AI to Eliminate Barriers

If you are an Apple fan or enthusiast, you probably watched the Apple live event on 9/7/22. As one executive introduced the crash detection feature on the Apple watch, they mentioned that a Machine Learning algorithm uses data from multiple sensors to detect a crash. A key point to note is that most of these sensors existed in previous versions of the Apple watch. Auto accident SOS feature is something that automobile companies have offered for a long time. When Steve Jobs walked on the stage with the first iPhone, no one in the taxi industry would have imagined that this small device could disrupt their industry. Smartwatches have chipped the medical devices industry in the last few years. And now, this feature is a foray into the market of a product that was long considered a forte of automobile manufacturers and their technology partners. A key question many may have is - What allows companies like Apple to keep transcending into territories of others with ease recently?

Technology is Making Barriers to Entry Obsolete in Technology-Based Products Markets

Barriers to Entry, in simple terms, mean that the investment and resources that a new player needs to enter a market are high enough to keep most prospects out. So what aspects have led to significant lowering or, most times, almost eliminating barriers to entry regarding technology-based products? There are three key ones at a high level:

• Commoditization of hardware
• Increase in computing power
• Power of algorithms

Commoditization of hardware: Rapid advancement in technology has led to the development of sophisticated hardware, like the sensors used in our devices, as well as in the industry, but it has also led to the commoditization of such hardware. You can buy a smartwatch with sophisticated features enabled by many sensors on many Chinese e-commerce websites for 1/10th the price of watches from name brands. These cheap watches obviously will not last as long as the hardware on the products from top-end manufacturers. Still, the key here is- someone can easily replicate the technology and hardware. But does that mean the barriers to entry have been lowered by hardware alone? No! The barrier to entry, in this case for someone offering an SOS feature for automobile crashes, is the ability to detect a crash using the data from sensors. That is where the companies that have long offered these products in collaboration with automobile companies, and many times, automobile companies themselves, have developed specialization. The ability to help the onboard computer decipher the data from multiple sensors to detect the crash was the expertise. And the effort it took to develop this expertise was the barrier to entry in this specific case. So while the commoditization of hardware is one aspect, it is not the primary driver behind lowering barriers to entry. The exponential increase in computing power: As a teenager, I frequently heard that the moon landing was an astonishing feat since the computers used in the landing craft were very close to the most advanced calculators available today in terms of computing power. While I never validated that the statement was true, computing power has made tremendous progress recently. The IoT revolution, TinyML, and AIoT have all been made possible because, in simple terms, you can pack more and more computing power on tiny devices. Again, a powerful enabler, but that still does not replace the core expertise that acts as a barrier to entry in this example- the "learning" accumulated from years of data to predict crashes accurately. The Power of Algorithms: The power of AI and ML algorithms is the real enabler behind this ability to make the "experience and expertise" barrier to entry obsolete. Advances in hardware and computing power mean that the data capturing part is easy and almost commoditized. And as I have consistently emphasized, it is the ability to put the data being captured by innovative use of AI and ML solutions that is the real competitive differentiator. Here, Apple executives would have brainstormed more creative ways to leverage the data that the watch or phone already captures. They can train algorithms in days to "learn" what we have learned over the years, provided the data used for training is right. And that is why, going back to the cheap smartwatches example above, these cheap watches do a horrible job of detection and prediction. Because while they can replicate the hardware and pack the same amount of computing power on their devices, the key here is the "learning" ability, the key here is the AI algorithm.

What Does This Mean for SAPinsiders?

Technologies like AI and ML algorithms, and a plethora of other emerging technologies, allow organizations to develop products and solutions beyond what they consider as their strength. Companies like Apple have an advantage in that they also have a massive user base. That also makes them potential disruptors for many industries. With their massive outreach that places a technology platform device in the hands and homes of millions of users, they can keep looking for innovative ways to disrupt industries that may not be considered within the realm of Apple's core expertise. If you want to be a digital disruptor beyond your industry, there are a few elements you should start exploring/strategizing about: Evaluate transferrable core competencies: Understand your core competencies and build a list of transferrable competencies. You can use various frameworks to evaluate your existing competencies and determine which competencies are agnostic to your core industry, and hence transferrable . This will be the starting point to planning the products, services, and innovations you can build leveraging these competencies. Build a digital innovation center of expertise: Irrespective of which industry you belong to, you must have an internal digital innovation center of expertise if you have the resources. It is important to have this CoE outside of your IT organizations to maintain the focus and culture of this team. This team will experiment with formulating products and services that combine your transferrable competencies with digital tools and technologies. This team will also explore how to use existing investments, like SAP BTP or hyperscaler infrastructure, to help speed up the creation of these products and services. Build partnerships: Because of the rapid pace of the evolution of technology, your CoE can not keep pace with innovation by itself. Partnerships are critical, specifically with large technology vendors that you currently leverage. These large technology companies invest extensively in technology R&D and are also looking to partner with organizations to test their new capabilities in the real world. Establishing these partnerships will help you stay ahead of the technology curve.