Until AI Automates the World

We’re all on the AI bus careening rapidly toward the End of Human Work.

Until we get there, we all still have a lot of work to do.

We talk about the transformation as if the goal is autonomous AI, automatically doing all our work. But I think there is no question that the most valuable results will be achieved by the AI-assisted super-human, producing work never before possible – or imagined. The most valuable applications of AI will be human-machine collaboration, where AI augments human jobs and humans augment AI tasks.

We are in for a long period of working with and around intelligent machines.

To date, we have mostly experienced master-slave relationships with our machines. We feed in scads of data, and machines pour out the orders, invoices, payments, computations, and categorizations we rely on to keep business going. Or, machines feed tasks to workers such as warehouse pickers who have no other motivation/control over what to do next.

Today, for the most part it is humans who see the information, have the insights, make the decisions, and take the actions. Nevertheless, in a few arenas machines are producing better results than humans. These are limited cases where algorithms are tested enough data is clean enough, systems are integrated enough, the problem is clearly bounded and context is sufficiently indicated. Sadly, these conditions are rare: The majority of today’s business systems suffer greatly from poorly integrated, context-poor, messy data deployed in support of poorly articulated strategies and goals. Improving those conditions is a gargantuan effort, an effort already underway for decades.

For decades to come, then, AI will augment most of our jobs, and automate very few. We are not even on the cusp of understanding how to do that. We need to learn how to effectively collaborate. We need new design patterns, methods, and metaphors for this new shared work.

Technology may be ready for an AI-automated world in my lifetime, but corporations, systems, and people will be struggling to catch up.

A few questions we urgently need to answer:

  • How does an organization learn to assess opportunities to apply AI, experiment, and measure the resulting impact?
  • How do organizations acquire the skills needed to be effective users of AI?
  • What are the design methods for sharing work with a semi-autonomous agent?
  • What are the design patterns for collaborating with a machine?
  • How do we design interfaces that encourage trust? Given that ML won’t make perfect decisions in every case, how do we make people comfortable enough to use systems?
  • How do we design interfaces that involve people at the right time, in the right way?
  • How do experience designers develop sufficiently deep understanding of ML to know which behavior and context information is essential to improving ML?
  • How do we design interfaces that evolve as machines learn over time, and yet feel consistent and reliable?

Published by Sue Aldrich


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