Overlooked in the digital transformation: Ethical, Societal, Legal, Regulatory issues

A friend and I were talking recently about the dramatic change that digital transformation will create.  The potential impact on our lives is tremendous, from autonomous vehicles that whisk us to and from work, to drones that deliver goods directly to our homes or other locations, to the insights generated from data harvested by IoT devices. We can expect significant upgrades in customer experience and business models from the use of data massaged by artificial intelligence and machine learning. 

Of course, on the flip side, there are concerns. Governments and private companies are gathering more data about individuals than ever before.  What will they do with that data?  Who decides who owns your image, and what your rights are to privacy?  What will Apple, Google, Facebook and others do with all the data they collect about your activities and selections online? 

We've reached a point where the technology and what it can do has easily surpassed the consideration of its consequences and the impact on our ethical and regulatory frameworks.  This isn't the first time this has happened, however.  Recall just a few years ago that a scientist manipulated genes in a fetus, causing an uproar in the scientific community.  The fact that we can do something because of advances in science does not mean that everyone is prepared for, or comfortable with, the ability to do it.

In the past, non-technical factors - ethical, moral, legal, societal, regulatory - slowed the advancement of science.  If we look back only a few centuries, we can see that ethical, religious and moral issues resisted science and what it could do or tell us. From Galileo confronting the church on the geocentric model of the universe to doctors desecrating graves to get access to human cadavers for dissection, science has often been held back by societal, regulatory or ethical concerns. 
 

Today, the opposite is true

Today, science and technology move so quickly that ethical, moral, societal, legal and regulatory frameworks struggle to understand what is happening, much less keep up with the changes.  Airbnb can enter a city and dramatically change the housing landscape before the city government determines its opinion on short-term leases.  Facial recognition programs tied to cameras in public places can capture millions of images and those images can be used to determine the attitude and potential behavior of citizens.  The speed of implementation has shifted - science and technology move faster than people, societies and beliefs.  The impacts that new science and technology have on how we live, the rules we agree to, the expectations we have about security and privacy are not aligned with all the new technology.

Confounding the new technology will be the fact that society in general, our politicians and the laws and regulations they create and other administrative rules and burdens won't change quickly, and will delay the promise of many of the emerging technologies.

Pushing more than one rock uphill

The use of autonomous vehicles and drones creates an excellent example.  The adoption rate of these devices is NOT based on technology.  Autonomous vehicles are at least as safe as human drivers are today, but that does not mean we'll see rapid adoption.  The barriers that exist are important and diverse.  They include:

 - Multiple jurisdictions.  Just because California likes and supports AVs does not mean that Arizona or Nevada will.  This could even be true between local jurisdictions or counties.  Who will risk buying a car that can be used in only certain locations?
 - Insurance.  Until the insurance industry can determine how to price the risk of autonomous vehicles, and more importantly, who bears the risk, it will be challenging to get a lot of autonomous vehicles on the road.  Moreover, who owns the risk?  Does the passenger in an AV bear the liability for an accident, or the company that is technically in control of the vehicle?
 - Blended traffic.  The perfect world for AVs is when every car is an AV, because they will be more consistent and predictable.  As long as AVs and humans are sharing the road, the level of danger and unpredictability goes up dramatically, meaning that more accidents are likely, which will probably be blamed on the AVs.
 - Standards.  While it is good to have competition in AV technology, we will probably need a unified set of standards so that cars and the devices that control them all work with and on a set of agreed standards. 

Thus, the full scale implementation of a technology that is already reasonably mature will not depend on the technology, but on the legal, jurisdictional, administrative and societal acceptance.  Who is doing the work to prepare the population, revise the laws, change expectations?

Why drones are an even more interesting challenge

Keeping the challenges of the autonomous vehicle in mind, let's make the problem slightly more difficult, and 3 dimensional, by considering the challenge of building a drone business.  We add the complexity of the AV, with the added issue of flying a large object overhead, where risks are greatly increased, and where there are even more regulatory bodies involved (FAA).  If you are trying to build a business in this sector, you are facing a problem that even Sisyphus would find difficult - pushing several different rocks uphill at the same time.  Two points are critical from this sentence:  Several rocks and simultaneous advancement.

Several rocks:  to win in this space you have to 1) demonstrate the technology works, and provides benefits over existing solutions first,   2) convince local, state and federal authorities that the benefits are worth the risks, and to change laws and regulations, 3) convince people within the industry that the new solution is worth adopting, and keep your end customer or consumer from turning against the technology, and 4) demonstrate to the consumer that the value of the new technology outweighs the cost.

Simultaneous advancement:  To win in these very complex technologies, you'll need to do all of these things relatively simultaneously. This is the definition of a "wicked" problem - one that has many participants and constituents. You don't want to gin up too much excitement in the consumer space and be unable to demonstrate the technology works or adds value.  You don't want to over invest in a technology only to find that regulators are unwilling to change the laws to accommodate your new technology.

Many public implementations of digital technologies - especially those that interact with the public like robots, AVs, and drones - need to consider the ethical, moral, societal and regulatory challenges.  While Asimov may have created the 3 laws of robotics, his stories don't consider how the population reacted to the advancement of robots, how they were compensated for the loss of their jobs.  While AVs, drones and robots are risky because they could interact with humans, other digital technologies like AI, facial recognition, natural language processing and IoT are also interesting and potentially problematic, because they could lead to a loss of privacy and security.

How do we prepare the population for the advent of digital technologies?

What we need is more thinking and more investment in the secondary and tertiary impacts of digital transformation - what does it mean that governments have more of our data and images?  How should they use them?  What could it mean that robots and other digital transformation eliminate jobs?  What risks are we willing to accept to live with and among AVs and drones, and what are unacceptable risks?  How do we condition people to the fact that technology will become more prevalent and more overt in their lives?

As we've seen, in the not so distant past, societal norms, religious authorities and governments had significant control over the pace and impact of new scientific advancement.  Queen Elizabeth (the first one) once rejected a patent for an automated loom because she was concerned that her population would lose jobs.  Today, the reverse is true - we base our hopes, companies and futures on rapidly emerging science and technologies, often with little understanding of how much change these technologies unleash, how unprepared the population is for the actual impacts, and how existing laws and regulations may limit the value or use of technology.

There's an opportunity in here somewhere for someone to do some serious thinking, and bringing together different constituencies to help provide a pathway for more information, more analysis of the impact of digital transformation, more sense of the changes necessary in legal and regulatory frameworks and more understanding of the risks to income, privacy and security.  Who is doing this work?

The best way to address this is to bring people from different disciplines together in one team or organization - people who are interested in the technology of course, and what it can do, as well as people from the political realm (who can change laws or create new ones), funding mechanisms, education entities (because we need to educate both young people and older people on the possibilities and impacts of new technologies), people who focus on privacy and ethics, sociologists and of course we'll need experts in the law.  It would work best if we could create integrated information about these topics, with these diverse perspectives, because the technologies will have impacts that cross all of these functions - and probably more.

In digital, there are opportunities both fast and slow

In my class today on digital transformation I was fortunate enough to have a great guest speaker, Ramesh Latan, who helped transform Bell+Howell into a digital company.  Ramesh gave a great talk and the students received it really well.

One of the key points he made was how much more efficiently (and rapidly) digital processes operate. While his key focus was the Internet of Things, he talked about speeding up a number of activities that support e-commerce:  picking, sorting, packaging, addressing and mailing products.  All of these activities were sped up by IoT and robotics, often resulting in cycle times moving from multiple minutes to a few seconds, while at the same time increasing product throughput dramatically.  It was really interesting to see the transformation.

After the class, one of the students asked a question which was interesting.  His question, somewhat paraphrased, was this:  what happens to people in the process when the process continues to speed up?  His concern was real - could people contribute to processes that are increasingly automated, increasingly data driven and increasing occurring at speeds that humans can't comprehend.  It reminded me of an old saying that a good friend used to share with me - you can think faster, but you can't fish faster.  This question gets at the heart of whether or not there will be opportunities for people as AI and robotics accelerate processes.

Why you can't fish faster

While machines may be speeding up processes that can be automated, there are plenty of activities that cannot be sped up, and will require a "human in the loop" as the saying goes, for quite some time.  For example, an e-commerce order may be received, picked, sorted, packaged and shipped in very little time - so quickly that we somewhat clumsy humans may simply get in the way of well-trained AI, robots, RPA and other activities that can be automated.  But no matter how fast the automated processes work, if a human is involved, the communication can only go so quickly.  Just because I've sped up the automated processes does not mean I can speed up the communication processes.  Humans can only gather, interpret and understand information at a specific speed, and that speed isn't necessarily increasing as we get more data and more technology.  So while the processes may get faster, the interactions and context to communicate and emote with humans isn't getting faster and demonstrates where humans play an important role.

