Why Decision Intelligence is Critical for Today’s Projects

Jareth Reeves

10/31/20245 min read

In today’s volatile, uncertain, complex and ambiguous project environments, traditional management methods often struggle to keep up. Projects are becoming more complex as they become more interconnected, requiring more than just the conventional controls as we know it, but intelligence in decision-making.

The Problem

The UK must dramatically scale up infrastructure investment to meet growing pressures such as net zero targets, stimulating economic growth and upgrade aging infrastructure. These pressures are compounded by the increasing volume of major projects with growing complexity and sensitivity - impacted by a multitude of factors - with scarce resource availability [1,2].

Does this mean we must do much more with much less?

In light of the Cost Report by the National Infrastructure Commission [3] published in October 2024, the message is unambiguous: we need a fundamental shift in how we approach project decision-making. Without clear strategic direction and effective early-stage decisions, the UK risks continuing a pattern where projects face expensive changes and redesigns during delivery – when modifications and change are most costly.

Can we afford to continue with suboptimal decision-making processes?

In today's infrastructure landscape, we face a critical challenge. The report reveals the reality that infrastructure costs in the UK have risen by a third since 2007, when we need to build more infrastructure than ever before. However, this cost escalation isn't inevitable - the report shows over 50% of potential cost reductions lie in decisions acted upon before construction began. These are during the critical decision-making phases of project planning and design.

The report finds the benefits are substantial if the early stages are handled effectively, with strong front-end planning, apparently, achieving 20% lower costs and deliver 10-15% faster than average.

Are we not spending enough time to plan or are we not planning well?

Bent Flyvbjerg, author of How Big Things Get Done discusses how the bigger the project the less suited for ‘thinking fast’ - giving some examples of big consequences. He claims, “it’s time that is the killer for projects, not size.” …” By acting fast, you can reduce your risks enormously, especially if you have been thinking slow.” [4]

From experience, I see large projects at the early planning stage are increasingly taking a thinking fast approach, or belief that projects can go into execution mode whatever the maturity at the time. A leading cause is a focus on outputs rather than outcomes [2]. I believe this is because there is an even larger gap in skilled planning stage resources. This begs the question: is the project profession primarily geared for execution, such as reporting and managing schedules, etc?

Where’s AI and exponential technologies in all of this?

There is a growing gap between exponential technologies and the project profession. Azeem Azhar made the case in his seminal book Exponential back in 2021 [5]. He described the ‘exponential gap’ between accelerating technology and stagnant professions built in the linear industrial age mindset.

In 2021 the IPA released the Transforming Infrastructure Performance: Roadmap to 2030 [2]. Supporting the claim that the construction sector has been slow to adopt advanced technologies like AI, machine learning, and digital twins, limiting opportunities for improved efficiency, productivity, and sustainability. Furthermore, the paper claims government departments and industry partners often work in silos, limiting opportunities for collaboration and innovation. Sound familiar? Have things changed, since we’re now a third of the way to 2030?

But our projects are data-driven, right?

While projects generate vast amounts of data, this information typically exists in isolated specialist domains - segregated by department, function, and software platforms. Risk data often remains trapped in risk management databases, while schedule information lives separately in planning databases. This fragmentation creates barriers to holistic project understanding and prevents the kind of systems thinking needed for complex infrastructure delivery. The Transforming Infrastructure Performance Roadmap to 2030 specifically highlights how these silos extend beyond individual domains – but projects themselves, government departments, industry partners and sectors. When critical project information exists in disparate silos, how can we expect to make truly informed decisions at the speed and scale today's challenges demand?

In summary, projects increasingly take a 'thinking fast' approach to early planning stages, rushing into execution regardless of maturity. This rush to deliver, combined with scarce skilled resources, data silos and slow adoption of advanced technologies, creates a deepening capability issue. The traditional project profession, still operating on industrial-age principles, struggles to keep pace with exponential technologies like AI and machine learning.

