The AI revolution, like every great revolution, starts with your people.


How & why engaging your people is the secret to successfully generating value from your AI efforts. 

The opportunity of a lifetime

When 40-something years ago computers started to really take their place in our (business) lives, the first things we started to utilize it for was … drumroll … computation! Computers, as their name might give away, made computation significantly cheaper. And have continued to do so for the past 40 years. Very consistently & exponentially – this is what people refer to as Moore’s law. Very quickly, computation was becoming exponentially cheaper, and it still is. This incentivized us to look at inherently “non-computation” issues and see how we might reformulate them as computation challenges. A clear example would be photography. Inherently this was a chemical problem, but digital photography turned it into a computational challenge. And we all know how that ended up for Kodak.

Today, we’re getting the opportunity to re-think everything in our daily (business) lives again. However, this time it’s not computation we’re working with. It is a prediction. You might ask an economist what AI is. The chances are high that they would shrug and say something like “AI is just prediction becoming cheaper…” Luckily, I’m not an economist & I say: “AI is making prediction cheaper! 85% percent of people feel emotionally disengaged at work, according to studies. Let’s transform business & the world for the better with that prediction, starting by taking the robot out of the human!” That is why we started to help do that translation.

70% people & processes – 20% surrounding tech – 10% core digital innovation

1,5 years ago, we came home from an AI summit with a foundational insight. All the vendors were saying they could build anything you could ever dream of. However, the crowd that was there to get inspired, had no clue what they specifically were supposed to dream. Does everyone need a chatbot? Our foundational insight from this was: AI adoption is held back, not because the technology is lacking but because organisations are unsure as to where it creates value or once they see the value, how to integrate it sustainably within their own context. “It’s very cool what Amazon is able to do, but how does that help my very different business?” The answer, we found, lies in starting with people & processes rather than the technology.

AI adoption is held back, not because the technology is lacking but because organizations are unsure as to where it creates value or, once they see the value, how to integrate it sustainably within their own context.

Let’s give an example

The second industrial revolution is a beautiful example of how we’ve already encountered this problem. When the first electric motors started replacing the steam motor, it took 30 years for a noticeable productivity increase. That turns out to be precisely the time to replace a generation of managers. These managers who grew up with these large steam engines had the reflex to simply 1 for 1 replace the steam engines. However, the strength of electric motors lies in the fact that you can build a lot smaller motors, generating value in the process in different ways if you choose to adapt the process.

They started from the technology and not from the people & processes. The focus was on optimizing the current paradigm (made it cheaper & safer). However, by doing so, these managers missed the bigger opportunity to start exponentially increasing productivity while still lowering cost ànd making work safer & simpler for their people.

That’s why today, we at Humain base our thinking on this rule: Successful digital innovation comes down to 70% people & processes, 20% infrastructure & supporting technologies around the core innovation & 10% the innovative technology itself, such as in our example here A.I.

Change: Opportunity or threat?

This is where employee engagement starts to take center stage. Suppose 70% of the success with digital innovation depends on people & processes. In that case, the first part is -obviously – to convince your people that these digital innovation projects will make their life easier. This is one of the reasons, besides the obvious fact that we just love humans, that as Humain, we really made it a point to center our work on this human-first approach.

There are three pillars that support this approach: Inspire, Engage and Transform. We take a coaching rather than purely consulting approach throughout our work and focus on educating “digital innovation translators” from within our client organizations. You wouldn’t hire a consultant if you’re trying to lose weight, so why would you when you’re attempting a significant business transformation?

In the end it comes down to training the organisational immune-system (OI) – you know, the thing that would do anything to stop significant change by yelling out: “I’ve been doing my job like this for 20 years, it’s not about now that you’re about to go change this!” – to recognise the new tools as an opportunity rather than a threat.

The Organisational Immune System & AI Fear

A huge proponent of the organizational immune system, just like any good immune system, is a fear of anything new. Anything that could harm the current set of operations going on within the body – or in our case the organisation. The big question often is: “Is AI going to take my job?”.

Studies show

There have been a couple of significant studies on this topic. The most relevant ones were Oxford University in 2013, the OECD (Organization for Economic Cooperation & Development) in 2016 and, more recently by PwC in 2017. Oxford said that 47% of jobs would eventually be automated, and the OECD said 9% and PwC claims 38%.

More interesting, however, is the reasoning & different approaches behind these results as we believe they say a lot about how we should reason about that big question and how they actually present an opportunity.

