Advancing Digital Solutions From Pilots To Practice In The Water Sector

By Pooja Mahajan and Ashwin Dhanasekar

Across the water sector, the same question echoes through utilities and organizations: "We've completed a successful pilot, but now what?" While digital experimentation has become commonplace, the journey from pilot to practice remains challenging.

A Cisco survey (2017) found that nearly 60% of digital and IoT initiatives stall at the proof-of-concept stage, underscoring a challenge that extends across industries, including water utilities, where promising pilots often fail to transition into full-scale implementation. Broader studies reinforce this trend: McKinsey reports that most digital pilots in utilities fail to build lasting momentum toward enterprise adoption, while BCG estimates that 70% of digital transformation efforts globally fall short of their intended objectives.

The proliferation of commercial solutions offering risk-free trials and limited-scope pilots has made starting easier than ever. But starting is not the challenge, scaling is.

Scaling a digital solution goes far beyond giving more users access. It means embedding the tool into real operational workflows, ensuring reliable performance over time, and creating organizational capacity to support and improve it. Many organizations experiment with promising digital tools like analytics dashboards, AI-enabled applications, and customer engagement platforms.

Despite initial enthusiasm, most organizations struggle to plan beyond the pilot phase. Resource constraints, unclear success criteria, and a lack of defined scalability pathways often result in stalled momentum. Even when a tool shows early promise, there's rarely a clear roadmap for making it sustainable, interoperable, or integrated into day-to-day operations.

In this context, scaling refers to transitioning a digital solution, whether developed internally or purchased, from isolated use to widespread adoption. This includes reliable performance across multiple users, seamless integration with existing systems, and long-term organizational ownership.

Two Pathways To Scaling Digital Tools

Most organizations fall into one of two scenarios when it comes to scaling digital innovation:

1. Building Internal Tools: Teams or innovators inside the organization (engineers, planners, or digital specialists) create their own dashboards, analytics scripts, or prototypes to solve specific problems. These are the innovators building in-house pilots that need to mature into enterprise-ready tools.
2. Adopting Commercial Solutions: Organizations purchase or license vendor-built tools, from plug-and-play applications to configurable SaaS platforms. These are the adopters focused on integrating proven tools into their operations and ensuring long-term value realization.

Each path faces distinct challenges, but both share the goal of turning promising pilots into scalable and value-driven digital solutions.

Part 1: For Innovators: Moving Beyond The Pilot

In many organizations, technical staff are building internal tools to solve urgent problems. An engineer creates a Power BI dashboard to track main breaks. A planner uses Python to forecast asset life. A digital team experiments with a chatbot to triage customer service requests. These pilots are valuable. They often move faster than formal procurement and solve real pain points. But too often, they hit the ceiling.

Why These Pilots Struggle To Scale

• Built Around Individuals, Not Teams. Pilots are often created by a single motivated staff member. When that individual moves on, the tool lacks ownership and continuity, leaving it unsupported and vulnerable to abandonment.
• Technically Fragile. What performs well on a personal laptop or small dataset can break under real-world conditions such as multi-user access, diverse data inputs, and enterprise security protocols. As data volumes grow, performance bottlenecks and instability emerge.
• Lack of Governance and Support Structures. Pilots rarely include formal processes for user onboarding, change management, or issue tracking. Without defined ownership, even successful prototypes fail to transition into maintainable operational tools.
• Disconnected from IT and Operations. Many pilots operate outside sanctioned systems to move quickly. While this fosters innovation, it often creates integration risks, duplicate data, and security vulnerabilities that block enterprise adoption.

Guidance For Internal Innovators:

• Design for Handoff. Use shared repositories (GitHub or SharePoint), comment your code thoroughly, and write documentation that others can understand without reverse-engineering your logic. Include usage instructions, key assumptions, and sample data. Think of this as creating a user manual for your future replacement.
• Partner with IT Early. Establish relationships with IT or data governance teams to ensure your tool meets minimum security and performance standards. Collaborate on user authentication, permissions, and infrastructure needs. This partnership prevents costly rework later and builds organizational support for scaling.
• Think Modular. Break the solution into logical components. Separate data ingestion scripts from analytics logic and visualization layers. This makes it easier to swap, scale, or improve parts without rewriting the entire tool. Modular design also allows different team members to contribute to specific components.
• Plan for Growth. Model usage scenarios based on increased data volume and user concurrency. Anticipate integration points with other systems (GIS, asset management, SCADA) and ensure your design can evolve. Document these growth assumptions and revisit them regularly.
• Build Your Business Case. Measure and document success metrics from day one. Track quantifiable improvements like reduced processing time, faster decision-making, or decreased manual effort. Use dashboards, short demo videos, or brown-bag sessions to share wins. Calculate time savings in dollars and present clear ROI projections for scaling investment.
• Anticipate Support Needs. Even small internal tools require ongoing maintenance. Identify who will update them, fix bugs, and train new users as staff turnover occurs. Budget for this support, because a tool without maintenance is a tool heading toward failure.

