Commentaries at 37,000 feet

The, conceivably excessively, kind challenge that PAPS extended me is one I take with equal high dose of great pleasure and utmost sense of responsibility. Responsibility for considering it a privilege I can only fervently embrace. But debuting on the realms of intimidatus is not the most uplifting feeling, rather a realization of an indisputable gap in merit when addressing a Society of Post Graduate members.

So, not to disappoint, decided to write about a topic most, if not all, of you are already well-versed or, at the very least, know something about. In my sole defense, often throughout life I have found value in (re)stating the obvious. Sometimes even great value!

Hence, would like to invite you to take a journey and ride the wave of the #1 topic (or subject, for lack of a better word) that, from a corporate perspective, have most amazed me during the last three or four years. Most amazed for its speed; most amazed for its scope and depth of being an absolute and resolute revolution, seting foundations as a clear reality of nowadays corporate world. By now you should know already what am alluding to (I have indeed been reminded before that preserving suspense is not a skill I can claim to master). Am noticeably referring to the complete transformation that arose with the massive (cannot emphasize enough this aspect) deployment of automation, digitalization and management of massive (I warned!) amounts of data (aka big-data) on the normal daily life of a corporation. Across functions and departments, impacting the real job of real people. Not in an ivory tower of some selected few or in a basement of IT geeks (no offense intended; I do like to think I proudly was one during several of my teenage years).

This is not the future, rather a reality today. Not simply a reality, but the reality of the best fit and apt to succeed in the corporate environment.

Astonishing explosions of Bots, RPAs, Scrips, APIs, BPM software, Blockchain, Cloud computing, Artificial Intelligence (AI) and machine learning, even merely the surge of smartphones… you name it! Unquestionably dizzying array of choices and alternatives. And pause for a moment to reflect about the impact of all these have on Productivity: doing much more and better with just more or same level of effort. It is a reality but one which is starting and, am convinced, we have seen only the beginning.

Consider when Romulus (and Remus) set the foundations for more than a thousand years of glory of Rome, or when Gil Eanes discovered a passable route around Cape Bojador, or the dramatic setback of Apollo 1 that paved the way to the famous 11 which presumably allowed for the biggest small step of mankind. Rome back then was far from the prestige of Augustus or Cesar golden age; Albuquerque’s mare clausum supremacy in the Orient was more than 70 years apart; and even acknowledging the superior technological advancement of the Sputniks, the Apollo program was the one to ultimately fulfill the dream of “going to the moon and do other things… before the decade is over”.

The prior are all cases of major break-throughs… the point is that all were just the beginning of something transformational later fulfilled more comprehensively with the latter. Far from proposing to compare in greatness these moments of the Human history with the topic being discussed in these lines; but one must agree, the prior examples are more grand illustrations of my point that a much more adequate parallelism with the Spinning jenny, the Watt steam engine or the Electrical telegraph.

But getting back to more mundane instances. First consider three ideas and sequentially as well three examples. First the ideas, as it always should:

1. Digitalization is not about technology (only). It is a way of organizing a corporation: more flexible, more agile, more capable to react to changes in the marketplace and the competitive landscape. More fit to a world in rapid chance that will not wait for long periods of “study” and evaluation.
2. Remarkable expansion of processing capabilities (either in in-house datacenters using virtual machine capabilities or, most notably, in the cloud) allowing for every and anyone to deal and process massive amounts of information (in the order of millions datapoints a minute, when and if appropriate). This data, if properly processed, becomes extremely powerful making rise to a world where data management must be a core capability of corporations and, emphasize again, a reality across departments, functions and practices.
3. Related with this, the proliferation of easy access / friendly use, customized and flexible software solutions which nonetheless offer astonishingly powerful functionalities and most surprisingly require a relatively low cost and short time to deploy. And more, due to its ease of use and customization by a functional (power) user, provide the ability to be used by a wide spectrum of the user community either internal or external. At the same time, the concept of web, network and collaboration is real and easily accessible to all.

So, extraordinary ease of use, fairly low maintenance and implementation costs and, equally important, clear empowerment of the functional user (vis a vis dependency on IT expertise) to develop tailor-made solutions for daily problems on automation or improvement of processes are now at the reach of any to grasp. Such a variety and profusion of software packages and tool-kit solutions simply didn’t exist, even in the close past.

