more efficiently
• Reduce operational costs
• Increase service levels and responsiveness
• Make consistent, data-driven decisions at scale

In areas like supply chain, logistics, workforce planning and maintenance planning, optimization directly drives performance. It connects strategy to execution in a way that analytics alone cannot.

The Last Mile of Digital Transformation

Digital transformation is often described as a journey, but many organizations stop just short of the finish line.

They digitize.
They analyze.
But they don’t fully optimize.

Mathematical optimization represents the last mile, the point where transformation delivers tangible, measurable results. Without it, organizations gain visibility, but not full control over outcomes.

Ultimately, digital transformation is not about having more data or better models. It is about redesigning how decisions are made and executed.

The question for leaders is no longer « What does our data say? » but « What is our data telling us to do? »

References

¹ McKinsey & Company, The State of AI in 2025: Agents, Innovation, and Transformation (November 2025). Survey of 1,993 participants across 105 countries. mckinsey.com

² Boston Consulting Group, The Widening AI Value Gap: Build for the Future 2025 (September 2025). Study of 1,250 firms worldwide. bcg.com