Patrick Schwerdtfeger is a business futurist specializing in technology trends and innovations including big data, artificial intelligence and blockchain. Patrick is the author of the book Anarchy, Inc.: Profiting in a Decentralized World with Artificial Intelligence and Blockchain (2018, Authority Publishing) and a former speaker for Bloomberg TV. He has lectured at various academic institutions including Purdue and Stanford Universities. Innovation is accelerating and business leaders need to learn how to adapt if they wish to survive amidst disruptive innovation. The macro trend from centralized to decentralized power structures is sowing the seeds of anarchy in our society and businesses need to understand these trends in order to thrive in tomorrow’s economy. Patrick’s keynote program on decentralization and anarchy will explain the tribal, individualistic and increasing self-reliance we see growing within our cultures, contributing to movements such as Brexit, Donald Trump, the Tea Party, Black Lives Matter and Occupy Wall Street.
Anarchy is coming! Will you be ready?
Profiting in the new economy will take vision, boldness, and knowledge which is why Anarchy, Inc. is a must-read for the leaders of today … and tomorrow!Jerry Ross
Past speaking clients include:
Speaking destinations include:
Excerpt from “Anarchy, Inc.” (which is being released in February 2018)
Innovation = ƒ(Capabilities)
Innovation is a function of capabilities. As capabilities increase, opportunistic businesses fill the gap with better and more powerful services. YouTube and other video services emerged when data-bandwidth capabilities made online video streaming possible. Countless mobile apps emerged after the iPhone was launched along with the App Store.
This reality—that innovation is a function of capabilities—makes it possible to anticipate the future. The capabilities in data have been remarkably predictable for the past sixty years. If data processing, bandwidth, and storage can be projected into the future, you can start to imagine what new services would logically be introduced along the way.
Moore’s Law predicts that the number of transistors on an integrated circuit doubles approximately every two years. That term was coined in 1965 and it’s been chugging along ever since. Today, we’re nearing the end of the current paradigm of microchip development. Transistors are currently just fourteen nanometers across, smaller than most viruses. You can’t get much smaller than that. But that obstacle will only lead to new paradigms in the future.
Already today, semiconductor companies are exploiting 3D chip designs and using graphic processing units (GPUs) rather than central processing units (CPUs) because they’re better suited to today’s machine learning algorithms. Google has now developed their own chips (called Tensor Processing Units or TPUs) specifically designed to work well with their TensorFlow machine learning framework, resulting in a 15x to 30x performance improvement over traditional GPUs (“In-Datacenter Performance Analysis of a Tensor Processing UnitTM,” Google, Inc., June 26, 2017). The increases in overall chip performance continue to evolve.
Then there’s quantum computing. Although the technology is far from proven, companies including D-Wave, IBM, and Google are pioneering research that would increase processing power by 1,000-fold or more, possibly much more. If quantum computing is ever perfected, it would represent a gigantic leap in capabilities in a single step.
The point is that capabilities continue to increase, so innovation will continue as well. What would be possible in your industry if data processing was 100 times faster than it is today? What about data bandwidth or data storage? Who would use the technology if it became ten times cheaper? These milestones will all be reached in the not-so-distant future.
The role of data in business is increasing all the time. Imagine the most cutting-edge data-driven solutions currently in your industry. Today, those solutions represent a competitive advantage. In five years, every single one of your competitors will be using that same technology. What currently represents a competitive advantage will soon become essential just to survive.
The first people to implement new technology pay the most. It’s new. It’s unproven. People don’t yet know where the return on investment (ROI) will come from. The process involves a lot of trial and error, and that costs money, not to mention the new systems, software development, and implementation costs. It’s all very expensive.
In the early days of the “big data” revolution, 2011 and 2012, the ROI of big data initiatives among Fortune 500 companies was mostly negative. They were losing money. They were investing millions but had yet to identify incremental profits. But all those investments—paying for exploratory work, proofs of concept (POCs), pilot programs, and new system implementations—yielded better results in the years that followed.
Behavioral targeting is all about understanding customers better. What are their lifestyles like? Besides using your product or service, what else do they like to do? By building a multi-dimensional customer profile, it becomes easier to target them in other settings, but that involves a huge amount of data.
The development of these targeted marketing strategies allowed endless niche markets to be accessed and exploited by data-driven marketers, yielding profits difficult to find using traditional methods. Imagine targeting fly-fishing enthusiasts or victims of identity theft through traditional channels. Both are easy to target online.
On top of that, behavioral targeting made it easier to avoid marketing to the wrong people, reducing wasted advertising dollars. Data-driven online marketing delivered measurable ROI. It was an early winner in the big data revolution.
