The only certainty about the future is uncertainty. The change was always constant occurring ever since the evolution of humankind. But what differentiates this era from foregone is the velocity at which change is happening. In my earlier blog post, I have shared my thoughts on customers’ needs, and outcomes are increasingly becoming moving targets – an unendorsed challenge of building enterprise products. A product that is built today is at a huge risk of becoming obsolete or irrelevant tomorrow. Product Manager cannot succeed in building products for static needs, there is a necessity to think ahead, think future and think audacious. Your product cannot be a lottery, there should be something definitive about the future.
Unfortunately, Product Manager is not Nostradamus to predict the future. Nevertheless, Product Manager can anticipate needs and customers of tomorrow by developing a thorough understanding of how markets evolve, how technologies evolve and how customers’ behaviors or their challenges evolve. Accordingly, ensure that the new product can scale and adapt for needs of tomorrow, markets of tomorrow and outcomes of tomorrow.
Please read my earlier post on moving targets before proceeding further, it asserts the necessity for building customer insights and building a scalable product architecture to tackle the challenges of moving target of customer’s needs and outcomes.
How THE FUTURE transcends?
Choose any product (e.g., Digital Camera, AirBnB) or a category (e.g., SmartPhone) that has caused major disruptions. What we can realize is that the fundamental need addressed by those products did not undergo many changes. The need for capturing an image has not changed, the need for accommodation while on business or tourism has not changed, the need for communication has not changed. However, what has changed is the scale and the outcome at which the new-age products addressed those needs leading to new business models and creating a new normal. Enterprise products are no different. While regulation, microeconomic, and macroeconomic factors contribute to either acceleration or deceleration of the demand for a need, e.g., France to ban all petrol and diesel vehicles by 2040.
- Scale – As the adoption increases, users demand more functionality which is seldom not possible to deliver without additional processing power, storage, etc. In addition to an increase in the scale of specific attributes, there is also a rise in additional needs or challenges as the adoption of the product increases. More IoT connected devices might lead to challenges in securing those devices and the need for an operational simplicity of managing those devices. Those are needs that arise at the periphery as the adoption grows. Accordingly, ecosystems evolve with the addition of new players.
- Outcome – Technology evolution creates immense possibilities of delivering a differential outcome not possible before, e.g., AI/Machine Learning aids in effectively and efficiently addressing specific needs such as autonomous driving vehicles, fraud detection, etc. New age companies like Uber and Airbnb while delivering a classic need created a new normal through providing differential outcome embracing technology.
I bet you pick a technology product from any industry. I could comfortably state that all the changes that the products in that industry had undergone can be categorized into i) scale and ii) outcome
What defines THE FUTURE?
The technology was always a catalyst determining how future evolved and will continue to be so. While the Internet defined mid-90’s, mobile defined the mid-2000s, and the Artificial Intelligence could define future ahead starting mid-2010s. Technology advancements always had and will continue to have far-reaching implications on how markets evolve, how customer needs and their behaviors evolve. Faster technology advancements are putting products at the risk of becoming irrelevant sooner. Since technology is continuously at the epicenter of causing a new normal in markets and customer behaviors, it is only appropriate for Product Managers to exclusively focus on forecasting or anticipating what technologies could define the next decade. A plethora of technologies emerge every day, and hardly few enter the mainstream market. But, why do specific technologies alone succeed, what factors could contribute to their success?
The enigma of technology emergence
There is always an enigma behind why few technologies successfully emerge while many others eventually fade away. Even among technologies that successfully entered the mainstream market, some took longer than the others.
- The invention of the first digital camera happened in 1975. Why did it gain acceptance only in the later 1990’s and early 2000’s? What caused the technology to replace the older film cameras 25 years after its invention?
- Why was the smartphone one of the fastest adopted technology?
- Why did Google Glass, Segway, and Amazon Fire Power fail?
- The creation of the 1st AI program occurred in the 1950s. However, it is adopted and successfully applied to address several problems only recently. What caused the slow rate of adoption and what would cause the creation of an AI system that would completely replicate a human brain?
