Jobs of the Future: AI and K&IM Professionals

Helen Edwards, Editor, K&IM Refer

 How will artificial intelligence (AI) affect jobs in the future?  Two new books from Harvard Business Review Press offer frameworks for looking at how humans and “intelligent” machines can help each other and the impact this will have on job roles.

 

In Human + Machine: Reimagining Work in the Age of AI, Paul R. Daugherty and H. James Wilson, technology leaders at Accenture identify what they call the “missing middle”.

Based on their research and experience with 1500 organisations, they describe six new ways people and machines collaborate with each other:  humans train machines to perform tasks, they explain machine outcomes, and they sustain the machines in a responsible manner. Machines amplify human insight and intuition by leveraging data and analytics, they interact with humans at scale using novel interfaces, and they embody physical attributes that essentially extend a person’s capabilities”.  These emerging jobs are more human than technical and turn out to involve many of the skills long held by knowledge and information professionals and are based on concepts central to the profession.

Three roles humans play in developing and deploying AI

Trainers:   Whereas in the past people had to adapt to how computers work, now the reverse is happening.  AI needs to learn to adapt to us, develop responses and understanding of language. This involves understanding what people are looking for in all sorts of contexts and modelling interactions. Trainers also need to correct for noise and hidden biases that can easily creep into systems.   Example tasks include: “clean data for upload, discover relevant data and data streams, have machine observe decision making, tag data for better use, work with HR to inform the design of workplace retraining initiatives.”

Explainers need to bridge the gap between technologists and business leaders.”  This involves interpreting machine outputs, adding “explainability” to interfaces and explaining the working of machines to stakeholders.  As systems become increasingly sophisticated, it is critical that their decisions can be explained and justified in terms that are accessible to customers and managers alike.

Sustainers play a critical role in “setting limits or overriding decisions based on legal or ethical compliance.”  Information professionals have long been involved in ethical and compliance issues and are well placed to represent the wellbeing of society and  “fulfil values such as increasing diversity and a commitment towards improving the environment.”

Three ways AI unleashes new levels of productivity

The authors describe three ways AI can give people superpowers by augmenting their skills:

Amplification:  “AI agents give people extraordinary data-driven insights, often using real-time data. It’s like your brain but better.” AI is being used to enhance the effectiveness of work and improve decision making processes. The authors describe three examples of how this works: matching(match resources, automate low level tasks); recommending(rank or design alternatives, prioritize resources, automate process change); patterning (identify trends in real-time, personalise offerings, identify anomalies, categorise and route data, augment strategic decisions).

Interaction:  AI tools employ advanced interfaces such as voice-driven natural language processing to facilitate interactions between people at scale.   Activities involve automating Q&A, allowing human workers to focus on high value interactions. The authors comment “once tedious, repetitive tasks are gone, management and leadership can reimagine workers’ processes around unusual, interesting, more nuanced customer services situations.”

Embodiment:   AI in combination with sensors that allow robots to share workspace with humans on the factory floor and engage with humans in physically collaborative work. The authors describe the new breed of “cobots” that make it safe for people and robots to work side by side.  Although these developments may have a less immediate impact on our spaces than those affecting the work of the mind, in the longer term, there may be exciting opportunities to develop shared knowledge spaces, especially as showcases of what is becoming possible.

Ravin Jesuthasan, a managing director at Willis Towers Watson, and John W. Boudreau, professor at the University of Southern California’s Marshall School of Business and Center for Effective Organizations, also address the issue of the optimal combination of human and automated work in their book Reinventing Jobs: A 4-Step Approach for Applying Automation to Work.  Their research shows that the question is not which jobs can be automated but instead which tasks are suitable for automation, and of those, which make strategic sense to automate.

The Four Steps 

  • Deconstruct jobs into component work tasks. Tasks can be categorised across six dimensions: repetitive / variable; independent / interactive; physical / mental, each with their own characteristics with regard to automation. The authors note that while some tasks (repetitive, independent) are generally known to be more compatible with automation, advances in AI and sensors are continually changing the boundaries.
  • Assess the relationship between job performance and strategic value. This involves asking “what payoff work automation can produce.” To illustrate this, the authors analyse the role of a research director in the pharmaceutical industry:  “one work element is research, where performance can range from moderate (being aware of leading research) to great (being a big thinker who publishes breakthrough research). Another work element is team leadership, where performance can range from moderate (providing input to the team) to great (creating collaborations that transforms breakthrough ideas into unique drug formulations). Depending on the strategic priorities, the return on improved performance, for example of an automated research alert, varies.  If it is critical not to miss an important publication, such automation creates the optimal payoff.  However, if the goal is to create unique breakthroughs, it may be more valuable to use an advanced AI “that can observe and interact with the research scientists, learning the patterns that lead to unique breakthroughs.”
  • Identify options for recombining tasks in the light of new technology or process. Only when tasks have been described and payoffs assessed should automation options be evaluated.  The authors use these three categories:
    • Robotic process automaton: for high volume, low complexity repetitive tasks.
    • Cognitive automation: applying intelligence like pattern recognition and language understanding, extracting meaning from Big Data.
    • Social robotics: interacting and collaborating by combining sensors, AI and mechanical mobile robots.  By matching specific tasks to the automation potential, it is possible to explore where automation can replace, augment or create new work.
    • Optimize work by putting it all together to reinvent jobs. The authors show how these ideas are being implemented in a range of sectors including banking, cancer treatment, the airline industry.  A particularly relevant example comes from the retail fashion industry: “human stylists do tasks where their performance can add value, like curation, improvising and relating to others. Cognitive technology takes on takes on tasks that humans do less well, such as gathering and analysing data and producing decision guidelines. More important, the results of the cognitive automation make the value of every stylist’s performance incrementally better, because they can use the decision rules and results of cognitive automation as their starting point.”  

Jesuthasan and Boudreau conclude that new, reinvented  work options fundamentally change the leadership profiles, strategy and culture of organisations.  They comment, in language especially pertinent to the information sector: “Norms such as “customers come to us because only we have the information they need” must change to “customers arrive with more information than we have, and come to us for a trusted and collaborative relationship.”

Human + Machine: Reimagining Work in the Age of AI

Paul R. Daugherty and H. James Wilson

Harvard Business Review Press, 2018

Reinventing Jobs: A 4-Step Approach for Applying Automation to Work

Ravin Jesuthasan and John W. Boudreau

Harvard Business Review Press, 2018

 

K&IM Refer 35(1), Winter 2019

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