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Artificial intelligence and machine learning bring critical insight to project bidding

About the blog

Sunit Mohindroo
Sunit Mohindroo is co-founder and Chief Product Officer at Citylitics.

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  • Artificial intelligence and machine learning bring critical insight to project bidding

North America is witnessing a once in a generation infrastructure investment boom. With that comes the opportunity to build smarter, more resilient, and more efficient solutions that will pave the way for future sustainability. This growth has created greater demand for project data and predictive intelligence at all levels, from sales and bidding processes, to design and construction.

Market activity has accelerated significantly as cities and utilities, with access to unprecedented capital, are making major infrastructure investments. As such, vendors and engineering firms serving the water industry find themselves submitting multiple proposals in an overly crowded field. Many times, those efforts come at great expense and effort, with little to show for it in the end.

Rather than relying on anecdotes and estimations to track market direction, prospective bidders need a granular depth of data to gauge market realities and pinpoint the highest value opportunities offering them the greatest odds of success. To have a fighting chance, they now have to “pre-position” themselves. To do so effectively, they must arm themselves with critical sales intelligence.

A bottom-up intelligence approach combining artificial intelligence (AI) and machine learning (ML) to sift through mountains of information is key. AI/ML allows providers to blanket the entire scope of market opportunities and pinpoint water infrastructure projects that are the best fit for their products or services.

With an AI-based sales effort based on contextual data, they can focus on the right opportunities that match their targeting criteria at an early enough stage to influence outcomes, build relationships and customize their strategy for the project in question. With the growing demand for automation, telemetry, and advanced technology capabilities, it is essential that smart water solution providers have the tools to place them at the forefront of the buying process, rather than end up lost in the RFP crowd.

AI/ML techniques offer the precision needed to prioritize opportunities and construct bids based on predetermined filters and conditions

AI/ML techniques offer the precision needed to prioritize opportunities and construct bids based on predetermined filters and conditions. An AI/ML-enabled document aggregation engine can ingest millions of public documents and data generated by tens of thousands of cities, utilities and public agencies, pulling from council meeting minutes, budgets, capital plans, permits, environmental reports, and more.

With an AI/ML approach, users are able to layer multiple open-ended criteria to look for relevant smart water and wastewater partnerships and projects. Inputs can include any number of variables, such as geography (national, regional, or local), climate, current leadership’s proclivity for smart technology, funding status, competitors’ involvement, design consultants-of-interest, facility treatment capacity, contract lifecycle, openness to alternative delivery, budget trends, EPA violations, smart water project keywords, and more.

Based on these system inputs, the AI/ML engine then surfaces early indicators of infrastructure needs, turning that vast store of raw data into meaningful, predictive market intelligence on infrastructure plans, priorities, projects, and more. These insights help identify the best opportunities to pursue, at the right stage, using the most effective approach and strategy.

AI/ML approaches offer the power to correlate multiple datasets in a way that was simply not available 10 years ago. And today, we are discovering new multi-dimensional insights that AI/ML-powered analysis has generated, well beyond what is capable by human means and traditional analysis models.

While there is a wealth of potential water infrastructure projects to pursue, companies have limited resources to chase down multiple projects that may or may not be a good fit. With artificial intelligence and machine learning, sales and engineering teams have the critical market intelligence to focus their efforts and energies on opportunities they are more likely to win.

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