The reference to fishing faster is an important one.  As a person who enjoys fly fishing, by law I can only have one rod in the water at a time.  It is a purposefully inefficient process.  What's more, if I am fishing for wily or easily spooked fish, I have to fish slowly and carefully, in order to present the lure to the fish at the right place and time without showing myself to the fish.  This kind of fishing cannot be sped up - it takes experience, craft and forethought, and is difficult to execute effectively.  In other words, to be more effective at catching fish when the conditions limit me to one rod, one lure, relatively difficult fishing conditions and smart fish, I can't fish faster, I have to fish smarter.  And that's an analogy to the opportunities for humans as processes speed up.

Fishing (and working) smarter

We humans will still have important roles, but the roles will shift.  While we aren't as fast as machines, we are far more flexible, more creative and less rule bound.  This means we can augment the machines, use our creativity and insight, become better interpreters or explainers of what's happening and do a better job of anticipating what will happen next.  Plus, we are better at communicating, putting things into context for our fellow humans, demonstrating understanding and empathy.  That is - for some time to come - we will work smarter than the AI or ML or robots that we work with, and that's where our opportunities lie.  In fact, there are many attributes of a process that will not speed up - which will continue to work at the pace of a human, but perhaps a human with a better understanding of the digital decision making and augmented processes.

To go back to the fishing analogy

Younger or less experienced people who fish think the activity is probabilistic - the more times I present the lure, the more chances I have for a strike.  In some wildly optimistic setting that is potentially true, but your arm will wear out long before you'll increase your catch.  It's not the number of casts, or to some degree even the placement of casts, but understanding the water, the hatch, knowing where the fish are in a particular hole or channel, understanding drift, the angle of the sun and other nuances.  More casts will simply spook the fish, and once spooked they won't bite. Fishing smarter and slower is the best recipe for success.

This is completely transferable to almost all kinds of work.  There will be components or attributes of the work that, like fishing, resist automation and place value on knowledge, flexibility, craft, insight and ingenuity.  And this is where the next working generation will thrive.

It's important that we understand what the emerging digital technologies can, and more importantly should, do.  There are many difficult, repetitive activities that will be replaced and done far more efficiently, faster and with more consistency by machines.  We need to be thinking now about the activities that don't translate to simple definition and automation, and building skills and competencies to do this work more capably.  No matter how fast or efficient some digital processes become, there will almost always be a human at the beginning and end of the process, and we will work at a much more sedate speed, but require far more expansive interaction, context and communication.

As digital transformation unfolds, it pays to be a generalist

I remember the hubbub surrounding Dan Pink's book A Whole New Mind, in which he stated that the future (this was back in the early 2000s) would belong to right brained people, because machines would ultimately automate anything that could be easily documented and that followed a standard process.  His argument was that everything that could be reduced to a defined process would be outsourced or automated, and in most cases he has been proven correct.  What remains to be seen from his prediction is whether or not the "winners" of this automation will be the people who have "right brain"skills - artistic, creative people who were most likely liberal arts majors.

What I think is another interesting and similar question is emerging in machine learning and artificial intelligence.  I think it is likely that the people whose jobs are most at risk from machine learning are the deep specialists, who have really deep but narrow knowledge.  As we train machines to interpret data and to approach an artificial intelligence, I think many of these instances will be deep learning around very specific problems.  Take, for example, breast cancer.  Machine learning and artificial intelligence may soon reach a point where detecting breast cancer from x-rays and sonograms is more consistent and less error prone than having a doctor do that work.  But unlike doctors, who are often good at many things simultaneously, the AI or ML application that's good at finding breast cancer will probably not be able to immediately also diagnose other health issues.  For a while at least AI and ML are often good, but one trick ponies.

Which raises the question - will we spend the time and effort to train the AI and ML applications in a wide array of very specific, very important and very narrow fields, where the benefits outweigh the cost of training the machine?  And, once all of these machines are trained, who oversees the transition from one model to another to do multiple diagnoses, or when will a generalist human be "good enough" because they can move between different issues more easily and perhaps more rapidly and effectively than machines?

Does it pay to be a generalist in an age of ML?

Of course this is not just an AI or ML issue.  When we think of the technologies powering digital transformation, there are a handful of technologies that are really efficient but frequently limited option solutions.  Most robots, for example, are good at one or two activities that are constantly repeated, whether that action is in 3-D space (picking and placing parts) or in automating data transcription using RPA.  IoT devices gather and transmit data effectively, but only the data that the sensors are meant to capture and transmit.

These deep but narrow competencies will eventually create highly productive and efficient but potentially fragile processes, where a small shift in focus or needs may expose the fact that these technologies, at least for the foreseeable future, aren't really good at rapidly shifting from one input, one data set or one job to another, even if the shift is somewhat inefficient.

We humans, however, have evolved to do exactly that.  For the most part we are multi-functional machines, capable of doing a wide variety of tasks without a lot of reprogramming, and we can shift from job to job, task to task relatively quickly.  It seems as though as digital transformation takes hold, the ability to adapt, to be flexible and the ability to shift quickly from one task to another will be important when working with machines and intelligences that are relatively narrow and somewhat rigid in their capabilities.

What happens to the specialist?

What happens to people who have deep, deep learning and experience in a specific field that AI or ML can rapidly learn?  At first they become the teachers of the technology, helping instruct the AI or ML on misdiagnoses, correcting errors and improving the model.  Then, once the machines become nearly as good as the humans at detecting issues, humans become the explainers, telling people how the machines made their decisions, often defending the machines.  Eventually, as machines can explain their decisions and provide sufficient evidence, humans in deep specialties may become much less valuable.  What happens to a deep but narrow specialist once explainability arrives?

Where humans will thrive

Where humans will thrive in this rapidly approaching future is in places where there is little previously documented experience, where situations and models change frequently and without a pattern, where data is messy or missing, where a fast but "good enough" answer will suffice, or where tasks are frequently changing and don't allow time for re-purposing or retooling.  These needs will still be filled by capable generalists who can apply a lot of intelligence, reasoning, creativity and dexterity to rapidly emerging challenges that haven't been seen previously or can't be adequately predicted. 

At a time when our education systems are increasingly focused on narrow fields of study, we need a more comprehensive education system that reinforces a number of good skills simultaneously and turns out people able to rapidly shift from one task or skill to another.  Instead of increasingly narrow PhD programs, what we need are robust programs that engage science, math, literature, technology, psychology and other disparate skills to prepare people for the challenges and opportunities they are likely to face as they increasingly work with intelligent machines.

There's gold in the data - but have we learned the lessons?

For some reason I can't get the song Clementine out of my head.  Perhaps it's because of the line - dwelt a miner, a 49er....  that keeps that song playing in my mind.  We are in a significant transition. One breed of miner - the hardhat wearing, coal digging, heavy equipment manager is leaving the scene, being replaced by large equipment, robots, issues with safety and the environment and of course greenhouse gas concerns.  In his or her place is emerging a new kind of miner - a data miner, a process miner, a bitcoin miner.  People who are expert at exploiting a new, but somewhat unnatural resource:  all the data that we create.

The reference to "49" in the song, by the way, is a reference to the gold rush in California.  Where there are a lot of valuable and unoccupied opportunities open for grabs, there will be a gold rush.  We are in the very beginning of one now - a gold rush to find, mine and refine data and turn the insights into value.  There are some interesting parallels between the gold rush miners in the 1800s and the people seeking to capitalize on all the data available today.

You may have heard it said data is the new oil

Let's get this out of the way - there must be at least one cliche in every blog post so there it is.  Data has a lot about it that makes it a good analogy for oil.  At the beginning of the oil boom, there were lots of deposits of oil that were easy to reach, close to the surface.  Oil as we know it today became popular as other types of fuel (whale oil in particular) were becoming more difficult to obtain and more expensive.  Plus, the oil was more plentiful and in most cases had more value.

Data is increasingly the same way.  Now there is more of it, since there are more devices and people generating more data (and more kinds of data).  Data is more valuable, because as the volume grows you can do interesting things and get interesting insights from the data.  And we are just entering an age where we have the compute power and memory access to really dig deeply into the data and find all that is interesting in there.

Of course we can mine our current databases - the CRM and ERP databases that contain so many transactions.  These should be gold mines after years of collecting data.  There are probably other significant master data bases that your company has that can be mined.

Just recently I became away of process mining - which is just data mining but based on process records.  So your order to cash process, or procure to pay process can be mined and evaluated for gaps or inefficiencies, or to discover new insights.  Even blockchain has its own miners, but they are in a different category.

Why data is unlike oil or gold

In the past, miners have been extractive, doing difficult and dangerous work to extract rapidly depleting natural resources.  These future miners are doing something quite the opposite - working in pristine conditions with the latest technologies, but more importantly working on a resource that is replenishing faster than it can be mined and understood.

The total amount of gold, coal or oil worth extracting is fixed, and the inexpensive stuff has already been extracted.  In contrast, we are only beginning to understand how to understand data, and the amount of data we create will experience exponential growth.  The problem miners will face is not where to dig, but assessing the potential value of data before digging.  If anything, data miners and data scientists risk becoming overwhelmed with data, as companies increasingly understand the potential value of the data in their organizations and make plans to capture and store more of it.

