You Can’t Optimize What You Can’t Trust

Why trusted infrastructure data is becoming the foundation for digital twins, AI, and operational decision-making. 

The telecom industry is investing heavily in technology designed to improve operational performance. 

• Digital twins.
• AI.
• Automation. 
• Operational intelligence platforms. 
• Advanced analytics. 

The promise is compelling. 

Organizations want to make better decisions, accelerate deployments, improve forecasting, and operate infrastructure more efficiently. 

Yet despite advances in technology, many teams continue to encounter the same challenge:
Can the underlying information actually be trusted? 

That question was at the center of a Connect(X) 2026 panel discussion focused on digital twins, infrastructure intelligence, and the future of operational decision-making. 

One observation stood out:
You cannot optimize what you cannot trust. 

The Foundation Problem 

Most operational initiatives assume information is accurate. Forecasts assume data is current. Analytics assume records are complete. Automation assumes workflows are built on reliable information. But infrastructure organizations often operate in environments where information has accumulated over decades. 

A single asset may have: 

• Lease agreements 
• Amendments 
• RFDS packages 
• Structural analysis 
• Construction drawings 
• Inspection reports 
• Asset management records 

Many of those documents were created by different organizations at different times. 

Some may contain conflicting information. Others may be outdated. Before organizations can optimize operations, they must first establish confidence in the information driving those operations. 

Why Digital Twins Depend on Trust 

Digital twins have become one of the most discussed technologies in infrastructure management. Historically, digital twins were viewed primarily as visualization tools. 

Today, expectations are much higher. Organizations want digital twins to support: 

• Planning 
• Forecasting 
• Asset management 
• Deployment coordination 
• Operational decision-making 

However, none of those capabilities are possible without trusted information. A digital twin does not create truth. It reflects the information used to build it. If that information is inaccurate, the value of the digital twin diminishes quickly. 

The effectiveness of any operational model depends on the quality of the data behind it. 

AI Doesn’t Eliminate the Problem 

The rapid adoption of AI has created similar challenges. Many organizations hope AI will help them unlock value from existing infrastructure data. In many cases, it will. 

But AI introduces a new reality. Poor data quality scales. Just as good information can improve decision-making, inaccurate information can create larger problems when automated systems depend on it. 

Organizations are increasingly recognizing that successful AI initiatives begin with trusted information. Validation is becoming a prerequisite rather than an afterthought. 

The Shift From Visibility to Confidence 

For years, the industry focused on visibility. Organizations wanted access to more information; more dashboards, more reports, more asset data. Today, many organizations already have access to large amounts of information. 

The challenge is confidence. 

• Can teams trust the information they see? 
• Can they make decisions without manually verifying every record? 
• Can they forecast outcomes accurately? 
• Can they automate workflows safely? 

The next stage of operational excellence is not simply improving visibility. It is improving confidence. 

Trusted Data Improves Decision Velocity 

One of the most overlooked benefits of trusted information is decision speed. When teams trust their information: 

• Reviews happen faster 
• Approvals happen faster 
• Forecasting improves 
• Coordination improves 
• Projects move forward more quickly 

Conversely, when confidence is low, organizations slow down. Additional reviews occur. Questions emerge. Manual validation increases. Every decision takes longer. 

Trusted data reduces friction throughout the organization. 

Validation Is Becoming Strategic 

Historically, validation was often viewed as an operational task. Something teams did before moving on to more important work. 

That perspective is changing. Organizations increasingly recognize that validation is not administrative overhead. 

It is a strategic capability. The quality of infrastructure data directly impacts: 

• Operational efficiency 
• Deployment speed 
• Forecast accuracy 
• AI effectiveness 
• Business performance 

As a result, validation is moving closer to the center of infrastructure operations. 

Looking Ahead 

The infrastructure industry will continue investing in digital twins, AI, automation, and operational intelligence. Those technologies will become increasingly powerful. But their success will depend on something much more fundamental. 

Trusted information. 

Before organizations can automate workflows, improve forecasting, or optimize infrastructure performance, they need confidence in the data driving those activities. Because the future of infrastructure intelligence is not determined by how much information organizations collect. It is determined by how much information they trust. And without trust, optimization becomes impossible.