There is a necessity to comprehend factors that could catapult specific technologies into the mainstream market, identify if there is a pattern that can help us predict or forecast the potential technologies that will emerge defining THE FUTURE. The first digital camera even though invented in 1975 gained acceptance only in the later 1990’s and early 2000’s, what caused the technology to replace the older film cameras 25 years after its invention. Probably improvements in image quality of digital cameras, the proliferation of PCs to store and process images, etc., it is always essential to comprehend factors that could accelerate the adoption of emerging technology. Imagine someone building a film-camera in late 1990’s. Even if made with impressive features, it would have been a sure recipe for disaster. History can be helpful to provoke our thoughts. Product Managers of film camera products could have anticipated the threat of digital cameras and took appropriate corrective action. Product Managers of digital camera should have identified factors essential to accelerate the adoption of digital photography. We are always on the cusp of significant technological changes. There is a necessity for performing structured analysis to differentiate fad from reality and comprehending which technologies are poised to become a reality and how. Later evaluate the impact of emerging technology on the new product both from a perspective of threat and opportunity.
Underpinning the dependencies for technology emergence
AI (Artificial Intelligence) is a vast domain with several tiers of intelligence according to the use-cases that it intends to address. Following three categories outline a broader classification of AI products.
- Artificial Narrow Intelligence (ANI) – Specializes in a specific task
- Artificial General Intelligence (AGI) – Matches the capabilities of a human brain
- Artificial Super Intelligence (ASI) – Exceeds the capabilities of a human brain. It is getting tough to fathom the exact potential of ASI.
Scientists and architects working on AI have embarked on an audacious journey to build intelligent systems (AGI) that can learn and adapt on its own just like humans do or even exceed the capabilities of humans through replicating neural systems of the human brain. What is the realistic possibility of building such an AGI system and when it could happen? For building AGI systems that behave as humans do, we have to make massive strides in AI algorithms which is interdependent on two other factors.
- Immense processing power at an affordable cost
- Availability of humongous data and corresponding big data systems to retrieve, store, model, process, and act upon that data in a fraction of sub-microseconds.
The industry is making tremendous progress on both (1) and (2). However, whether the development is sufficient purely depends on the computing requirements of AGI systems that we are building. We have to analyze the kind of progress (1) and (2) are making and what factors could further accelerate or decelerate the progress that could eventually determine whether AGI systems are hype or reality. Such analysis can also throw light on the possible duration for AGI systems to become a reality.
There is a clear indication that the computing systems that can mimic the human brain at an affordable cost will probably evolve around 2030. If we are looking at building AGI systems, then we know when it is feasible to build those systems successfully. Simultaneously, we can also anticipate that there is no possible threat at least until 2030 from AGI systems to ANI systems. However, after 2030 there could be intelligent systems that could do much beyond than just playing chess and driving cars replacing white-collar jobs probably. Simultaneously it is essential to undergo similar analysis to understand when big data systems required for AGI systems will evolve. Does it happen before 2030 or later? We should always perform such analysis for comprehending which emerging technologies could enter the mainstream market and when. Accordingly, identify both threats and opportunities confronting the new product.
Unraveling the enigma of technology emergence
The earlier analysis concluded that it is not possible to build an AGI system before 2030. Nevertheless, computing systems are not the only dependency on the advancements of AGI systems. Moreover, a decade is too long for making any predictions and blindly sticking to it. We need to keep a close watch on whether computing systems are growing as expected by identifying the dependencies that determine the progress of computing systems.
Conceptualization of a wide range of ANI use-cases, and successful adoption and proliferation of related products such as Tesla, Alexa, and Nest, etc. will accelerate the demand for more advanced AI use-cases. The initial euphoria of AI products gaining customer acceptance lure investors to make huge bets on AI companies, thereby increasing investments, fueling further innovations in computing systems and other related AI infrastructure making it feasible to harness additional use-cases that were eluding humanity earlier. It is a cyclic reaction, initial adoption of ANI systems by B2B and B2C customers will fuel more investments, leading to better technology advancements and in turn, creating better AI products through improvement in computing systems and HW/SW entities related to AI.
Initial success fuels more investments and additional investments beget further progress. The cycle rotates until the success or progress decelerates, or investments slow down. For better prediction, we (Product Managers) should be able to predict what could break, accelerate or decelerate the cycle. Certain possibilities are, technology improvements do not happen as predicted, the economic slowdown might hamper in-flow of money, enterprise customers stop investing in AI devices, etc. On the contrary, check what could accelerate the cycle, killer AI use-cases for B2B and B2C making AI products indispensable. Killer use-cases will ensure that AI products will be the last category to take a hit while either B2B or B2C customers decided to cut down their spending. Meanwhile, keep collecting exhaustive data that can indicate how the AI adoption cycle performs. Below are few snippets of relevant data i) Phenomenal increase in AI funding over the last five years, and ii) Increase in share value of NVIDIA.
