Towards the next Industrial Revolution

While the first three industrial revolutions took hundreds of years to supersede, the fourth industrial revolution only became a term in 2011. We saw the emergence of digitalization, spearheaded by technologies such as IoT, cognitive computing, and the combination of Big Data and AI - and we built conceptual frameworks to help us utilize technology.

And while Industry 4.0 and the technological decentralization and interconnectivity are still at full speed, it is natural that it will be inevitably succeeded by Industry 5.0: The full integration of the human touch of business and intelligent systems.

The combination of humans and machines will merge the potential accuracy of full automation with the critical and cognitive skills of business leaders.

Reason enough for us to write about it!

What is Industry 5.0 and why should you care?

The growing integration of Artificial Intelligence creates a multitude of challenges and opportunities for the future of the workplace. Just as work and life are no longer strictly separated, it is no longer enough to focus (often passively) on a single sector or industry one operates in, but to work towards seamless interactions in the background.

Simply put, Industry 5.0 adds a personal human touch to the two main pillars of Industry 4.0, automation and efficiency. It refers to people working alongside robots, smart machines, and technologies. This personal touch is the core element since technology will never fully replace the human workforce.

This is not to say that we should underestimate all that AI offers but rather move the conversation toward how we can make this work the best for us.

Industry 5.0
Moving towards Industry 5.0

We see this in practice when we take a closer look at AI capabilities. There are a number of rather well-known benefits of implementing AI, most of which have been essential to the design of Industry 4.0, such as:

  • Productivity gains through automation
  • Scalable business practices
  • Data-driven decision-making
  • Division of cognitive labor, and
  • Utilization of higher computing capabilities

Interestingly, when we focus on the design principles and goals of Industry 5.0, a more internalized perspective is taken. Some of the integral aspects are:

  • Moving towards interconnected empowerment - the focus has shifted to a human/machine collaboration as a way that drives value from the grass-roots up. Think on the lines of a supercharged human/robot collaboration that exceeds purely manufacturing pipelines and trickles down to the smallest teams and business units.
  • Cognitive support - and not elimination - of humans - increased decentralized decision-making through technical assistance that is not caused by reducing headcount. Over-automation is a well-documented threat - and one that leaves us with problems that are even harder to solve.
  • Circling between automation bias and cognitive bias - as Hans Rosling put it: Let my dataset change your mindset. Data is everywhere, but it requires a certain aspect of judgment that should be leveraged to bias that occurs. As most of us remember from our statistics course - correlation is not causation but we need both computing and cognitive power to find the meaning in the data.

It is clear that Industry 4.0 leverages technological components to create value - and is often defined by them. What makes the shift towards Industry 5.0 significant is the re-added component in the mix - the people in your organization - and how they interact with the technical world around them.

This is where Artificial Intelligence will make the biggest change both vertically and horizontally. Even though critics argue that Deep Learning is still in its infancy in some areas, its daily application power is enormous. And while it’s true that it is still mostly leveraged by large corporations with high resources, this is challenged by the no-code movement.

But how do you make this transition to Industry 5.0 as frictionless as possible? In short, by setting up procedures that drive value not by division of labor but by integration.

This often lies within the leadership of an organization but...

Leadership as we understand it is changing – and fast

The biggest challenge to define and implement any change mainly lies in leadership. It is great to incorporate AI & ML into your practices, but doing so for the sake of doing it provides no benefit to anyone. Questions regarding job (dis)placement, economic status quo, developing and redefining crucial skill sets, and transparency in complex decision-making – are all framed in the context of your organization - and all fall into the deliberate design of leadership.

When we talk about leadership in broader terms, things can get a bit technical. Gone are the days when leadership refers to the over-glorified traits of individuals like Jobs or Churchill and is changing towards the understanding that leadership is what leadership does.

We have identified four core competencies for leadership to take into consideration while moving your organization towards Industry 5.0 which we present in the following.

Core competency 1: Know your processes

Not every problem in an organization needs an optimized solution – AI is only as good as the defined outcome, the set goal for the process you aim to automate – and defining the outcome in some instances, is the most difficult task (and you can read more about how to do it here).

This requires leadership to ask themselves how various jobs can be broken into tasks and how many of these (repetitive or not) tasks an organization truly needs to automate. Issues of scalability should be taken into consideration, as automation for the sake of automation will fast become a costly experiment.