When To Partner With A Solution Provider

If your internal tool solves a recurring problem and others in the organization want it, but scaling it would require full-time support, cloud infrastructure, or 24/7 uptime, it may be time to seek a commercial solution.

This doesn’t mean starting over. Bring your prototype, lessons learned, and performance data to the conversation. These insights become powerful input for defining requirements, evaluating vendors, and ensuring that any purchased solution builds on your internal success rather than replacing it.

Part 2: For Solution Adopters: Adopting And Scaling Commercial Tools

Many organizations choose to adopt commercial solutions rather than build custom tools. Often, this begins with piloting and testing a solution before moving toward broader adoption and scaling. These tools range from plug-and-play SaaS platforms to custom-built solutions delivered by consultants or vendors.

While the technology is externally developed, the responsibility for achieving outcomes still rests with the organization. The challenge lies in expanding adoption, integrating with existing organizational workflows, configuring systems for diverse users, and maintaining consistent performance, security, and reliability over time.

Many utilities are also experiencing growing pressure around data infrastructure costs. What begins as a reasonable investment can become difficult to sustain as storage needs rise, queries slow, or tools struggle to accommodate new data types. Without thoughtful architectural planning, scaling becomes cost-prohibitive as data volumes grow and maintenance complexity increases.

Common Pitfalls When Adopting And Scaling Commercial Tools:

• Demo vs. Reality Gap. A solution that performs well in a pilot or vendor demo may falter when connected to real operational data, approval workflows, and user variability. Scaling reveals integration and performance issues that pilots rarely test.
• Over-customization. During early implementation, teams often tailor tools to meet every local need. While this can speed up pilot success, it introduces long-term complexity, making upgrades costly and hindering future scalability.
• Under-investment in onboarding. Pilots often rely on a small user group. Broader rollout requires structured training, internal champions, and user support. Without this investment, adoption stalls, and benefits remain localized.
• Siloed implementation. Pilots are usually tested within one department. When scaling, failing to integrate the tool with enterprise systems (e.g., GIS, SCADA, billing, or customer service) limits visibility and value across the organization.
• Data infrastructure misalignment. Pilots often run on simplified or static datasets. In full deployment, tools must handle larger, dynamic data pipelines. Misaligned formats, update frequencies, or hosting requirements lead to rising costs and operational friction.

Guidance For Solution Adopters:

• Evaluate Scalability During Selection. Assess tools' ability to accommodate growth in users, data volume, and functional requirements. Verify API availability, single sign-on support, and multi-departmental capabilities. Request references from organizations with similar scaling challenges.
• Prioritize Configuration Over Customization. Understand how tools adapt through built-in settings rather than custom development. Configuration maintains upgrade paths and reduces long-term technical debt. Request demonstrations of similar organizations' configurations.
• Invest in Organizational Capability. Identify internal champions and super-users early. Develop training materials, user guides, and support processes. Plan for staff turnover and knowledge transfer. Successful scaling requires internal expertise beyond vendor support.
• Design Integration Architecture. Map data flows between the new tool and existing systems. Work with IT teams to establish secure, reliable interfaces. Plan for both initial integration and ongoing synchronization requirements.
• Establish Success Metrics. Define measurable outcomes beyond initial deployment. Track user adoption rates, process improvements, and operational benefits. Include both quantitative metrics (time savings, error reduction) and qualitative measures (user satisfaction, decision quality).

Final Thoughts

Pilots test and prove ideas; scaling demonstrates impact. Organizations that successfully bridge this gap not only improve operational efficiency but also build resilience against evolving challenges including climate change, regulatory pressures, and workforce transitions.

Whether building internal tools or adopting commercial solutions, success depends less on the technology itself and more on how it is governed, integrated, and embraced across the organization. Those that scale strategically will shape the next generation of digital water management.

Key principles for sustainable scaling include:

• Configuration over customization for long-term maintainability
• Governance structures that ensure longevity and continued value
• Integration strategies that connect systems rather than create silos
• Organizational commitment that extends beyond initial implementation

The question isn't whether to scale, but how to scale effectively for lasting operational transformation.

Pooja Mahajan is a Product Manager at Stantec specializing in digital solutions for the infrastructure and environmental sectors. She brings a unique perspective to digital product development, with a professional background in civil and environmental engineering and over 12 years of experience. At Stantec, she leads the development of innovative digital tools that integrate traditional infrastructure practices with cutting-edge technology. Additionally, Pooja has taught a professional training course on IT Foundations for Water Utilities and co-authored the book Secrets of the Construction Industry.

Ashwin Dhanasekar is a Principal at Brown & Caldwell serving in Research and Innovation, and Digital Solutions. Ashwin’s expertise encompasses biosolids management, microplastics, intelligent water systems and the energy-water nexus. Ashwin also co-found a company, Ajax Analytics, which provides data-driven environmental monitoring solutions. Ashwin possesses a background in chemical and environmental engineering, combined with over 15 years of professional experience in the water sector, providing solutions to a diverse array of clients