What these miners have in common

What all of these data miners have in common is that they are trying to find, collect, assess and refine the data and information in large databases within corporations.  The potential to do this well emerges from a couple of new concepts:  data scientists, who are interested in what the data contains, and new tools like predictive analytics, machine learning and artificial intelligence, as well as natural language processing.  These tools help identify interesting trends or relationships in the data in ways that humans can't do at scale.

What these miners and their companies lack

While there is great promise in data mining and process mining, many companies lack the basic infrastructure to benefit in significant ways from their data.  This is true for several reasons:

First, the depth and range of data.  While many companies have a lot of data, it often isn't reflective of a long period of history over a consistent set of customers or vendors.  Just having a lot of data does not mean much if the contents vary a lot.

Second, the quality of the data.  While some data is of relatively high quality (most likely the data in your ERP system) other data may be suspect (your CRM data is more suspect than your ERP data) or questionable (data you acquire versus data that you create).  Differences in quality can create significant errors in analysis later.

Third, the validity of your processes.  When a friend suggested I write about process mapping, I almost laughed out loud.  There's nothing wrong with the idea - in fact it's a good idea, but it assumes that 1) there are defined processes that 2) people follow to the letter and 3) that all the data within that process is captured effectively.  Since we know that SOPs are often written but rarely reviewed, many processes don't do what we think they do.  Be careful about believing that your documented processes reflect reality.

Fourth, the ability and capability of your data scientists.  I don't know about the market where you live, but if you want a job in Raleigh, all you need to do is claim to be a data scientist.  There are far more jobs than experienced people, which is another problem with the gold rush.  Lots of people who went to California had little or no experience mining for gold.  Turns out, it isn't simply lying there on the ground waiting to be picked up.  You needed experience to find it, and even then there were no guarantees.

I suspect that there will be some isolated successes in every company that has good data and good processes, and is patient with the people who are seeking value in the data.  There are a number of caveats there.  Few companies have good consistent data, and even fewer have the patience necessary to allow a lot of experiments and learning in the data.  It could be another few years before we see really interesting results from data mining from firms that are not pure data plays.

One small warning

It will be interesting to see who becomes the Levi Strauss of this particular mining rush.  Strauss became rich not from mining operations but by selling clothing and equipment to miners. There's a lesson here for current day data and process miners.  Very few of the miners in the California gold rush got wealthy.  The people who reaped most of the benefits were the people who supplied them, sold them supplies and entertainment.  The unexpected outcome of the gold rush was a growing, thriving and eventually diversifying economy, which I think might happen in this data mining rush.  I wonder how much that history will repeat itself in this rush to mine data.  It remains to be seen who benefits or ultimately profits from all the data mining that will happen.

Innovation building blocks: diverse perspectives and voices

I've been writing a series of blog posts focused on some of the most fundamental building blocks.  These fundamentals are necessary to sustain innovation in corporations, and are most often overlooked or treated as an afterthought.  Some of the building blocks I've reviewed are:
 - having a cultural bias for innovation
 - encouraging discovery and exploration
 - ensuring enough time, and the right kinds of time, for innovation

Today I am focusing on the importance of diversity for innovation success.  When I refer to diversity, I am speaking of a spectrum of concepts:  different perspectives, different experiences, different educations, internal and external perspectives, perspectives from people who are new to the problem as well as from experts, and so on.

Let's first talk about why diverse voices and perspectives are so rare in many corporate innovation teams, and then look and what a little more diversity can do.

The usual suspects

One of my favorite movies is Casablanca, and in one of the most telling scenes, the police chief acknowledges a crime has been committed and tells his men to round up the usual suspects - in other words, the same criminals they rounded up for any crime.  In many ways, innovation teams are often built this way.  Executives want the work to go well, so they round up the usual suspects to participate.  But these usual suspects aren't criminals.  No, they are the best and the brightest.

In a corporation, the "usual suspects" are typically high performing people who have demonstrated their ability to deliver high quality outcomes at low cost, typically running an existing process or product.  These folks are tightly bound to existing rules, thought processes, markets and corporate history.  This makes them the perfect suspects if we are trying to emulate an existing product or process, and perhaps the worst  participants for an interesting innovation team.

It's very often the case that these people are not very diverse.  When I say they aren't diverse, I don't mean diverse in terms of race or ethnicity, but in terms of how they think, what they know about customers or market needs, the amount of risk they are willing to bear, how creative they are when allowed to think about new ideas, their experiences and so on.  Far too often these teams are formed and have great homogeneity in the way the participants think, and that leads to limited innovation.

Let me give you an example.

Clarifier, Ideator, Developer or Implementor

I've used the Foursight personality model to help my customer teams gain some understanding of this issue. The Foursight model helps identify four personality types when it comes to developing a new product.  A clarifier seeks clarity - asks questions about strategy, purpose and scope.  An ideator loves to generate ideas but may not be all that committed to realizing an idea.  A developer likes to develop raw ideas as prototypes or pilots, but may not enjoy implementing the idea.  Implementors are the ones who see a prototype or pilot over the finish line to become a new product or service.

All of these capabilities are necessary for innovation.  If everyone is an ideator, it is unlikely the idea will progress to become a new product or service, as an example. 

In one company where I led innovation work, and where people were assigned to the innovation team, I used the Foursight model to determine the interests and proclivities of the people on the team.  Of the 8 people on the team, seven were either developer or implementors.  This was a challenge for a team that was expected to create radical new ideas!  Only one member of the team even enjoyed generating new ideas, much less thinking creatively and divergently.  I say think not to belittle the skills of the other participants, but to note how likely it is that the "best" people are often not the right people when it comes to innovation.

The Foursight analysis represents just one aspect of diversity.  It reflects the role people like to play in an innovation activity.

Another Example

In another case, I worked with a team creating new medical devices.  This meant we had to have people on the team who had 1) product development and engineering skills 2) business and marketing skills 3) medical experience and 4) legal skills.  This is also a diverse team, and without all of those skills on the team - working in some harmony - the project would fail.  Of course, each of these functions represents different skills and experiences, but they also have different language, different expectations and sometimes very different ways of working, so diversity does bring with it some challenges.

Diversity - of ability, race, ethnicity, education, age, experience and so many other factors is what creates truly interesting ideas.  So why don't companies invest more and plan more for diverse teams?

There are a couple of reasons.

Why homogeneous teams are often created

While some managers and executives will recognize that diversity is important, I'd guess that at least 75% or more of the innovation teams I've worked with were fairly homogeneous.  There are several reasons why homogeneity is so often the case.

First, the usual suspects are often the most favored and recognized people.  Many innovation teams are staffed with people known and valued by the management team.  Dissonant voices or people not well known to the management team aren't often included.  This reflects concerns about speed, comfort and awareness of the skills and attitudes of the individuals.  It's logical to want to seed the team with successful people, but often these very people limit the scope of work.

Second, some innovators or people with divergent ideas or voices are considered troublemakers or malcontents, so few managers want to appoint people to teams who may create conflict. Yet many of these people have the insights or knowledge you need.  But it requires identifying the skills and vetting the members, and perhaps taking a risk on one or two team members.  Few managers like taking risks on team members in high profile projects.

Third, diverse teams often take longer to form and norm.  By their very diversity of approach and experience, it may take longer for such a team to form and norm.  But when they do, they have the chance to perform far more effectively and creatively.  Again, an issue of speed and cost.

Finally, I think there's a real lack of understanding about the value and importance of diverse ideas and voices.  Some of that is based on how much risk and variance have been removed from business processes and management, and some is just a lack of engagement and understanding by management teams. 

Diversity - of many kinds - is a critical building block

Diverse people with widely different experiences, perspectives, education and backgrounds are more likely to spot needs, understand customers, generate newer and more valuable ideas and find the issues and challenges in an idea more quickly than more homogeneous teams. 

The problem today is that building these teams is more work, takes more time and creates more risk for managers, who prefer a predictable, quick and engaged team, even if that means the opportunities for innovation are far lower.  If your team wants more and better innovation, include more people, who have more diversity of thought, perspective, experience, education and other factors, as quickly and as often as possible.

Innovation fundamentals: using time effectively

I've been writing an occasional series on some of the fundamental building blocks for innovation success - at least those that I think are critical, intangible and often overlooked.  I've written previously about having a bias for innovation in your culture, seeking and finding important and unmet needs or opportunities, and introducing discovery and exploration in your innovation process.  These concepts all have something in common, which is the next building block I am going to discuss:  time.  Good innovation, like good cooking or the development of a human being, takes time, and time is something that is so constrained that most innovation projects simply compress time into manageable blocks, and in doing so compress and constrain good ideas.

There are at least four concepts of time I want to discuss that matter to innovation:  elapsed and committed time, development and launch time, and the opportunity window. All are important to understand and all are critical to successful innovation.

