The productivity promise of AI in tomorrow’s workforce

Kamila Hankiewicz
Nov 7 · 6 min read

Artificial intelligence promise huge quality jumps in health, safety and productivity. But will its wider adoption initiate next industrial revolution? Many seem to think so. While there is no question that AI and Machine Learning already transform many aspects of the way we do work and play, whether this technology will have the same disruptive impact on the workplace as the assembly line or the internet remains to be seen.

We got used to media’s shouting headlines of new technologies as something that will change the world, but around 90% of technology evolution comes incrementally.

AI has been in development since the 1950s, when a group of academics at Dartmouth College began studying how to teach machines to learn. For the past six decades, researchers have been developing tools that enable computers to learn and adapt so they can eventually take over common workplace tasks. Yet, it was only in the last years that computing power became more available due to cost drop, hence the development and adoption of AI algorithms increased.

The process that never ends

Truth to be told, most business leaders are still unclear about how and where AI will transform their operations. There is a common understanding that AI can bring a level of efficiency and mitigate risks, but many decision makers have spent their career treating every new technology as a new piece of software with very specific features and expectations. That’s not how AI works. This technology is applied, not installed, and it requires an endless process of checking outcomes against desired results and further tuning.

Business leaders that understand that and can adapt company’s processes to this new way of thinking can expect to generate significant productivity gains, along with other benefits.

Unlike software, AI engines are not programmed to do a specific function. Rather, they are taught to understand how to do a variety of tasks, and with every interaction they learn more and get better at the job. That’s what makes AI fundamentally different from any other form of technology.
AI won’t just improve human productivity. It will stand alongside humans as a form of virtual labor in its own right.

Contextual intelligence

At a basic level, AI-driven chatbots use Machine Learning and Natural Language Processing algorithms to respond to customer or company’s internal queries. The algorithms study historical interactions to find patterns in questions and whether the responses resulted in a positive outcome. That “knowledge” allows them to craft appropriate responses. Supported by guidance from the human agents, bots learn from every interaction, so they get better at responding to requests over time.

This same model of machine-based learning is being applied to far more complex workforce tasks. AI algorithms are now used in law firms to scan millions of documents for specific data or patterns, in health care to review medical images for signs of cancer, and to identify compounds that could be developed into successful treatments. Machine Learning and AI is successfully used within company’s internal affairs too. It can give employees access to e.g. a much wider information about the project by using contextual intelligence — think of a Pinterest where all the most important (often — previously hidden) pieces of the project and data links between them are can be gathered all in one place. AI can allow smart collaboration that retrieves and delivers the right information at the right time.
The beauty of AI is that its engines are able to finish largely mundane tasks faster and with fewer errors than their human counterparts.

All of these applications of AI in workplace have clear — and in some cases astonishing — productivity gains. In one example, LawGeex, a legal contract review company, challenged 20 attorneys to compete against an AI engine to identify 30 legal issues in five nondisclosure agreements. The AI engine delivered more accurate results and was able to complete the challenge in 26 seconds, compared to one-to-three hours for the attorneys. In another example, a team from the Universities of Manchester, Cambridge and Aberystwyth has demonstrated the potential of artificial intelligence by using it to discover that a compound shown to have anti-cancer properties might also be used in the fight against malaria. Robot scientists are a natural extension of the trend of increased involvement of automation in science. They can automatically develop and test hypotheses to explain observations, run experiments using laboratory robotics, interpret the results to amend their hypotheses, and then repeat the cycle, automating high-throughput hypothesis-led research. Robot scientists are also well suited to recording scientific knowledge: as the experiments are conceived and executed automatically by computer, it is possible to completely capture and digitally curate all aspects of the scientific process.

For 1000 jobs lost, there will be >1000 jobs more

Artificial Intelligence’s ability to ingest and review data in a fraction of the time brings obvious productivity advantages, particularly in areas where human workers simply do not have the capacity to review that quantity of content. 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 causing a drag on growth.
Experts at the World Economic Forum liken advances in AI to the next “Industrial Revolution” using digital tools to automate tasks in the same way that earlier revolutions used steam, electricity and assembly lines to mechanise production. But as with all revolutions, the latest generation of AI-driven productivity gains will come at a cost.

According to research conducted by McKinsey few years ago, 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.

However, it also shows that 400 million jobs will be replaced by automation, and many more jobs will be altered by this technology, often requiring a higher level of skill and education. From blue collar jobs to retail workers and counter staff, to paralegals, radiologists and information analysts, AI could cut a large pool out of the workforce, forcing employees to get educated in new skills and requiring companies to rethink how they find and train new staff.

Government should play an active role in shaping the workforce of the future

Regardless of the impact, no one needs to panic about the coming AI revolution — at least not yet. While there is already a lot of mismatch between open jobs and skills in the labor pool, with incentives from governments, companies can retrain and educate their workforce on the future of work.
China seems to be taking the lead, as its government has big plans for artificial intelligence. In July 2017, its government issued an ambitious master plan to lead the world in AI research and deployment by 2030. The road map outlined the steps by which AI will be deployed in areas such as military readiness and city planning, and the government announced that AI courses would be included in all primary and secondary schools. In response, the Chinese Ministry of Education has drafted its own “AI Innovation Action Plan for Colleges and Universities,” calling for 50 world-class AI textbooks, 50 national-level online AI courses, and 50 AI research centres to be established by 2020.

While AI technology hold huge promise for productivity gains — and potentially threaten the livelihoods of some low skilled, entry level workers — the changes won’t occur over night. While many business leaders are paying attention to AI advancements, few are even dabbling in AI applications. It will be years before this technology transforms the way we work.

Business leaders interested in taking the lead, should begin by identifying real business problems that AI can solve, then considering whether they have the people, processes and technical expertise to make AI work. Companies must make the necessary time, expertise and access to data to create and train algorithms to do a specific job.
Even if you have a lot of data (and as I’ve mentioned my my previous article — majority of it it’s dark data), it’s not easy to grasp the value hidden within.

At the moment, most uses of AI in the workplace relate to scanning paperwork and responding to customer queries. However, the next phase will be to focus on uncovering the next layer of value — with help of AI companies will be able to e.g. identify new sales opportunities or deliver one source of truth for company knowledge.

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Kamila Hankiewicz

Written by

AI advocate with passion for Japanese knives | @untrite.com @japanahome.com, @hankka.com, co-MD london@girlsintech.org

Untrite

Untrite

Uncovering valuable information in what you already have

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