· the fast track towards algorithmization ·

Fighting the hype by understanding the basics.

Someone had to say this...

Zoom-out, it all makes sense: vol. 4

About the controversial lack of impact from Digital

Vol. 1

About the controversial lack of impact from Digital

June, 3rd, 2022

The markets haven't been particularly generous with those companies that have prioritized Digitalization in their business strategies and, furthermore, some regulators are already asking incumbents to stop using the large amounts of money spent on the field in order to merely explore and learn. As such, almost everyone we meet, from central banks and large hedge funds to mid-caps and SMEs, ends up asking us why Digital has not truly delivered yet.

Well, the question is tricky and the answer certainly polemic but, to me, here’s why. A rather long one.

Let me this time start with a spoiler: Digital is not a target.

It is not a target, it is just a first step - a necessary but not sufficient condition to unlock impact. Impact requires fine detail analysis. It requires analyzing and orchestrating legacies in a coherent manner with the strategy of the company. It requires change resistance management, gamification and communication all at once. And more, far more.

So, the question is, why can't all that be embedded in the Digital layer in one go? Talent. The average person hired in Digital is not suited for all the above tasks. It is rather suited for storytelling, marketing, some data science use cases, etc. And that's ok. In fact, she often can do these tasks better, with more interest than those profiles capable to bring the hardcore impact to the company. The latter should be keener on designing the right tech architectures (beyond standard bigtech slides), on finding the right angle to tackle the problems (beyond science applied)... than going to events here and there (which is also relevant).

Being that the case I always explain that the impact unlock, belongs to the Algorithmization of the companies - a step further to Digitalization.

In order to have a clearer reasoning and in order to avoid the (often overwhelming) terms of Python and Mongo and Spark and whatnot, you have to look at it as if your company started onboarding Excel 2.0. Because, that's pretty much it - a comparable jump to the one that decades ago went from physical notebooks to Excel. At first it looks great - you can now do tones of things with it upon your data. And you get exited - largely pumped, by the way, by all the marketing spread through Forbes, HBR, strat consultancies' decks, etc.

But then, in an ex-post second iteration, you realize that there is a problem - none of those sources tell you who is going to do what, how, what for...

There is a large number of external consultancies that know how to onboard Excel at your company. They have partnerships with Microsoft and they would come and do an ad-hoc installation for you. But they wouldn't know what to do with it. Because it is not their nature to deal with such challenge any further.

Unlocking impact from your data is a difficult task that requires tones of judgement and creativity in hardcore transformation (tech + AI + economics + vertical).

And who has judgement and creativity in hardcore if those consultancies don't? Well, at this point, since you can't figure it out, you go to yet another consultant that sells you a brand new pack of transformation. Now, it proposes you to hire a Head of Excel, to create a unit of Excel Innovation and to hire a factory of Excel Formulas Analysts. Interestingly enough, the Power Point deck is not only expensive but pretty much the same solution in spite of your size - whether you are large or mid cap. And, not surprisingly, it is precisely here where the end of your transformation quest lies.

Because there is a major problem in transformation: you only have one shot to do it right.

When you do things wrong in Power Point, well... you future is not that compromised. But transformation leverages tech and protocols/culture. And you should never mess with those. When you do things in tech you build a legacy utterly expensive to remove in the future. Not to mention that if you think that change resistance is an issue you can't imagine the change-of-the-change resistance. We are living in an extremely interesting business era. If you do nothing, you are done. If you reach Digital, you can keep playing - you have the basics. If you reach Algorithmization, you can take it all - maximal efficiency. But, can your Digital team take the lead onto Algorithmization?

So, recapping, at this stage you had a tech legacy around your ad-hoc Excel 2.0 and a culture legacy around the Game Theory of a bunch of people who includes externals trying to sell you more projects and internals trying to survive, basically, creating new Excel formulas. At this point, one has to understand four things about these people:

1. They may lack creativity: it is a must to be able to pose the problem in a creative way in order to avoid wasting resource. It is the only way to increase the hit/miss ratio when facing new challenges. But who can blame them? Creativity is one of the scarcest skills.

2. They probably lack technical skills: again, can you blame the persons you have hired? Can you blame yourself? The Power Point that you were sold didn't disclose that juniors are being educated in a (suboptimal) hurry because there is an excess of demand and, as such, they currently are more a breed of libraries-users than deep experts on applied science. Can you at least blame the senior managers? Well, you should. But the Power Point didn't say either that the seniors in this field are in fact unicorns. There are far more startups valued over $1 billion than senior experts in hardcore transformation. So, the ones that took the Digital job often feel they are not fit for the position. Because they are not. But, still, can they be blamed? I don't think so. The challenge is far too much for any person without years of very accurate training. They surely worked their hardest in their positions up to their maximum. Only at that very frontier, their maximum, can you start blaming someone internal at last: if they are knowingly trying to bite more than they can chew, hacking the company (agency costs) leveraging the overall Digital lack of judgement (and a worryingly scarce number of transformation experts in the boards of the companies, chasing downstream), they should indeed start being blamed. And there are already a few of these that I know already trespassing that frontier. I'll explain below how to avoid this risky fact.