Time, in several dimensions

When I am talking about time I am actually talking about several different "kinds" of time.  There is first elapsed time - the time from the first recognition of an opportunity or need until the product comes to market.  For most companies, the preference would be that all innovation projects last no more than 88 days - a project should begin one day into the quarter and finish to deliver outcomes one day before the same quarter ends.  This is one reason why ideas like agile and rapid sprints seem so compelling.  We have an artificially imposed time constraint on innovation activities, forced upon us by the financial reporting mechanisms of the stock market.

There's another time that is also important.  That is the time that individuals and teams can commit to any specific innovation project.  Employees in most businesses are fully loaded with plenty of existing work and responsibilities, and most will prefer to focus their efforts and energy on their day jobs rather than new or unusual innovation work.  If people cannot or will not commit the necessary time to an innovation activity, then it will either fail or the scope will decrease to meet the time they can commit. This means a lot of interesting and reasonably important activities like problem and scope definition, exploration and discovery, trend spotting and customer insight work are hurried or in most cases avoided.

So, time is being compressed from beginning to the end of the project (the elapsed time is constrained) and the time committed to a project by individuals and teams is being constrained (total committed hours by team members).

Time is a valuable commodity

If time is money, or if time is a valuable commodity, and increasingly time is not invested in innovation activities in at least two dimensions, what is the logical conclusion that people assigned to innovation tasks should draw?  That time is better spent in other activities.  This is a relatively simple and obvious conclusion, and a message that far too many companies are sending to their innovation teams.

But what about lean startup, rapid sprints, agile

But aren't there entire methodologies that are meant to get to good ideas faster?  Agile, lean and rapid methodologies meant to accelerate the innovation cycle?  The answer is:  yes, properly implemented and resourced, concepts like Lean and Agile can be introduced into an innovation process, but the concepts should not simply take time out of the equation.

For example, the idea of short sprints that result in viable, testable concepts and prototypes makes sense as you build to a larger solution.  But what often happens is that the first sprint creates a solution that is "good enough" and little discovery or incubation of ideas happens after the first sprint.  Too often companies are taking the philosophy of agile and lean and using those concepts simply to remove time from an activity, without fully engaging all of the rationale of the approach.

Take all the time you need

The best advice I can give here is that teams must have the right to frame a problem correctly, explore trends and emerging needs and gather customer insights.  These activities take both 1) elapsed time and 2) project team member time and commitment  in order to be completed effectively.  This isn't news - we all know and understand this, and simply accept the inevitable when the elapsed time is shortened or the commitment levels of team members is reduced.  Likewise, as these reductions happen, the scope and potential outcomes are most likely to be reduced.

Don't get me wrong - I'm not saying that more time equals better ideas.  In fact sometimes moving quickly with a really interesting idea is the best answer, because it gives the existing culture and bureaucracy less time to fight back.  In that regard we can see that often the time that is most critical is not elapsed time but the amount of time committed by engaged employees, who are hopefully following a defined process and not spending time fighting culture and bureaucracy.

It is the case that a few really deeply engaged people, who have the permission they need to explore and discover, talk to customers and identify needs, and the skills to generate ideas and create rapid prototypes can create really interesting new solutions relatively quickly (in elapsed time).  But these teams often have people who are spending significant time on the project and who are relatively senior and experienced in their area of expertise.

Development and launch -  time innovators don't control

However, even if you can get to a really good idea quickly, there is another time that matters, and that is the time a good idea sits waiting to transition into development, and the time it takes to fully realize and launch a new idea as a new product or service.  It's often the case that the real time culprit in this regard is simply how difficult it is to get a new idea into product development and then through the product development process.  This is especially true with tangible ideas in areas like medicine, medical products, food, pharma and other areas where oversight is high and the regulatory burden is high.  Getting to a good idea can be a relatively short affair.  Getting a product to market can take years or even decades.   Again, agile and lean development and launch processes are important, but there's not nearly enough focus on reducing product development time, which brings us to the final time consideration:  the opportunity window.

Opportunity Window - the clock is ticking

One of the final aspects of time for innovation is the opportunity window.  That is, there is a sweet spot for new technologies, products or services.  Introduce a new product too early, or before customers are ready or recognize a solution, and you create an expensive beachhead that others will profitably exploit.  Introduce new products or technologies too late and you enter a red ocean, where many of the early profits have been harvested, and all that's left is to compete on price.

The "smart" play is to be the second or third player to introduce a new offering, allowing the first or second player to establish the need and solution and educate the prospect base.  Every company wants to be the smart fast follower, but most spend far too much time defining and validating a market, so they enter too late.

The opportunity window is increasingly important, because new technologies and solutions rise, gain market share and fall out of fashion very quickly.  Whereas product life cycles used to be measured in periods of decades, new products are now in and out of fashion in 18-24 months.  This means entering the opportunity window early, with the right product or service, is the only way to gain the lion's share of the profits.  Again, an issue of timing.

Who determines or controls the pace of your work?

Here's the vital question - who controls the timing and pace of your innovation work?  Corporations have people whose responsibility it is to manage people resources, funding and equipment, all of which are important inputs to any project.  But who controls and sets the time element?  Or do we simply revert to the time commitments and expectations of familiar projects, and try to reduce time commitments to innovation to get back to the day job, or to cut costs and scope?  If "time is money", then why isn't it managed as such?

Getting the time right for innovation, across these and other considerations, is critical to sustained innovation success.

The Big Nine: digital transformation, opportunities and perils

I've been reading a fair number of books and articles lately about digital transformation.  The concept of digital transformation is still somewhat nascent, still being defined, but increasingly it seems that many of the significant underpinnings of whatever digital transformation ultimately becomes are rapidly coalescing, and controlled by just a handful of companies in the US and in China.

While many companies may create more revenue, cut costs or create new relationships with customers because of new digital techniques and better use of data, almost all of these companies are ultimately relying on infrastructure - could we say an operating system - developed and produced by a few companies that have a significant lead in digital.  It seems no matter how much work any company invests in becoming more digital, they will be paying a toll or relying on this same handful of companies in order to fully deploy digital solutions.

If this sounds a bit dire, try reading the book entitled The Big Nine by Amy Webb.  Perhaps the subtitle will give you a sense of what she is thinking:  How the Tech Titans and their thinking machines could warp humanity.  I think the last bit - about warping humanity - is overkill, but a lot of what Webb is talking about is important for individual consumers of technology to understand, because it impacts what happens to our data and our privacy.  Further, it is important for any corporation conducting digital transformation to understand, because the tech titans that Webb identifies control so much of the data flow and the tools used to conduct digital transformation.

The Big Nine

Webb identifies many of the usual suspects in her rogues gallery of tech titans of digital.  Included in that list are Google, Amazon, Facebook, IBM, Microsoft and Apple, leading her to the acronym GMAFIA. These are all well-known, well-established companies with plenty of technology know-how and varying degrees of trustworthiness when it comes to collecting and managing data.

Webb also identifies three large Chinese companies which she postulates, with good support, are on the same plane as the GMAFIA.  These include Baidu, Alibaba and Tencent, leading her to christen these the BAT.  So her big nine focuses on leading American companies, darlings of the stock market and leading high tech Chinese firms, which command great market share and control in China but which aren't significantly active elsewhere yet.

What these Big Nine have in common is a significant head start over many competitors when it comes to managing data, extracting value from data, and in many cases creating platforms that generate data.  Amazon and Alibaba are essentially large virtual hypermarkets, conducting thousands of transactions a second, giving these companies incredible insight into consumer demand and a rich data stream.  Google is a purpose-built data company, and has been harvesting data through search and email for years.  Microsoft has made a significant transition from a desktop operating systems to become a real player in the cloud.  IBM has made a similar transition from hardware to software and cloud.  Apple is of course a fan-boy favorite with a closed platform, capturing data but slightly aloof from the more aggressive companies.  And of course we all know how Facebook has captured millions of users and their data.

Building platforms to use data effectively and then monetizing the data to create new customers and new channels is just a virtuous circle for many of these companies.  But simply existing as platforms is only a part of the story.

Building the operating system

Companies like Google are increasingly building the key components of the operating structure for other companies that want to do digital transformation.  Clearly, many of the GMAFIA already provide a range of services including managing data in the cloud.  Many of the basic building blocks of AI and ML are being developed by these companies as well.  Take TensorFlow, an open source software library that supports many machine learning algorithms.  TensorFlow was developed by Google.  Webb goes on to point out that more and more of the core components of digital transformation aren't simply provided by the GMAFIA as a service, but are built and offered by them as core infrastructure, what could become an operating system.  She is concerned about the amount of power that a few companies could control in the digital transformation space, and the control they could possess over other companies and the economy.

Three scenarios

What I found especially interesting was the last third of the book, where Webb creates three alternative scenarios for the future based on key trends and potential actions taken by the GMAFIA, the BAT, governments and consumers.  The three scenarios are a happy-go-lucky scenario where digital transformation creates new opportunities for everyone and everyone cooperates and shares nicely, a middle scenario with less cooperation and more competition but with a number of benefits from digital transformation, and the final scenario, where we welcome our new Chinese AI overlords.

