Scaling a business depends on controllable factors

 One of my favorite musicians is (was) Warren Zevon, who had a great sense of humor and was also a good musician.  You may be familiar with his song "Werewolves of London" but perhaps one of my favorite songs of his is "Lawyers, Guns and Money".  In the song, he describes what it will take to get out of a particular jam, and notes that neither lawyers, guns or money will get him out of his predicament.

While the song is humorous, it made me think about the reverse question.  Why are some songwriters or musicians so successful, while others, equally talented, aren't successful.  And, if we take the analogy further, can we ask:  what are the factors that make some companies so successful, while others, in the same industry, with the same information, aren't successful?

It's not lawyers (although they may help), it certainly isn't guns, at least not yet, and while money can appear to be an advantage, just remind yourself that Yahoo! was the big search engine when Google was conceived, and Yahoo! could have purchased Google for a reasonable figure in the early days.  Money is helpful but not decisive.

What are the factors that dictate the success or failure of a business, and which of them can we control?  Luck and timing play a part in the success or failure of any business, but what's more important is management focus, alignment, communication and setting priorities.  Easier said than done.

What we can control vs what we cannot control

In any venture, there are factors that the leadership can control, such as when to spend money and what to spend it on.  There are also factors that the company could control, but through inaction, distraction or simple lack of acknowledgement they fail to control.  There are also externalities that cannot be controlled, some observable (competitive actions) and some potentially random (timing and luck).

We ought to ask ourselves as leaders in any company:  what can we control?  Do we have line of sight, awareness and accountability on everything we can control?  Next, what can't we control that we need to be aware of?  What externalities could impact us?  What shifts or trends could reshape our ability to compete?  And finally, what factors are important but perhaps unknowable?

We shut down because...

This takes me back to a funded startup in the early 1990s.  I was working as the global VP of Marketing for a software startup doing data mining on large data sets.  We had the right ideas and were just beginning to show value on customer experience and preventing churn, when the attacks on 9/11 happened.  While our product/market fit wasn't perfect, we were learning and getting better, but at that point we needed another round to get the product exactly right and ready to launch.

We couldn't anticipate what would happen during the attacks, or the way that VC funding dried up after the attacks.  So, about eight months later, the company was no more.  

Looking back, I can say with some certainty that with another round, we probably would have created a compelling product, but with equal certainty that we had more to do to create the software the market wanted.  The unexpected and uncontrollable event doomed us.

It's easy to blame the failure of the company on the lack of VC funds, and many companies shut down and blame externalities, or issues that were out of their control.  These factors do cause issues, for startups and larger firms, but my work over the last decade has taught me that while we often blame these unforeseen and uncontrollable issues for failure, the reality lies much closer to home.

Controlling the controllable

After working in innovation and strategy for over 20 years, a few things are clear.  First, most companies don't really understand what factors they can control, and most leadership teams need help prioritizing and controlling the factors within their control.  A list of some of the things that need focus and control include:

• What is the company's core value proposition?  Why does what you do matter and why are you truly different than competitors?
• What is each person's role in the leadership team and next level down?  Where does one person's job or role begin and another one's end?  Are there areas of uncertainty or discord between roles and jobs?  Can you build a RACI model for your business that everyone agrees on?
• What are the fewest, most important things (priorities) that the leadership team must focus on to achieve their goals?  What shiny objects or nice to haves are regularly paraded about, and how does the leadership team dispose of things that aren't top priority?
• Is your leadership truly aligned to the same outcomes and goals?  Does their compensation and evaluation align to the company's goals?  Are there disagreements or gaps in the leadership team about strategy, execution or measurement?
• Is there clear accountability, and are people held accountable to plans and execution?
• Does the leadership team communicate well within the team, and equally well and equally consistently to the rest of the organization?

If you've read the bulleted list, you'll be thinking just about now that these are basics.  A good leadership team should be doing all of these well.  Yet I can tell you that in my experience working with leadership teams (from VC backed startups to rapidly scaling firms with PE investment to Fortune 500 companies) that these issues recur over and over again, and are the main reason that companies fail to achieve their goals.  It's not lawyers, guns or money, or luck or timing that creates success.  It's focusing relentlessly on the basics.

There is a saying that a well-aligned executive leadership team, with a good strategy, can do just about anything, and I believe that is true, if they are truly of one mind, aligned to a great strategy, with clarity about roles and responsibilities and willing to hold themselves and their teams accountable.  When a leadership team focuses on these basics and does them very well, they are controlling for the things they can control, optimizing and anticipating issues and dealing with them quickly and directly.

The problem is the people

Which leads me to another content favorite - Hard Core History with Dan Carlin.  If you don't listen to Hardcore History, you should. It's a great podcast.  I'm listening now to his account of the war in the Pacific in World War II, and he makes a great point.  Both the Japanese armed forces and the US armed forces were filled with great leaders, good soldiers and weapons.  He points out the problem wasn't with the weapons but with the people.  He makes the case that the war could have ended much more quickly, or differently, "if it was fought by Vulcans".  His point is that emotion and ego often created conflict.  A good example is MacArthur, who let his ego cloud his ability to work with the US Navy.  Carlin's premise, and it's one we all should pay attention to, is that if Vulcans, who decide purely on logic, were in charge, things would be done differently.  Instead, humans are in charge, and we decide based on emotion, knowledge, logic and a host of other factors.

My brief segue about Hard Core History aside, the same concepts are true about leadership teams.  What gets in the way of success are different agendas, misalignment, different priorities, poor communications, distrust, lack of clarity, lack of accountability, poor execution, lack of measurement.  When I talk to leaders in businesses of all sizes, these are the issues that really create difficulty and drag the business away from what it could achieve.  These factors exist in small, underfunded startups and in large, public Fortune 500 companies, because, ultimately, we are people with selfish interests trying to work together to create a company that creates value and rarely do people see eye to eye on everything.

These conditions exist in good times and in bad times, by the way.  When a company is profitable and the markets are good, it's easy to overlook or ignore issues or challenges in these factors.  Then, when hard times hit and it's important to button up and become more effective and efficient, poor habits or lack of focus is hard to change, since it has been ignored previously.

