The only certainty about future is uncertainty. The change was always constant occurring ever since the evolution of mankind. 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 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 future.
Unfortunately, Product Manager is not Nostradamus to predict 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.
What defines THE FUTURE?
The technology was always a catalyst determining how future evolved and will continue to be so. While mid-90’s was defined by the Internet, the mid-2000s was defined by the Mobile and future ahead starting mid-2010s could be defined by the Artificial Intelligence. 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 forecast or anticipate what technologies could define the next decade. A plethora of technologies emerge every day and hardly few enter the mainstream market. But, why do certain 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 emerge, some take longer than the others to enter the mainstream market.
- The first digital camera was invented in 1975. Why did it gain acceptance only in later 1990’s and early 2000’s? What caused the technology to replace the older film cameras 25 years after its invention?
- Why was smartphone one of the fastest adopted technology?
- Why did Google Glass, Segway, and Amazon Fire Power fail?
- The 1st AI program was created 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 certain 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 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 adoption of an emerging technology. Imagine someone building a film-camera in late 1990’s. Even if built with awesome features, it would have been sure recipe for disaster. History can be helpful to provoke our thoughts. Product Managers of film camera products could have used the data to anticipate threat and take corrective action. Product Managers of digital camera should have used the data to identify factors that could have accelerated the adoption of digital photography. We are always on the cusp of major technological changes, a structured analysis is required to differentiate fad from reality for analyzing which major technologies are poised to become a reality and how. Later evaluate the impact to the new product both from a perspective of threat and opportunity.Product Manager has to build customer insights through experiments and observing customers in their natural habitat, immersing in their business, assimilating their business process, problems, and challenges and not just listen to what they say but to read between the lines to understand what they did not say. Customers might not be able to articulate what business challenges they might face in future. Based on trends affecting the product and general understanding of customers’ business environment, Product Manager should anticipate customers’ requirements and ensure that the new product will optimally address the requirements of tomorrow. Product Manager can do so by looking outside the boundaries of existing customers and trying to establish a generalized view of how the market evolves because of changes in external factors influencing technologies and customer behaviors. One plausibility to understand future is to comprehend what has caused the present to diverge from past and use that as a reference to anticipate what will cause future to diverge from the present.
Let me pick a contemporary example – AI (Artificial Intelligence). AI is a vast domain with several tiers of intelligence according to the use-cases that it intends to address. Following three categories outline 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 attempt to build intelligent systems (AGI) that can learn and adapt on its own just like humans do or even exceed the capabilities of humans replicating neural systems of the human brain. What is the realistic possibility of building such an AGI system and when it could happen? To build AGI systems that behave like humans, we have to make huge strides in AI algorithms which is interdependent on two other factors.
- Huge processing power at an affordable cost
- Availability of huge data and corresponding big data systems to retrieve, store, model, process, and act upon that data in a fraction of microseconds.
The industry is making huge progress on both (1) and (2). However, whether they are sufficient or not 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. Accordingly, we can either determine the threats that AGI system could pose to ANI systems if we are discarding it as a hype or determine when it is possible to build a new product that behaves like humans do.
There is a clear indication that the computing systems that can mimic 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 successfully build those systems. 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 probably replacing white-collar jobs. Simultaneously it is essential to undergo similar analysis to understand when big data systems required for AGI systems will actually evolve. Will it happen before 2030 or later? We should always perform such analysis for comprehending which emerging technologies could actually enter the mainstream market. Accordingly, identify both threats and opportunities confronting the new product.
Comprehending THE FUTURE – Unraveling the enigma of technology emergence
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 strong symbiotic relationship between how the evolution of technology, market, and customers coalesce together defining THE FUTURE. Understanding future is tantamount to diligently anticipating the following albeit not independently but in relation to each other.
- How do customers’ needs evolve?
- How do technologies evolve?
- How do markets evolve?
- Who are customers of tomorrow?
- What are customer needs of tomorrow?