Now the examples, starting with a classic:

1. Retail stores can predict, with reasonable level of confidence, teenage pregnancy well before parents do. By collecting data on consumption patterns, retailers for long have targeted which coupons more likely will resonate with each consumer. Target store has managed to take it to the next level by date-mining its way to figure out if one is having a baby way long diapers need to start being included on the shopping list. This is achieved by simply cross referencing some shopping patterns, such as pregnancy tests, baby registries, large quantities of unscented lotions, diet supplements and others. This is a classic example of predictive analytics for future shopping purchases (in this case, diapers which constitutes one of the most profitable product lines).
2. Drones equipped with high definition cameras than scan the sky photographing, with the eye of an eagle, infrastructure projects (such as bridges, buildings, etc) or industrial large equipment (such as wind turbines, communication towers, etc) to detect faults or signs of cracks. And what is most remarkable is the technology allowing for the processing of millions of images and wisely discerning which should then be subject to further human detailed examination. It is prime example of exponential processing capabilities in service of artificial intelligence. It is the announced end of Moore’s law, by a significant factor.
3. A more personal example would be when, in 2007, I had the privilege to lead a multi-million dollars project to implement a company-wide strategic planning tool for what was, at the time, the largest US Utility. I was working in the Finance department and this project was a big thing for me: involved several departments within corporate functions, two consulting firms (one for planning and one for execution) and, with great modesty, was by all measures a success for all of the larger team involved. And I did learn a lot.
   The point is, making fast forward into 2017-18 with same scope, bit smaller size organization (but not much smaller): primarily only internal resources, about same year and one sixth to one fifth the cost. Not to mention the flexibility to make improvements and further customizations as needed. No bid deployment, no special internal infrastructure needed, despite the increments of complexity and multidimensional scenario planning features.

All these ultimately provide evidence of substantial and measurable Productivity gains. Two quick data points on a high-level view tone:

1. 50% of the time currently spent in labor related tasks is subject to automation using actual existing technology. Low-level decision making is to become for the most part automated in the close future, I dare to add.
2. 87% of senior global executives assess their organizations do not currently have internal capabilities to properly tackle this opportunity. It is noteworthy, executives don’t say technology does not exist nor is not available. They seem to have a fairly clear perception of possibilities and what is available in the market to capture these productivity gains and market opportunities.

It is clear as well that to excel (or shall I say survive?) on the labor market, a worker needs to have these capabilities. Data mining, ability to process huge amounts of information, familiarly with Bots and other automation tools (from a functional perspective) are already a must. Digitalization is as well a way of thinking, behaving. Nothing to do with being fashionable or trendy, rather flexible and nimble. Several companies make it mandatory already as a of the required skills for an increasingly wider range of functions. “Digital natives” are starting to fill the employment ranks, understandably fiercely. The best comparison I can make is with English overseas, for non-native speakers, some decades ago. It is of most elementary literacy and this, far from being a secret, is still a message worth stressing. This transformation is not expected to come without pain: almost 40% of Americans are in occupation categories that could shrink by 2030 and the largest occupational categories naturally have the highest potential displacement rates. It is of most importance to keep this in mind, for a large variety of reasons (if for nothing else, since society needs to address it as a reality). Although of chief importance, it is not in scope of these present considerations.

Ultimately, digitalization and automation enable significantly higher productivity by applying similar levels of human effort, when outcome is exponentially more substantial or relevant. And more, additional visibility to data supported facts, discipline organizations to become better and more accurate in their estimates. One final, but prime, example of that is the fact that major banks offer, already at present time, corporate treasury home-banking solutions which include treasury forecasting functionalities. By analyzing historical series of past monthly receivables and payables, the software is able to offer predictive analytics on weekly / monthly short-term cash flow. And this out-of-the box and at no extra cost for a medium size organization. Remarkable: even if one cannot rely solely on the output of the tool, clearly helps steering internal forecasts and soon enough, like many other software, the functionality will be able to learn from human corrections to the automated outcome. Treasury and cash management will continue the trend to become more like algorithmic trading, with systems doing the job whereas human intervention is required only for key decisions or systems failure. Fairly simple machine learning, but at its best in terms of practical functionality.

Final word to emphasize, once more, that to capture several productivity improvements, one does not require to be a tech genius. It is available to everyone and all sorts of industries, even the most traditional ones that have prevailed the test of time. This revolution is well beyond technology, it is one of mentalities and possibilities. Not about dreams but about realities and what needs to be done for innovations to have pragmatic and ordinary applications in behalf of the common world.

Since it is evident by now that genius is not required to embrace a topic I stated in advance to be one we all are already knowledgeable of, I finish by requesting the help of Walt Disney’s practical intellect: “the way to get started is to quit talking and start doing”. That’s all folks!

Bernardo Goarmon
Portugal-U.S. Chamber of Commerce