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As machines enabled by technologies are increasingly becoming commonplace, work by humans are expected to become more versatile and creative. The reasoning is humans — at least the ingenious set — have always shown a willingness to adapt and innovate to new situations. But how will the relationship between humans and machines play out?

The following is excerpted from a new book published by Harper Collins titled The Future Ready Organization: How Dynamic Capability Management Is Reshaping The Modern Workplace by Gyan Nagpal. The book offers some interesting insights on the subject.

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The very nature of dynamic capability thinking is collaborative. Taking it one step beyond human capability involves embracing both human and automated contributions. A principal reason that a vibrant gig economy even exists today is due to web-enabled communication and collaboration. The same is true for outsourcing too. The entire industry for transnational services outsourcing grew from a standing start in 1990 due to advances in telephony, software and data mobility. It stands to reason that this trend will continue and with it the reshaping of human contribution by technology will also grow exponentially. Of the nine sources of human capability within the dynamic capability framework, there are five that will be totally reshaped by technology over the next ten years. These are given in the next illustration (fig. 7.2).

We know this because in each of them, the core disruptive triggers are already in motion. Let’s examine eight predictions of how the human–machine partnership can reshape how we organize work over the coming decade.

Machines will dominate repetitive and rule-based work. Humans will shine at creative and non-linear pursuits.

Key technology: Robotic Process Automation (RPA)

RPA generally refers to the automation of rule-based and algorithmic work using software. By and large, these robots are dumb — they must be programmed, turned on and provided with instructions. Still, RPA has the potential to totally transform the transactional outsourcing industry by 2030. One level above RPA sits Intelligent Process Automation (IPA), which while still rules-based, now involves a level of machine learning or ‘intelligence’. This could mean a program autonomously reaching out to a parallel system, or maybe even a human, for additional information.

Intelligent Machines will raise human intellect and productivity to new levels.

Key technology: Artificial Intelligence (AI)

Experimental AI programs like AlphaGo have already demonstrated how programs can learn complex games to eventually outperform the best human champions. It came to a head in 1997, when the Deep Blue computer beat the world champion Gary Kasparov at chess. We are discovering AI’s potential by the day. Recently, an improved version of AlphaGo, AlphaGo Zero, took just three days to learn the complex Go game from scratch and beat its predecessor 100 games to zero. These events have provided new impetus and learning directions for both game designers and players. In other words, machines can teach us to go beyond presently known boundaries of human potential. Go players are already relearning a thousand-year-old game according to Alphago Zero’s patterns of play.

As opposed to dumb robots, AI relies on a level of machine learning which makes the system self-aware and capable of rational reasoning. It does so by mimicking a neural network, processing options and the consequences of those options. This is what Simon believed AI’s true potential to be. Could AI programs one day be seen as a source for human learning—with an ultimate aim to improve human decision-making and eventually the human mind itself?

Machines will program directly for us.

Key technology: Natural Language Processing

Laboratory programs today can already recognise handwriting almost as well as humans do and describe the contents of pictures, diagrams and schematics at astonishing levels of detail. The key tipping point seems to be in natural language processing, which allows software to understand and interpret human speech in multiple languages, an advance which could allow computers to write code one day and lead to the complete obsolescence of programming languages such as Java.

The human cloud will exist within a much larger mechanical cloud.

Key technology: Natural Language Generation

The next step to Natural Language Processing is Natural Language Generation, or a machine’s ability to interpret and write commentary on data independently. It is here that automation starts to overlap with traditional white-collar jobs, such as journalism or data analytics. A very good example is Quill, a program which writes journalistic style commentary based on underlying data. Developed by Chicago-based Narrative Science, Quill is already writing up 10–15 page reports for regulators and investors at companies like Credit Suisse and T. Rowe Price. Think of the implications in live sportscasting and security intelligence. It shouldn’t surprise us that one of the investors in Quill is the Central Intelligence Agency (CIA).

