AI / ML – Past, Present & Future – Part 6

 

Just a recap, we learnt in my last update, the different ways to harness the new age machine to enhance market competitiveness of your products and services. In this excerpt we are going to dwell upon the different ways to harness the new age machine in by and large the innovation quotient that we need to invest upon.

As we have seen throughout this series of posts, the innovation related to the intelligent systems and digital economy is both a catalyst for and an outcome that will allow your organization to discover opportunities that were never before visible or addressable. Innovation being the center stone, it can’t be a side project which is nice-to-have but its central to remaining relevant in the great digital build-out that we are experiencing and of course lies ahead of us. While machines will do more and more of our work, the process of innovation will allow us to discover entirely new things to do that are impossible to imagine and hard to predict but they will be at the core of what we do in the future.

Today, a new economy is emerging with a flurry of job categories that even a few years ago would have been hard to predict; social medial consultants, search engine optimizers, full stack engineers, content curators, and chief happiness officers. These all jobs that the tech economy’s equivalent to the Charles Babbages of the early starters would have imagined. AI is changing our world already, but in reality we have only begun to scratch the surface of where it will take us over the next 20, 50, 100 years. Your job is to imagine the new forms of value you can create with the new machines of the new revolution. Institutionalizing the role and importance of being open to the fruits of innovation is a hugely important role that you as a leader of the future need to play. The openness to innovation is not just a job of the company’s formal R&D department. It has to be a culture of innovation that each and every one of you should exhibit.

We have learned right through this series that the new machine will be your platform of innovation. Once you are instrumenting, automating, tracking and analyzing the core operations of your business an applying machine learning, innovation opportunities will be consistently unearthed. Innovation is thus a rich term with many different attributes and applications. It can be applied to many areas including some I have listed below.

With product innovation, team will gain continual insight as to how your products are being used, also what customer frustrations points exist, and where the obvious areas for improvement exist. These inputs definitely can’t be gauged without a thorough AI system in place. Once you have automated, instrumented and enhanced your company’s activities, the associated AI engines can be applied to innovation. The team will get greatly enhanced by the application of the new machine, primarily because it radically accelerates the scale and speed of the innovation process. As such, when the new machine is soon widely adopted, the rate of human progress in the 21st century (as defined by the cumulative growth of human knowledge and he pace of the innovation) will be at least 1000 times the average rate of the 20th century. Obviously the general factors affecting innovation in the organization (opinions, ideas, emotions, organization’s inertia etc) can bring down above prediction to may be by two orders of magnitude. Still the innovation quotient of 10 times is way higher than traditional R&D ways.

One of the core principle in these posts is that machines can do many things but that practical application should be focused on specific business processes and customer experiences. When you are making discovery investments, start at the process and experience level and imagine how the process can be restructured and reinvented with digital.

Discovery can be a risk. Invest too much in the wrong ideas and you go broke. Wait for somebody else to do it and you can miss the market opportunity of a lifetime. So what’s the best practice for bringing about this new form of innovation? We find too many managers looking for the new “the next great breakthrough” but that doesn’t work. The opposite approach is to ask how the new machine adds the most value – that is, by looking for continuous, incremental improvements or looking to hit singles on a consistent basis. This primarily caters to “change for better” but is implemented as small, continuous improvements that in time have a large impact. Your goal should be to become a Know-It-All business via instrumentation, sensors, big data and analytics. In the real world, organizations should establish a portfolio of initiatives focused on discovery, with a clear life cycle methodology that manages these initiates from inception through o ultimate success or failure. Central to their generative acts will be the belief that something better can be created. The true core of discovery is, after all, hope.

Obviously, please don’t forget that with the Gods come the devils as well. While most part of my posts are ushering in that AI is an age of miracles and wonder of technological marvels but in the hindsight we should also see a world of robots, more powerful human like machines taking over. Thus strike a balance.

In these posts, we have argued that the information technology innovations and investments of the past 4 decades are merely a precursor to the next waves of digitization, which will have truly revolutionary impacts on every aspect of work, society, and life.

As the last S-Curve’s growth rate continues its inexorable journey south, the new S-curve is gather momentum, and so are the companies  poised to lead this new charge. These are the companies that have learned how to master the 3 Ms, how to align the new raw materials of the digital age (data), the new machines (intelligent systems), and the new models (business models that optimize the monetization of data-based personalization).These are the companies that understand how to build and operate a know-it-all-business, that understand that intelligent machines aren’t to be feared but embraced and harnessed, and that are energized by the unwritten future rather than just trying to hang onto the glories of the past.

Below are the few mandatory steps that any organization should embark upon and leaders from organizations should help them implement.

The companies that are getting ahead are the ones acting on these ideas. Some companies we work with emphasizes one ‘play’ over another, while others recognize the holistic connection between all of the plays, automation enables enhancement, discovery uncovers how to achieve excessiveness, and so on. All of them, however, understand the need to act now, to not wait for more certain times ahead, more clarity over exactly what AI is, and what it will become. All of them recognize that the rise of the machine intelligence is the ultimate game changer we face today. All of them know that inaction will result in irrelevance. All of them know that fortune favors the brave and punishes the timid.

This is the last blog in the series on AI/ML and the related space. Hope you all enjoyed reading through the posts. Stay tuned while I come back with yet another series on a technology topic.

Authored by Venugopala krishna Kotipalli

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