We face a stark choice: transform how we make decisions and orient outcomes or accept continuing cost escalation when we can least afford and settle for reduced outcomes.

Through the lens of People, Process, and Technology (PPT) [6], we can expose the interconnected challenges facing infrastructure delivery today. Such as unprecedented resource scarcity while being asked to deliver more complex projects, still rooted in industrial age thinking and technology advancement creating an 'exponential gap'.

The Solution: Decision Intelligence

Decision Intelligence (DI) bridges these systemic gaps through a transformed PPT framework: empowering project professionals with enhanced capabilities, evolving processes to handle growing complexity, and making advanced technology accessible and practical. Where traditional methods leave teams struggling with information overload and rushed decision-making, DI provides a framework that augments human expertise, enables 'thinking slow' during critical stages, and ensures technology serves rather than overwhelms. This integrated approach directly addresses the scarcity of skilled resources, the inadequacy of traditional planning processes, and the challenge of technology adoption.

Kaleido's approach to DI harmonises three reinforcing capabilities:

• Integrated Modelling,

• Advanced Analytics and,

• Knowledge and Data Management.

Integrated Modelling represents a data-driven step-change in how organisations approach planning techniques through-life. Providing the ability to consistently ‘think slow’ and provide the value of forward-looking scenarios and robust risk mitigations. By taking a systems-driven approach, we could ensure traditionally siloed data is integrated, informing and enhancing those vital decisions early on. This holistic view would enable project teams to understand complex interdependencies across portfolio-programme-project and common systems, plan right to left to orient outcomes, and identify variance to plans before they occur. Enabling informed decisions before significant commitments are made. Additionally, through iterative refinement of underpinning project deliverables—from estimates and schedules to design and engineering documentation—we ensure projects remain adaptive while maintaining strategic alignment.

Advanced Analytics approaches transforms vast amounts of project data into actionable intelligence through new and emerging capabilities. Although I generally remain software agnostic, strategic partnering with some leading vendors and having an in-house development capability helps us develop bespoke and deployable products. Custom GenAI solutions are rising in popularity and provides clients with Enterprise AI. This dramatically increases productivity from automating routine analysis to enabling intelligent information retrieval in seconds rather than hours. Through sophisticated machine learning algorithms and intuitive visualisation tools, you will identify patterns and predict outcomes that would otherwise remain hidden until it's too late - when changes are most expensive. This shift from reactive to proactive management means teams can focus on strategic thinking while technology handles the complexity, bringing simplicity to teams.

Knowledge and Data Management underpins how organisations harness collective intelligence, bridging the white spaces between traditionally disconnected project domains and respective databases. Understanding the relationships between risk, schedule and cost with requirements, designs and other project definitions, we transform these isolated silos into an interconnected knowledge ecosystem. Through sophisticated database innovations like knowledge graphs and ontologies, we are able to map and manage these connections, making previously hidden relationships visible.

This unified approach directly addresses the scarcity of skilled resources by making Enterprise Knowledge accessible and meaningful across the organisation, regardless of functional boundaries. By breaking down silos and connecting related information, we enable teams to understand the fuller context of their decisions. These technologies also form the foundation for Enterprise AI, serving as an enriched knowledge engine that fuels more sophisticated AI models.

In an environment demanding dramatic scaling of infrastructure investment with increasingly scarce resources, DI provides the framework needed to enhance human capability and transform how projects are conceived, planned, and delivered in an increasingly complex world.

[1] https://assets.publishing.service.gov.uk/media/62d6bba4d3bf7f28630924f9/IPA_AR2022.pdf

[2] Transforming Infrastructure Performance: Roadmap to 2030 - GOV.UK

[3] https://nic.org.uk/studies-reports/cost-effective-delivery

[4] https://www.apm.org.uk/blog/bent-flyvbjerg-s-secrets-of-project-success

[5] https://businessofsoftware.org/talks/exponential-gap

[6] https://whatfix.com/blog/people-process-technology-framework