Even the automation vs. jobs theory isn’t stacking up. Layoffs and subsequent redundancies they are making news every day. Front-line managers are finding that automation creates more unpaid non-core business work just in finding fixes. The simplest description of the hype about the transition to automation is that it is absurd. Tech pundits have said over and over that automation simply can’t and shouldn’t do many things on its own.
Labor experts agree. Many critics have said that even the idea that automation immediately replaces jobs simply proves that management knows little to nothing about those jobs. They often only see reports, not the realities of the job.
Carnegie Endowment for International Peace has very clearly and patiently expressed the mix of perceptions about automation and the future of work. The future looks very uncertain right now.
Perceptions vs facts
Carnegie nailed several issues regarding perceptions of automation very effectively. The fear of substitution can now be called a global psychosis, especially at the white-collar level. Nor is the role of AI well understood in any practical context related to actual work roles.
There is an emerging view of AI as “remote workers,” according to Carnegie, for example. Given the continuing somewhat hysterical and overpriced prejudice against human remote workers, it’s interesting to watch this logic drown itself in self-contradictory arguments.
This is a huge unsolved cultural problem, and the problem, not the solution, is making decisions on the fly. This myopic worldview clearly lacks practical sense, and “ideological executive blindness” based on unrealized and often ill-defined perceived savings is making it worse.
Are people freer and better?
The basic theory that automation saves money by reducing labor costs is so wrong and so off target as to be utterly absurd. It can also be the opposite, and brutally expensive.
To start with:
How is it cheaper to adopt an entire class of major technologies at an upfront cost much higher than the fixed costs of existing jobs?
People’s jobs can be designed to deliver value on a clear cost-cost basis. Automation starts as a cost, and you need to get value out of it.
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The most basic operational rules, practices and laws related to automation and work are barely in the fetal stage. even China recently passed laws banning and limiting AI layoffs.
Humans don’t need the kind of 24/7 immeasurable overhead that automation imposes. Workplace technologies inevitably need costly evaluations, maintenance, upgrades, on-call SaaS and eventually replacement in relatively short cycles.
Technique is one continuous purchasing process with infinite mixed results in direct and indirect costs. Technologies become redundant faster than humans, especially in AI.
Then there’s automation fitting into that tactless thing called business reality. Most business technology is a patchwork of different years of technology, either secure or insecure for use in the modern business environment. It’s just cheating becoming a technology sector in its own right, let alone table errors.
AI makes and often cannot fix its own mistakes, especially when those mistakes show up on the balance sheet and require more expense. These mistakes can be based on situations and issues that any experienced person would automatically avoid. Expertise is a real value, not a perceived value.
The stark takeaway from this elegant and meticulous presentation of the obvious is that “uber alles automation” is definitely no way to run a hot dog stand. The lack of depth in the due diligence assessment of automation is downright dangerous.
Finding the right fit for real-world applications
Every business, every market, every customer base, every job and every workplace is different. Just there I can’t be one size fits all in automation at every level.
Productivity is an important case in matching jobs with people. Let’s start with HR. Trying to fit a human being into a job is not common practice. The person is more likely to enter a job with varying degrees of good fit or otherwise. High staff turnover means lots of bad crashes. You cannot call it productive in any sense.
Now there is also an AI tool for predicting staff resignations. This somewhat ironic development reflects the need to manage experience, expertise, task handling at all levels, and the most basic fluidities of workplace production. Skills learned in-house are essential for smooth workflow.
These almost invisible skill sets dictate real productivity throughout the food chain of doing business. Circulation loses those skills and their productive values.
So losing people is likely to be a net own goal, especially when you lose all your productive in-house fluidity. Again, automation does not solve these problems. It makes them more difficult to manage. Good fits for people are the only way any business has ever worked.
It’s not automation or work. They are both.
The picture that emerges is very different from the “work vs. automation” scenario. According to MIT, the positive impacts are emerging even in the much misunderstood world of coding time usage and productivity. Reduced burnout was one of the findings. He also strongly rejects the idea of cutting costs, especially for junior-level staff. Training was actually improved using generative AI, adding skill values.
The clearest indicators are that automation is by reconfiguring work, not just automating it. Short-term thinking based on cost just doesn’t work. An isolating effect of AI was also seen as a problem, reducing essential collaboration.
There is another horizon here and it is the real story that has not yet been written. Jobs are not static things. Tasks change, objectives change and priorities change. Unfamiliar roles and completely new environments are likely to be the new frontier of work.
