It’s not hard to imagine the potential of AI to transform design, engineering, construction and asset management. Even small improvements in efficiency, quality and decision-making across tasks and project stages can add up to significant overall benefits.
AI could be the catalyst for a new era of efficiency and innovation in construction, overcoming the productivity stagnation that has plagued the sector for years.
Organisations that don’t adapt might get left in the dust. But leveraging AI effectively across the project lifecycle isn’t as simple as flicking a switch. Getting it right will demand clarity and consistency of processes, strong governance, and people with the skills to integrate AI in meaningful ways.
AI across the project lifecycle
Generative AI has the potential to transform most aspects of the project lifecycle by lifting productivity (speed, quality and quantity), improving decision-making based on data-driven insights and recommendations, and helping team members acquire new skills and stay up to date with industry trends and best practices. It can also be used to quickly explore options, brainstorm innovative ideas, and identify potential biases.
Early in a project, AI can provide inputs for feasibility studies, resource allocation and risk assessments, and support preparing and evaluating tenders. In design and engineering, experts can use AI to rapidly generate and iterate on multiple concepts, significantly speeding up the ideation process. For decision-making, AI can process vast amounts of data to identify patterns, which experts can then interpret to make more informed choices.
During delivery, there are many opportunities for using AI to optimise construction schedules and logistics, and enhance quality control and safety monitoring. Even at closure and handover, AI can assist with commissioning and testing, automate project documentation, and support evaluation and continuous learning.
Smoothing the AI journey
For many organisations, adopting and integrating AI is exciting but daunting. Many organisations are uncertain about the return on investment for implementing AI or aren’t sure what AI can and can’t do. You don’t need to know everything there is to know about the technology, but you will need to put some careful thought into how to apply it for maximum benefit in the context of your business goals.
The following steps will set your AI journey on a good footing.
1 – CONDUCT AN AI READINESS ASSESSMENT
First, you’ll need to explore whether you’re really ready for AI by evaluating your existing processes, data infrastructure, policies, and team capabilities. A key factor is your data quality, security and standardisation. Good-quality data leads to better outcomes, while poor data will hinder AI’s effectiveness.
Organisations should start building a solid, future-proofed data infrastructure to get the most out of AI’s capabilities and maximise the benefits for project delivery. This might involve creating a centralised data repository and investing in existing, off the shelf AI tools that allow employees to experiment or building something bespoke to address a specific need.
2 – CREATE A CUSTOMISED AI INTEGRATION ROADMAP
Based on the findings of your readiness assessment, develop a tailored AI integration roadmap that aligns with your business objectives. As part of the roadmap, assess any gaps that emerged in your readiness assessment and how you can address them.
Look at your entire project lifecycle and think about how generative AI could fit in. Define the problems that may be solved by AI and identify small pilot projects to begin testing. Then you can gradually expand based on what you learn and what succeeds. It is worth establishing some specific AI-related key performance indicators (KPIs) so that you will be able to measure the impact of adopting AI.
3 – DEVELOP OR REFINE PROCESSES AND GOVERNANCE FRAMEWORKS
Before generative AI or any digital tool can be applied effectively, you will need to establish clear governance structures, define roles and responsibilities, and develop robust processes. AI is not a silver bullet to fix broken processes; instead, AI will improve and optimise processes that are already working well.
Robust governance frameworks will be crucial for safely and successfully implementing AI – so this is the time to establish or refine your frameworks for AI governance, data management, security, ethical use, and alignment of AI with your business objectives.
4 – DELIVER SUPPORT AND TRAINING
While AI can quickly generate ideas, draft content, or analyse large amounts of data, the human expert brings important context, industry knowledge, and critical thinking to the table. Humans need to understand AI’s limitations and potential biases, and learn how to craft effective prompts, validate outputs, and integrate AI into their workflows.
For some individuals, new ways of working can be intimidating or anxiety-provoking, especially if their technical skills and IT literacy are on the lower side. But for AI to be implemented effectively across your operations, it’s important that no-one is left behind. Talk with your people about AI’s role as an assistant rather than a replacement, how their roles might evolve, and how they can be involved in the AI adoption process.
Develop training programs tailored to your workforce culture and to individual needs, fostering an environment where continuous learning and adaptation is the norm. This could include workshops, coaching and ongoing guidance to ensure teams have the skills and knowledge necessary to drive successful AI adoption.
5 – UNDERTAKE CONTINUOUS MONITORING AND OPTIMISATION
Given the lightning speed of technological progress, implementing AI in your organisation can’t be a ‘set and forget’. You’ll need to plan how and when you’ll review the performance of AI and gather feedback from expert end users who can validate its outputs. This will no doubt uncover some opportunities for improvement so you can maximise AI’s benefits and adapt to evolving needs and challenges.
As well as exploring what benefits AI has delivered, look carefully at the challenges that have emerged and whether there has been any change in terms of risk. Consider whether extra mitigations are needed to improve data privacy and security, protect against inaccuracies and algorithmic bias, and stay on top of maintenance and updates.
There’s no time like the present
We believe that embracing AI is no longer optional for organisations that want to stay competitive in the construction sector. Businesses and project teams that are slow to embrace the technology run the risk of falling behind and missing out on the edge that successful AI integration can offer.
Thanks to AI, the vision of a world in which building and infrastructure project teams make better data-driven decisions, manual processes are automated, and assets deliver expected benefits consistently is within reach. The journey begins with understanding the technology’s potential and crafting a strategic approach to adopting it.
Are you ready to step into the future?
About the author
Danny Liston is a Director at WT’s Portfolio and Program Advisory practice. Danny leverages AI and automation to streamline processes, optimise reporting, and enhance planning and decision-making, ensuring project success and stakeholder satisfaction. He is committed to training and capability building, advocating for the responsible use of digital systems to boost productivity and effectiveness. With a background in engineering and project management, Danny brings nearly 20 years of experience spanning planning, design, procurement, delivery, and operations for buildings and major infrastructure projects in Australia, Europe and Asia.
