The combination of AI and business intelligence reporting has the power to improve decision-making, efficiency and risk management in construction, but only if the data powering that combination is robust. How then should construction businesses make the most of AI and business intelligence?
A new white paper from digital construction consultancy Acumine outlines the considerations and steps businesses should take to ensure they are procuring AI and business intelligence reporting tools effectively and how to ensure their data that feeds those tools is robust.
The white paper is written by Acumine director and co-founder Alistair O’Reilly. In it, he lists the benefits of AI and business intelligence reporting as being accurate, real-time insights; proactive risk management; optimised resource allocation; and better decision-making.
But these benefits can be missed if the data being fed into the tool is poor, leading to cost overruns, project delays, inefficient resource allocation, compliance and regulatory failures, and loss of competitive advantage.
So, what are the key features construction businesses should look for in an AI or business intelligence solution?
O’Reilly lists the following:
- Data storytelling – interactive visualisations and dashboards that present data in a clear and understandable format.
- KPI monitoring – tracking of key performance indicators, such as project budget vs actual cost, schedule adherence, and resource utilisation.
- Reporting and analysis – ability to generate custom reports and perform ad hoc analysis to answer specific business questions.
- Advanced data analytics – business intelligence dashboards, potentially AI-enabled, that provide real-time insights into cost, schedule, and compliance metrics.
- Predictive and prescriptive AI models – AI systems that not only identify risks, but also suggest corrective actions.
- Automated compliance tracking – rule-based or AI-driven monitoring of data conformance and process compliance.
- Mobile and cloud accessibility – secure access to AI or business intelligence solutions from any location, ensuring project teams and executives stay informed.
Robust data governance
For AI and business intelligence to deliver true value, O’Reilly notes, contractors must implement a robust data governance framework that should include the following:
1. Data standardisation – establish consistent naming conventions, formats, and validation processes across all systems.
2. Data integration – the ability to connect to and consolidate data from multiple sources, including project management software, accounting systems, CRM and other relevant applications, reducing silos.
3. Data security and compliance – implement controls to ensure tools comply with GDPR, the Building Safety Act and other UK regulations.
4. AI transparency and explainability – ensure AI models provide clear reasoning for recommendations, reducing the risk of “black box” decision-making.
5. Continuous monitoring and improvement – regularly audit AI-generated insights to ensure accuracy and refine models accordingly.
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