This last scenario is based on a couple of facts and suppositions.  Two facts that matter to Webb are 1) the Chinese companies have an exceptionally large market to practice on with limited competition, giving the BAT an unfair advantage and 2) the Chinese government is investing and and hopes to be the world's leading economy in AI and ML and other digital capabilities, while the US and Europe take more market driven approaches.  I think I have a couple of concerns with her scenario in that regard.

First, while the Chinese market is large, and having a lot of customers and data helps improve AI and ML, the fact that Baidu or Alibaba are successful in China does not necessarily mean that they will be successful elsewhere, for a lot of reasons, many of them political or societal in nature.  The Chinese government is known to be active in these companies and many governments and consumers may be concerned about sharing data with these companies.  Look no further than the current Huawei issues with 5G.  Second, we've seen some of this movie before.  The Japanese decided in the 1980s to corner the manufacturing and technology market, creating a governmental body that heavily invested in selected companies and industries.  Yet the global market is not under the thumb of Japanese manufacturing or computer titans.  While there is some benefit to centralized planning and control, there are also benefits to competition.  I doubt that the future is as bright and cheerful as Webb's positive scenario but I also doubt we'll find ourselves under the thumb of a Chinese AI conglomerate, no matter how many science fiction books William Gibson turns out.

Conclusion

If you are just turning your attention to the emerging force that is AI and ML, and want to learn more about the rapidly growing power of key players in the market today, this is an excellent book.  It points out the growing consolidation of power in digital transformation, and highlights two very different approaches to dominance - the market and competition driven approach in the US and the centralized, command plus competition approach from the Chinese.  Strangely, the Europeans, Koreans and others seem completely shut out of digital transformation leadership.  I wonder if that is the case, or if Webb simply choose to focus on a classic mano a mano battle.

I'd highly recommend this book if only for some of the work Webb does to highlight what's going on, and how major companies are starting to consolidate power in this space.  She does on to illustrate issues with data security and privacy that are important as well.  While I may quibble with her scenarios, she has done good work developing them and they paint somewhat realistic alternative paths, but I think leave out important potential actors and ignore rapidly emerging companies all together.  This book reads like a warning, and for many people it probably should be.  Are we really OK with the amount of consolidation that is already underway?  Do we trust these firms to keep and manage our data?  What benefits are there to a more engaged federal government, defining and leading digital transformation?  Is market competition the right way to encourage digital transformation?  These and other questions are at least asked, if not always answered, in this book.

Demographics is your destiny

In a short break from my recurring posts on innovation building blocks (see articles on innovation bias, finding important unsolved problems and discovery and exploration) I was motivated to write today about demographics and destiny.  As a person who really enjoys understanding how the future will unfold, there are few more important trend lines and signals than demographics, yet far too many companies ignore the clear signals that demographic change indicate.

Of course, to understand what any trend tells you, you and your team must be gathering and interpreting trends.  Some trends are easy to see - what's the hot toy for Christmas this year?  Other trends emerge over several years.  New technologies take a few years to gain traction and cross the chasm to the majority of users.  Other trends are always in motion, moving at a somewhat more glacial pace, but arriving with the destruction and reshaping force of a glacier.  Demographics is one of those more glacial trends that you should be paying more attention to.

The lesson of Oldsmobile

In the good old days of the automotive industry, GM had a product for every segment and buying population.  You might start with a Chevrolet, and then move to a Buick and eventually to an Oldsmobile or Cadillac.  This was a graduated strategy, moving early buyers to more sophisticated and higher end brands.  Eventually, it also had to do with age.  Older people graduated to the more stately Oldsmobile and status symbol Cadillac.  A car for every age and stage.

But what happens when new entrants (BMW, Mercedes) and new causes (green, ecofriendly) and new rationales (grandkids) happen to your target audience?  European and Japanese "luxury" automobiles competed for a slice of Oldmobile and Cadillac's business and market.  Today, most of my elderly friends and relatives seem focused on ecofriendly cars - the Prius and others are often a car of choice.  Plus, many older buyers are moved to buy minivans and SUVs to cart their grandkids around.

These social and demographic trends (luxury, ecofriendly, family pressures) have led to the demise of Oldsmobile and cause a radical rethinking of Cadillac's branding.  Now Cadillac offers ads that tells you that your posse needs to roll in a Cadillac.

But what also killed Oldsmobile and damaged Cadillac was demography.  Their potential buyers aged out or passed away, and Oldsmobile failed to capture the next generations - the Boomers and the Gen Xers - who might have followed their parents to buy Oldsmobiles and Cadillacs. Except, of course for the fact that Oldsmobiles were their parent's cars, and were for older people.  Boomers and Gen Xers rejected the brand, partially because of the branding, fit and finish and positioning of the cars, and partially because their generation had different goals and needs.

The population implosion of Russia and Japan

If you want to see the power of demographics at work, look no further than Russia or Japan.  While many think of Russia as a powerful force in the global economy, Russia is on track to shrink in population by close to 30% over the next 30-40 years.  Russia currently has a population of about 144M people, and with the existing immigration rates and birth rates, that population is anticipated to fall to 130M by 2050.  Few people are migrating into Russia, many are migrating out, and the birth rate is below the replacement rate and has been for years.  Russia as a country will soon face very dire demands for care for its elderly, and only demands for oil will keep the Russian economy afloat.

Japan faces the same dire challenges from demography.  Japan limits immigration and it is aging rapidly.  The Japanese have a higher standard of living than the Russians, so the elderly will live longer and demand more resources from the state.  Both Russia and Japan face a rapidly aging population with few younger people to support the aging population or replace retiring workers.  This is demographic change you can watch, witness and predict.

What demographic and societal trends should we be paying attention to in the US?

Thanks to immigration our country is still growing in population.  Currently, we are just at the replacement rate (births versus deaths).  We have a radically changing population, however, which should influence products and services that are created now and in the near future.

The generations that built our current economy - the silent and the greatest - are rapidly leaving the scene.  These generations are represented by people who are today over 75 years of age.  Their wants, tastes, attitudes, perspectives and more were formed in the Depression, World War II and the aftermath of the war.  Even the boomers are starting to age.  The oldest boomers are now in their early 70s and many will be leaving the workforce and their consumption will decline.

This means that Gen Xers and Millennials will create the environment for new products and services.  It is important to note just how different these generations are from their parents and grandparents.  They are:

• more exposed to and more tolerant of different races, religious beliefs, etc
• more exposed to more opinions and more media
• far more experienced with information, information technologies and social media
• far more willing to share information publicly
• far more experienced with international travel and work
• far more diverse themselves as compared to their parents and grandparents
• far more educated than their parents and grandparents

All of which will lead to different needs, different expectations and different demands for products and services.

While other generations witnessed a lot of change - think about the change someone born in the 1930s in rural America has seen over the lifetime - the pace of technological change, of societal change and of government change in the US in just the last 15-20 years has been even more dramatic.  Thus, we are losing generations of the population who were accustomed to stability and sameness over time, and our markets and governments are increasingly full of people who are used to dramatic, rapid change on a near constant basis.

These social and demographic changes have impacts for your products and services

It should come as no surprise that these demographic and social changes will have a real impact on your products and services.  The good news is that some of the demographic segments are large - the Millennial generation is a large cohort and will be in consumption mode for quite some time.  The challenging news is that their wants, tastes, habits and expectations are pretty different from their parents and grandparents, and those differences are more fickle than before.

For example, Millennials will need homes, just like other generations did, but are probably more likely to rent longer, and buy smaller homes in more urban settings than their parents.  They will need clothes, but will be much more likely to buy and consume online rather than in stores, and use and discard clothing far more frequently than their parents and grandparents.

I don't know all of the implications - this is why you need to do the work of sorting out what trends are going to emerge, what expectations and needs will emerge and what importance these emerging generations place on products and services.  I only know that they will resemble their parents's needs and wants, but will not replicate them.  Understanding these subtle differences is what will separate the winners from the mere survivors.  As Twain said and I frequently write - history does not repeat but it does rhyme.

Innovation Building Block #3: Discovery, exploration and novelty

I'm writing a series of posts about what I consider to be the fundamental building blocks of innovation.  The building blocks I'm writing about are core to doing innovation well over a period of time.  The first building block I wrote about was creating a bias for innovation in your culture.  The second was on gaining the context for innovation - identifying an emerging opportunity or a problem.  Today I am writing about the third building block:  introducing discovery, exploration and novelty to your innovation work.  For many businesses, discovery and exploration belong in the R&D group, if anywhere, and novelty is rarely embraced.

Let's examine some of the challenges to discovery, exploration and novelty.