3. They surely lack the M in ML (Machine Learning): even when you are very good at the previous two, you cannot transform your company if there is not an appropriate machine, a software that orchestrates legacies and new developments (RPA, data science...) along with new protocols for all experts involved in a transparent and green way (don't forget the green part!) so that everything you do stays in the long run and can be exploited by any department (synergies are the trick in tech). For those of you who are geeks, here is another way to explain all above: Digital focuses on bringing together the data required to apply ML and start with use-cases of the L in ML - i.e. eloquent low-hanging-fruits, wrongly called quick wins, of data science. But when you are an ML expert you soon realize that you lack the M to go further with your improvements. No one is paying enough attention to the provision of an optimal M for you to move forward - hint: Jupyter labs of Python notebooks are not quite it. And well, the same happens in transformation: no one is looking at the orchestrating machine that allows you mixing models with experts, with regulation, with reporting, with cybersecurity, with the company's future strategy...

4. They often lack previous experience in the vertical: even when you have the creativity, the tech skills and even the M, if you don't have previous experience in the field that you are transforming you have a major burden: a massive dependence on your expert colleagues. If they simply don't have the time or they are not willing to collaborate, to find new ways for them to augment the machine, to embrace evolution... you can't use the only lever that unlocks the impact: coopetition. And you can only lose in the game of change resistance.

And because all theses challenges erode anyone involved in the transformation process, the equilibrium moves quickly onto the status quo.

Which means, negligible impact. Hence, the aforementioned wonder of our clients.

Everyone is happy again. There are a few low-hanging-fruits in this and that department and the only major change, overall, is that now there are new people in the house talking about the business from an often self-marketing point of view. But nothing really happens. Interestingly enough, of those new people only the seniors don't change. The juniors keep leaving at a worrying pace. But, hey, you simply have a laugh at it, complain a little and that's that - you don't need to change yourself so, good news. Because you are still projecting the momentum of perceived (lately proved non-real at a number of incumbents) employment stability.

But then, as time goes by - and errors in management strategy typically take at least 5 years to recover from, so there's plenty of time to witness the error - you realize that they may not be good news after all. And you start thinking of the benefits of hardcore transformation. Because there are benefits.

Once you have gone through Digital, you have to try to reach the real target: to become an algorithm towards maximal efficiency.

An algorithm where humans and machines chase each other towards a healthy equilibrium. An on-platform-protocol that helps govern the company more accurately by orchestrating how (almost) everything is done.

Because in the Digital world, it all boils down to synergies and efficiencies. And only those that take the right steps on that direction will be the survivors of this movement - which, as anything underpinned by tech, will only give room to a handful of players.

And then you realize how important it is to get the ad-hoc Excel 2.0 right. And you wonder, for the first time, whether the vitae of those who created the decks had the track record in hardcore judgement and credibility required to claim the right to win in this arena.

You didn't do the due diligence on the individuals and you regret.

You simply believed in a brand to follow your future path. But, I'm afraid, now you know this challenge goes well beyond brands. It is all about human unicorns.

And you start wondering... What will happen with the company if we do not adapt? Is it too late to try to change and make it right? How many legacies do we have now in terms of politics, games, tech...? Are they killers?

Well, I don't know. I would need to see it case by case - there are very capable digital managers out there. But I can tell you something: recovering will be utterly expensive. And aggressive. Hence, the importance of:

1. Doing it right from the very start: seek the high hanging fruits to be able to exploit synergies downstream - guess why I am a fan of entering into business with mid-caps and SMEs with much lighter legacies than large incumbents or none. Underdogs. Especially those funded by funds - whether venture capital or, very interestingly, private equity. They should be able to take over many positions of the top tier one positions in the coming years.

2. When 1) is not possible, distinguishing between Digitalization and Algorithmization. You leave the as-is Digitalization to the ones who currently own it. But you assign the Digitalization growth to a new breed, more native ready to deliver the strategic tech right - the high hanging fruits. Both groups should be able to co-live, eventually. The former will leverage big names to avoid the risk of making unique decisions while the latter will leverage open source to squeeze the budget and exploit individual talent.

Hereon, simply recall the importance of having a flexible technology (ad-hoc, open source, cross-departments...), optimizing business protocols upon it, augmenting the machines with experts, minimizing the science to achieve a certain target (small data matters too, pose the problem right...), embrace employees rotation (that is here to stay) in your protocols, etc.

This one was long but easy. Good for you for getting here. Next one is short - promised!

Probably you have realized by now that the journey is massive. And so is the impact to be unlocked thereon.

Thanks for reading!

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