Human-Machine Partnership. I love that phrase. It is in the title of the recent research report from the Institute for the Future and Dell Technologies. I wrote about the technology side of the report in my last post. This post will highlight the human and partnership sides of the report.

Takeaway: The way we work with technology in the near future will evolve into a partnership that can enhance human training and education building a workforce that is effective and focused on continual learning.

Challenge: The optimism in the report needs to be tempered by the question—will this only benefit the few self-motivated youth and those whose parents push them? How can we remake our institutional education (which is now global) such that we can provide mentors and a different way of motivating kids and young adults (as well as us old guys) into continual and self-paced learning?

From the report:

Recent conversations, reports, and articles about the intersection of emerging technologies and society have tended to promote one of two extreme perspectives about the future: the anxiety-driven issue of technological unemployment or the optimistic view of tech-enabled panaceas for all social and environmental ills. Perhaps a more useful conversation would focus on what the new relationship between technology and society could look like, and what needs to be considered to prepare accordingly. By framing the relationship between humans and machines as a partnership, we can begin to build capacity in machines to improve their understanding of humans, and in society and organizations, so that more of us are prepared to engage meaningfully with emerging technologies.

However, it would be a fallacy to assume that technology is making human effort redundant. It’s doubtful that computers will have fully mastered the fundamental, instinctive skills of intuition, judgment, and emotional intelligence that humans value by 2030. Over the next decade, partnering with machines will help humans transcend their limitations.

Human-machine partnerships will enable people to find and act on information without interference of emotions or external bias, while also exercising human judgment where appropriate. They’ll learn to team up with technologies integrated with machine learning tools to help activate and deactivate the resources they need to manage their daily lives. And they’ll partner with AR/VR technologies to develop necessary work skills, blending experiential media with human judgement to perform well at work.

Their ability to evaluate talent will also be bolstered by VR/AR technology, which will increase managers’ ability to evaluate a worker’s aptitude for gaining new knowledge or learning new skills and applying this knowledge to a new scenario.

By 2030, populations’ needs and resources will be orchestrated by self-learning, digital technologies, allowing humans to take the role of digital resource conductors. Technology will work as an extension of people, helping orchestrate, manage, and automate many day-to-day activities. And because the technology will be woven into everyone’s lives (some will even be implanted), and personalized to the individual, some needs will be met often before people even realize they have them. These digital technologies will be integrated with machine learning to create a population of digital orchestration systems, harnessing technology to arrange and direct resources to produce a desired result.

By 2030, many will be savvy digital orchestra conductors, relying on their suite of personal technologies, including voice-enabled connected devices, wearables, and implantables; to infer intent from their patterns and relationships, and activate and deactivate resources accordingly.

By 2030, expectations of work will reset and the landscape for organizations will be redrawn, as the process of finding work gets flipped on its head. As an extension of what is often referred to as the ‘gig economy’ today, organizations will begin to automate how they source work and teams, breaking up work into tasks, and seeking out the best talent for a task.

As the transfer of knowledge will be increasingly offloaded to emerging technologies, individuals will shoulder the burden of using these new technologies to acquire necessary skills to demonstrate proficiency. As a result, people will need to know how to access information and learn through immersive and experimental media such as AR and VR. (Big fear, can we restructure education so that we lessen the divide between digital haves and have nots? Or do we continue to stratify society?


  • Contextualized intelligence: nuanced understanding of culture, society, business, and people
  • Entrepreneurial mindset: applying creativity, learning agility, and an enterprising attitude to find workarounds and circumvent constraints
  • Personal brand cultivation: a searchable and favorable digital identity as basic work hygiene
  • Automation literacy: the nimble ability to integrate lightweight automation tools into one’s own work and home life
  • Computational sensemaking: ability to derive meaning from blended machine and human-based outputs


  • Business-driven security: embed security as a business strategy
  • Eliminate latencies: exceed consumer expectations for real-time delivery
  • Algorithmic branding: ensure algorithms align with organizational values
  • Diversifying value of work: reset assumptions behind the value of work
  • Inspire innovation: incent workers to deviate from machine-learned systems


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