Experts at doing more with less

As I've said many times, over the last 30 years, large corporations have become experts at doing more with less.  Generating more productivity from fewer inputs.  Gaining more output from fewer hours of work.  Outsourcing and right-sizing businesses.  And this is to the good - it means that businesses are more efficient.  But it comes at the expense of discovery, exploration and novelty.

To gain more efficiency, companies have to curtail discovery and variance.  There becomes "one way" to do a particular job, and the methods are fairly constrained.  If the job is to build a good product in the same way each time, then a lack of variation is valuable.  However, these factors create real hurdles to creating interesting new products and services.

Blame it on the people

One of the first arguments we'll hear in this case is that the people simply aren't that innovative or creative.  More likely, the reason your teams fail to explore or discover new opportunities is that they can read their evaluation forms and compensation models.  Most companies recognize and reward efficient operations over interesting exploration and discovery.  It's not that you lack interesting or creative people, it's that the processes, evaluation and risk models all keep them under wraps.

Discovery and novelty stand out

Beyond the cultural imperatives, it takes a pretty confident individual or team to commit to new research or exploration.  Innovation savants like Steve Jobs may be able to conduct research or talk about beginner's mind, but few people have the chutzpah to actually try it at work.

Yet this is what businesses need most - people and teams willing and able to explore, discover new needs and new technologies, to look at business problems and opportunities with a sense of wonder and novelty.

Not just a technology

What's important to note here is that exploration, discovery and novelty don't belong exclusively to the R&D team.  Good innovators should be seeking and solving problems and opportunities and exploring the means to create new products, services, experiences and business models far beyond the realm of physical products.  In fact, with digital transformation emerging, it's more likely that really creative new ideas and solutions will be intangible.

This means that it is becoming increasingly important for discovery and exploration to happen in the non-technical, service and business model oriented parts of your business.

The three horizons aren't dead

I read occasionally that the three horizons model of innovation is "dead" - that the format or framework of three horizons doesn't work anymore.  What I'd like to know is if any business doesn't think about or consider three timelines:  today, tomorrow and the future. Because that's one way to think about the three horizons.  Exploration and discovery will deliver for tomorrow and the future, but they have to begin today.

Conclusion
To understand the future, you've got to explore and discover today, act with a sense of novelty and openness to unfolding or future trends.  You've got to establish the right to do this kind of thinking, find the people who are willing and able to do it well, and ensure that they do it in the right context, for the right reasons, and that the work generates insights the company can use.

Discovery, exploration and novelty are vital to the mid term and longer term health of your business.  Identifying teams to conduct exploration and discovery, in R&D but also across the organization, will ensure that your teams are constantly identifying meaningful opportunities and identifying emerging needs and technologies.  Ignoring this important component and skill set, or simply delaying discovery and exploration will simply lock you in to the existing products, business models and industry competition that you have today.

Innovation or ERP: which path will digital transformation follow?

I've been thinking a lot about the concept of "digital transformation" recently.  You can't help but encounter it on Twitter or LinkedIn, in business publications and in discussions with customers and prospects.  Everyone wants to know about digital transformation. Admittedly, teaching a class on digital transformation keeps it forefront in my mind.

From all of this interaction, I've been wondering which management phenomenon digital transformation will be more like:  the ERP adoption of the 1990s or the innovation phase from 2005-2015 or so.  Both ERP and innovation had significant impact on businesses, both were in the press a lot, but so far I'd have to say that ERP did a lot more for businesses than innovation has, a few companies excepted.  In fairness, these two things aren't alike, except that they were both a key focus for management and promised dramatic change.  ERP is software which required a mindset change.  Digital Transformation is a philosophy backed by data and software applications.  However, they share some common features and make some similar promises.

Digital transformation has attributes and aspects of both the switch to integrated ERP, and the energy and passion (and promise) of innovation.  How it plays out, and the impact it creates, remains to be seen.   While many observers and vendors hope it will follow the ERP playbook, I think it is more likely to follow the innovation path. Here are a few ways digital transformation is like, and unlike, the two recent management eras.

Accelerating Existing Processes/Enterprise Capabilities
1.  ERP was vital and successful because it was integrating data and disparate processes, while automating existing capabilities.  All three of these ideas are important.  ERP built on an existing business process framework.  People were already processing purchase orders, running MRP and so forth.  In that instance, ERP built on and extended existing capabilities in a way that innovation did not.  As innovation became interesting to companies, it was evident (and remains evident today) that few companies have real experience or defined processes for innovation.

Digital transformation is a basket of ideas, philosophies and applications.  Some will build on existing actions or processes (RPA and Robotics).  Some will introduce new processes (blockchain).  Some may improve the use of data (Machine Learning).  Since digital transformation is not reinforcing and improving existing capabilities for the most part, it looks and its experience will probably be more like innovation's to date.



Organizational Strategy and Commitment
2. Doing ERP required the full commitment of the entire organization, while innovation is rarely an enterprise commitment.  Switching all your systems and many business processes to an enterprise application is difficult and requires complete commitment, across the organization and up and down the management hierarchy.  You cannot be successful if some groups adopt your ERP and others refuse to use it or ignore it.  Innovation, on the other hand, typically thrives in some pockets and is routinely ignored in others.  Few companies have a deep, continuing commitment to innovation.  In this regard, digital transformation is again more like innovation.

There is no one digital platform, and for the short and medium term most digital transformation will be done as pilots and proof of concept in small teams and functions, divided up into robotics, augmented reality, blockchain, machine learning for specific tasks and so forth.  Rather than integrate everything, digital transformation in the short and medium term will create new siloes, where some teams or functions are vastly more experienced and gain more benefits from exercising digital transformation, while others lag behind.  ERP benefited from the fact that virtually everyone was impacted, and most shifted to a new application that they all shared, like it or not.  Digital transformation will not operate in the same way.

Clarity of Purpose
3.  ERP has a unifiying purpose - integration and efficiency, while innovation focus is more diffuse - incremental changes to existing products and transformative new solutions.  In this regard, innovation is more scattershot, able to make solutions across the company in diverse ways, while ERP is focused on unifying and creating common ways of working and using data.  While digital transformation both uses and creates data, it will not necessarily create a unifying platform, and may solve many discrete problems but neglect to unify the company around a clear direction.  I think many early digital transformation projects will focus on efficiency, and then eventually customer experience.  In this light again digital transformation looks and feels a bit more like the innovation experience of the last 10 years or so, rather than the unifying activity of ERP.

Single Source of the Truth
4.  ERP provided a single source of truth about data in the company, creating one aggregated way to gather data about anything in the company.  Innovation does not necessarily create data, or create ways to think about the company or customer differently.  Digital transformation does deal with data, both consuming and using data to create new insights and opportunities, and using data as inputs to fuel activities through RPA, robotics, autonomous vehicles and other means.  Until a unified digital transformation platform emerges, however, every instance of digital transformation will address a narrow need or function, not consolidating or simplifying data globally.  Digital Transformation shares with ERP the need to clean and standardize the data before its use, but gains far less enterprise value from the data generated, and often requires new data in order to generate meaningful insights.  In this digital transformation shares many of the downsides of an ERP implementation (aligning processes, cleansing and preparing data) without all of the enterprise upside.

As more AI and Machine Learning becomes available for your data, the risk is that there are multiple interpretations of the new data, rather than a single source of truth.

Observable and Valuable Impact
5.  Cost reduction, efficiency, revenue gains?  ERP helped gain efficiency and put businesses on a more robust footing for better operations and allowed them to grow without adding headcount.  Thus, ERP has always been focused on process efficiency and cost reduction.  Innovation promises new organic growth, and is more reasonably positioned as a revenue enhancer, but often is used to drive efficiencies as well.  In the short run, most digital transformation will follow the path of ERP, cutting costs and creating efficiencies.  It is possible as more data is gathered and interpreted that some digital transformation applications and solutions can create new revenue streams, or perhaps new business models and services that create new recurring revenue.   But one really disruptive idea converted into a new product or service from a good innovation team will drive far more growth than digital transformation is likely to for quite some time.

Thoughts and Conclusions

From this short analysis I think digital transformation will follow the architectural, implementation and data path of ERP and will focus primarily on efficiency and cost reduction, but will in the short term look and feel more like the innovation era.  Digital Transformation will create a lot of promise, but much of that will be lost to small prototypes, capabilities and benefits that are overhyped by vendors, a lack of enterprise engagement and eventually the really different ways that disparate teams and groups implement digital transformation internally.  Rather than unifying and simplifying, digital transformation may, again may, make organizations more stovepiped and make it more difficult for adjacent teams or functions to interact effectively because they will have implemented different forms of digital transformation at different speeds and have radically different insights from their data.

There is clearly a lot of promise in digital transformation, but in many ways much digital transformation activity remains as point solutions rather than an enterprise play.  Rather than consolidating and clarifying the data, it will create new ways to interpret the data and also may demand the acquisition of new data to operate effectively.  The disparate deployment options and ways digital transformation can be used may create wide disparities of digital capability and gaps in the same organization.  A much more thoughtful approach to digital transformation is required, more enterprise-level, more consolidated and more concerned with business processes and the interaction of disparate teams and functions.

