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Humanity augmented

Step into the street today and you’ll appreciate the huge impact machine intelligence is having on the world around us. Autonomous vehicles are emblematic of the way machines are displacing humans in a number of ever more complex tasks.

Part of our Seismic Shifts series

Despite some alarmist predictions, this does not herald a jobless future. We envision a future where people and machines work together, each playing to their strengths, to achieve greater outcomes. We call this “Intelligent Empowerment”.

Organization won’t replace workers, instead it will team them up with specialized machine intelligence. Algorithms will take on tasks that people formerly performed and weren’t usually very good at or didn’t enjoy doing. This will produce dramatic results: it could boost productivity by 30% in many industries, while cutting manufacturing labour costs by up to a third.

For instance, ThoughtWorks is helping a client classify millions of legal documents, identifying which are contracts as well as the legal clauses that have changed between revisions of the contract. For human lawyers this is painstaking, slow work, but can be rapidly accelerated through machine learning.

By collaborating, humans and machines deliver better outcomes. Understand what the combination of human strengths like intuition and machines’ abilities with data will do for productivity.

People will use these tools to accomplish tasks quicker and more accurately, freeing them time to focus on areas requiring human ingenuity, intuition and experience. Additionally, we will solve problems in new ways and allow people to do things they could never do before, opening up new opportunities.

Data comes first

Data is at the core of these new intelligent systems. Your infrastructure will have to support storing large volumes of data and a scalable processing layer for different types of workload, from batch to more real-time and streaming needs.

We have seen many businesses invest in Big Data infrastructure without changing their organization structure or build the skill-set required to fully leverage new Data Science and analytics applications.

We think you’ll want to go beyond batch-oriented thinking and traditional data warehousing techniques. You need to bring data thinking into the core of your system architecture.

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