Working it out

While leaders in every organization have a tremendous amount of work on their plates, it makes sense to check in on these factors at least quarterly.  Just as people exercise to keep their bodies fit, companies should review their interactions, alignment, execution and accountability to ensure that the organization works at peak performance.  There are simply too many factors, that are too critical to success, to allow these to atrophy or to gloss over non-compliance.  

And, as these factors become clearer and more closely evaluated, two things will happen.  The organization will work more effectively, freeing up time for executives to focus more on the strategy and less on the day to day, because they are no longer bridging gaps, and, as the engine of the business becomes more efficient, growth and scaling is actually easier to accomplish.

You can't burn data

 As the concept of digital transformation takes root, you may frequently hear comparisons between data and oil.  After all, both are abundant commodities that can create value.  This comparison was strong enough to lead Wired magazine to define data as the new oil in a magazine article some years ago.

On the surface, this comparison seems to make some sense.  Both data and oil are commodities, and exist to some degree in large volumes.  Both have the ability to create incredible wealth when harnessed appropriately.  Both can be used for good purposes or misused.  Both data and oil have interesting issues and side effects, pollution in the case of oil and loss of privacy in the case of data.

But the more you consider the two, the faster the analogy breaks down.  That's because the analogy works at the surface, but when you carefully think about the value propositions and the issues associated with oil and with data, you'll see that there are some really interesting similarities but some stark differences.  We gain value from oil by burning it to generate heat, light or kinetic energy.  The conversion of data to value simply isn't that straightfoward.

Let's look at some interesting similarities first.

Similarities

I'm not a data scientist, but I did start my career developing enterprise systems and worrying about the value and quality of data that flowed through those systems.  Data quality and data volume may not seem like interesting concepts, but when we talk about using data to "transform" a business or process, the data needs to be of the highest quality if decisions are to be based on the data.

Most businesses have a range of data types, data quality and data management, none of which lead to high quality data.  And, as stated previously, without good quality data, you cannot automate or transform anything.  So, all the talk of digital transformation is talk until companies normalize, standardize, simplify their data and ensure it is of the highest quality and veracity.

Another problem many companies face is the Volume and Velocity of data.  And yes, I am capitalizing the Vs because a good way to think about data is in Vs:  Veracity, Volume, Variety and so on.  Veracity or truthfulness or data quality is obviously key, especially when training a machine learning algorithm.  However, veracity is difficult if the data comes in a variety of types and the sheer volume of data is so high that people and machines cannot determine what data is useful and what data isn't.

In other words, most companies need to refine their data, in much the same way that oil coming out of the ground needs to be refined.  Venezuela has perhaps the largest oil reserves on the planet, but since most of its oil is thick and loaded with sulfur, the oil there is of less value and needs to be refined in order to be used effectively.  Oil straight from the ground can be refined into a number of different grades of fuel we consume, from bunker oil for ships to gasoline for cars to home heating oil and many other types.  But raw oil isn't all that useful until it is refined, and sweet crude from Texas is easier to turn into marketable products than Venezuelan oil.  In the same way, some data is more valuable, and easier to put to work, than other kinds of data.  As Einstein said, not everything that is countable counts.

Like oil, data need to be managed, refined, combined and prepared to be used effectively, and few firms have a strong handle on what it takes to manage data and ensure that the data is of high quality when it goes into a system, and remains high quality as it is used, combined with other data and manipulated.  When you stop to consider that the most common reporting mechanism in companies is Excel, where any cell can be changed or any calculation can be written incorrectly, there are thousands of opportunities for even high-quality data to be mis-used or reported incorrectly.

Differences

While it is a simple metaphor to compare oil to data, it is also grossly misleading, for several reasons.

First, oil is a non-renewable commodity.  That is, there is only so much oil in the ground, and it is getting more difficult to extract what's left.  On the other hand, data is getting created every day, by billions of people, about millions of topics, in hundreds of types.  Data is not only renewable, but it is almost inexhaustible, limited only by human creativity and need. Each month we created as much cumulative data as was created from the dawn of writing until last year.  The problem isn't data, the problem is the volume and veracity of the data we are creating.  With so much data generated from so many platforms, how do we know what data is useful and meaningful, and what data is created merely to distract or confuse?  I can imagine it will soon be possible, if it is not already, to create AIs specifically for the purpose of creating seemingly valid information that has little or no basis in reality, to confuse or distort economic projections or scientific inquiry.

Second, while oil was the basis for carving up nation-states in the Middle East after the first World War, data will be the dividing line in the future.  Oil to a great extent is a monopoly based on accidental or purposeful geography.  Some countries - Saudi Arabia, Venezuela and others - have lots of oil, while others - Japan for example - have little or no oil reserves.  Japan has been a major success for a country with limited natural resources such as oil, but data is unlike oil.  Anyone can create data, and almost all of us do so every day.  Increasingly, it's not countries or geographies that control data, but companies.  One could easily say that Meta is the Saudi Arabia of the data stores, since it has so much data about so many individuals.  Nations, which once coveted oil, are just waking up to the value of data and realizing how much power data provides and wondering how virtual companies like Meta and Google have managed to hoover up so much data and become so powerful.  Why is Mark Zuckerberg advising Congress on data?  Because Meta controls more data and has more insight than most agencies in the US Government.

Third, oil has a provenance and a supply chain, for the most part.  That is, we talk about "sweet Texas" crude or Saudi oil or Venezuelan oil.  While oil is a commodity, it typically has a provenance that indicates its value, and further there is a value chain associated with oil.  Oil moves from a driller through a pipeline or other distributor to a refiner and then on to another distributors, a wholesaler and a retailer.  In other words, there are specific value-added components that make up the oil to marketable commodity (such as retail gasoline) that we consume.  Data does not necessarily come with a provenance and does not need a supply chain to reach a consumer.  Anyone can post a data set that they've created, create their own research or surveys, and make that data accessible to almost anyone, instantaneously, on the internet.

