Talent, time & trust

How to deliver an AI transformation strategy in a Covid-19 construction world. By John Spooner

As the Covid-19 situation unravels, its impact on the global construction industry appears formidable. The industry is predicted to experience difficulties with material supply chain disruption, labor shortages and the city-wide shutdown of construction sites.

Artificial Intelligence (AI) and Machine Learning (ML) are key technology pillars to enable the industry to address future uncertainty, as the re-emergence of AI led transformation projects promises to reduce cost and drive productivity, post Covid, but challenges remain.

The uptake of Artificial Intelligence
AI has evolved from being the latest technology buzzword, to a commercial reality. Whilst many global organizations are already building AI-led applications, only the most progressive construction firms are grabbing the AI mantle. Whilst adopting the latest technology can be daunting, AI and ML have short- and long-term business value for the construction industry.

Amidst the AI hype, progressive business leaders are looking to embrace innovation, even now, and develop a AI transformation strategy, but challenges remain.

Talent comes first, as firms need to hire, train, and bring on board the right team skills. Time is second, as it’s important to assess how fast you can achieve js1business results with the AI strategy. The final challenge is Trust, as in an increasingly uncertain and data-cynical world, keeping regulators and broader stakeholders on-side is key. So, what’s next?

Building a data driven culture
To effectively use the volume of business data generated to support workstreams, companies need to encourage a data-driven culture to evolve, with these considerations.

Effective Questioning: Asking the right questions is key to building the right company-wide data culture. This may include how we go about project planning, predictive maintenance or demand forecasting. Assessing the business problem is key, so companies need creative people that have an analytical mindset and understand the business constraints.

Data Capture & Access: Firms need to proactively collect data from a variety of sources, and to make it accessible to the right teams. The data should be presented in a way that enables the relevant people to glean actionable insights.

Expertise Search: Data is a team sport, so while companies need experts to build machine learning models, they also need people with commercial abilities, who will uncover useful data insights. Instead of jumping to re-build a team from scratch, companies should secure data scientists from the expanding labor market or the existing employee talent pool.

Ensure a competitive AI strategy
The construction sector is on the verge of digitalization, which is both disrupting traditional processes, and presenting valuable business opportunities. AI and ML are set to increase efficiency throughout the value chain, including demand forecasting, predictive maintenance, the production of building materials to the design, planning and construction phase. Using AI will save time and money, and give you a competitive edge, however, set-up issues remain.

Determine outcomes: Asking pertinent questions will shape what outcomes can be generated from specific applications. The priority is to capture the high-level goals, translate them to a business process automation challenge and determine the ideal results.

Measure Success: Companies must identify success metrics. The definition of success may vary, but the goal remains the same; cutting costs, delivering value and improving profits.

Connect with the IT Ecosystem: Community plays a vital role in driving change. There are many ways to connect with the ML community, which will enable IT specialists to exchange knowledge and learn from each other.

Establish trust in AI
ML models should not be seen as ‘black boxes’. We should be able to explain them coherently, and identify the logic behind business predictions. Eliminating bias from the results will help establish trust in AI.

Open Source or not: When companies start on their AI journey, they will need to decide between open source or proprietary software, or both. Using open-source provides a good starting point, but mission-critical construction led applications will need to evolve to commercial platforms, with the associated support.

Cloud or on-premise: If you are starting from ‘ground-zero’ and have no existing infrastructure, going the Cloud route makes sense, given the security and maintenance issues. However, if you have an IT foundation, the on-premise option can reduce costs. Many companies embarking on an AI initiative may opt for a hybrid model of cloud and on-premise.

Construct your AI journey
We predict that AI will continue to drive significant change across the entire construction value chain. Progressive firms will embrace innovation, especially where the disruption potential is highest, in the areas that are characterized by repetitive tasks with limited uncertainty. However, even when construction companies have identified the AI potential, the three key challenges of Talent, Time and Trust, will help underpin the strategy, and kick-start your staged AI transformation journey.

John Spooner is Head of Artificial Intelligence, EMEA, at H2O.ai, an open-source leader in AI and automatic machine learning with Driverless AI, and its mission is to democratize AI for all. H2O.ai is transforming the use of AI with software, with its category-creating visionary open-source machine learning platform, H2O. More than 18,000 companies use open-source H2O in mission-critical use cases for Construction, Finance, Insurance, Healthcare, Retail, Telco, Sales, and Marketing. H2O.ai currently partners with leading technology companies such as NVIDIA, IBM, Intel, AWS, Azure, Google and more.

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