Harnessing AI to Make Knowledge Workers Effective

Artificial Intelligence

Artificial intelligence (AI) has long gone past the rudimentary capabilities. Today’s AI is packed with capabilities to recognize concepts and contexts opening up new pathways for collaborations between knowledge workers and machines. So much so that, experts can provide their inputs for training, quality control, and can dictate the fine-tuning of AI outcomes.

AI-powered machines can increase the expertise of their human collaborators and help create new experts. These machines are now pretty much adept in mimicking human intelligence and proving themselves to be more boisterous than the previous-gen big-data systems.

Knowledge Workers & Stats:

Knowledge workers are people who reason, create, design, and apply insight in non-cognitive processes. Studies also hint that these intelligent machines could affect 48% of the workforce of knowledge workers in the US alone and over 230 million knowledge-worker roles globally. It is time for organizations and companies to redesign knowledge-work jobs and processes to take advantage of the possibilities of the AI of the current generation.

The study also states that 70% of knowledge workers will require need training and reskilling due to the new compulsion for working with artificial intelligence. C-suite execs must get involved in the overall effort of redesigning knowledge work roles and processes. While the executives reimagine leverage knowledge work through artificial intelligence, there are certain principles they can apply:

Related: Future lies in Collaborative Intelligence

1. Let human tell AI what they care about

To give an example, AI has already become a vital part of medical diagnosis. Despite AI offering a diagnosis to the doctor who in turn has to explain the diagnosis to the patient. This is called the black box problem. However, Google Brain has conceived a system that dissects the black box and provides a translation for humans. The Google tool lets medical experts enter concepts in the system they think is important to test their hypothesis.

2. Make models amenable to common sense

With increasing concerns of cybersecurity, organizations have increased the use of instruments to collect data at various points in their network to analyze threats. Unfortunately, most of these data-driven techniques do not integrate data from multiple sources, nor do they include the common sense knowledge of cybersecurity experts. The cybersecurity experts often know the range and diverse motives of attackers, understand typical internal and external threats, and the degree of risk to the enterprise.

But researchers dealing with data science and artificial intelligence are striving to change that. They have been using a Bayesian model, where a method of probabilistic analysis captures the complex interdependence among the risk factors that include the large number and types of devices on the network and the knowledge of the organization’s security experts about attackers, risk, and related things.  

These researchers are pursuing ways to represent and incorporate expert knowledge throughout the system. The current method is where various AI-driven cybersecurity systems subsume human decision-making at the last moment.

To give an example: The expert understanding of security analysts on the motivations and behaviors behind an IP theft, and how it might differ from multiple scenarios such as a denial-of-service attack, are explicitly programmed into the system from the beginning. This human knowledge in collaboration with the data sources from networks and machines can be used to train more effective cybersecurity defenses.

Related: Keep your Website Hack-Free in 2020

3. Use AI to help turn novices into recognized experts

Beginners can turn pro with the help of AI. With customer service agents learning new skills, the AI incorporated in the function automatically updates their expertise. By this, the need to manually update their skills profile in the HR record is eliminated. As the agent becomes more knowledgeable, the software learns to route the more complex problem to the agent.

The software constantly fortifies the agent’s expertise and the AI’s cogitation of ‘micro-skills’ increases the efficiency with which the expert trains the software.

4. Use data-efficient AI techniques to map the work processes of human experts

Large sets of data cannot be produced as experts are scarce. However, deep learning and machine learning powers AI’s advancements through the acquisition of mountains of data to train and build systems from bottom up.

We could see more top-down systems that require far less data for their construction and training, enabling them t capture and embody workers’ specialized knowledge in the future.

In conclusion, experts across disciplines and sectors are designing AI that can be easily trained and evaluated. These newly designed AIs can collaborate with experts who incorporate their scare, but valuable knowledge. Organizations will have to allocate their AI to spend judiciously as they begin to take advantage of these new possibilities.

Like how today’s machine learning systems amplify the capabilities of ordinary workers, tomorrow’s systems will heighten the performance of knowledge workers to previously impassable levels of uniform excellence.

Related: AI Trends To Watch Out For In 2020

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All Comments

  • Avatar Dan Begue

    Cool!

    7 months ago | 18 April, 2020 4:48 pm Reply
  • Susie McCrea Susie McCrea

    This article really helped me to understand AI as I continue to learn. Thank you.

    7 months ago | 18 April, 2020 12:34 am Reply

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