We can continue looking at other relevant data i) Adoption of AI products (both B2B and B2C), ii) Increasing rate of AI startups, iii) Technology improvements, and iv) Acquisition of AI companies, etc. It is essential to evaluate each data in correlation with others and connect the dots to predict the progress of AI. Conduct such analysis of all possible emerging technologies related to the new product using the below model. I have identified five major areas that can either impede or advance the emergence of any technology.
Figure – Emerging technology canvas
AI was an example to illustrate the structured approach of analyzing the possibility of any emerging technology entering the mainstream market. Nevertheless, we can generically examine the potential of an emerging technology under the following five broader parameters.
- Technical – Is the technology not matured yet, what is stopping the technology to reach its desired level of performance? Building AGI systems are not possible until computing systems with the required performance levels are built.
- Customer behavior – Does technology requires a change in customer behavior. Virtual reality requires a change in customer behavior. The technology even though exciting, expects customers to experience the world in a way that they are not accustomed.
- Regulation – Is the technology has to undergo any regulatory compliances g., autonomous vehicles. The existence of red tape can have a negative impact on technology emergence
- Economy – Does the wider adoption of the technology is dependent on the economic state of its target segments. Products typically build with emerging technology for segments at the bottom of the pyramid should give lots of consideration for economic factors.
- Lack of use-cases and ecosystem – Ecosystem is critical for the wider adoption of certain technologies, g. autonomous vehicles is dependent on the presence of charging stations. Adoption of HDTV was delayed because of the absence of an ecosystem. Lack of high-definition cameras, archaic broadcast standards, and older production and post-production infrastructure delayed the emergence of HDTV. Most importantly, the presence of killer use-cases that could entice customers to migrate to newer technology. Can the outcome delivered by the technology is good enough for customers to discard the status-quo and embrace new ways of doing things.
Connecting the dots
Technology is undeniably the protagonist of our discussions, but technology alone does not define THE FUTURE, it is not a one-way street. There is a symbiotic relationship among the evolution of technology, market, and customers and how they coalesce together defining THE FUTURE. Understanding future is tantamount to anticipating –
- Who are customers of tomorrow?
- What are the customer needs of tomorrow?
Through diligently analyzing the following albeit not independently but in correlation to each other.
- How do customers’ needs evolve?
- How do technologies evolve?
- How do markets evolve?
Customer needs, technologies, and markets do not evolve overnight. They do evolve at a linear pace. However, there are certain forces at play that culminate together suddenly pushing the evolution of customer needs, technologies and markets on a trajectory path reflecting a hockey stick. Especially for high-tech products, Clayton R Christensen has clearly outlined that when the performance of new technology outpaces older technology, it gains adoption. Similar to performance, Product Manager had to identify several such factors that would result in the evolution of new markets, new technologies, new needs and thereby bringing in new normal completely replacing older way of doing things.
In the earlier blog post, I have independently provided models to understand how the customers’ needs evolve by categorizing whether the need is a dormant need or emergent need. Comprehending the demand drivers of the need, and the factors making it feasible to address a dormant need should facilitate Product Manager to anticipate how the demand drivers evolve in future, what existing demand drivers extinct, what new additional demand drivers surface in future and what emerging technologies could deliver better outcomes. Similarly, for technology, I have provided a model to anticipate which emerging technology will most likely attain mainstream market and what new outcomes they could deliver. Now identify how emerging technologies and emerging needs could amalgamate creating emerging markets.
Figure – Product Canvas – Building for the future
The prospects of higher scale or newer outcomes in future are the causal effect of the interplay of how markets evolve, how technologies evolve and how needs evolve (or rather how the demand drivers of the need evolve). Product Manager has to assess how smaller changes in each of them could lead to the next significant change that defines the future.
Please drop your thoughts or experiences on how you managed to build enterprise products for an unknown future.
 Source: http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html?utm_source=share&utm_medium=twitter&utm_campaign=sm_share