The quantified organization: Grail or Big Brother?

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Thanks to the growing mass of data they capture, businesses are in a position to understand how their employees are doing and how they function, and to implement a process of continuous improvement on both organizational and human dimensions. But they can’t do this without gaining the trust of their employees.

You can only manage what you can measure“. Peter Drucker’s phrase may have been criticized, but we have to admit that he hit the nail on the head. Measuring anything and everything, just because it can be measured, has led to excessive complication in organizations, and to obvious managerial and decision-making errors. When a measure becomes an objective, it ceases to be a good measure (Goodhart’s Law).

But we’ve also seen its relevance in an increasingly digitalized world, dominated by knowledge work, where we see organizations that are totally dysfunctional for lack of being able to give materiality to what goes on within them. Nothing in business has ever been as poorly managed as people on the one hand, and knowledge work on the other(Knowledge workers, the excluded from operational excellence?).

Towards the quantified organization

While I was thinking about how to properly evaluate employees in today’s world, without really finding a satisfactory answer in a Deloitte study dealing extensively with the subject, I continued to look for runways to new ways of understanding productivity. This led me, again at Deloitte, to a study entitled “Beyond productivity: The journey to the quantified organization“.

I’d already touched on this subject in connection with the secondary data collected as employees use their work tools, so it was a good time to return to the subject.

What is the quantified organization? For want of a better one, I’ll use Deloitte’s definition:

A quantified organization takes a strategic approach to measuring what it should, not just what it can. It takes a responsible approach to using new data sources and AI tools to create value for stakeholders across the organization, improving workforce trust and driving the organization forward to new levels of financial, reputational, and operational performance.

A bit wordy and keyword-laden, isn’t it?

Let’s try to be a little more concrete.

1°) Businesses (potentially) have data on everything you do

You probably suspect that businesses have a wealth of data on your day-to-day work , but you can’t imagine just how much.

We can find out who you talk to, how often, what the tone of your exchanges is, when you use or don’t use your mouse and keyboard, how often you have meetings, how long they last, with whom, we can analyze the tone of the exchanges you have in your online meetings, we can analyze and understand your daily routines, the way in which a particular workflow unfolds…

But this goes beyond white-collar workers. Cameras can identify potential lapses in attention from a truck driver or an operator on a production line.

What makes this possible is simple. Firstly, the omnipresence of IT in just about every business, or rather, of data. As soon as you use a machine or software of any kind, data can be captured. Then there was a gas pedal: the COVID, which prompted 78% of businesses to set up monitoring tools for employees teleworking on their computers. A frightening figure, but one that needs to be put into perspective if we imagine that it is much higher in countries where almost anything can be done with employees’ privacy and personal data….and much lower in countries with a different culture and legislation on the subject.

2°) They can (in theory) learn from this data

Having tons of data, often unstructured, is one thing, knowing what to do with it and having the means to process it is quite another. That’s where AI comes in!

To put it pragmatically: we can use it to see, analyze and understand how everyone works and behaves, individually and collectively, and draw all the conclusions we want from it.

The invisible finally made visible for the benefit of all

I like to repeat that if, in our open spaces, we could see with the naked eye, as easily as in a factory , information flows, strocks, work-in-progress, bottlenecks, etc., we’d think we were working in the wrong way and press the “stop” button to rethink everything from scratch.

Well, that’s the promise of the quantified organization: to give materiality to the invisible, to make it measurable and therefore manageable. Which means it can be improved.

So, for someone like me, it’s a kind of Holy Grail, enabling us to understand how people really work, to understand how people impact on processes (or should), to reconcile data from “systems of engagement” and “systems of records” to understand the impact of collaborative dynamics on business and operations, to identify bottlenecks and risks of information overload…. and so on.

The benefits are varied and easy enough to understand: improving processes, identifying risks of disengagement, understanding the reality of the organization (how people work vs. organization chart), leadership development, etc. etc.

What trade-off between ethics and business?

When, more than 10 years ago, I wrote that there would be no data business without data ethics, I didn’t think that the future would prove me so right. But as is often the case, the emergence of a new technology brings with it a wave of technological solutionism, leading people to think that all problems will be solved “automagically“, before realizing that technology will never solve problems whose very nature is human.

So how do you think quantified organization and AI are going to provide answers to such complex issues? Well, as I said earlier: by capturing data on virtually every move you make.

Every time you use your computer, keyboard and mouse.

Every time you interact with an application.

By using your phones and connected objects.

Using your webcam

By knowing who you’re interacting with and what you’re talking about.

By reading your emails to understand their tone, by listening to your meetings, by watching your expression.

And, of course, promising us confidentiality of personal data.

Etc, etc.

Do we have to sacrifice everything, or do we have to say no to the promises of the quantified organization?

If the answer were simple, we wouldn’t be asking the question. Once again, it’s a question of cursor, of compromise.

No AI without trust

Fortunately, the maturity of users on the subject is now real and, in many countries, legislators have already gone through the process of explaining what can and cannot be done.

Where I agree with the study is that it alerts businesses to the issue of trust: the quantified organization won’t work if employees don’t trust the employer. And I would add, by ricochet, if the business doesn’t trust its AI supplier(s).

The study explains the extent to which the most successful businesses are those that have the trust of their employees, and that the most trustworthy businesses have the most engaged employees. For those who doubted it.

More interestingly, it explains the pillars of this trust:

Is that enough? In terms of pillars, yes, but that doesn ‘t take into account the notion of acceptability of the approachon the part of the employee. Some will be very open, others totally recalcitrant, and in the middle there’s a cultural dimension that comes into play.

For me, the cultural dimension is essential when it comes to business AI. Which means one thing: the inherent difficulty of global deployment in an international company.

Moreover, this cultural prism is generally well reflected in national legislation. I don’t know if this is still the case, but I remember that in the early 2000s, most enterprise Social Network Analysis tools were not deemed compliant with French legislation … at least in businesses that had taken the trouble to get their CNIL correspondent involved.

But there’s a real risk that we’ll find ourselves with cases of use that are possible in some countries and not in others, with data that can or cannot be used depending on the user’s country or the “localization” of the data… What a headache!

In any case, according to McKinsey, only 18% of businesses have set up global AI governance bodies… so it’s urgent to act.

Conclusion

Les promesses de l’organisation quantifiée sont énormes et ce serait un moyen très puissant de libérer le potentiel des entreprises. Mais sa mise en place généralisée sera rendue compliquée par l’acceptabilité du traitement massif des données des collaborateurs et les législations nationales sur le sujet.

Bottom line

The promises of the quantified organization are enormous, and it would be a very powerful way of unleashing the potential of businesses. But its widespread implementation will be complicated by the acceptability of massive processing of employee data and national legislation on the subject.

Image : quantifiée organization by iQoncept via Shutterstock

Bertrand DUPERRIN
Bertrand DUPERRINhttps://www.duperrin.com/english
Head of People and Business Delivery @Emakina / Former consulting director / Crossroads of people, business and technology / Speaker / Compulsive traveler
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