Digital transformation is not an enterprise solution, but a set of capabilities and technologies that may enhance point solutions or data sets.  There is no one enterprise digital solution, rather a collection of digital capabilities that may support a digital strategy.  In this regard, digital transformation looks a lot like innovation - something with a lot of promise, that could create great impact, but often on a case by case basis in disparate settings, rather than fully integrating and creating a common platform.

Innovation Building Block 2 - important, unsolved problem or opportunity

I'm going back to basics for a handful of blog posts - back to what I call the innovation building blocks.  In the first blog I wrote about the importance of defining an innovation bias in your culture.  In this episode of the continuing series on innovation building blocks, I'm going to be focusing on the importance of an important and unsolved problem or opportunity.

To which you'd say:  no kidding.  And in general terms you'd be right - this should be obvious, but in so many ways innovation sponsors and innovation teams miss the mark on what should be a predicate to doing innovation work.  It's a lot more challenging to create, define and validate an important and unmet need or opportunity than you might think.

There are a handful of reasons for this.  I will be addressing three or four in this blog post.

Our technology or their need?

One of the first fallacies is that everyone needs what you've got.  Just as the old saying goes - when you have a hammer everything looks like a nail - so too with your capabilities or technologies.  There is far too much technology push when it comes to innovation.  Companies believe that since they have a new technology, simply getting the technology into the marketplace should be considered innovation.  Worse, since it is their technology and by all accounts a very interesting and valuable technology, it should be easy to push into the marketplace and gain rapid adoption.

What customers want and need are solutions (not technologies) that help them address real challenges or problems, that help them do things with less effort or more efficiency.  In the end, they could care less about the technology.  Do you really think most people understand how Google works, or how it makes money?  Start with the idea of market pull or finding unmet needs rather than pushing technology.

Starting with the customer in mind

OK, if we don't start the work with our technology in the forefront, what do we do?  Start with the customer or prospect in mind.  Understand their wants and needs, and the importance and relevance of the gaps or challenges in their lives.  This will sometimes be obvious, and will sometimes require real insight or even a leap of faith.

Identifying needs comes from real empathy and interaction with customers and prospects.  Rarely will asking a customer what they want or need lead to new insight.  It will require deeper observation, using ethnographic and design thinking skills to uncover unmet needs.  Few large companies do this work well because it is not a common research task and because it is qualitative, not quantitative.

I've also used with some success the strategy canvas from Blue Ocean Strategy, to identify a range of undermet or overmet needs.

However, identifying an unfulfilled need is not enough.  Unless you are Steve Jobs and can intuit what customers want, you'll want to do some prototyping and testing to validate the need and customers' willingness to acquire a new product or service.

Getting there first

Sometimes, it makes sense to skate to where the puck will be, rather than where it is.  This is what Gretzky said about hockey, and it is true in innovation as well. Some opportunities emerge based on shifts in the marketplace, technology introductions that transform competitive landscapes or demographic or societal shifts.  Whole segments can emerge and disappear relatively quickly.

How can we skate to the opportunities even before they emerge?  You have to work on future trends and identify emerging needs or opportunities and identify them before others do.  This work requires carefully watching the markets, assessing trends and forecasting some likely scenarios, to understand how the future may unfold, and what new opportunities may emerge, what new needs may be exposed due to those changes. When you see this opportunity, moving quickly to fill it before others do provides a significant advantage, what my co-author and I called pre-emption.

Innovation without context is opinion

Far too frequently, companies conduct innovation work without trying to find an important and unmet need or opportunity.  Gaining this information is what I call gathering innovation context. The needs, wants, priorities, unmet gaps and emerging trends provide context to let good innovators know where the opportunities lie.  With this context, creating new ideas or identifying new solutions or technologies is simplified.  Without this context, all innovation is supposition, guesswork and opinion.

It's difficult to do good discovery work (what I've discussed in this post) without creating a bias for innovation (which was the topic of the previous post). These building blocks aren't simply foundational, they are also mutually supportive.  Without a bias for innovation, teams and executives will skip past the context setting to get more rapidly to what they consider the main event - the idea generation and validation.  This is why so many innovation projects seem to produce the same results - without new context, the old context is substituted or adopted, and the same ideas appear to be valid.

The digital revolution will not be evenly distributed

I'm writing today about digital transformation, and starting with two of my favorite quotes.  The first, referenced in the title, is from William Gibson, the author of Neuromancer and other great sci-fi books, who wrote:  "the future is here, it's just not evenly distributed".  That is, we experience glimpses of the future everyday, and some places or companies are more advanced than others.

Mark Twain said that that history does not repeat itself, but it does rhyme.  I feel this describes the talk about digital transformation.  Please don't misunderstand me - I think the emerging technologies that support a true digital transformation are amazing, and have the power to provide more benefits to customers and to create more insights and more profits for companies than ever before.  I'm perhaps a bit jaded because it feels like we've been here before, but in an entirely different way.

Almost 30 years ago I was fortunate enough to be part of one of the first implementations of SAP (R/2) in the United States.  At that time, software applications were stovepiped.  There were financial applications, manufacturing applications and customer service applications but no unified, enterprise application that integrated systems and data across all the functions.  SAP changed that, taking the market by storm and changing our expectations about software solutions and data integration. 

When we would talk to clients about the investments and potential benefits of implementing SAP, we'd always look for the means to generate new revenues and profits as part of the benefit, but the truth was that most of the benefits were driven by automation, efficiency and cost reduction.  Strangely, however, in most instances SAP installations did not dramatically reduce employment - the jobs and roles simply shifted to higher value work.

I think some of the same phenomena are at work in digital transformation, but on a much larger scale.  In all honesty there is no one digital transformation "solution", but a host of tools, methods and applications to make a company more digital.  The funny thing about all of this is that the benefits aren't from being digital, but should result from becoming smarter, faster and more nimble.  We'll see if the digital tools and solutions create those benefits. 

This is about transformation

But let's not neglect the fact that "digital" transformation is really about transformation.  The digital aspect merely points out that new tools and new methods are mostly consuming or creating data, or integrating or using data.  But what's going to get transformed is the revenue model, the customer experience and ultimately the business model.  What many firms are going to discover is that you can't bolt new digital technology onto an outdated, slow and bureaucratic operating model and expect benefits.  New technologies and richer data will require companies to change how they operate, and right now many companies think that digital transformation belongs with the IT organization, because it is driven by data.

What's going to happen is that many of the underlying capabilities or tools will create more data, which will lead to new services, products and experiences, which can be delivered through new channels and create new customer relationships, which will lead to new business model opportunities.  The operating model of the business will need to transform at least as quickly as the implementation and use of the underlying technologies.  Most companies do not understand this and are not prepared for the amount of structural and business model change that is going to occur.

Why will this happen?  New data streams will create opportunities for new services, new experiences and new revenue models.  Increasingly physical products will be offered as services on recurring revenue streams rather than as one time purchases.  Products that were once valuable will be given away in order to extract data and monetize the data stream.  All of these factors will radically change existing business models.

This is about the data

Digital transformation is a two edged sword - it both gives data and consumes data.  Some applications, like IoT, will create massive amounts of data that must be gathered, stored and interpreted.  However, for that data to have meaning, other data must be acquired and appended.  Other applications like robotics or Augmented reality will require new data streams that must be created.  Many of these solutions do not yet exist and in many cases are specific to the task at hand.

Many companies face at least three significant challenges where data is concerned:

1. The data they have is noisy, inconsistent and incomplete, meaning that the existing data cannot be used effectively for digital tools like machine learning until it is cleaned and standardized.  Much of the historical data is not useful unless it is radically improved.
2. Most companies don't have a lot of experience managing the volumes of data that will be generated, or acquiring and ingesting other data that will enrich the core data streams.  Few companies truly know which data is important and which data is not important.
3. Most companies lack experience creating value from data.  This is the holy grail - creating revenue streams from harvested data.  Yet, value is still barely understood and the skills don't exist in most organizations to do this well.  Few companies have deep knowledge and experience monetizing data streams.  This concept is one of the most anticipated value propositions of digital transformation, yet probably will be one of the most elusive.

Back to the ERP analogy

If I could revert to the ERP analogy, then, we can make assessments of what is likely to happen based on past experience. 

In the early ERP days, many companies had imperfect or incomplete data when ERP was implemented, so SAP and other ERP applications were often implemented with only the minimum amount of data necessary and older systems were kept functional to refer back to.  I think there will be a fair amount of parallel operations as digital systems come online for the same reason.  This is likely to slow full digital adoption because of the costs of supporting new systems and maintaining legacy systems at the same time.

In the early ERP days, there were few companies with the internal staff that could manage the new IT technology, so large consulting firms grew in coordination with ERP companies. Accenture, Deloitte and others should benefit from large implementations.  The good news here is that we should have learned something from those implementations and should be smarter about how to go about installing and bringing the digital tools online.  Human capital will be at a premium.

