Data center portfolio management: Optimizing efficiency with AI

Managing a portfolio of data center construction projects is no small feat. With tight timelines, high technical demands, and a need for consistency across multiple sites, success depends on precision and efficiency at every stage. Owners must balance resource allocation, risk mitigation, and compliance while ensuring projects stay on track and meet operational goals.

This is where AI progress tracking transforms data center portfolio management. Its benefits go far beyond ‘project control.’ It allows all data center owners (hyperscalers, wholesalers and independents) to control projects remotely, make continuous improvement a reality, and build and enforce scalable processes.

Standardize data collection and reporting

Data center designs are often highly standardized and repeatable. In this respect, data center construction resembles the manufacturing industry, where achieving consistent output is a top priority. In a factory setting, the first step of control and optimization is sensors and consistent data collection. A similar principle applies here when standardizing construction data across builds. When you collect the same progress data in the same way, it’s much easier to use information consistently and not rely on people collecting data all over the world in the same way. You get clear, accurate comparisons to inform benchmarking, improve forecasting accuracy, and better allocate resources – more on this below!

It’s also worth noting that AI progress tracking platforms provide a dependable, comprehensive record of construction progress, documenting how the project evolved from day one to completion. This helps you meet stringent industry regulations and maintain a transparent audit trail.

Optimize schedules and delivery

We all want to avoid making the same mistakes twice.

AI progress tracking helps you do this through the power of historical data. In other words, you can use insights from each past data hall, phase or project to refine timelines, logistics and design for future data center builds. You can also set realistic benchmarks to ensure continuous improvement becomes part of your day-to-day. It can help to think of continuous improvement like compound interest in your savings account. Even incremental improvements have a huge impact over time – a 3% efficiency gain per project translates to almost 35% gained over ten projects. 

Consider this example from Intel. Given the design similarities between many of its fab projects, Intel sees ample opportunity to share learnings between them. This includes creating optimal work sequences and benchmarks for continuous improvement in its construction organization. The below graph illustrates continuous improvement in action by comparing a single activity’s progress across three successive projects.

This type of organization-wide analysis sets top-performing owners apart from the rest. Integrating all project data into a single dashboard allows you to achieve full transparency at both the project and portfolio levels, empowering you to make high-level, strategic decisions confidently.

Identify portfolio-level risks 

AI progress tracking can help owners detect and prioritize risks across their entire portfolio. One way is by comparing multiple projects’ risk levels and using this data to prioritize resources toward higher-risk projects. For example, if one trade is holding back the critical path on a particular project, you can choose to concentrate your resources here.

A similar principle applies to allocating equipment and supplies. Often, data center builds are held up due to sourcing delays. With AI progress tracking, owners can ensure that equipment and supplies are always allocated most efficiently. For instance, you can identify that Project A – the original destination for an equipment delivery – is eight weeks behind its planned critical path. Meanwhile, Project B is on schedule and will, therefore, be ready for equipment installation earlier. So, instead of letting the equipment for Project A sit idle, you can reallocate it to Project B. This data-driven decision prevents delays in Project B while giving Project A time to resolve its issues. Once Project A catches up, you can coordinate the next available shipment of equipment to maintain its progress, thus minimizing disruptions across the portfolio.

Enforce organizational processes and improve designs for future projects

AI progress tracking allows owners to flag any BIM model deviations, ensuring compliance and accountability by analyzing every element in your model and indicating if it was correctly installed. Even minor model deviations can impact compliance, maintenance and reliability, and given the scale of data center projects, it’s unrealistic to expect even the most eagle-eyed project teams to spot small discrepancies.

On a longer-term basis, owners can incorporate deviations and lessons learned from past builds to refine designs and reduce inefficiency in upcoming data center builds.

Improve estimation accuracy to save money on future data center projects

The comprehensive data AI progress tracking provides isn’t just helpful in boosting efficiency. You can also use it to improve estimates for future projects, ensuring your budget is as accurate as possible.

AI progress tracking – lay the foundations for success

With a streamlined system for accessing and cataloging data, you gain a single source of truth for all data center projects. This enables you to identify trends, analyze projections, and make ‘continuous improvement’ the norm across your data center portfolio, so each project completes faster and is of better quality than the last.

Discover how AI-powered progress tracking helped Intel reduce delays and improve efficiency. Explore the case study here.