How To Help Businesses 'Sweat Their Assets' Leveraging A.I.
Kodak: From Blue Chip to Bankrupt [Source: https://www.youtube.com/watch?v=wwfwr8eYP50]
Ah, the benefits of hindsight. Let me begin this blog by giving you three examples of companies that reacted differently to technology disruption and, in some cases, paid the price for it.
Imagine if you could have gone back in time to warn the executives of Eastman Kodak Co. that they might be better off leaving the photography business altogether, earlier than they did. They wouldn't have taken you seriously - even after numerous strategies failed to save the company's traditional line of business. The Eastman Kodak brand was synonymous with photography. The truth is that when a business is built on a legacy technology that is categorically different from the market's new standard, even perfect foresight (in this case, the demise of film or CDs) would not have solved the core problem that digital replacement is fundamentally less profitable.
Then there are the success stories. In the late 1990s, PolyGram was one of the world's top record labels, with a roster boasting stars from Bob Marley and U2 to many of the world's top classical artists. In 1998, however, Cornelis Boonstra, CEO of PolyGram's Dutch parent, Koninklijke Philips, flew to New York, met with the investment bank Goldman Sachs, and arranged to sell PolyGram to Seagram for $10.6 billion. Why? Because Boonstra had come across research showing that consumers were using the new recordable CD-ROM technology (which Philips co-invented) largely for one purpose: to copy music. In hindsight, this is a good example of how, in the early stages of disruption, demand begins to 'purify' and lose the distortions imposed on it by businesses.
Another example of how a company can effectively cut through the chatter and distortions of legacy technology in order to embrace rapid change is General Electric. The conglomerate's former CEO, Jack Welch, made it known during his tenure that if a GE subsidiary was not in the top three of businesses in that industry, the company would sell it off so that it could better focus on those businesses with which it did dominate the market. Welch did not allow a sense of legacy sentimentality to get in the way of his razor-sharp strategy and business acumen. According to recent reports, AirBnB, by the end of this year, will be five times larger than the largest hotel chain in the world. Imagine the speed of change. Two years ago, how many of us knew about the company?
Today, in the new digital world, change is at its slowest ever in the context of what awaits the future. The three business examples I have just mentioned are proof that enterprises need to look at adapting to change rapidly. Long gone are the opportunities for enterprises to conduct a multi-year transformation program to adapt to change. In the precious time it takes to run the program, both the business model and the technology would have dramatically changed.
In my mind, the following four core aspects will drive digital transformation within an enterprise and across its business partners:
- Frictionless experience: It is about providing a customer experience that is fluid, intuitive and delightful.
- Ecosystem: No company can succeed in isolation. Today's business is about leveraging a network economy to maximize benefits for all partners in the ecosystem.
- Insights: Business intelligence is like driving a car by looking at the rear view mirror. How could predictive insights steer tomorrow's business and make them more agile and adaptive?
- Automation: Driving cost efficiency and, at the same time, delivering consistency and reliability within and across the organizations in their everyday processes.
In this blog I will focus on the core - Automation - which is disrupting the way enterprise business processes will run in the future. With significant advances in computing technology and associated affordability, Artificial Intelligence (A.I.) capabilities like machine learning as well as others, will fundamentally change more and more business processes to help enterprises become truly digital and touchless (using automation).
Here is a quick example snapshot of what things look like now in an enterprise: Applications have become more complex in the context of an enterprise. Under the hood of a single business process there might be many applications running on different Virtual Machines, operating systems, middleware, and storage technologies. Add in many switches, multiplied by the accelerating speed of changes under the DevOps paradigm, and the issue the firm faces is that context is always lost between the various layers. So finally the ability to solve problems is limited to how quickly the IT development and IT support teams can process large amount of information, both for problem diagnostics and problem prediction.
These kinds of rapid capabilities can only come from an A.I. platform that is tailored for enterprises. The A.I. capability set will not only change the way the enterprise operates, but will enable the enterprise to transform but leverage constantly curated knowledge of the enterprise, all while taking automated actions.
This is where the A.I. platform tailored for enterprises will play a crucial role. At the core, machine learning and A.I. rely on two key ingredients: advanced algorithms and data sets to train those algorithms. Novel algorithms are increasingly making their way into the public domain in the form of open-source libraries. The key differentiator for companies (including startups) working in this space and ultimately their long-term competitive advantage is access to proprietary data sets and use cases.
In an enterprise scenario, proprietary data sources that are essential to train next-generation machine learning models are easier to amass in the enterprise space rather than the consumer realm. The most persistent and longest running example of this scenario is the security space, where many start-ups have built lists of exploits and attacks. Because these attacks evolve over time, no single company owns a monopoly on the exploit database. Thus, an A.I. platform acting as a 'system of systems' will help enterprises reinvent themselves for a challenging and dynamic future, to ensure enterprises are able to 'sweat their assets' in the most contextual way.
True, the exploit database is not dominated by any one organization. Entrepreneurs, did you just read the previous sentence? Fertile territory for the right innovator...