Next, oil is a commodity, and certain grades of oil are priced in global markets, regardless of where they are drilled.  Data, on the other hand, is often not quantifiable as to its value or price, and the same data set can be more valuable or less valuable, depending on where it is, who has it and who needs it. A list of names can be very important, if the list is a list of spies in a foreign country, or conversely very unimportant if it is a grocery list for a suburban family.  But if a company could compile thousands of lists of grocery shopping for families in similar circumstances, that compendium of data would become valuable to grocery chains and the brands that sell on grocery store shelves.  The value of data is in the eyes of the beholder, and the value of data varies with a number of factors.   

Finally, since oil is a commodity, it is easy to acquire in a specific grade at a specific price.  Data, on the other hand, is difficult to aggregate, difficult to grade and almost impossible to price.  I recently acquired a list of prospect companies and executives that promised to be up to date and highly accurate.  As I scanned through the list, I quickly found several mistakes or omissions that should have been easy to identify in just the first fifty to one hundred names.  It is hard to keep many types of data accurate and fresh, difficult to validate data without human intervention, which makes almost all data stores suspect to some degree and in need of constant evaluation and refreshing.

Thanks for the analogy, what does this all mean?

In the end, the analogy between oil and data falls apart.  Oil is a standard commodity that for the most part we burn to gain heat or kinetic energy.  We transform oil into one or two potential outcomes, at specified temperatures and pressures.

Data, on the other hand, is not a commodity, is perishable, is constantly renewing and generative, is difficult to price or establish a value for, and we find it difficult to create real quality metrics for data.  Plus, data is not a commodity.  The same data sets have different values at different times to different users.  

What this means is that the talk of digital transformation - using data to fuel a new era of economic growth, in the same way that oil spawned economic growth and benefits in the 20th century, is optimistic or true in some circumstances but not in others.  Companies that want to transform themselves to be more effective and to create new revenue streams based on data must first address the questions of veracity of data, volumes of data, varieties of data.  In a time when analysts suggest than less than 10% of the data that companies currently possess is being used to create value or insight, what leaps of technological advancement and data quality improvement are necessary to base a new economic model on driving competitive advantage from data?  After all, we aren't converting a commodity into heat, light or kinetic energy.  We are transforming trillions of data sets from millions of data sources into useful insight, which companies can act on.  

This means that the benefit to data will be widely and unevenly distributed, highly beneficial in some industries or market segments or niches, and almost impossible to understand in other segments of the economy, for decades yet to come.  Those companies and industries that cleanse their data, understand the variety and volumes of data and who can make sense of the data to convert it into useful insights that indicate future actions will benefit to an enormous degree.  The rich will get far richer.

Many industries will spend billions of dollars trying to get the same benefits, but will lack the basics - good, high-quality data, understanding which data matters, being able to capture and use the most effective and useful data, and being able to convert the data into beneficial actions. 

Sneaky innovation

 I'd like to write today about what I call sneaky innovation.  I define sneaky innovation as the innovation work that is often started and completed in disparate locations in the business, is not strategic and is often completed without a lot of fanfare.  Sneaky innovation is about doing small but impactful innovation, without asking for permission or waiting to see who will approve.  The fact that sneaky innovation accounts for probably 30-40% of most of the innovation that gets done should tell you something:  there's a significant amount of need for new ideas and for innovation generally, but not a lot of will to seek permission, or a good sense of what the answer will be if the request is made.

Eventually the good news about sneaky innovation is that teams can demonstrate that they can get new things done.

The good things about sneaky innovation

There are several very positive attributes of sneaky innovation.  When innovation is small, kept quiet and worked on by a small team, the ideas can flow freely and new insights will emerge.  Often, starving innovation teams of resources, time and attention will require them to think more creatively and generate ideas or solutions that would not have been generated otherwise.  Sneaky innovation is often focused on process improvements and customer service or experience problems that teams can define and implement without a lot of approvals or permissions, and demonstrates that teams can identify needs, generate ideas and implement solutions often without the management team knowing, at little cost or risk.  So, sneaky innovation demonstrates that the capability to innovate exists, as well as the ability to put good ideas to work.  

The problem with sneaky innovation

There are, as you might imagine, a few problems with sneaky innovation.  For instance, sneaky innovation can't work on physical products or business model changes.  No matter how sneaky you are, someone is bound to notice a new feature or a new revenue stream or cost component.  So, if you rely on sneaky innovation for your business, you are limiting yourself to innovations and implementations that few people will notice until after the fact.

Sneaky innovation is, almost by definition, a local phenomenon.  That is, different groups will perform their version of sneaky innovation at random times, and in random ways.  There's no way to scale success from sneaky innovation, and few ways to describe how it works or how others can learn from success, since the point of sneaky innovation is not to raise too much attention to the fact that innovation is going on.

Sneaky innovation will almost always be starved for funding, but not for personnel.  People like to pull one over on their managers, so they will commit time and energy if they think the idea will succeed.  This means that ideas cannot cost a lot of money to implement or test, but can take a significant portion of peoples' time and attention.

Bottom up or top down innovation

In the past, I've tried, with great passion and hopefully deep logic, to try to illustrate why I think innovation should become a business process, ordained by the corporate executives, sustained at all levels of an organization, encouraged by culture and incentives.  However, that vision will require a new set of leaders who may be emerging, but it does not seem to sit well with existing corporate leaders who are happy to isolate innovation in R&D, or dabble in occasional innovation projects but fail to build innovation capabilities and capacity.

If we cannot build the idea of innovation as a repeatable process and a cultural phenomenon, we need to go guerilla.  Find the executives and manager who are willing to take risks, to create smaller, sneakier innovation projects.  Ideate, generate ideas and implement under the cover.  Only claim the results once the benefits are clear.  Perhaps it's time to build from the ground up, rather than from the top down.

Five reasons why few companies are data driven

 For years now, we've heard of the importance of becoming a "data driven" organization.  Being data-driven, we are told, means making decisions not based on gut instinct or what managers believe is true, but based on evidence, on hard data.  This, it would seem, should be simple.  After all, businesses have had robust IT systems and teams for years.  The advent of ERP in the 1990s and beyond created sweeping systems that automated many sectors of the business.  Even smaller companies that did not need the larger ERP systems like SAP could find solutions in Salesforce, Workday, Netsuite and others.

