Digital technology has transformed transportation, healthcare, education, energy, and entertainment. Those industries provide valuable lessons for the digital transformation we are now witnessing in the water sector, where we work. However, the realities of this transformation can be challenging. Roadblocks include the readiness of the workforce for training and a company’s culture of innovation (or lack of one).
Yet there’s still momentum in the digital transformation of water. Some trends fueling it include:
- The need to be more efficient with water use within operations due to scarcity
- Increased scrutiny of corporate water use by stakeholders (think: non-governmental organizations, civil society, public agencies)
- The overall movement away from analog solutions
- Increasing need for sustainable and resilient business operations and for environmental, social and governance reporting (ESG)
Opportunities and Challenges
While operational excellence in resource use and assets is a central reason for adopting digital water technologies, the value proposition is more robust. Digital water technologies such as AI enable greater support to the workforce, decreased business disruption, and contribute to more sustainable and resilient operations.
The challenges center on the availability of data, the capabilities of the workforce, a culture of innovation, and company commitment and investment. These are not insignificant challenges, but they can be overcome.
AI Value Creation
Companies in the industrial sector have been grappling with AI for many years. Business leaders are often faced with the challenge of implementing AI in a practical way and that creates value for the company. For any new technology to work within an organization, it needs to start with a narrow well-defined scope. These success templates can then be used to drive enterprise-wide adoption. AI should be packaged into bite-sized pieces so that the teams within the organization can consume it.
The playbook to create value with AI has several components:
- Implementation and risk management – One of the biggest hurdles in championing a new technology or service inside an organization is the need to clearly explain the business case. Presenting the justification is a significant undertaking involving detailing the costs, risks, advantages and disadvantages.
- Don’t make it only about water. Include other KPIs, such as production and quality. For decision-makers to realize the full potential of applying AI and other digital solutions, the initial business case cannot simply focus on the economics of water. The initial impact from water stewardship will often be seen as too minimal for such a major resource investment. In reality, water is only part of the overall digital transformation needed to optimize a company’s operations. The real KPI to include at the forefront alongside water is production – specifically, how much water is used in relation to a given amount of production. In other words, you can’t digitize your process related to water without adopting other digitization efforts, such as factory automation. Without digitalization for the production side as well, your ROI will fall flat.
- Focus on the need for a long term execution strategy. Getting information on a highly visual dashboard may initially drive engagement for end users. But this “curbside appeal” does not last long. Once the excitement wears off, so do the actions needed to follow through on building the “dream home” for your data. It will ultimately be people who drive results.
You can proactively avoid this misstep by highlighting the actual use cases and the user experience. Clearly outlining the journey from end-to-end is not easy. It involves a great deal of planning and understanding, especially around the kind of design thinking process which is not widely known in industrial settings.
When you begin to ask “How do we get insightful information at the right time to the right people?” you will start to shift your focus from short- to long-term.
4. Don’t underestimate the amount of work required. Account for factors such as culture and skills. Turnkey solutions are common in certain industries, and for good reason. What company doesn’t want to bolt on an existing solution instead of building a new toolbox? Manufacturers, for example, may look to external sources for digital support. However, if a company approaches digital transformation or sustainability initiatives from a turnkey mindset, it will likely experience major disappointment.
The amount of the culture change needed to embrace these technologies should not be underestimated. New ways of working with data and maintaining data/sensors require different skills. It’s likely that upskilling employees will be needed.
The realities of operational transformation can be challenging, even with increasing interest in practical solutions such as digital water technologies. In order for AI to work within an organization, it needs to start with a specific desired outcome that is measurable and can quickly show direct impact on the business. To successfully achieve long-term sustainability and economic goals, a project also needs a well-defined scope that considers workforce readiness with regards to training, culture, and risks. But the payoff is well worth it.