When Natural Language Generation merges with other areas of cognitive computing, such as pattern recognition or self-learning systems, the future potential is unmistakable. With massive gigabytes of data available to analyse or visualise, and an internet of things (IoT) revolution on the horizon, I can imagine programs such as Quill will one day have a seat at the boardroom table, for the immediate analytical insight they can provide.

Management spans-of-control will exceed a hundred times.

Key Technology: Smart Agents

Smart automation can help both delayer the organisation as well as radically widen management impact, through the automation of old supervisory tasks like review and reporting. This frees up managerial time and also promotes greater autonomy and sense of self-direction for employees.

Once a project is scoped and agreed upon, smart agents can easily take on the task of scheduling project deadlines, aligning priorities, dependencies, communicating critical reminders and even handling escalations independently. Management can be much more interventional as a consequence and oriented towards real value-added behaviour like problem-solving and coaching for outcomes.

Applications will fade to the background and be replaced by digital assistants.

Key Technology: In-App and Mobile Searches, Smart Agents and Chatbots

Across industry today, it is common to drive the use of self- service applications. This augers well for customers but can also lead to a huge cluttering of applications. Smartphone users’ app downloads number close to 185 billion a year — each fulfilling a narrow purpose. This can be overwhelming. Most of us like how Google searches and catalogues the internet for us, yet apps don’t behave in the same way. They resemble independent books on a shelf rather than a seamless web. Industry experts now suggest that Search engines could soon have the power to crawl through, parse and index data stored within an app. This makes the app itself obsolete. Once this happens, content could potentially reach the smart agent directly, without the user ever needing to download the app itself. This will result in both simplification and speed. Instead of hundreds of enterprise applications to trawl through, more and more functions will make their data compatible with virtual digital assistants, operated by voice commands and designed to meet the needs of customers or even a specific type of talent, for example, a new hire, a virtual worker operating from home, a salesperson, short-term contractor or a manager supporting a mix of internal and external talent.

In Singapore, DBS Bank’s Digibank chatbot that operates across a variety of platforms including Facebook messenger, claims to resolve 82 per cent of all queries without human assistance. OCBC, another Singapore bank, has generated over 50 million in-home loan leads via its chatbot Emma. Similarly, the top insurance companies are using chatbots to help their agents and financial advisors resolve client queries in real time. Customer information on claims, past premium histories or policy values are all available through what is now seen as a virtual sales assistant.

Learning becomes practical, customised and just-in-time.

Key technologies: Virtual Reality and Augmented Reality

Virtual Reality (VR) based immersive simulations have already started changing the way we learn. For example, Oculus VirtualSpeech helps improve public speaking skills by mimicking live stage experiences with responsive virtual audiences.

Augmented Reality (AR) is the next step, which involves superimposing data or images upon what we see in real life. For example, imagine workers repairing complex machinery in a remote location being able to superimpose step-by-step schematics or training manuals on the machine itself. The implications of AR all the way from school classrooms to operation theatres in hospitals can save both time and lives through just-in-time learning.

Intelligent machines make humans safer at work.

Key Technology: Intelligent wearables

In an Internet of Things (IoT) environment, intelligent wearables are often the smartest safety investment that companies can make, particularly in high-risk work environments. For example, Australia-based SmartCap Technologies has developed a solution which can constantly monitor operators of heavy machinery, like truck drivers, for micro-sleeps and other signs of fatigue. Similarly, wearable proximity alarm systems can help workers stay away from danger or prevent mishaps on locations as diverse as oil rigs, ship decks, underground mines and high-rise construction sites.

In conclusion, the potential for collaboration between human capability and intelligent machines is limitless and if used correctly, can transform the impact of both people and the organisations they work for. It is important that we don’t see intelligent machines as the enemy, but recognise that human frailties, like our propensity to satisfice and our desire for creativity over tedium, are the real reason we need them.

The excerpts have been published with permission from HarperCollins Publishers India.

 

Published Date: Sep 13, 2019 06:09 AM | Updated Date: Sep 13, 2019 12:09 PM IST

Tags : #Artificial Intelligence #Augmented Reality #future of work #machine learning #virtual reality #Wearables

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