Capital program management, focused on the planning and building of public infrastructure and facilities, is an area traditionally steeped in manual processes and has been slow to embrace digital change but AI presents a revolutionary opportunity for it.
Investing in new infrastructure and facilities has become a top priority for governments around the world, creating a need for construction projects that are delivered on time and on budget, giving due attention to the needs and expectations of key stakeholders. According to one study, almost 70% of capital projects are delivered late and over budget, but with so much investment now on the line, the status quo is no longer sustainable.
Coming in late or over budget isn’t necessarily a problem, rather, it’s the failure to foresee hold-ups and budget constraints, and adequately communicate them with relevant stakeholders in good time. Doing so creates issues. The ability to make accurate forecasts around planning and development, accounting for variables such as supply chain disruption, adverse weather, natural disasters, and more, is becoming increasingly important to decision-makers in the sector — particularly as the focus on public sector infrastructure grows. Initiatives such as the Infrastructure Investment and Jobs Act (IIJA) in the U.S. mean that public sector construction is in high demand, but to put that demand to good use, public agencies will need to be able to demonstrate a data-driven approach to capital planning and adequately manage the expectations of their stakeholders.
With the climate issue high on owners' agendas, public stakeholders also want to know how “green” a development project will be. Where are the materials coming from? How long will it take? What will the impact be on local transport and how much waste will there be?
These are complex questions that manual processes cannot sufficiently answer. Change comes in waves, and right now, capital program management is ripe for digital disruption.
Unlocking productivity
Despite the vast scale of capital infrastructure planning and building, its productivity growth rate has been locked at a mere 1% per year over the past two decades. This sluggish growth is largely due to the aforementioned reliance on manual processes and its slow adoption of digital technologies. However, the tide is turning, and the potential for disruption through digitization is immense. It is estimated that embracing technologies such as AI and machine learning (ML) could boost productivity by 50% to 60% in the sector.
These productivity gains alone might be enough to significantly increase digital adoption in the sector, but what’s more important are the new capabilities digital disruption will open up — from accurate forecasting and enhanced budget management to transparent communication and automated reports so that key decision-makers and project stakeholders can be kept in the loop.
Precision planning
Accurate estimates of time, budget, and resources are essential to avoid cost overruns and project delays — as are project selection, funding, public feedback, and program approval. Traditionally, these estimates have been based on experience and intuition, but AI and ML offer a more scientific approach.
AI and ML can leverage historical data to create models that accurately predict these factors. An ML model can calculate the time required for a task based on resource availability, location, and supply chain constraints. These models can adjust to changes in inputs, providing insights into the impact of each factor and enabling detailed “what if” analyses. This approach can significantly reduce errors and ensure that public agencies meet their objectives by collecting data on all aspects of a task, including workforce, material requirements, and budget.
Optimized design
The design phase of a project is another area where AI and ML can bring about significant improvements. Generative design AI can use ML algorithms to explore numerous design alternatives and build three-dimensional models that fit together perfectly. This technology can identify and mitigate clashes among models created by different teams, reducing the need for rework and improving design efficiency.
Moreover, ML can recommend specific design solutions, considering various factors such as the total cost of ownership, the timeline for completion, and the likelihood of mistakes or defects during construction. This approach gives owners and contractors more information to work with to make informed decisions.
Prioritizing, Funding, and forecasting
In capital planning, AI's role in decision-making is becoming increasingly significant. For project prioritization, AI evaluates data from past projects and stakeholder feedback, assisting decision-makers in selecting projects that align with strategic objectives and meet public demand. When it comes to budgeting, machine learning offers enhanced accuracy. ML algorithms can predict multi-year Capital Improvement Plan (CIP) budgets by considering variables from previous projects, such as resource allocations, market conditions, inflation adjustments, and unexpected expenses. Furthermore, AI's forecasting capabilities provide a proactive approach to potential challenges. Whether it's supply chain disruptions, regulatory changes, shifts in the political climate, or evolving industry trends, AI ensures that project stakeholders are well-prepared and informed in advance, enabling them to address challenges head-on.
Maintenance and operations
Maintenance of infrastructure is a critical concern to ensure the long-term use and safety of the assets being built. AI and ML can implement predictive maintenance strategies to proactively identify and address maintenance needs before they become critical issues. For example, drones equipped with high-resolution imagery, thermal imaging, and AI algorithms can detect structural weaknesses, identify potential maintenance requirements, and collect valuable data for further analysis.
Capital program management is uniquely poised for major digital disruption. By leveraging AI and ML, oiwners will not only overcome its immediate challenges, but pave the way for a more efficient and sustainable future that prioritizes data and communication. Transparency, cost-effectiveness, and data-driven decision-making will become the new standard, making it easier to create project roadmaps and gain stakeholder buy-in. Digital disruption in the sector is inevitable — now it’s a question of how that disruption is handled, and which agencies come out on top.
