The Technology Adoption Curve in Water Is Speeding Up
Part 1 of the BIV Series on Water, AI and Digital Technologies
Conventional wisdom says that water utilities are slow adopters of new technology, and historically, that has been true.
Water utilities have generally preferred to let other industries test emerging technologies before deploying them in mission-critical infrastructure. The pattern has repeated itself across the internet, cloud software, mobile applications, smart metering, machine learning, and more recently artificial intelligence.
This caution is understandable. Utilities are responsible for public health, operate under tight regulatory oversight, and manage infrastructure expected to function reliably for decades. Failure carries significant consequences, while the upside from being an early adopter is often less obvious. As a result, adoption cycles in water have historically been measured in decades rather than years. Yet something appears to be changing. I haven’t done the big study and my insights are purely observational at this point, I will work on making the case with evidence, forthcoming!
My observations is that although water utilities are not suddenly becoming technology enthusiasts, they are increasingly being forced to confront a set of external pressures that cannot be solved with traditional approaches alone. Rising capital costs, persistent labor shortages, aging infrastructure, climate-related disruptions, cybersecurity threats, and growing regulatory requirements are creating a level of operational stress that many utilities have not previously experienced.
The economics of inaction appear to be changing. For many utilities, advanced digital technology appears to be shifting from discretionary and for the 'innovators" to necessary operating tools.
The Classic Framework for Understanding Adoption
Paul O'Callaghan's Water Technology Adoption (WaTA) model provides a useful framework for understanding how technologies spread through the water sector.
Paul categorizes adoption drivers into three broad groups:
Crisis-driven adoption
Value-driven adoption
Tipping-point adoption
His model suggests that most water technologies require between seven and twenty-one years to achieve widespread market penetration, depending on the urgency of the problem being solved and the clarity of the economic value proposition.
Historically, this framework has proven remarkably accurate, particularly for hardware-based technologies that require significant capital investment, operational changes, and lengthy procurement cycles.
My view is that the confluence of factors are compressing these timelines overall (technology agnostic), and that we are seeing an increasing number of digital technologies reach the tipping scale in an even tighter time scale. My anecdotal estimate is in under five years time, from early pilot to wide-scale adoption within a given market.
The reasons being that the water sector is experiencing a convergence of crisis-driven and value-driven adoption dynamics simultaneously. Utilities are facing mounting operational challenges while, at the same time, modern software and AI tools are becoming easier to deploy, easier to validate, and capable of generating measurable returns on investment. With these two forces at play, adoption accelerates.
The question is no longer whether utilities should adopt technology. The question is which technologies have demonstrated enough value and reliability to justify deployment.
Anecdotal Evidence that Adoption Is Accelerating
One way to observe changing adoption patterns is studying the technology categories that move from pilot projects to repeatable commercial adoption in just a few years. As historically, many water technologies spent years in demonstration mode before gaining broad market acceptance. My observation is that a growing number of new -wave software-driven businesses appear to be moving through that cycle much faster (i.e. not billing, SCADA or ERPs).
Categories that come to mind are:
ML and AI -based pipe condition assessment
Cloud SCADA
Network digital twins (drinking and collections)
Dispatch management
Asset management
Companies such as Daupler, SewerAI, Stormharvestor, Waterly, and others are demonstrating the ability to secure early utility customers, prove value, and then expand across customer segments at a pace that would have been difficult to imagine a decade ago.
The common pattern is not that utilities are becoming less risk averse. Nor is it true that procurement has become faster or that you can skip pilots. Even with these things staying constant, adoption can become faster.
Instead, technology providers are becoming better at eliminating perceived risk. We are seeing the best of entrepreneurs navigating both the sales cycle, phased deployments, training, and explanations strategies much more adeptly than in years past.
Successful digital companies increasingly demonstrate a few characteristics:
Clear economic value - whether financial or otherwise, such as water quality, the value must jump off the page.
Rapid deployment - minimal needs for third party consultants (we love quick contract to cash) and this is a massive cost savings for the utility too.
Proven operational reliability - impeccable case studies and references, close to zero churn.
Strong cybersecurity and compliance posture - Get the SOCII or whatever compliance the IT departments will use to not be able to say no.
Once technological risk is retired through successful deployments, adoption appears to accelerate more rapidly than historical norms would suggest. We are seeing that rather than a timespan of a decade or more for broad market acceptance, some software-centric solutions are reaching meaningful market penetration within three to five years.
Why AI May Follow a Different Path
Artificial intelligence represents an especially interesting test case. The technology itself is not new. Utilities have used predictive analytics, optimization models, and machine learning techniques for years. What has changed is accessibility.
Modern AI tools can be wrapped around existing workflows, require less infrastructure investment than previous generations of software, and often deliver value without requiring utilities to replace physical assets. This lowers barriers to experimentation and deployment.
At the same time, AI is arriving during a period when utilities face unprecedented workforce challenges. Experienced operators are retiring, hiring remains difficult, and institutional knowledge is leaving many organizations faster than it can be replaced.
Technologies that help utilities operate with fewer people, make better decisions, or automate repetitive tasks are being evaluated against increasingly urgent operational needs. Our portfolio company TeamSolve, for example, has a gen AI -based technology to help manage workflows for field work that combines work orders, asset management, and access to technical resources into one application - massive time and headache relief for their clients.
Conclusion
Water utilities remain cautious buyers of technology and they should be.
Desperation may spur innovation, but in water, adoption still requires proof. What is changing is that the bar for proof is being cleared faster by software businesses that can demonstrate ROI, security, and reliability without asking utilities to bet the plant.