So, why is it that so many companies cannot make decisions based on their data?  If we've known about this need for decades, if we expound upon the importance of data and its use in decision-making, if robust systems exist to allow us to create, gather and use data effectively, why are so many companies no closer to becoming a data-driven organization.  There are several reasons:

1. The executive team does not like what the data tells them.  In many cases, data contradicts what management wants to do, so in those cases the data must be "wrong".  The data is inconvenient to current thinking or perspectives.
2. The executive team does not trust the data, and has good reason not to trust the data because it is out of date or incomplete.  The data is full or errors, untrustworthy, incomplete.
3. The executive team trusts the data and the data is accurate, but the data does not inform current or future decisions.  It is good data, just not the necessary data.
4. There is too much data and not enough information to inform executives.  The team is inundated with data and has too little information.
5. The executives have accurate and up to date data that is meaningful and easy to understand but they do not know how to interpret or act on the data.  The decision makers do not have the perspectives or tools to interpret the data and make decisions.

In some cases, all of the above is also true.

From these five reasons, there are three categories of issues:  problems with data itself, problems with policies about data, and problems with people.  The first two are the easiest to solve.

Problems with data

Every company has lots of data, often spreadsheets full of data.  And therein lies the problem:  there is no single source of truth, and data is highly compartmentalized and curated for each team or department.  When decisions need to be made at an enterprise level, but all data is managed and manipulated at a team or departmental level, obtaining a realistic view of the data or information a company has, and what that data means, is difficult because when aggregated, the data has passed through so many filters or lenses that it is often contradictory or meaningless, or so out of date as to be useless.

The problem is also part of the solution:  many managers and executives grew up in a time when IT managed all the data, and IT can be monolithic and slow to respond, full of data governance rules and policies.  So, many companies have gone "free range" with their software selections and data management.  Every department selects its chosen software, deploys how they care to, and does not worry about enterprise data or data management.  The problems with this approach will emerge within 6 to 12 months of the third or fourth departmental deployment, when executives request a single view of the business on one sheet, and no one can provide it.

Problems with policies

Face it, no one likes being told what to do, and no one likes policies, until those policies actually benefit them.  Managers and executives don't like data lexicon or nomenclature, or rules about data usage or data governance, because they impede speed and deployment and impose rules.  However, months or years later, they inevitably look back wistfully and wish they had had the coaching to implement data governance early, because imposing these rules and policies later in a company's life is difficult.  My advice:  start defining your data policies and rules early.  Keep your definitions consistent and clear, your calculations the same across teams and divisions.  Trying to impose data policies, data governance and data quality on a company years later is like trying to fit a grown adult into the clothes they wore in their youth.  What you need is a good suit of clothes that can be upgraded and tailored from youth through adulthood.

Problems with People

Even if your data is correct and your policies are strong and the data is timely, if managers or executives cannot interpret the data or cannot describe the data or information they need to run a business, all the other infrastructure work will fail.  There are plenty of people who are experts at a very narrow range of business metrics - sales people know sales metrics, financial people know financial metrics, but few of us get education or experience in managing metrics across a business - we are often simply too siloed.  But in a small but growing business, understanding all of the metrics is vital.  

As much as we'd like to believe that we educated humans are rational actors, we are attracted to data that reinforces our biases and reject data that calls into question our wants and goals.  A data driven organization needs a Spock-like character who can make decisions not based on desires or emotion but on pure logic, and most of us aren't able to do this reliably.  When you realize that NONE of us are logical or Spock-like, and we all have our own perspectives and biases, you can see how difficult decision making influenced by data becomes.

That's the data, what does it mean?

Finally, being able to understand the data is one thing, understanding what it means, what the data is telling you is another thing entirely.  Here, we can't simply wish our way to intelligent AI bots that will tell us which actions to take based on the information we are receiving.  Good managers and executives will need to combine experience, history, data and information, emerging trends and some gut instincts in order to evaluate data and make determinative decisions and actions.  Most of the existing management cadre in business today are familiar with backward looking data (reporting) and historical trends and precedents but are not familiar with the existing pace of change, or the amount of data and the requirements for interpretation of the data, much less trying to determine future actions based on the volume of data they are receiving.  

Solving for the three big data issues

There are two approaches to solving for these issues - bottom up or top down.  My recommendation is top-down.

The bottom-up strategy will focus first on getting the policies correct, then correcting the systems, how the systems are integrated and the validation of the data.  It's not really all that helpful to start improving the data and systems unless there are policies that will sustain the changes you make.  Once the data, systems and policies are in place, then you can ensure your leadership team can interpret and use the data.

The top-down strategy requires that the executive team and senior users and managers of the data can describe what they need in order to make better decisions, and how they need the data presented to them.  Then, they can create or reinforce policies around data strategy, data quality and data governance, which will influence how systems are implemented and how data is exchanged and aggregated.

Understand that all of these things need to change - the systems and their integration to get better, faster data; the policies to ensure that they data is useful and meaningful; and the users, to ensure that they are educated in how to interpret the data and how to make decisions based on data.  This is yet another holistic change that needs to occur.  Changing policies without changing systems, or educating the executive team without improving the quality of the data leaves the solution incomplete.

Objects in the mirror are closer than they appear

 I'm constantly struck by how much faith businesses put in numbers, especially numbers about the past.  All businesses want to be "data driven" and the volume of reporting that occurs at the end of a week, month, quarter or year is astounding.  Of course, we want to know if we made our numbers, or how key metrics compare to metrics from other months or quarters.  But the focus on numbers about the past, even the recent past, pales in consideration to the focus on what's going to happen next.  And there are several reasons for the emphasis on looking back at numbers rather than looking forward to possibilities and probabilities.

There are several problems with a focus on numbers and compilation and analysis, when looking at a quarter or month just completed.  First, most companies don't have great data quality or data flow, so a lot of the metrics they are looking at require a lot of massaging and human manipulation, which eats up an enormous amount of time.  Second, the data is all hindsight - did we make the quarter just past?  How does that compare to the previous quarter?  Of course, these questions are important, but just as important is:  what's going to happen next? 

