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Putting AI to Work: Technology and Policy for Enabling the Workforce of the...

Artificial Intelligence (AI), machine learning, and assistive technologies will transform the future of work, with wide-ranging effects on employment, wages, and income distribution.

Panelists will consider a range of issues regarding workforce, technology and design trends for target populations, and policy questions for a new era of automated and adaptive workplaces.Preliminary results from a CITRIS white paper will be presented, sharing case study examples and recommendations for the private and public sectors to ensure development and application of AI that supports a diverse workforce and further strengthens social safety nets for all.

Agenda (Participant Bios) 10:00 Registration 11:00 Welcoming Remarks 11:15 Keynote: Jennifer Granholm, Distinguished Adjunct Professor, Goldman School of Public Policy and Former Governor of the state of Michigan 12:00 Break (Box lunch served) 12:20 Pathways to Work: Recruitment and (Re)Training This panel explores AI’s potential to support new platforms for discovering employment opportunities, increasing skills, and reducing bias to achieve a workforce more inclusive of older adults and persons with disabilities.

AI, automation, and the future of work: Ten things to solve for

As machines increasingly complement human labor in the workplace, we will all need to adjust to reap the benefits.

This executive briefing, which draws on the latest research from the McKinsey Global Institute, examines both the promise and the challenge of automation and AI in the workplace and outlines some of the critical issues that policy makers, companies, and individuals will need to solve for.

Our research suggests that society needs these improvements to provide value for businesses, contribute to economic growth, and make once unimaginable progress on some of our most difficult societal challenges.

In summary: Beyond traditional industrial automation and advanced robots, new generations of more capable autonomous systems are appearing in environments ranging from autonomous vehicles on roads to automated check-outs in grocery stores.

AI has made especially large strides in recent years, as machine-learning algorithms have become more sophisticated and made use of huge increases in computing power and of the exponential growth in data available to train them.

These technologies are already generating value in various products and services, and companies across sectors use them in an array of processes to personalize product recommendations, find anomalies in production, identify fraudulent transactions, and more.

An analysis we conducted of several hundred AI use cases found that the most advanced deep learning techniques deploying artificial neural networks could account for as much as $3.5 trillion to $5.8 trillion in annual value, or 40 percent of the value created by all analytics techniques (Exhibit 1).

Deployment of AI and automation technologies can do much to lift the global economy and increase global prosperity, at a time when aging and falling birth rates are acting as a drag on growth.

Labor productivity growth, a key driver of economic growth, has slowed in many economies, dropping to an average of 0.5 percent in 2010–2014 from 2.4 percent a decade earlier in the United States and major European economies, in the aftermath of the 2008 financial crisis after a previous productivity boom had waned.

AI and automation have the potential to reverse that decline: productivity growth could potentially reach 2 percent annually over the next decade, with 60 percent of this increase from digital opportunities.

For example, explaining decisions made by machine learning algorithms is technically challenging, which particularly matters for use cases involving financial lending or legal applications.

Among countries, US investment in AI ranked first at $15 billion to $23 billion in 2016, followed by Asia’s investments of $8 billion to $12 billion, with Europe lagging behind at $3 billion to $4 billion.

Adoption will continue to vary significantly across countries and sectors because of differences in the above factors, especially labor-market dynamics: in advanced economies with relatively high wage levels, such as France, Japan, and the United States, automation could displace 20 to 25 percent of the workforce by 2030, in a midpoint adoption scenario, more than double the rate in India.

We developed scenarios for labor demand to 2030 from several catalysts of demand for work, including rising incomes, increased spending on healthcare, and continuing or stepped-up investment in infrastructure, energy, and technology development and deployment.

These scenarios showed a range of additional labor demand of between 21 percent to 33 percent of the global workforce (555 million and 890 million jobs) to 2030, more than offsetting the numbers of jobs lost.

For example, the introduction of the personal computer in the 1970s and 1980s created millions of jobs not just for semiconductor makers, but also for software and app developers of all types, customer-service representatives, and information analysts.

Our research suggests that, in a midpoint scenario, around 3 percent of the global workforce will need to change occupational categories by 2030, though scenarios range from about 0 to 14 percent.

High-wage jobs will grow significantly, especially for high-skill medical and tech or other professionals, but a large portion of jobs expected to be created, including teachers and nursing aides, typically have lower wage structures.

The risk is that automation could exacerbate wage polarization, income inequality, and the lack of income advancement that has characterized the past decade across advanced economies, stoking social, and political tensions.

Government, private-sector leaders, and innovators all need to work together to better coordinate public and private initiatives, including creating the right incentives to invest more in human capital.

How will AI change the workplace of the future?

The landscape of the modern workplace is likely to be totally transformed in the coming years by AI.

Its far-reaching research covers topics such as cognitive systems, deep learning and machine learning, virtual and augmented reality, and, of course, AI.

“For example, one AI agent may automatically respond to simple email requests and another may manage your appointments.” According to Lawless, AIs are creating “a whole slew of professions that don’t exist”.

I’m sure that the equine industry was outraged at the development of the automobile,” said Lawless. “However, I believe, and Gartner agrees with me, that AI will help to create more jobs than it replaces.” The rise of chatbots is a particular trend that Conlan notes.

“They are very task-oriented and still tend to hand-written [queries] as glorified if-then-else programmatic statements.” Still, even if imperfect, chatbots are good at fielding low-level queries, things a human employee may go into autopilot while doing anyway.

Conlan is optimistic that saving this mental energy will greatly benefit both workers and enterprises. “This will free up employees to do the things we’re best at: being creative, being inspired, handling grey areas, communicating with others.” The relationship between AIs and workers is set to be a symbiotic one.

The AI learning will absorb all of this, but tends to be very task-focused and can be brittle when required to act outside of what it has learned.” An AI programmed to approve mortgages can be trained based on the reams of data created over the past five, even 10 years.

This has many potential downsides with regard to employee freedoms and privacy, but has lots of benefits both for the employer and employee … It will lead to a more effective and productive work environment.” AIs could be used to monitor how employees interact with and move around their working space “to identify the best office layout to support collaboration and teamwork”.

Artificial Intelligence – the Future of Automation in Workforce Management

Artificial Intelligence (AI) has already started to influence processes and automate decision making in manufacturing, health care, finance and customer service industries.

While the technology is still nascent, the building blocks exist to suggest that machine learning could ease the burden of complex analysis, surface insights, and trigger actions on behalf of managers in the workplace.

required to offer employees open shifts, accept open shifts, inquire about availability, resolve timecard exceptions, approve time and more, can be had between the system and the employee, and it can happen with much greater speed, volume and frequency than a human can handle.

small share (15 percent) of HR leaders already use technology with AI elements, but 40 percent of them expect to adopt it within the next five years, according to a survey by the recruiting firm Harvey Nash.

For example, many HR vendors have released or are about to release products that review data, such as social media posts and cover letters, to help determine which applicants might best fit an organization's culture.

Today, a manager would need to research accrual balances, look at existing time off requests, analyze coverage and skill sets, and other factors to attempt to arrive at an outcome that works for everyone and for the business.

After analyzing available PTO, coverage needs, skill sets, years of employee tenure and attendance policies, the system could bundle those recommendations for the manager to act on with the click of a button, or better yet, to have the system allow for auto-approval when no conflict will be created.

The technology will see what is happening in your business before you do by monitoring KPIs such labor hours, overtime rates, absence rates and even sales and service, and continue to deliver insights and generate recommendations that should mirror managers'

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