Future-proof project controls: balancing best practices with innovation

Project controls is about using project data to deliver insights, support decision-making and steer projects to success. It has become a cornerstone of modern project management, particularly in capital-intensive industries such as construction, infrastructure, energy and pharmaceuticals. Over the past decade, the field has evolved rapidly, driven by advancements in tools, techniques, and data analytics.

Today, most large projects have clear contractual requirements for scheduling, critical path analysis, earned value management, and risk integration. Project controls best practices are widely adopted and expected.

Best practices provide structure and consistency in project controls, offering a common framework that supports transparency, accountability and contractual alignment. They help standardize reporting, define expectations, and serve as a basis for resolving claims and disputes.

However, over-reliance on best practices can limit innovation. As new technologies like AI offer alternative ways to gain insights and improve project outcomes, we must ask ourselves: are we focusing too much on the 'how' rather than the 'why'?

The risk of over-reliance on best practices

Best practices exist for a reason. Established methodologies like CPM, earned value management, and risk-based scheduling have proven their value. However, they are often applied rigidly, without questioning whether they remain the most effective and efficient way to achieve their purpose. This rigidity can limit innovation.

Take the Critical Path Method (CPM) as an example. For decades, CPM has been the industry standard for scheduling. While it is a valuable method that serves its purpose, it is also time-consuming and requires a specialized skill set. With the rise of AI and data-driven insights, is it still the best way to predict project completion?

Yet, many industry requirements, such as the insistence on XER files, passing DCMA 14-point assessments or adhering to predefined scheduling standards, are built around best practices like CPM, reinforcing their use without questioning their effectiveness. If we continue to enforce these requirements, are we truly enabling the most value-adding solutions?

AI and the future of project insights

Today, AI’s practical impact on project controls is still limited by challenges such as data quality, industry adoption, the natural complexity of project environments and open questions about its contractual justification in claims and dispute resolution. These challenges must be addressed, but they should not overshadow AI’s undeniable potential to transform the field.

AI has the potential to enhance project controls by using historical and contextual data to improve forecasting accuracy. It may help identify trends, recognize patterns and anticipate risks in ways that complement conventional methods. While its full impact is still emerging, early applications suggest that AI could support better decision-making, provided that these challenges are addressed.

What sets AI apart is its ability to process vast amounts of data and uncover insights beyond predefined models. Unlike traditional methods such as CPM that rely on structured algorithms, AI can generate predictive insights without strictly following these rigid calculations. This opens the door to alternative ways that do not necessarily adhere to the same predefined rules, methodologies or compliance requirements as conventional techniques.

As technology evolves, we should remain open to the most effective approaches, whether they build on traditional methodologies or introduce entirely new ways of working. While established methods remain valuable and will continue to play a role in managing complex projects, the goal should be to adopt the approaches that deliver the best possible outcomes.

Compliance vs. innovation: finding the right balance

The challenge is that contractual requirements and industry standards are often built around existing best practices and established software tools. When project professionals focus too much on compliance, they may miss opportunities to explore better methods. If we only measure success by ticking off best practice checklists or relying on the same familiar software, we risk limiting innovation and overlooking more effective solutions.

So, what should we do?

• Rethink how we measure success – The success of project controls should not be defined by compliance with best practices but by its actual impact on project outcomes. We should move beyond rigid checklists and predefined metrics, focusing instead on KPIs that truly reflect how well project controls contribute to project health, efficiency and value creation.

• Strengthen data quality – AI and advanced analytics can only be as effective as the data they rely on. Organizations should prioritize improving data accuracy, consistency and integration across project systems. By ensuring high-quality, structured data, we create the foundation for more reliable AI-driven insights that complement traditional project controls.

• Encourage experimentation & phased adoption of AI – AI should enhance, not immediately replace, traditional project controls. Organizations should test AI-driven insights alongside existing methods, comparing results and identifying where AI adds the most value. This phased approach builds trust, refines AI models, and helps maintain contractual compliance while gradually increasing reliance on AI-driven decision-making.

• Define AI’s role in contractual settings – As AI-driven project controls gain traction, it is crucial to determine how their insights fit into contractual frameworks. AI forecasts should be traceable, explainable and auditable to ensure they can be used in claims and dispute resolution.

• Collaborate on neutral AI models – To make AI-driven insights contractually reliable, stakeholders should work together to develop neutral AI models and industry-wide standards. Shared, unbiased AI tools can reduce disputes by providing a common data-driven reference for claims and project performance assessments.

• Outcome-driven contract requirements – The goal of project controls is not just to produce deliverables but to provide the best possible insights that drive project success. Instead of requesting existing best practices and methodologies, project owners should focus on outcomes. Contractual project controls requirements should define the value they seek while allowing flexibility in how it is achieved, enabling teams to adopt the most effective tools and approaches.

• Rethink procurement practices – IT departments and procurement teams often draft RFQs in a way that favors legacy systems over newer solutions. Instead of specifying long lists of functional requirements, procurement should focus on the value that solutions must deliver, allowing room for innovation and better-performing technologies.

• Invest in continuous learning & development – Project controls is evolving, and professionals need to keep up. Encouraging training and knowledge sharing ensures that teams remain adaptable and open to new ways of working. As emerging technologies like AI and data-driven methods become more prominent, staying informed and continuously improving skills will be essential to delivering the best possible project outcomes.

Rethinking project controls: the Proove approach

At Proove, we believe in challenging the status quo. Best practices and established tools provide structure, but they should never stand in the way of innovation. As the industry evolves, our role as project controls professionals isn’t just to follow the rules, it’s to push boundaries, rethink approaches, and find smarter ways to maximize project value.

Are you ready to rethink project controls with us?