I have earlier predicted that it might not be possible to build an AGI system that can mimic human brain before 2030. Nevertheless, 10 years 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. Identifying 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 other related HW/SW entities. Initial success begets more investments, with additional investments comes additional success, the cycle rotates until the success rate 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 any data that can provide an indication of how the AI adoption cycle performs. Below are few snippets of relevant data i) Phenomenal increase in AI funding over the last 5 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) Acquisitions etc. Any data cannot be viewed independently, we need to evaluate the data in correlation with others and connect the dots to predict the progress of AI.
Customer needs, technologies, and markets do not evolve overnight and they do evolve at a linear pace. However, there are certain forces at play that culminate together to suddenly push the evolution of customer needs, technologies and markets on 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 needs and thereby bringing in new normal completely replacing older way of doing things.
While trying to understand the impact of technology either from a perspective of threat or from a perspective of opportunity over a definitive timeline, it is essential to do some structured analysis as shown above. Such analysis is possible only if we could understand the dependencies that underpin the evolution of various technologies. I have exclusively focused only on technology. Nevertheless, Product Manager should focus on regulatory, customer behaviors, the purchasing power of customers, economy etc. while anticipating how future unfolds and determining how all those factors will influence the evolution and acceptance of a technology. Many companies such as Kodak has gone into oblivion because they could not anticipate the threat that digital photography can have on their products. Such analysis could have provided Kodak clear hindsight of when digital photography is ready for mainstream market and what factors can aid its adoption. Accordingly, Kodak could have switched gears to embrace digital photography. We now knew that self-driving cars would eventually become a reality. What about a decade ago while self-driving car initiative was still very nascent. Was it possible to identify factors that could make self-driving car a reality and anticipate approximate duration for such possibility? I presume traditional companies making cars might have done such analysis to evaluate the threat matrix. Sometimes it helps to look back at history to derive some meaningful insights that can help us connect with the past in order to comprehend future.
The quantum of changes that will occur in future is much higher than what we have seen in the past. Rightly, the changes that have transpired in the last decade is much higher than the changes occurred in the last five decades. Yet, looking into the past will definitely help Product Manager to connect the dots to anticipate how smaller changes can combine and what factors could bring those smaller changes to take a bigger form. In today’s world, technology is one of the biggest drivers of changes. It has caused many changes in customer behaviors, markets etc. No industry or product is immune to technology advancements. One way of identifying future is to anticipate changes in technology landscape and understand how it could impact existing markets or create new markets, change customer behaviors or create new needs etc. Ideally identifying factors related to technology would be a perfect start to understanding future.
Any transformation in customer behaviors, market or technology would cause a paradigm shift. Understanding causal-effect is estimating the quantum of such shift by thoroughly anticipating all causal factors and analyzing how those factors could cause the paradigm shift and when. No change is independent. There is always a correlation of smaller changes to coalesce into something bigger – X –> Y –> Z –> BIG CHANGE i.e. paradigm shift. There are always some elements acting as a catalyst to combine smaller changes (‘X’, ‘Y’ and ‘Z’) into a bigger change. The smaller changes need not essentially combine linearly or sequentially, it can sometimes be a complex tree structure. For simplification, I choose a simple linear model of combining smaller changes. Along with identifying the smaller changes, Product Manager also had to identify the catalyst that can combine those smaller changes to spur a bigger change. Evolution of technology, market and customer needs will have so many connected pieces that Product Manager has to identify those pieces and identify what connects those pieces together to anticipate bigger changes.
There are two ways to do it
- Identify smaller changes and later anticipate what connects those smaller changes to coalesce into something bigger
- Product Manager has to be all ears and eyes to spot signs signifying smaller changes and use scenario analysis to anticipate how those smaller changes could culminate into something bigger
- Anticipate a potential bigger change and work backward to identify what smaller changes could combine to cause those bigger changes
- Analysts provide quality data on trends that might dominate the future. Their data can be a probable source of truth.