Far too little data and analysis goes into what's going to happen next.  Yes, there are attempts to build pipelines and forecasts, but those are often wrong by orders of magnitude.  We aren't up to using data to be prescriptive about the future and have too many people focused on what just happened - reporting, rather than figuring out what's going to happen - predicting.

This focus on historical data isn't a problem in settings where there are few uncertainties and little change.  The emphasis becomes a larger problem when data is uncertain, people are at a premium, and understanding what's going to happen next is more difficult.  Welcome to your VUCA world, where things are changing faster than ever, and often in unexpected ways.  Now we need data and people to help us understand what is going to happen and how to prepare for and anticipate what's going to happen, because factors are changing in unpredictable ways and the rate and pace of change is accelerating.  

Worse, we've trained a cadre of managers who are good at reviewing and analyzing data about the past, but don't have the tools or skills to look forward with any degree of certainty.  When I do trend spotting and scenario planning with my clients, it's interesting to hear people talk about the future and what will occur and the ideas they create as if the possibilities are unlikely and far in the future, when many of the ideas and situations they create are actually happening in real time.  As William Gibson has said, the future is already here, it's just not widely distributed.

Doing well in business in the coming years will be more about using data, both quantitative and qualitative, to understand and predict what is likely to happen in the short run, and to develop scenarios and pathways for the longer term, again based on emergent data and observable trends.  This is not yet an area where AI or ML will be as helpful, because the data and patterns are not yet as established.  Analyzing and predicting based on past behavior in somewhat stable environments is a trainable activity.  Anticipating events and scenarios where there is no history or data, with uncertain inputs and conditions requires a significant amount of adjacent thinking and exploration of possibilities.

The old warning on rear view mirrors was that objects in the mirror were closer than the image in the mirror might suggest.  I think we need a new warming, on the forward windshield of your business:  Objects in the future are closer, and moving faster, and more erratically, than you expect.  Is your business prepared?  Is it capable of being proactive, understanding what might happen and being nimble enough to address rapid changes in stride?  Does the word "surprise" show up often in your 10Ks and 10Qs?  If so, it may be time to change your emphasis from reporting to predicting.

Identifying and overcoming the innovation resistance

 All good stories need a protagonist and an antagonist, the "good' character and the not so good character to develop tension.  Eventually, in any story, the tension is resolved - the protagonist wins (in the happy ending stories) or the antagonist changes sided, or the antagonist wins (unhappy resolution).  Every story with any meaning or tension has these two opposing forces. 

So, it seems strange to suggest that there are innovation protagonists, and innovation antagonists.  We are led to believe that "everyone" wants innovation.  Everyone likes new things, new ideas, new experiences.  But with every new creation, something or someone is put at risk.  Even people who claim to like and appreciate innovation can become resistant or antagonists to innovation when new ideas threaten their cherished products or positions.

Why innovation is like Star Wars

I grew up with Star Wars, with Luke and Darth Vader as the key actors.  I guess it's not strange that Luke is always shown wearing a white shirt and Darth is always depicted as wearing a black cape.  Much like the old westerns where the good guy wore a white hat, and the bad guy a black hat.  

The strange thing about innovation is that there are Lukes - people who really believe in the power of the innovation force, as a means for good, and people who, mostly in the moment, see innovation as a threat, or a force to be managed or turned to their own devices.

I don't think anyone sets out to be a Darth Vader for innovation intentionally, but many people play the role of Darth when innovation threatens the projects, products or positions that are important to them. Let's consider why, and when, innovation becomes a force that will create resistance and Darth-like characters may emerge.

What causes the rise of the Dark Lord of innovation?

Innovation seems to be a very positive force, creating value and opportunity, creating new products and services that most consumers want and need.  Like ice cream and puppies, it's kind of hard to imagine that anyone would resist innovation, and I suspect that even the people or teams who do resist innovation often find themselves in an uncomfortable situation - resisting a force that they know is meant for good, but in the moment appears as a threat.

There are at least three instances where innovation resistance will arise, and it will arise mostly within a company (although sometimes from external actors) and mostly will arise as a reaction to a perceived threat. 

The "locked in" people and products

You've encountered this before.  Some people are resistant to change, even when you can show them a better and brighter future.  They resist change, and its uncertainty, not necessarily the idea or solution itself.  To these individuals or teams, change itself is the enemy, and innovation is simply another attempt to create change that they will have to deal with.  The language of these folks is:  "if it ain't broke, don't fix it".  Change of any form creates challenges and uncertainty.  These individuals revere the past glory of their product or company, and are very concerned about the future.

The leaders of products put at risk by innovation

In the zero sum game of most corporate budgeting, a new product needs to get funded, and those funds almost inevitably come from an existing product.  Rather than recognize that all products go through a life cycle, some product managers become affixed to their products, and seek to defend them from all new opportunities and ideas.  While these individuals aren't afraid of change and are in fact often open to innovation, innovation that threatens their sacred cows will be fought on all fronts.  The language of these people is:  yes, let's innovate, but not at the expense of my product.  In another setting, these resistors can be innovation champions.

The bean counters

Another segment of the population that will rise to fight off innovation are the people who wield the letters R-O-I like a light sabre.  If an idea cannot guarantee a specific return on investment in a ludicrously short time frame, the idea must be rejected.  These individuals evaluated nascent ideas on impossibly stringent metrics, that they often don't hold even existing projects and products to.  Their language is about investment and risk, while they miss projects and products that have enormous sunk costs.

Defeating the innovation resistance

It's crucial to anticipate each of these dark forces arising to do battle with your ideas, and to understand who will attack your ideas and how to either win them over or to defeat them.  The most difficult to win over are the people who simply resist change, because these individuals often aren't fighting fair.  Like Sith Lords, they show up in multiple disguises and with unusual weapons, but their ultimate resistance is in their lethargy, their foot-dragging and their disdain for change.  The simple fact is that a corporate bureaucracy will not change if it does not want to, unless it is forced to, or the bureaucracy is changed.

