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February 12, 2018
AI changes everything. Or does it?
By: Kristy Kinney

Artificial intelligence (AI) is poised to transform the way government agencies deliver services. AI can help streamline routine processing of applications for services. It can reshape customer support in contact centers, facilitating better self-service and fueling greater efficiency. And it can make it easier to serve all citizens thanks to real-time translations into virtually any language.

Given the potential for innovation, it’s tempting to think that AI brings a set of entirely new challenges. Yet successful AI is derived from traditional building blocks, including sound data and compelling design.

In other words, when it comes to AI, data and analytics, some things are changing. Others are timeless.

What’s changing: Machine learning is a form of AI that makes it possible for computers to dive in and “teach” themselves about data. It’s opening up opportunities to use extensively large datasets that we previously couldn’t leverage. With machine learning, it becomes possible to analyze massive sets of unstructured data, including video footage.

What’s not changing: At the end of the day, the goal of AI is to enable federal agencies to realize the potential of one of their biggest assets—data—to help solve mission and business problems. Whether you’re building business intelligence or artificial intelligence, you must first identify what data is available and where it resides, and then extract and prepare that data to be analyzed and used to derive insights.

What’s changing: It’s more important than ever to engage all of your “customers”—citizens, employees and other stakeholders—in designing AI-enabled experiences. Putting the individual first will ensure meaningful use and understanding while facilitating the level of trust and confidence needed when employing AI tools. To drive adoption and maximize outcomes, let your customers help in “training” your AI.

What’s not changing: Data scientists remain crucial to designing and implementing AI. Their expertise is essential in creating models and algorithms and defining the training data that powers AI tools. The key will be applying the right methods and technology to the areas of business that have the highest potential of impact coupled with adoption.

Agencies that have strong data science capabilities may have a head start in benefitting from AI. However, even agencies with less advanced analytics can benefit by aligning initial AI projects to quick and impactful mission and business wins.

In either case, focus on two central and timeless success factors: using human-centered design approaches and putting fundamental analytic principles to work to yield actionable, high-impact insights. Together, they will help create successful AI that reflects the needs and priorities of decision-makers and enables more informed, data-driven decisions at every level of the agency.

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