However, and this is where there is a significant departure from the ERP model, digital transformation tools are not monolithic.  An ERP application might replace three or four legacy applications.  Digital Transformation, bringing online IoT, blockchain, robotics, machine learning, big data and other tools and technologies, will simply layer on a number of new and discrete technologies and data streams on top of the ERP/CRM platform.  In other words, rather than integrating and harmonizing all the data in one application, these tools and methods will create new data streams with different focus and different purposes, potentially requiring a new means to capture and standardize all of the data from all of the different digital functions.

And it's hard to get value from data until you normalize, clean, standardize and interrogate the data.

The implication here is that the existing IT structures in most organizations will not be able to manage all of this data, in all of these streams, to create meaningful value from the data in their current organizational structures and forms.

Fulfilling the promise of digital transformation

Let's go a bit further - what happens when everyone has been promised the ability to gain more insight into the data using machine learning, and everyone wants to interrogate the data coming from a wide variety of data streams?  What happens when many IoT devices go online and products start sending packets of data back to home base?  What happens when marketers and sales teams want to start generating new value from the data being generated?  And all of this has to occur while the company continues with its traditional operations, to fulfill existing products and services?

There is a LOT of change coming, and I worry that the digital tools, while they have tremendous opportunity and promise, will overburden legacy companies which are struggling to compete today.  Most companies are not very nimble or agile, not accustomed to big change, so it will be interesting so watch this unfold.  The biggest opportunity - more data and more value from the data - is also one of the biggest challenges given the state of existing databases and data capabilities today. 

From this analysis we can expect to see lots of very small digital transformation pilots, because companies need to learn how to implement these tools, how to gain value from them and most importantly how to manage the data they generate and use the data they have.  Digital transformation will be a spot solution for at least the next few years, rarely an enterprise solution unless supported by an enterprise application.  Machine learning, robotics, IoT, blockchain, augmented reality and other technologies are not enterprise applications, but meant for specific tasks, and will be implemented in that manner and generate data specific to those applications in the short run.

Culture and innovation bias - the first building block

I'm writing a series of blog posts about the fundamentals of innovation success.  In my previous article I wrote about the importance of fundamentals in any activity, using music and practicing scales as an analogy for understanding the fundamentals of innovation. 

In this post I'm going to be writing about two very interconnected issues - organizational culture and the bias for, or against, innovation.  This is a critical fundamental attribute for successful innovation, not just on a project by project basis, but enterprise wide.

Definition of Culture

I won't spend a lot of time on the definition of culture, but it is worth describing what culture "is" in this context and why it matters.  Culture is the aggregation of all the historical record of the company, the stories the company tells about itself, what it values, what it rewards.  It is all of the formal and especially informal decision making apparatus.  It is the set of beliefs and behaviors that form and structure how decisions are made and how work gets done.  In this regard, culture is an exceptionally powerful but intangible force.

What our cultures focus on now

Over the last 20-30 years, we've trained our cultures to value efficiency, cost reduction, a lack of risk and variance and especially stability.  This has been brought about by a range of management thinking, including Six Sigma and Lean work, right sizing, outsourcing and cost containment.  There is nothing wrong with these ideas - they have made many organizations profitable and effective.  However, they do not lead to growth or differentiation, and have established a very conservative mindset in many managers and reinforced the concept of efficiency in the culture.

How to assess your culture and its bias

If you want to know what your culture reinforces about certain ideas or topics, it is easy to get feedback.  Simply suggest a more radical idea and wait the feedback.  If the feedback starts with "tell me more" or "we could" or "how might we" then your organization may have some tolerance for exploration and risk.  If the responses start with all the reasons why the idea won't work, or worse, why the existing bureaucracy or organization simply won't tolerate the idea, then you'll know the bias of the organization.

Most companies and organizations have a bias for efficiency, predictability, low variability and low risk.  This mindset and cultural preference conflicts with innovation.  So, the prevailing bias for many companies is not necessarily against innovation, but for efficiency, and cultures and their biases are difficult to change.

Readiness to change/bias for innovation

To innovate once, your teams can often scale the organizational bias against innovation momentarily, but to innovate repeatedly and in many sectors of your business you must address the prevailing cultural bias.  As anyone who has worked on culture can tell you, changing a culture happens in only two ways:  really slowly over time, just as the culture was built, or really rapidly.  The problem with the "really rapidly" option is that culture only changes really rapidly under an imminent, observable threat that cannot be avoided. 

Companies and people will jettison long-held beliefs or operations when their company's existence is at risk.  However, this is a really difficult time to innovate because everyone is focused on simply surviving.  So we will have to accept that cultural change and embracing an innovation bias is something that happens over time.

How can you build a bias for innovation

Building a bias for innovation in your organization is relatively straightforward, but it takes management engagement and perseverance.  Just as a bias for efficiency was built over decades, a bias for innovation will be built over years, not weeks or months.  There are a few key activities or attributes you can focus on to help:

1. Communication.  If innovation is important to the organization, then it should be communicated constantly, and followed up with action.  Communication should occur at all levels, reinforcing the importance of innovation, and identifying the innovative actions that are happening.  Companies should communicate both the innovation successes, and the projects that may not have been successful, but where valuable learning occurred.  
2. Compensation.  If you want your organization to change its behaviors and bias, then you must change its reward structure.  People and organizations do what they are rewarded to do.  Thus, look at your compensation and rewards structures and programs to see how you can more frequently and more transparently reward innovation, outcomes and activities.
3. Evaluation.  Every organization evaluates its members, employees and teams.  Most organizations do not evaluate their people or teams based on innovation activities or outcomes.  It's rare to find innovation as a focus of an annual review or an evaluation.  If people or teams aren't evaluated on innovation engagement, then they'll turn their attention to what they are evaluated on.  If you want to shift your bias toward innovation, change what you evaluate.
4. Find the flag pole people and encourage them to lead.  Every organization, every team has what I call "flag pole" people.  These are people that may or may not be in formal positions of power but whom others in the organization look to for influence and ideas.  Identify these influencers and use them to shift the thinking and behavior of others.  
5. Adopt good analogies from other businesses.  While I don't know if Microsoft's approach to "learn it all, rather than know it all" is working, it is definitely a step in the right direction.  As the CEO keeps repeating this mantra, he is establishing a theme for the business and setting direction for the company, opening up to new concepts and new ideas not developed at Microsoft.  This is a simple theme with profound implications.  It sets out a new way of thinking and working (learning and discovery) and allows executives and managers to gently influence and correct people who are still in the older mindset (know it all, internal focused, not exploring or learning).

Infection or conversion

Your company can be infected with this thinking, in that new thinking and some new cultural phenomenon can be demonstrated by one or two teams momentarily.  However, you will find that like many other infections the anti-bodies will soon seek to snuff the infection and revert to the norm.  Your organization will need to find a few teams or business units to try out a new cultural approach and bias, and will need to infect them through some of the steps listed above, but will also need to wall off or inoculate these teams from the cultural bias that will react to new thinking.

As you do that, the rest of the culture will be watching, watching to see what the executives do and say, watching to see the results and what happens if the results aren't stellar, waiting to see if this is just another flash in the pan or an ongoing change with deep commitment.  In other words, is the attempt to change a cultural bias a one time activity that is easily snuffed or is there real management commitment behind it?  Will the organization commit to converting to a new normal - a new bias for innovation?

Conclusion

Any team or organization can innovate once or in good times sporadically.  That's because the prevailing culture will ignore or tolerate some dissonance, and times may prevail on the culture to ignore or overlook risk and variability periodically, but cultures will inevitably snap back to their status quo.  Corporations simply cannot afford to fight their own cultures or innovate only every once in a while or based on what the culture is willing to tolerate.  We are in an era where consistent, continual innovation is a matter of survival.

Without the building block or fundamental attribute of a bias for innovation within the culture, you simply cannot sustain innovation, especially transformative or disruptive innovation that drives new organic growth.  With this realization, there are a couple of questions to answer:

1. Is it worth the effort to introduce change to the culture to create a bias for innovation?
2. When should we start?
3. Where should we start?

The answers in reverse order are:

1. Start with the most important influencers in your business.  These may not be the most important executives but the people that others look to for cues about how the business operates.  You will need executive support as well, but moving the influencers and important middle managers makes all the difference.
2. Yesterday.  You should have started yesterday, but today is a good day to start as well.  Waiting only postpones and perhaps increases the amount of work you need to do.
3. The investment and costs associated with changing a culture are surprisingly low.  The problem isn't out of pocket dollars, but management time and attention over a period of time.  That's the investment side.  The return side on the inevitable ROI equation is:  can we return more on this investment than we put in?  Evidence shows that good innovators in every industry have better profit margins, better stock prices and grow revenue faster than their less innovative competitors, so you'll need to be the judge, but evidence shows creating a bias for innovation is a consistent winner.