The most difficult battle will be with the product or project leader who feels threatened in the moment by a specific innovation project.  In these instances, the individual or team whose product is threatened by a new innovation will literally "go to the mat" to save their product or project, resisting innovations that make sense.  In this instance, the only way to win is to bring in bigger guns - the executives who will make decisions and prioritize projects.  

The most subtle battle is with the funders - the accountants and financial people who will want to understand the potential return of the investment in innovation versus continuing to invest in a proven commodity that exists.  Here, you will need to turn their tools against them, to do your homework to demonstrate that your idea has financial merit, that the investment pays off in a reasonable timeframe, and that alternative investments aren't as good as yours.  Which means you'll need to get one of them on your side, because you'll need to bring an accountant or financial manager to a finance fight.

There's no Death Star

Unlike Star Wars, the innovation resistance doesn't have a Death Star and isn't really seeking to destroy the concept of innovation.  Instead, the battle is really more of a thousand cuts, constantly questioning the value, the direction, the focus, the support or the need for innovation.  Instead of one climatic meeting, innovators have to be ready with all their tools all the time to meet and overcome the resistance.  Or, be ready with a lot of Jedi mind tricks to get the management team to play along.

You can expect an explosion of innovation in the next 18-24 months from June 2023

 I am going to go out on a limb.  Not a big limb, but any time you make a written prediction that people can come back and check, you put yourself at risk.  My prediction has to do with innovation.  Predicting innovation will seem unusual and risky for many people, because innovation seems so random and difficult to anticipate, like a lightning strike.  But if you understand the conditions that make a lightning strike possible, suddenly the strikes don't seem so surprising.  The same is true with innovation.

Why innovation will flourish in the next two years

Innovation will flourish in the next two years for a couple of reasons.  First, we are going into (perhaps already are in) a recession.  As you look around, you can see many large companies cutting staff, in preparation for leaner times.  People are asking questions about hard and soft landings, about the length of the recession and so on.  History shows that most recessions are relatively short, about 18 months, and the economy often rebounds with great strength.

But I'm not forecasting recessions, I am forecasting innovation.  Another thing we know about recessions is that lots of new companies get created by all the people who are released from larger firms.  People start up businesses and pursue ideas that they would not have done otherwise.  While startup money is tighter in a recession, expect thousands of new companies to be created.  This will lead to new products, services and business models.  To be honest, this isn't a prediction as much as it is a recitation of fact - almost every recession in the last 40 years has led to immense innovation.

There are some other factors to consider that make this time period different, however.  First is artificial intelligence, ML and ChatGPT and all of the new technology that will make people obsolete.  Just as the loom did not replace all workers - in fact created more jobs - AI will not replace all workers but create new roles and new jobs, as well as new products, services and business models.  

Innovation creates and spawns new adjacent jobs as well.  New technologies and new services require new marketing and sales channels and messages, new support structures, new payment mechanisms.  The interwoven layers of technology will create new jobs.  New robots require maintenance techs to ensure the robots are working correctly and programmers to improve how the robots operate.  

Creative destruction

What we are going to witness over the next few years is what Schumpeter called "creative destruction", which means that new ideas will reach the market as new products, services and business models and will destroy existing industries and jobs.  That sounds scary, and it can be, for those unwilling to understand the emerging possibilities.  In every transition from one technology to another, people have rebelled.  The printing press was considered a threat to copyists, the automated loom to weavers.  And the people who held those jobs were right - those jobs were under threat, but other, better jobs emerged.

We are likely to see creativity unfold in ways we've rarely seen previously in these tectonic technology shifts, because we have layers of technology that other technology is built on - computers support the internet, which enables machine learning and artificial intelligence.  We are gaining exponential benefits from previous investments in innovation, going up a learning curve.  That learning curve and its impacts will have difficult implications for some jobs but will create other opportunities as a by-product.

We are likely to see the destruction of jobs and industries as well.  Increasingly, white collar work will become threatened by the power of artificial intelligence, and there could be a resurgence of high-tech blue collar jobs.  Machines can already read x-rays as well as humans, and much more quickly and far less expensively.  Plenty of white collar jobs gathering, analyzing and making sense of data will be at risk.  

Bifurcation

There are gaps, however, in this possible future, the biggest of which is the growing gap between those who are dialed in to new technologies, either as the technologists or knowledgeable users, and those who do not have the skills or capacities to understand or harness the strength of digital transformation.  It's becoming critical that we rethink how we educate people and how we introduce them to what is about to occur in technology, so they can be involved either in the creation of new AI and ML, or at least become knowledgeable about how to use and deploy these tools and other manifestations such as robots.  If too many people are left behind, unable to create these technologies and uncomfortable or uncertain about their implementation and use, a widening gap between those who understand and control technology and those on the outside will threaten the stability of the markets.

There is a huge opportunity here, and when opportunities emerge, there is another opportunity for innovation.  We cannot afford to create two classes of citizens, one that has access and understanding of emerging technology, and one that is cut out from any engagement of technology other than as a consumer.  I think we'll see a lot of innovation around education and training people to become more adept at the development of AI and ML, user experience solutions to simplify the interface to AI and ML, and methods to learn to use AI and ML more effectively.

Whole Product

Finally, there is the "whole product" model to consider.  Many of these emerging technologies are used by scientists, technologists and early adopters.  As fans of Geoffrey Moore know, that market represents only 10-15% of the total market - the larger segments are in the early majority.  As the title of his book indicates, these technologies need to "cross the chasm" from technologists and early adopters to the early majority of customers.  What the early majority wants are complete products, easy to use, with plenty of support.  Thus, creating the whole product for AI, ML, robotics and other technologies creates an entirely new industry and valuable opportunity.  AI and ML will need to leave the back office and the lab and become everyday products used by people everywhere, and to do that, those products and technologies will need to consider user experience, support, interface management and a host of other challenges that technologists skip over.  Why do you think there are so many LinkedIn posts about how to create useful prompts for ChatGPT/

Will we see it as an opportunity or a challenge?

