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7 ways artificial intelligence heightens the need for people<\/h2>
As artificial intelligence projects roll out, organizations will need to rethink the definition of the \u201cwork\u201d that people will do. The future of work will become one of the largest agenda items for policy makers, corporate executives and social economists, says Sanjay Srivastava, chief digital officer at Genpact, a professional services firm focusing on digital transformation.Despite the strong and inherently negative narrative around the impact on jobs, the bulk of the impact from the automation of work through AI will result in a \u201cdisplacement\u201d of work, not a \u201creplacement\u201d of work. Srivastava offers seven areas in which human knowledge and expertise will be a critical requirement in AI projects.<\/p>

Being an ethical compass<\/h2>
First and foremost, selecting revolves around the question of whether we are using AI for the right reasons\u2014to help people with special needs by enhancing eyesight, to provide translation in hearing aids for better communications across cultures, to increase diversity in the hiring process? Or are we using it for the wrong reasons, say for influencing elections? People provide the ethical compass needed to determine AI\u2019s use cases.<\/p>

Bringing context<\/h2>
People must orient AI towards the right goals. This happens already with supervised learning, where people label the datasets in advance before running AI algorithms. But even in unsupervised or reinforced learning, people need to ensure the algorithm is leading to results that matter for the business, for instance, in pharmacovigilance, when prioritizing between life-threatening and benign secondary effects of taking medicine. In addition, we need people to contextualize AI\u2019s results. It is more powerful to combine a moderately strong AI algorithm with human domain expertise than to use the most powerful deep learning model without any such context.<\/p>

Providing governance<\/h2>
In a digital workforce, planning should take into account the inputs, outputs and various exchanges involved with every process. We cannot just toss a robot into the mix and let it do its own thing. Mapping out how machines and other systems will work together can prevent potential hiccups and obstacles. Since AI is virtual, it becomes harder to tell whether or not a robot is working. In a hybrid workforce, we should be able to easily see if and how AI is working, just as much as human employees. People drive governance.<\/p>

Handling complexity<\/h2>
The new man-meets-machine dynamic will make current jobs easier to do, but the focus of people will shift to higher value and more complex jobs. With AI taking over some tasks, employees are then free to look at bigger issues and concerns. This evolution towards more complex work is not new; when spreadsheets were invented, the financial analyst\u2019s role went from reporting to planning.<\/p>

Preventing bias<\/h2>
The two big reasons for AI bias are the lack of diversity in data samples and rushed or incomplete training algorithms. In both cases, people with domain knowledge are key. Industry or process experts can help think through potential biases, train the models accordingly, and govern over the machines to see that they don\u2019t fall out of line. Diversity in the teams working with AI can also address training bias. By bringing in a team with different skills and approaches, we can have a more holistic, ethical design and come up with new angles.<\/p>

Managing change<\/h2>
Managing this next-gen hybrid workforce is very different from just managing people. We have to think about how we deal with the change, make sure robots are holding up their end and see that employees have the right skills to work with their new AI coworkers. Putting seasoned employees alongside machines is a dramatic change. AI design is moving from \u201chumans in the loop\u201d to \u201ccomputers in the group.\u201d Sometimes we forget to consider change management because we are eager to get AI going and start seeing some results. Projects have to start with a clear change management plan to prevent problems.<\/p>

Connecting creativity and compassion<\/h2>
When Apple introduced the iPhone, an entirely new creative industry of applications developed\u2014ecommerce, ride sharing, wearables, online communities, video gaming. Just like creativity boomed after the iPhone\u2019s launch, new creative applications will emerge from AI. We don\u2019t know what they are yet, though people will drive that future. Much has been written about AI\u2019s ability to create. AI can help enhance creativity, although humans bring the compassion that goes hand in hand here.<\/p>

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7 ways artificial intelligence heightens the need for people<\/h2>
As artificial intelligence projects roll out, organizations will need to rethink the definition of the \u201cwork\u201d that people will do. The future of work will become one of the largest agenda items for policy makers, corporate executives and social economists, says Sanjay Srivastava, chief digital officer at Genpact, a professional services firm focusing on digital transformation.Despite the strong and inherently negative narrative around the impact on jobs, the bulk of the impact from the automation of work through AI will result in a \u201cdisplacement\u201d of work, not a \u201creplacement\u201d of work. Srivastava offers seven areas in which human knowledge and expertise will be a critical requirement in AI projects.<\/p>

Being an ethical compass<\/h2>
First and foremost, selecting revolves around the question of whether we are using AI for the right reasons\u2014to help people with special needs by enhancing eyesight, to provide translation in hearing aids for better communications across cultures, to increase diversity in the hiring process? Or are we using it for the wrong reasons, say for influencing elections? People provide the ethical compass needed to determine AI\u2019s use cases.<\/p>

Bringing context<\/h2>
People must orient AI towards the right goals. This happens already with supervised learning, where people label the datasets in advance before running AI algorithms. But even in unsupervised or reinforced learning, people need to ensure the algorithm is leading to results that matter for the business, for instance, in pharmacovigilance, when prioritizing between life-threatening and benign secondary effects of taking medicine. In addition, we need people to contextualize AI\u2019s results. It is more powerful to combine a moderately strong AI algorithm with human domain expertise than to use the most powerful deep learning model without any such context.<\/p>

Providing governance<\/h2>
In a digital workforce, planning should take into account the inputs, outputs and various exchanges involved with every process. We cannot just toss a robot into the mix and let it do its own thing. Mapping out how machines and other systems will work together can prevent potential hiccups and obstacles. Since AI is virtual, it becomes harder to tell whether or not a robot is working. In a hybrid workforce, we should be able to easily see if and how AI is working, just as much as human employees. People drive governance.<\/p>

Handling complexity<\/h2>
The new man-meets-machine dynamic will make current jobs easier to do, but the focus of people will shift to higher value and more complex jobs. With AI taking over some tasks, employees are then free to look at bigger issues and concerns. This evolution towards more complex work is not new; when spreadsheets were invented, the financial analyst\u2019s role went from reporting to planning.<\/p>

Preventing bias<\/h2>
The two big reasons for AI bias are the lack of diversity in data samples and rushed or incomplete training algorithms. In both cases, people with domain knowledge are key. Industry or process experts can help think through potential biases, train the models accordingly, and govern over the machines to see that they don\u2019t fall out of line. Diversity in the teams working with AI can also address training bias. By bringing in a team with different skills and approaches, we can have a more holistic, ethical design and come up with new angles.<\/p>

Managing change<\/h2>
Managing this next-gen hybrid workforce is very different from just managing people. We have to think about how we deal with the change, make sure robots are holding up their end and see that employees have the right skills to work with their new AI coworkers. Putting seasoned employees alongside machines is a dramatic change. AI design is moving from \u201chumans in the loop\u201d to \u201ccomputers in the group.\u201d Sometimes we forget to consider change management because we are eager to get AI going and start seeing some results. Projects have to start with a clear change management plan to prevent problems.<\/p>

Connecting creativity and compassion<\/h2>
When Apple introduced the iPhone, an entirely new creative industry of applications developed\u2014ecommerce, ride sharing, wearables, online communities, video gaming. Just like creativity boomed after the iPhone\u2019s launch, new creative applications will emerge from AI. We don\u2019t know what they are yet, though people will drive that future. Much has been written about AI\u2019s ability to create. AI can help enhance creativity, although humans bring the compassion that goes hand in hand here.<\/p>

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7 ways AI will change how people work with technology

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