Google AdWords is the perfect example. Small businesses can leverage enormous data to target potential customers at an affordable clip. They can show tailored ads to demographically specific users in geographically specific locations during specific hours when those users search for specific keyword phrases. The targeting is incredible. This would never have been possible before Google’s AdWords advertising platform. Today, it’s readily available.
Predictive maintenance is about anticipating maintenance requirements before machines actually break. This was another area for quick ROI. When machines break down, it can grind the entire production line to a halt. That costs a lot of money. By avoiding unexpected work stoppages, predictive maintenance was an early winner for big data initiatives.
Logistics was a third area for positive ROI. By optimizing delivery routes, monitoring traffic conditions, and identifying and selling excess capacity, companies were able to squeeze more profit from regular distribution operations.
This can be seen on the airlines. It used to be common to have empty seats on flights. It was rare to have every seat taken. Not anymore! The airlines have become incredibly good at filling their flights. How are they doing it? They’re monitoring search activity, ticket sales, and travel trends. They’re using dynamic pricing models and adjusting flight schedules. It’s all driven by data.
Businesses are using data to optimize the marketing and delivery of their products and services. The situation is different when the data is itself the product. Credit rating agencies such as TransUnion, Equifax, and Experian come to mind. Their product is the data, so they were among the first to leverage big data technologies. But for all other companies who were analyzing data to look for new efficiencies, success stories were slower to emerge.
Innovation propagates through use cases and success stories. As the early adopters try new things and eventually identify positive ROI, everyone else follows suit and implements similar systems in their own businesses. The competitive process mandates it. If your competitor exploits new technologies with positive ROI, you have to do the same or lose out in the marketplace.
Before long, enterprise software providers incorporate successful use cases into their platforms. Customer relationship management (CRM) platforms including Salesforce and Zoho contributed to the marketing applications, and enterprise resource planning (ERP) platforms supported the operations applications.
CRM and ERP software platforms then deliver these new capabilities to small and medium-sized enterprises (SME). This is the point when new technologies propagate from the industry giants down to the mid-market players. Of course, as the cost of new technologies come down, so do the associated benefits within the marketplace.
If you’re the only one using a new technology in your industry, you enjoy a competitive advantage: a monopolistic position, either attracting new customers or earning outsized margins. But as the technology propagates and all of your competitors start using it too, the advantage erodes and eventually shifts to a disadvantage for those who fail to adopt it themselves.
Think about your industry. You have large competitors—dominant players in your industry—and small competitors. You also have high gross-profit competitors and low gross-profit competitors. Generally speaking, the large and/or high gross-profit competitors will embrace new technologies first. They’re the only ones who can afford it. They enjoy the “first-mover advantage,” but they pay a high price for it.
Over time, their trial-and-error investments make it cheaper for other companies to follow suit. Adoption works its way down the gross profit ranking until it reaches the small and/or low gross-profit commodity producers.
Keep in mind that gross profit comes from two different business strategies. Some companies have impressive margins built into their pricing strategy. Apple is a great example. They have impressive profit margins.
Other companies sell huge quantities of goods or services at lower margins, but the sheer volume results in significant gross profit anyway. Amazon, Walmart, and Costco are good examples. Their margins are thin, but they sell tons of stuff. This is common in China as well. They want volume! With higher quantities, lower margins still generate sufficient gross profit to innovate early.
For the old industry stalwarts, the greatest obstacle to innovation is often their old legacy systems. This can relegate high gross-profit companies to the bottom of the innovation list. You can’t endlessly apply patches on top of outdated legacy software. Eventually, you have to replace the whole system, and that’s a daunting task.
Innovation has to come from the top. C-level executives need to drive change if the big investments are to materialize. Legacy systems might be holding the process back. If so, someone with authority has to pull the trigger. Someone has to make change happen. Make note of the companies that have that leadership, and those that do not.
This is a valuable tool to anticipate when investments need to be made. Create a list of your competitors, sorted by gross profit. Think about the newest technologies in your field and who’s exploiting them at this stage. In all likelihood, the high gross-profit and progressive players are leading the charge. Previous technologies were probably embraced in the same order, and that order will likely continue into the future.
Having a list of industry participants in order of innovation adoption is extremely useful. Remember, the first ones to embrace new technology pay the most. Costs drop over time. Everybody talks about the first-mover advantage, but there are substantial benefits to the second-mover advantage as well. By monitoring new technology adoption in your industry, you can plan when to invest in new technologies and minimize the cost required.