Innovation is about to unfold in ways we cannot appreciate, due to the stacking of proven technologies to lead to greater computing advances.  Further, a host of people leaving larger companies will be starting their own companies, creating a range of experiments that will pay out over the latter half of 2023 and into 2024.  Emerging AI, robotics and other technologies will feel like an assault on existing jobs and industries, and a wave of protectionism is likely, but ultimately futile if past is any indication.  We will need to ask ourselves:  is all this change an opportunity or a challenge?

My innovation journey (so far)

 I think a re-introduction is in order.  I've been leading innovation work for close to 20 years, writing about it on this blog and in my book (Relentless Innovation).  I've made some great friends along the way and been influenced by a range of great innovation thought leaders.  Over the last year, I needed to take a step back, to focus on some other things, and to get a new perspective.  Too much time at the innovation coal face was making me a bit cynical.  I'm ready to dive back in, to write about innovation, to lead innovation projects and to build up an innovation competency in the firms where people want to get good work done.

My innovation journey

My journey started in a rather inauspicious way.  A good friend and I were eating lunch, and talking about concerns that there wouldn't be any good or interesting jobs for our kids, because of the usual technology advances - robotics, automation, machine learning and other factors.  We felt that to remain competitive, larger companies needed to recapture the sense of wonder and adventure that working in the "front end" of innovation would create.  My friend, who founded OVO Innovation with me in 2004, was really passionate about the idea of reviving innovation in corporations.  I have to admit, I was skeptical at first.

Doing new things in large corporations is difficult.  There is too much pressure to toe the line, to continue to work in highly efficient and well-understood processes.  Market evidence will tell you that most of new product innovations fail in the first year.  Most managers will ask:  why take the risk?  Managers can squeeze out a few more percentage points of profit on existing products without significant risk.  Any innovations kicked off now will only pay off - if they pay off - down the road.

But, we got started, getting some initial training from Tim Hurson, who wrote Think Better, a great book on innovation thinking, creativity and facilitation.  We won a few small projects at first and had the idea that what companies needed was a great place to store and manage ideas, so we built an online idea generation and brainstorming platform, and an idea management solution, which completed with industry leaders BrightIdea and Spigit and Imaginatik back when.  What we quickly realized was that while companies could acquire and use our software, they lacked the infrastructure, the processes and the teams to do anything with ideas, so we quickly scaled up a consulting capability, more focused on how to do innovation, rather than how to capture and manage ideas.

Innovating on Purpose

With this learning in mind, we created a methodology called Innovate on Purpose, which was meant to illustrate that innovation is not accidental, it isn't something that happens only in R&D and isn't a black-box activity.  Defining a process and the meaningful steps, and training good people on the thinking styles and tools can accelerate innovation and, more importantly, make it a repeatable activity.  In other words, rather than consider innovation an unusual, once in a lifetime accident, we believe innovation can become a more consistent process within a business, easily taught, frequently repeated and far more successful as teams experience innovation work and come up a learning curve.  We simply need to take some of the fear, risk and uncertainty out of doing innovation.

We worked with a number of Fortune 500 companies, mid-sized companies and even some startups, to deploy our methodology, to build innovation competency teams and implement processes.  I'm happy to say that many teams and individuals we worked with are still innovating, but certainly not all of them.  What we found over time is that frequently innovation is simply a word that executives and corporations want to use to appear to be creating new products and services, while neglecting the people and the investments necessary to create new products and services.  Just as "green-washing" takes place to make companies appear more ecologically friendly, so to does innovation theater happen in many companies, to make it appear they are doing more new product development than they are in reality.  This practice, of course, has led to its own moniker - innovation theater - and to rising cynicism on the part of people who want to get more innovation done.

The next shiny object

I started to get a little frustrated and perhaps a little disillusioned a few years ago, when I was asked by an executive in a company I was working for to blend "digital transformation" into our innovation offerings.  Digital transformation was becoming the hot buzz word, and I worried that it would detract from innovation - creating new things of value - to switch the focus to digital transformation - implementing systems and technologies to cut costs.  And you can guess which version is winning.  Digital transformation can be innovative, but most digital transformation is about cutting costs using technology that exists.  Not too innovative.

Now, of course, there is a new buzz phrase - machine learning and/or AI, especially focused on ChatGPT.  Many businesses are now trying to grapple with what generative AI or applications like ChatGPT will mean for their business.  It's not a wonder that many executives, confronted with significant wage and cost inflation, a divided economy, trouble in China and a major focus on AI, are not quite certain where to place their bets.  Any investment in innovation, or digital transformation, or AI, or reworking a supply chain, will take time and mean there is less investment for other activities.

Innovation still remains

However, one thing is certain:  except for champagne producers, every company needs to create new products and services in order to stay relevant.  Whether those products, services, business models and customer experiences are leveraging digital transformation, or are delivered through artificial intelligence, we still need to spot ideas, ensure they align with emerging customer needs and to take risks creating new products, services and business models.

When we finally cut through the noise and begin to identify what is real, what drives value, what creates new products, services and business models that customers want and are willing to pay for, at higher margins than existing products, we'll see that innovation is still as vital as it ever was.  This is illustrated in some way by the number of entrepreneurial firms creating new products, services and business models.  Will larger firms continue to focus on their core products, ignoring the need to innovate, as time compression and customer demands makes the near future more volatile?  I think we'll need to watch carefully.

To end, I'll just note that the revered management thinker Peter Drucker said that businesses really only have two important functions - marketing and innovation.  According to Drucker, everything else a business does is a cost.  Let's get back to doing the important work - innovating products, services, experiences and business models.

Relying on outdated tools or conditions

 In uncertain times, where do you turn for insight and answers?  Most often, it seems, we turn to what we know and trust.  Older tools and models that have proven themselves over time.  In the uncertainty we face today (this post was written in May 2023), many business leaders are facing uncertainty about what strategies to implement, what plans to make.  Inflation is still running high, there is a significant amount of political uncertainty in the US, a war in Ukraine and rising tensions between the US and China.  

Supply chains that once seemed so efficient now seem easily disrupted.  Costs, for people and equipment are rising.  When we face all of these uncertainties, what tools or methods help companies understand the current state and plan for the future state?  What do managers turn to for answers?