Let’s take an example of visual quality control in a manufacturing setting, a process often defined by manual eyeball search that takes time, people, and money. Or, if you are a large corporation with infinite funds, you might have fully automated this process - but it is still likely that you have someone sifting through images in front of a computer screen for hours.

This job more often than not requires judgment: your employees may need to know how the product operates in different settings, what potential malfunctions can occur or whether various specifications are met – this is all highly valuable data! And mistakes happen, even with highly skilled professionals.

Taking all this knowledge and training an AI model on your data, technology can do most of the heavy lifting through personalization. And you cut down that screen time for your employees to deal with edge cases.

Leadership in the era of AI
Data-driven decision-making creates the bridge between leadership and AI. Photo by Franki Chamaki

The key here is to understand what it is that makes processes of that product line specific to your company – and what knowledge goes into decision-making. Once you understand what level of complexity the tasks have, efficiency and productivity will follow seamlessly.

Core competency 2: Know your design

Now that you understand your processes, and are exploring technological tools, you should focus on what can be improved in the first place – and how it must be framed. This is for one simple reason - people really hate change.

Culture is like the wind. It is invisible, yet its effect can be seen and felt. When it is blowing in your direction, it makes for smooth sailing. When it is blowing against you, everything is more difficult. - Bryan Walker and Sarah A. Soule in HBR

Whether it is an in-house design of intelligent systems, design of the organizational structure and flows, or design of human and non-human agency collaboration, deliberate action is crucial for culture management. Process management studies also agree - intelligent systems need to be incorporated seamlessly for them to operate seamlessly.

Framing is a powerful tool to identify or create synergies and collaboration, so once you’ve identified the human/machine collaboration and outcome, you should:

  • Define the purpose of the change - and align it with “the why we do it”
  • Demonstrate the wins
  • Design the space in your organization for experimentation
  • Identify the tools with the lowest adaption friction

Core competency 3: Know your (future) employees

AI will not take away the humanness of any job – but rather restore that humanness in daily work for many organizations. People are the most valuable asset of organizations, and a sense of empowerment and belonging is becoming increasingly important for the workforce. Both up-skilling and re-skilling are seen as leadership functions that need to be fulfilled, and this consequently allows for a shift towards more meaningful tasks that involve creativity and critical thinking.

Finally, utilizing non-human intelligent systems as employees that have been hired to perform a specific task or job must be framed as such – there must be transparency in algorithmic processes to encourage collaboration. A way around this is through the fast adaptation of no-code tools which have proven their ability to change how organizations do things.

Core competency 4: Know where you stand

Expectations of leadership may be collective and socially constructed, but leaders must be sensitized to the traits and behaviors expected of them. We should not disregard the importance of some level of technical knowledge (or at least curiosity!) - at least to the level where you can have a conversation with your employees about technical integrations.

Don’t worry - you don’t need to hold a Master’s degree in Data Science - but you should be able to understand the value data holds in your organization.

Just as one cannot employ humans without speaking the same language, leadership cannot expect that from implementing intelligent systems – because their intelligence is valuable. Collaboration is not a modular process and requires active management, and something to remember – collaboration means various things to different people and comes in many forms.

Intelligent systems may not need to be designed with humanness in mind, but Artificial Intelligence in the bigger picture needs to be humanized to an extent, or framed as such. Designing a shared context, be it organizational goals or productivity output lead to a higher engagement rate through collaboration - because this is what it really boils down to.

The future of AI and leadership does not have to be complicated…

Industry 5.0 is inevitable – and just as we are changing the way we do business in this next economic shift, we should in parallel change how we see leadership. The role of leadership, just as any other organizational function, must adapt.

In the context of AI, the higher focus has often been placed on functions such as onboarding and designing the workforce, knowledge management, work-learning, creating and managing transparency, managing agility, and change as well as collaboration and communication. But as we mentioned before, there are multiple ways to lower adaption friction.

And even though the theoretical definition of leadership may be ambiguous, the practical implications can not. The role of AI in the organization through process and workflow automation must be contingent on the complex nature of how we do work.

Taking humans and machines as two sides of the same coin will impact the benefits we can reap from Industry 5.0.

Now that you're here

Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you'll love Levity.

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Now that you're here

Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you'll love Levity.

Sign up

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