Machine learning is the driver and enabling technology for several emerging technologies. Machine learning is a back-end technology for a front end consisting of augmented reality, virtual reality, chatbots, digital assistants, sensors, data visualizations, prescriptive analytics, and many others. It’s the driver of an ecosystem but the question facing business is, when is it time to act? Is this ecosystem ready now, in three years, in five years?
Innovative early adopters like Google, Facebook, Amazon, and startups are a good indicator that a technology is worth monitoring. They have been building machine learning, augmented reality, virtual reality, and internet of things devices for over five years. When products start hitting the market, it’s time to assess the potential impact of these technologies on the market and business. This is where most companies find themselves now; in the assess and observe phase. At some point, they know they must invest real money and resources into these new technologies but there is uncertainty about timing.
In my last post, I talked to vendors and machine learning strategists about how to explain the new software paradigm, driven by machine learning, to their clients. Once they kick off the conversation, the burden shifts to senior leaders within those client companies. They’re challenged with making the business case for action on emerging, highly complex, volatile technologies. I this post I want to bridge the gap between conference enthusiasm and real world decision making.
The leading indicators of new technology adoption & maturity.
There are significant indications that 2017 is when these technologies move from the abstract to impacting multiple markets.
· Intel’s new focus on machine learning and the IoT
· IBM, Verizon, and Microsoft making significant strategic moves to position themselves as partners and enablers of these new technologies
· Investments in new technology capabilities by consulting companies: Infosys, Deloitte, Accenture, etc.
· Non-tech, Fortune 100 companies are investing in new technology capabilities: Ford, Disney, Wal-Mart, P&G.
· A consolidation & shakeout phase in the new tech market.
Intel’s AI Day last November was their way of saying to the industry, ‘We’re putting a lot of our eggs into the new technology basket in 2017.’ That’s a significant change in strategy for them. They made a major acquisition, Nervana, to fast track their machine learning capabilities. They have optimized their latest chips for machine learning and deep learning libraries. They have made the case that quantum computing architectures are most practical using existing silicon technologies, like theirs. Their investment in putting on a drone show at the Super Bowl and Slam Dunk Contest are the most recent examples of a full court press to position their chips at the center of the new technology movement. All these steps have happened in the last six months. It reads like a significant indicator that new technologies are ready to make an impact.
IBM, Microsoft, and Verizon are all putting the framework in place for new technologies. Like Intel, these businesses are enablers for one or more of the new technologies. IBM has been working on the Watson cognitive computing initiative for several years. It’s been applied successfully in the medical field and they have recently branched out into other markets. They’re also applying machine learning to every aspect of their core product lines. Microsoft has several initiatives from Azure for machine learning to R-Studio to open source contributions that highlight their commitment to facilitating new technologies. Verizon’s 5G initiative is aimed at providing mobile connectivity that enables the IoT and advanced machine learning capabilities on these devices.
The combination of established, non-tech Fortune 100 companies investing in new technologies and large consulting companies ramping up capabilities is a potent indicator that new tech is starting to impact diverse markets. Ford, Disney, Wal-Mart, and P&G have all made significant investments in IoT and machine learning based products or services. Accenture, Infosys, Deloitte, as well as smaller consulting companies are ramping up their machine learning capabilities to meet this growing services demand. When mainstream, established businesses make multi-year investments in new tech, it is a powerful signal that change is coming.
New technologies in the consumer space. Do people actually want this?
All of this points to the business world being ready and willing to adopt these new technologies. What about the average consumer? For this to really be the time to get off the fence, consumers must be part of the movement. Amazon sold over 5 million Echo devices over the last two years with nine times as many sold in 2016 as 2015. Google Maps and Waze have hundreds of millions of users. Cortana, Siri, and Google Now are installed on hundreds of millions of devices. Pokemon Go was embraced on day one by millions of players. Samsung VR has sold 2.3 million units last year alone.
These anecdotes are interesting but easily countered by just as many failed products. Why are some products being adopted by the masses while others rejected? The critical trends separating success from failure are:
· Ease of use and quality of functionality.
· Obvious improvement over older generation products.
· Power of experience and ability to share that experience.
Consumers adopt new technology when it’s easy to use with high quality functionality. Most chatbots and personal assistants aren’t ready for prime time. I saw a phrase on a bag from a chatbot conference that read, “What do we want!? Chatbots! When do we want them!? I’m sorry, I didn’t understand your question.” That’s where most chatbots are right now and that’s why only the best of the pack are being embraced by consumers.
The quality of experience relies heavily on those experiences being novel. For any new tech to take hold in the consumer market, it must be an obvious improvement over previous generations. Where Oculus has failed in the market is here. The average early adopter consumer doesn’t see a reason to shift from console or mobile gaming to their product. With Samsung’s VR, customers can experience an NBA game or TV show content in a way they can’t using any other device. That’s what’s driving the adoption of one while the other languishes.
Novelty rapidly wares off so for a new technology to have legs, the experiences must be powerful. Powerful experiences come in two forms: highly useful and highly social. Google Maps and Waze both offer highly useful navigation and traffic info features. Personal assistants provide a voice interface for a range of functions. In many cases, users find that interface to be more convenient that traditional visual interfaces. Pokemon Go had groups of people walking around the real world together, looking for monsters, stops, and gyms in the virtual world. It was a social experience as much as an augmented reality experience. These experiences are powerful because they create habits in consumers which are reinforced through continued use. Once the habit is formed, the user becomes a loyal customer.
What’s different now? Why should businesses act?
In five years, the market has moved from business intelligence, to analytics, to data science, to machine learning, and recently to deep learning. The A350 has around 6000 sensors throughout the aircraft and generates 2.5 terabytes of data per day. In three years, the company expects those numbers to triple. Autonomous cars have become a reality in less than five years. Chatbots in just over two years. Compare that to mobile phones which took twelve years to get to the first Blackberry and another eight to reach the first iPhone. Amazon was founded in 1994 and it took over a decade for the disruption of ecommerce to hit the retail market. Emerging technology is getting to market faster and more frequently than in the past.
Technology used to move in single waves; the PC, the operating system, the internet. Then we saw enabling ecosystems like ecommerce, mobile, and social media. Now we’re seeing more of these ecosystems like machine learning, cloud, and the IoT. Technology used to emerge in serial evolutions. What we’re seeing today is parallel, amplifying ecosystems.
That’s a key point to understand when looking at how emerging technology is impacting business. The evaluation process needs to examine how multiple emerging technologies can create an ecosystem and what the impacts of that ecosystem will be on the marketplace as well as customer behavior. That is exactly as complex as it sounds.
For example, the capabilities of generative adversarial networks to turn text into images combined with programmatic advertising and mobile search. This trio has the potential to enable hyper targeted ad experiences by generating (not finding premade images but building them on the fly) personalized images served to the user based on their search query. It will take about three to six months for the technology to mature and be ready for production applications. If advertising is part of your core business, being able to offer your clients this capability will mean significant revenue and advantage. While programmatic and mobile search are familiar to most agencies, advances in machine learning for image and text processing might as well be in Sanskrit.
This is a single micro example in a sea of others. There are opportunities, both for revenue and cost savings, emerging from multiple technology vectors. That’s the driver and justification for businesses to act as well as the thesis for an emerging technology strategy.
In the current technological reality, change is exponential rather than linear. Technology ecosystems are producing opportunities for businesses to generate new revenue and create competitive advantages. In my next post, I’ll cover the emergence of the collaborative business model. While an emerging technology strategy allows a business to identify these new opportunities, the collaborative business model enables them to monetize those opportunities.