Amplified intelligence has been saved
A public sector perspective
Some of the most promising and valuable uses for analytics will come not from the field of artificial intelligence alone, but from “amplified intelligence.” That’s where the effort and intelligence of public sector employees can be augmented with machine-generated data-driven insights that can help improve people’s decision-making and efficiency.
Going from artificial to amplified
The public sector has historically led the way on adopting artificial intelligence (AI) in critical areas such as defense and national intelligence. However, despite their AI adoption, public sector organisations tend to lag behind their commercial counterparts, many of which have started using artificial intelligence and natural language processing to improve their operational and analytical capabilities in a wide variety of ways – both large and small.
It is one thing to augment warfighters and astronauts with eye-catching AI technologies, but how can similar technologies be used to create value in the everyday operations of government?
Amplified intelligence will be enabled by analytics, which itself is enabled by data and human intelligence. Today, machines still struggle to make sophisticated connections and find higher-order patterns within data; yet those are precisely the kinds of sophisticated and creative analyses where human experts excel.
- Identify clear questions. Start with a hypothesis, and don’t just look at data with the vague notion that “we have so much data, there must be something we can do with it.” Begin with a specific problem or, better yet, a specific question. Amplified intelligence can help accelerate you toward an answer
- Formulate your path. In the public sector, it might take a few steps before agencies fully embrace amplified intelligence. Implement visualisation and then move on to cross-system insights; from there, amplified intelligence can play a bigger role
- Tackle tedium. Speak to analysts, and find out where they spend their time. Identify areas that are repetitive or tedious, and then determine where a machine could help augment or accelerate tasks – freeing up humans to focus on higher-value analysis and insights
- Widen your circle. Look at your data and data governance models, and see where you can leverage data sharing or crowdsourcing to tap into other internal or external skills that might uncover new insights in your data.