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To deliver value to customers, companies perform a wide range of operational activities, which generally take place in a standard set of functional units such as R&D, innovation, design, engineering, manufacturing, operations, procurement, logistics, marketing, sales, servicing, IT, human resources, and finance.

The vast majority of business processes today are operated by human beings. But in the not-very-distant future, almost all business processes will be heavily augmented, or even operated, by smart machines, many of which will be running AI algorithms. Machines can do much of what humans do, although they are best suited to singular tasks rather than multifunction jobs, and they can often perform faster and more efficiently. Smart machines can sense (with data sensors), remember (with data lakes), make decisions (with data analytics), and take action (using robots, drones and autonomous vehicles). Most important, smart machines learn steadily (with artificial intelligence). More and more of these functions can be accomplished robustly at low marginal cost.

But smart machines and their AI algorithms must be fuelled by data, so datafication is an essential strategic piece in any value-delivery model. The amount of data in our digital world is exploding so exponentially that is triggering the reinvention of traditional delivery models by means of datafying every operational activity, from innovation to marketing. Data-centric organizations are reinventing the traditional delivery model by datafying every activity and making artificial intelligence and smart machines work symbiotically with humans.

Datafication vs. Digitalization 

Historically, the concept of ‘data’ refers to a description of something in a numerically quantified format and it has always been closely related with humankind’s intrinsic need for evolution. It’s about the idea that the world can be addressed in a technological way, through data. So, data are not just there to be gathered, first the world has to be imagined as data.

Therefore, the datafication of any phenomenon means putting it in a quantified format so it can be recorded, monitored, modelled, analyzed, understood and improved. This is the reason why humans have been datafying their reality and analyzing data for millennia. Since biblical times, the ability to datafy anything is one of the lines of demarcation between primitive and advanced societies. Written language, basic counting and the measurement of length and weight were among the oldest datafication tools of early civilizations.

But despite the continuous breakthroughs in mathematics, physics, accounting and in many other fields of science and technology, processing data in analog formats has being enormously resource intensive, costly and time consuming. Before computers, experimentation was usually limited to testing a small number of hypotheses, so innovating, learning and evolving required seemingly infinite patience, or at least a life-long dedication.

The quantum leap towards big datafication and experimentation at scale came with the advent of digitalization, in the form of converting analog information into the zeros and ones of binary code so computers can handle it. The arrival of computers brought digital measuring and storage devices that made the datafication of anything at least ten times better, easier, faster and cheaper. This exponential advance has also improved the time-to-market and time-to-value dramatically, since data collection and analysis that once took months or years can now be done in minutes, seconds, or even less.

So datafication in the digital era is changing the game of business. Things that could never be recorded, monitored, analysed, optimized and automized before are becoming datafied. Harnessing vast quantities of data opens the door to a new scale of innovating, learning and evolving.

Making AI work symbiotically with humans

AI is all around us because this transformation is playing out in every single business process. People interact with AI systems many times every day without being aware of it. If it all suddenly disappeared they would notice, but AI’s omnipresence has become unremarkable, like air. Alibaba efficiently matches buyers and sellers in both on-line channels and brick-and-mortar stores. Uber intelligently matches drivers and users to provide more convenient, faster, and cheaper rides. Netflix learns from each user’s viewing habits, matches this with whats it’s learned from millions of other users, makes recommendations, and then uses AI-based learning to guide its decisions about what original movies or series to produce in order to appeal to most users. Facebook’s learns from your posts and likes and then populates your timeline with feeds and ads you’ll probably want to see. These experiences are driven by the AI capability of making data work for the business through smart algorithms that interact with people and machines in a natural way, optimizing decision-making chains and automating the execution of activities. AI seems to be the next frontier and the days of companies that won’t adopt it are probably numbered.

After decades of false starts and unfulfilled expectations, AI has now gone mainstream, and could be considered to be the electricity of twentieth-first century. During the past century, almost all daily activities were being completely reinvented by electricity, and society changed in ways no one could have anticipated. In 2007, as we started to move more of our lives onto digital formats, AI could finally make an impact that rivals that of electricity in the early 1900s.  As smart machines and AI take over more of any given company’s operations, the role of humans inevitably will change. Many new jobs will be created for people to design augmented and automated processes and improve them over time. Indeed, over the medium to longer term, we expect to see a fundamental shift in the nature of work, from processes operated by humans to processes designed and optimized by humans.

AI is becoming as transformational as the internet was twenty years ago. In the near future, it’s going to be at the core of business operations. But perhaps, the most common barrier to envisioning the potential of AI is to focus this exponential technology only on automation rather than augmentation. AI can be effectively used to assist and augment humans, not replace them. Companies that view intelligent machines merely as a cost-cutting tool are likely to push them into all the wrong places. In the near future many aspects of our daily life will be augmented by computer systems that today are the sole purview of human judgment. This includes not just driving, but even more complex tasks. After all, Amazon can recommend the ideal book, Google can rank the most relevant websites, and Facebook knows our likes.

So, Value Hackers are reimagining value-delivery models wherein humans improve the performance of AI exponentially and, in turn, smart machines give humans superpowers at scale. Up until a few years ago, the focus was on using machines to automate routine activities in a value chain. Now, scalable experimentation relies on moving from solid and mechanic operations to fluid and living organizations, where symbiotic teams of augmented humans and smart machines are continuously innovating, learning and evolving.

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