Nowadays analysts do a fantastic job of predicting how customer needs, technologies, and markets evolve in future. Product Manager can rely on analyst information, but instead of overly relying on the analyst data Product Manager should try to rationalize their predictions by identifying what elements or factors could cause their prediction to come true. I did attempt to rationalize what would cause virtual reality to enter the mainstream market by 2020.
Virtual reality is supposed to be a huge market by 2020. Firstly, let us understand why virtual reality has not entered the mass market today.
- Is the technology not affordable? Is the technology not mature?
- Is there not a relevant and appropriate use of virtual reality technology?
- Has the virtual reality ecosystem not evolved completely?
Understanding what stops virtual reality from entering the mass market today will help Product Manager identify the gaps that could be bridged propelling virtual reality to attain mainstream market by 2020.
- How can someone ensure affordability of virtual reality technology?
- How can the technology mature? Can affordability and maturity of the technology be good enough factors for adoption of the technology in B2C space?
- What would be the appropriate use of virtual reality that can attract a mass market?
- In which segments, would there be demand for VR devices? Existences of what business drivers would cause the demand for virtual reality products in those market segments (particularly B2B)
While I was building the new product (HW appliance, back in 2013), NFV (Network Function Virtualization) was an emerging technology that was holding lots of promise (especially in Service Provider market). NFV was predicted to be a billion dollar market over a period of 3 – 5 years. Naturally, we want to have a pie of that growing market through building a virtual appliance. However, I attempted to a make a rational analysis to understand the real possibility of NFV gaining tremendous adoption in service provider networks. We analyzed what stops customers from adopting virtualization in service provider networks. We drew following observations that prevent the adoption of virtualization in service provider networks. Below observations, reflect scenario as in 2013 and not as of today.
- Lack of killer use-cases
- The inability of virtualized products to meet desired performance levels required for delivering right RoI
- Lack of products to orchestrate, manage and load balance the traffic to virtualized instances and
- Lack of clear and tangible advantage over HW appliances
We later analyzed the presence of what factors would allow bridging of above gaps to increase adoption of virtualization in service provider networks. We did analyze that (3) cannot hold ground. When there are even remote signs of customers adopting virtualized products, companies building products to perform (3) will automatically burgeon. The primary aspect that was blocking adoption was performance. Without improvements in performance, which will eventually lead to doing more processing on a single core CPU, it is tough to accelerate the adoption of virtualization. The performance improvement was on increasing trajectory and it was merely a matter of time before it reaches desired levels. Meanwhile, we decided to leverage that time focusing on conceptualizing use-cases to determining the product-market fit. Conceptualizing use-cases require a discovery process along with customers to understand their business environments and challenges that virtualization can tackle uniquely. To do so, there is a need for a tangible product. Without anything tangible, a mere whiteboard discussion will not yield results. Therefore, we attempted to create an MVP version of the virtualized product (i.e. a software appliance running within a virtualized environment). The existence of a real product can help articulate value while allowing customers to experiment with the product to derive real use-cases leading to a better product-market fit.
Even though analyst will outline when technologies such as Big Data, IoT, Self-Driving Cars, Virtual Reality etc. would reach mainstream marketing, Product Manager should independently assess how those technologies will attain mainstream and in which market segments will they achieve mainstream. The idea is to assess what factors would make the technology affordable and usable, which segments would contribute to demand of those technologies. Accordingly, Product Managers can evolve their products to capture a majority of the predicted growth. Product Manager should always be inquisitive and curious constantly asking ‘WHY?’ with insatiable quest to unravel the enigmatic future of markets, technologies, and customers.
Why look into future?
Focusing on future needs and identifying those needs can help Product Manager anticipate customers of tomorrow and needs of tomorrow. Accordingly, Product Manager can conceptualize a product architecture that is scalable for future needs. What I had noticed is that the fundamental need does not undergo many changes, what changes are the scale and the outcome. While regulation and economic factors contribute to either accelerate or decelerate the demand for a need e.g. France to ban all petrol and diesel vehicles by 2040.
