From Complexity to Simplicity: AI’s Role in Making Work Meaningful Again

My day job gives me the fortunate opportunity to speak with many leaders in marketing communications and with multiple experts in the field of AI. You are somebody that I’ve previously had engaging interactions with on the topic of AI - my goal with this newsletter is to share the thoughts and insights I gain from these conversations. This time:

Anthropologist David Graeber suggests in his 2018 book “Bullshit Jobs” that 50% of jobs in today’s world are meaningless and contribute nothing to society. This is a bold but at the same time unsurprising statement that is somewhat backed up by regular employee surveys suggesting a similar number of people at least feel the same way about their own work.

Many theories along with numerous debates attribute various reasons to this phenomenon. You don’t need to agree with Graeber (or the surveys) to observe that many organizations are not as smart or as efficient as they should be, and that employee engagement is often lacking.

The Process-Driven Organization

One significant factor contributing to this sense of meaningless work is the overwhelming reliance on process within organizations. A process-centric approach often reduces employees to feeling like mere cogs in a big machine—integral parts of an impersonal and mechanistic system where individual contributions seem insignificant.

Processes are excellent for creating consistency and repeatability. Consistent, repeatable performance is what enables managers to sleep soundly at night, so organizations invest in designing, implementing and measuring processes with great abundance.

The problem is that consistency and repeatability are not the same as business performance. Performance is a broader metric that encompasses innovation, growth, creativity and problem solving - qualities that processes often hinder rather than promote.

To be cliché, processes treat the symptoms instead of the illness - poor teamwork, skills, initiative, incentives, leadership or whatever other dysfunction you care to name - making sure that the right boxes are ticked without regard to the actual outcome.

Processes also act as a substitute for thinking and human intelligence as they deliberately constrain the scope of human creativity and agency. There is a better way: ask anybody who has worked in a small, tight, highly effective team where there are no processes, no approvals, just talented people getting great work done together quickly and efficiently. Processes are usually the last things needed in this case.

Small is good. Whereas processes dull our brains and are slow, inflexible and ultimately lead to that sense of individual pointlessness that is the difference between a valuable work contribution, and a meaningless one.

None of this is helpful for an organization. Yet firms are often trapped in a spiral of process dependency. Processes don’t retain the most talented people and mediocre talent can’t sustain an organization without the need for comprehensive processes.

And so the more process reliant an organization becomes, the less likely it is to behave intelligently.

Intelligence and Organizations

Mark Andreessen in his essay Why AI Will Save The World writes of how intelligence creates better human outcomes:

Andreessen goes on to explain how AI can enable better outcomes for everybody, for example:

By the same logic AI also has the potential to make organizations more intelligent and so less process dependent. Yet AI has been with us for a while now and this clearly didn’t happen so far - in fact today you are more likely to hear AI and process mentioned in the same sentence.

There are two reasons for this:

First, the most common forms of AI used today (Chat GPT, or it’s Co-pilot relative) are not as advanced as those that Andreessen envisions. “Infinitely helpful” is only achievable by specialized AI applications or likely by the next major generation of tools. Although this is only a function of time.

The more relevant reason is that organizations are more likely to put AI inside a process box than use AI to help climb out of one. This is a result of our thinking about technology in general.

The Way We Think About Tech Today

When I talk to communications and marketing professionals about their existing tooling infrastructure there are several things I often hear: that there are too many tools, that the tools are too complex, and that they are not being properly or fully used.

All these things are correct. The evolution of modern tools has been shaped by the increasing complexity of the media and social media landscape. As communication and marketing processes have ballooned in response, tools have been designed to support these intricate workflows leading to a race to the bottom in terms of efficiency. This process dependency has become so deeply ingrained that software applications and complex processes are now inextricably linked.

However, the emergence of AI presents a new alternative. AI's ability to access vast amounts of knowledge and to consistently reason and make data driven decisions enables a shift towards smaller, multi-competent and autonomous teams that can focus on delivering outcomes rather than being shackled to process steps.

By leveraging the capabilities of a new generation of AI tools, teams can work independently, tackling complexity head-on and producing tangible results without being bogged down by extensive process dependencies.

Paradoxically many people are put off investing in AI implementation because of the perceived complexity, because of the need to re-engineer processes around a new concept and because of the challenges of integrating something new into existing operations.

Yet the real choice is between something that is unused due to complexity and the chance to radically simplify things.

AI represents a chance to massively simplify. Simpler AI driven applications that can integrate knowledge and process steps = simpler processes, or no processes at all, and the potential for more autonomous and delivery focused teams.

The compounding effect that working in effective, autonomous teams  would have on human motivation and performance, together with the resulting management simplification has the potential to create dramatically better outcomes.

What Intelligence Means for BS Jobs

What this means for BS jobs - in contrast to many predictions of job losses - is that AI provides the opportunity to make jobs meaningful again.

Today we use processes to optimize organizational performance. If we can use (artificial) intelligence instead, by giving people access to information, insight, advanced reasoning and knowledge then we can better leverage our uniquely human abilities and capital and design jobs that meaningfully contribute. If we consider the economic impact of making 50% of jobs actually productive (instead of redundant) then AI becomes even more compelling.

Furthermore, imagine a world with a fulfilled workforce empowered to bring new levels of innovation, creativity and productivity. Basically a happier and better world..

Closing thought

If you are still reading… The issues I have outlined here represent unique and difficult challenges for leadership and turns many best practices upside down. The playbook for this has not yet been written and the theory is definitely not proven. I’m hoping to explore this in subsequent newsletters, but in the meantime I would very much appreciate any thoughts that you might have.

As I’ve said before, my goal with this newsletter is not be right, but to be less wrong - thanks for listening to me play out my thoughts.

Thanks,

Mark.