  • Oxford asked ML expert to evaluate the likelihood that seventy occupations become automated in coming years. They combined this with engineering bottlenecks in ML. They used a probability model to extrapolate this to 632 other occupations. 47% of jobs would become automated, concludes Oxford.
  • OECD thought that Oxford’s approach was brittle. They believed that the Oxford approach overlooks the many different tasks an employee performs that an algorithm cannot. Some examples are working with colleagues in groups, dealing with customers face-to-face, etc. That is why they believe it to be a more robust approach that is task-based. This leads them to their conclusion of 9%.
  • PwC also used a task-based approach. However, they have a strikingly divergent result from the OECD’s 9 percent, with 38%. This stemmed simply from using a slightly different algorithm in the calculations. Like the previous studies, the PwC authors are quick to note that this is merely an estimate of what jobs could be done by machines and that actual job losses will be mitigated by regulatory, legal, and social dynamics.
  • In 2018, Bain conducted a study taking a more macro approach & said that these macro factors, such as regulatory, legal & social dynamics, would, from the 50% of automatable jobs, result in an actual rate of 25% of automated jobs.

[…] the main takeaway from this in our eyes is that we’re a long way from the automation of entire jobs. What we are sure of, however, is that tasks can and are being automated away.

Time will tell

Time will tell who was right, but the main takeaway from this in our eyes is that we’re a long way from the complete automation of entire jobs.

What we are confident of, however, is that tasks can and are being automated away. We believe that tasks should therefore be the cornerstone of the automation journey. Let that be a brilliant opportunity to see the AI change as an opportunity and engage your workforce.

The AI Opportunity

According to a 2017 Gallup study – State of the global workplace, 85% of people feel emotionally disengaged at work. Eighty-Five. That means that a mere 15% feels fully engaged in what they like to do. If you know that we spend 40% of our workday on primary tasks and 58% of people feel too swamped with day to day which leaves no time to think, you might start to hunch why this disengagement sets in.

“85% of people feel emotionally disengaged at work.”

What if we could get rid of that 40%. All those things that slow you down, that you don’t like doing, and that add little to no direct value? What if we could work to automate those? What if we created processes that work for us instead of us working for processes? Can you imagine a company like an operating system that empowers its people to be their best selves as humans & not as robots? Humans who think creatively, connect deeply & act with kindness & love. Sounds fluffy, I know. But to me, that’s exactly the problem. It sounds fluffy, while if made real, this is undoubtedly the better situation – we wouldn’t go for anything less.

Both the process of detecting and rethinking these processes as well as the outcome of that rethinking our excellent employee engagers. Because your employees know these processes better than anyone, they also know what they like doing the least and who wouldn’t want to work at a company that helps you eliminate tasks you don’t like allowing you to focus on your job’s essence?

Turning Vicious Cycles Into Virtuous Ones

We took that simple idea very serious at Humain. Therefore we started working out a methodology that centers around inspiring & engaging people. By allowing them to reshape their jobs on a task-by-task basis and aligning that with the higher-level business goals.

Typically, before this methodology, management decided that the AI opportunity is huge. They set up a task force to explore some Proof of Concepts to “learn by doing”. Loudly & proudly, management communicates about these PoC’s. The innovative efforts are a vision of the next big thing. However, it never goes past the trial stage. By the third project, people in the company frown and barely take any of these efforts seriously. This feeds into those efforts never going past a trial stage. A vicious cycle.

The AI revolution, like every great revolution, starts with your people. When you start by engaging your people around their day-to-day challenges and then provide agency over how to (re)shape the job, that’s when you begin to see virtuous cycles.

We found the culprit. Starting from the technology rarely works. The AI revolution, like every great revolution, starts with your people. When you start by engaging them around their day-to-day challenges and then provide agency over how to (re)shape the job, that’s when you begin to see virtuous cycles. Because you start to co-create a vision of a better process that is empowered by the technology rather than constrained by it.

Instead of using the technology as a hammer looking for a nail, we take a step back and ask your people what kind of challenges they face & co-create tools to tackle them. It’s really hard not to go past the trial stage when your people are dreaming about the envisioned experience they’ve helped co-create enabled by the tools we’ve helped define.

Interested in more?

To be able to start from people & processes rather than from the technology, we designed a custom set of workshops and a whole methodology that we call a flywheel methodology aimed at creating impactful momentum throughout your organization. Are you interested in engaging your people to help them shape your organization’s future? Then feel free to reach out for more information!