- Scale – Support for more users and functionality generate demand for more processing power, storage etc.
- Outcome – Technology evolution facilitates delivering differential outcome e.g. AI/Machine Learning is drastically changing how certain needs (autonomous driving vehicles, fraud detection) are addressed efficiently and effectively. New age companies like Uber/Airbnb while addressing a classic need creates a new normal through delivering differential outcome embracing technology. While building a new product, what new outcomes are possible with the emergence of new technologies and what possible threats or opportunities confront the new product. Accordingly, Product Manager has to ensure that the new product is capable to embrace potential opportunities or thwart potential threats.
Focusing on future needs and identifying those needs can help Product Manager anticipate customers of tomorrow and needs of tomorrow. Accordingly, Product Manager can conceptualize a product architecture that is scalable for future needs. Every product has certain scale parameters and every scale parameter has an expiry date. Product architecture plays a crucial role in determining whether those parameters can scale beyond their initial limits. Some of the parameters have a soft limit i.e. the parameters can scale beyond their initial range without requiring too much rework. Consider SaaS products, demand for a SaaS product can rise from 1000 customers to 1 million customers very quickly immediately after determining the product-market fit. With cloud providers like AWS, Azure scaling of SaaS products is never difficult. SaaS companies can deploy and distribute multiple instances of their products globally. Cloud providers have efficient load balancers to manage those instances. Therefore, it is sufficient to build a SaaS product for few thousand customers and scale them as the demand arises. However, scale parameters of few other products (mostly HW products) have a hard limit. Increasing them requires a lot of rework, requires Product Manager, architects, and developers to hit the drawing boards. What we should essentially consider is the cost of revisiting the drawing boards. There could also be a counter-argument that what if customers would never require the scale that the product delivers. True, we are then not adapting lean development methodologies. We are wasting resource attempting something that customers never require. It is for Product Manager to make those trade-offs. Ideally, I would recommend to first determine product-market fit before making any decision on scaling. The thumb rule would be to nail the product before scaling it.
Building new product involves lots of decision-making. Certain decisions are irreversible or reversing them will cost a lot. Other categories of decisions are always reversible. Decisions involving scale parameters with hard limits are irreversible decisions and the decisions involving scale parameters with soft limits are reversible decisions. Product Manager has to make both kinds of decisions during the course of building the new product. However, certain irreversible decisions are dependent on anticipation of how customer requirements evolve in future. Irreversible decisions cannot be made irrationally. The decisions are taken thoughtfully after analyzing all possible risks. However, for a well-informed decision-making, Product Manager should develop deeper insights about customers to understand how their future requirements might evolve. Developing customer insights is like unearthing those deep truths about customers that customers themselves might not have acknowledged directly.
Considering the lifetime of an HW product or a complex SW product could at least be for five years with a possible extension of support for a couple of years, anticipating how future might affect the new product is crucial for ensuring that the new product is scalable for precise future needs of target customers. Furthermore, longer the relevance of the product in the market, better the ROI, as the incremental cost of building additional HW product is minimal. For SW products, the incremental cost of building additional software is almost zero. So building scalable products that can sell more for a longer duration is required for better revenues with higher margins while adhering to lean practices without wasting any resources unnecessarily through developing better customer insights.
The ultimate goal is to create a mental map of all possibilities of future and then identify the factors that determine the likely occurrence of each of those possibilities. The biggest responsibility of Product Manager is the ability to narrow down the possibilities to just one future that is most likely to occur based on the identification of corresponding causal factors that is highly likely to occur. The fundamental idea is that we should not leave anything to chance while building enterprise product that is future proof.
Please drop your thoughts or experiences on how you managed to build enterprise products for unknown future.
 Source: http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html?utm_source=share&utm_medium=twitter&utm_campaign=sm_share