An increasing momentum of digitisation across industries is seeing a major cash injection into digital initiatives, but with results failing to materialise many are asking why it is that digital transformation projects fail.
Although digital transformation begins and ends with data, the importance of a well-executed data strategy is often underestimated.
The COVID-19 pandemic ushered in instant changes to the way we work, moving most people from a traditional office-based commuter model to a working from home set-up, almost overnight. Web-cams got a work-out, the likes of which they had never before encountered, and people became accustomed to remote engagement with colleagues, clients and stakeholders.
This swift shift is an example of the type of change that would have ordinarily taken years to implement. Many businesses have realised this type of work environment is not only viable, but has in some cases, led to increased productivity. A study by Gartner has found around one third of newly remote or hybrid employees say their organisation’s culture has changed since starting to work remotely, and 76 per cent of them say the cultural change has been for the better. Some organisations have also become aware of the incredible power of artificial intelligence (AI) in supporting change within their business.
With the Federal Government banking on major infrastructure projects to ensure Australia’s smooth economic transition from the impact of the COVID-19 pandemic, now is the time for the industry to embrace Artificial Intelligence (AI) and all the benefits it brings.
According to the Urban Development Institute of Australia (UDIA), “An aggressive agenda to fund and build infrastructure projects needed to boost the liveability and productivity of major cities and regions will be essential to Australia’s recovery from the economic effects of COVID-19.”
Construction and infrastructure are complex, human capital intensive industries in which technology advancements are underutilized, resulting in poor productivity and project outcomes. Although a bleak summation, it is one that is regularly reported by those who work in the industry.
With machine learning (ML) and artificial intelligence (AI) here to stay, construction and infrastructure industries could look to embrace and benefit from new software techniques.
One of history’s most notable innovators and business magnates, Henry Ford is quoted as once having said, “If I had asked people what they wanted, they would have said faster horses.”
In 2021, it remains true that the future can be difficult to visualise, when it looks too different from the present. But if 2020 has taught us anything, it is that technology is something we need not fear nor feel overwhelmed by. In fact, businesses, Governments and industries which choose to embrace and champion the immense potential that artificial intelligence and machine learning offer, will thrive in a post-pandemic world.
Anecdotally, government and private sector projects overrun by up to $2 trillion globally, each year.
In addition to this being a significant misuse of time and resources; for government projects, these overruns present a huge impact on broader social needs. To put it in context – the current $2 trillion wastage could build 70,000 new schools or 1,000 new hospitals.
Companies rely on successful delivery of projects to remain competitive. As a company board member, how do you identify the real risks affecting the projects that you tender and deliver?
Recent headlines around companies who have been unsuccessful in delivering projects are adding up, creating reputational damage and legal friction. At worst, companies go into administration. Some lose significant shareholder value, some are sold, and others move away from high risk project revenue.
Cultural change can be an overused term, though project management and delivery teach us what is perceived as best practice. Despite the huge range of project management, cost control, scheduling, risk and safety management systems, tools, procedures, and standards, they still fail to rectify under-performances.
A new project will be as good as the team appointed to manage it. They will bring their own experience and personal data biases to the project. People are optimistic by nature and suffer what is termed optimism bias.
It is an enlightening experience when customers realise that our AI technology works, and that is has the capacity to identify problem projects. Reaching this point is a significant milestone. Octant AI’s predictions are then referenced in project review meetings to analyse the differences in AI and traditional predictions.
These early warnings highlight the importance of project managers exploring the details and taking an involved approach with this technology.
The journey of AI adoption is gathering pace as the company moves forward through piloting and testing Octant AI. Target customers are led by very experienced professionals and they seek information on how the technology operates. We run several tests to demonstrate what Octant AI can predict on projects it has never seen nor been trained on. The test results demonstrate predictive accuracy.
Showing individual project managers AI predictions for past projects they managed, truly demonstrates the power of Octant AI. It helps overcome the perception that AI is just a black box.