Another major theme the company is seeing among infrastructure clients is the focus on managing long-term horizon objectives, such as achieving an operator’s energy commitments and net-zero goals in 2050.

These require significant decisions about the health and state of the network, and what investments the companies have to make. Data and AI help them to make those decisions, including by enabling them to model and then manage these changes as they go forward, and to leverage the changes in information flows as they progress, Helmbold adds.

“This is one area AI techniques can help. While digital twins typically aim to precisely replicate all aspects of the network, we also use a different approach that uses simplified versions of the network to ask overarching questions.

“For example, a simplified version allows our clients to ask broad questions about the impact of population trends and water demand, and they can start planning according to different possible scenarios and thereby inform and improve their decision-making processes,” he says.

Further, AI and data techniques and simulations allow for many different scenarios and variations to be modelled and analyzed, he adds.

“For example, we are working with a US telecommunications firm to model the most effective way to roll out fiber, based on demand forecasts, uptake estimates and cost effectiveness. However, these same techniques can also be used in greenfield fiber network rollouts, such as in developing countries, to model the eventual operational expenditure and thereby shape the rollout of the fiber network, albeit in a different way than that of a developed country,” Helmbold explains.

This highlights the business use case and value of AI and data techniques, specifically in helping to model how to optimize a network that is being developed. This information may influence the design of the network and investment decisions to ensure the most appropriate capital investments and eventual operating costs.

Additionally, when infrastructure companies use AI and data techniques as part of their operations, they are able to test their assumptions as the project progresses against the real-time data they are receiving, he highlights.

AI techniques rest on the use of data to solve specific challenges. This has provided a significant tool for infrastructure operators to use.

“Our work with current clients presents a chance to generalize our systems across industries. For example, the UK water sector is heavily regulated but is also cooperative, which presents an opportunity to collaborate across the industry,” Helmbold highlights.

There is a drive in the water and wastewater sector towards innovation and exploring new ideas.

“The tailwinds driving this are data and AI techniques based on sensor data, and we are able to make the data valuable by linking it to business drivers within a strategic context,” he notes.

However, to make use of any AI and data-related techniques, the technology side must understand the business. This means that collaboration between skills is critical.

Engineers do not need to be experts in data science, but must be intimately involved in the processes, as this allows for more and more effective solutions to be identified and developed, he adds.

“Data scientists and software engineers must work with the business’ engineers around developing a product, system or solution. This operational vehicle can also drive a lot of innovation and change, as product development becomes a part of the company’s digital transformation mindset,” he says.