There are several answers, and none of them are perfect.  Many managers and executives will look to the past, to see if there are lessons learned from previous, similar episodes in history.  Others will look to their education and the frameworks and tools they learned in college or graduate school, to help them understand the markets and their shifting demands.  Still others will look forward, to try to understand what the future may bring, and how to prepare for an emerging future that is unfolding.

I've written previously about VUCA - Volatility, Uncertainty, Complexity and Ambiguity - and how the military and government will use a VUCA model to begin to decipher what they are facing.  Today, there are several factors that business face from within the VUCA framework:

• Volatility - in the US, there is volatility in the market, especially with what has traditionally been a relative rock of stability - the banking industry.  As SVB and now First Republic go under, a cornerstone of the US economy now seems more uncertain, more volatile, and that causes concern not just for businesses, but for households as well.
• Uncertainty - in an economy with high inflation (well, high compared to what we've experienced for the last 30 years), there is a lot of uncertainty about what the Fed will do, what results its actions will create, and what the near future will look like from a growth or recession point of view.  No one has great certainty now, unlike just a few years ago when interest rates were approaching zero and the market was growing.
• Complexity - many businesses are unraveling or rethinking systems, processes and supply chains that have been implemented in the last 20 years.  China, once a low-cost production location, has become more expensive and a competitor, both on a business level and on a national level as well.  
• Ambiguity - This is the one factor that I think is less worrisome.  I don't think our challenges are ambiguous.  We may lack the correct models or frameworks or experience to decipher them, but that makes them complex, not ambiguous.

With all of this said, let's go back to the three ways managers often try to interpret their setting and the risks associated with each.

Looking to the past

The last time we had high inflation in the US was during the late 1970s and early 1980s.  While our recent inflation was challenging, it was nothing compared to the inflation of that era.  It makes sense to review what managers and executives did in that time, but that insight comes loaded with a lot of freight and uncertainty.  The 1980s were an inflection point in the US, a shift from the more liberal 1970s to the Reagan economy in the 1980s, where tax rates fell and spending on military and technology increased.  Also, the 1980s were the beginning of the end of the Cold War, where the US was increasing spending and the USSR was going bankrupt trying to keep up.  There were two "poles" in the world at that time.  China was a low-cost producer of inexpensive goods, and Mexico was just beginning to take advantage of NAFTA.  

What the Fed did at that time is what the Fed is doing now, raising rates to slow inflation, which led to a recession.  In some regards, history will repeat itself now.  But, as Mark Twain said, history doesn't repeat itself, but it does rhyme.  Meaning we can learn from the past, but we should not expect the present situation to repeat itself exactly.  

Understanding the present and especially looking forward to the future based on how governments and businesses reacted in the 1980s does not take into account the tremendous changes that have happened since then - the establishment of the EU, the advancement of the internet, the rise of China and the diminishing Russian economy and threat.  We should use the past as an example, but lift only those learnings that are still relevant to today's environment.  Relying too heavily on lesson from the past, when the past conditions were so different from today's, introduces ideas and concepts that may not be relevant or useful.  

Relying too heavily on lessons from the past can lead to mistakes, what the military calls preparing for the last war, and failing to understand that the next war will be different.

Looking to your frameworks

Many executives in charge of businesses today were in graduate school in the 1990s and 2000s.  This meant they learned at the feet of Michael Porter and others and were educated on the concepts that were prevalent at the time.  As a reminder, Jack Welch was held up as an avatar of great business competence during and immediately after this period, only to see GE break into several parts when it was realized that most of GE's profit came from financial engineering.  Tool like Porter's Five Forces and newer models like the Business Model Canvas can provide frameworks for analyzing situations, but this analysis requires deeper thinking and more complex synthesis in a time when inflation is higher and VUCA factors are more extreme than when we were in graduate school.  Note that I used a tool in the introduction - VUCA - and provided some basic analysis.

Again, nothing wrong with applying the frameworks we learned, as long as we apply them in the right way and take away lessons that align with the world we live in, and do the deeper analysis that is required.

Looking to the future

My bias is always to look back to the past, to leverage my tools, but also to try to understand what is going to happen next.  Understanding the future, what is likely to unfold, and to move proactively to address emerging needs and challenges is both profitable and risky.  Risky because the company needs to place bets on what future will unfold and what new opportunities or threats could emerge.  Profitable because getting these bets right, investing and moving into new opportunities or spaces before competition means higher revenue and margins.

The problem with looking to the future is that so few people understand how to do this work well, and too often we simply forecast the existing conditions into the future - what I like to call straight line future.  We'd like to assure ourselves that we can understand the future, and in the absence of good research and thinking, the best way to forecast the future is to determine it will be more of the same.

Which tools to use?

So, in one instance (looking to the past) we have rock solid evidence of how decisions played out, but the circumstances today are likely different.  In the instance of the frameworks learned in college, those too may be relevant for a specific timeframe and competitive state that may or may not exist.  When considering the future, the most necessary and relevant information is to understand future conditions, but we have the least experience and least amount of trust in that data.

Which suggests that most companies ought to get much better at understanding trends and scenarios, interpreting market conditions and future actions, and analyzing competitor actions.  If relying on the past is dangerous due to shifting conditions, and relying on frameworks could be situational, then understanding the future playing conditions is probably the best approach.

ChatGPT did not write this blog

 There are a lot of folks thinking about the implications of ChatGPT, the new AI that has many writers worried about losing their jobs.  I've seen posts online where people provided prompts to ChatGPT and found the resulting document to be comparable to a well-written, college level paper.  Which has led, inevitably, to the claim that AI will replace humans and take their jobs.  All that is left now is for the Luddites to grab their spanners and wreak havoc on the knitting machines.  

As someone who writes a lot, I am concerned about any AI with the ability to write or argue points in the same manner, voice and frequency that I do.  In fact, it might be interesting to load all 1200+ posts I have written and train an AI to simply continue to write posts for me, while I while away the afternoons sipping pina coladas.  Don't judge me - I like coconut.  And, yes, some jobs are likely to be replaced, but in case no one has noticed, AI has been writing short new snippets and even full-page web articles for several years.  Daniel Pink wrote years ago that anything that could be broken down into a defined process would be first outsourced and then automated.  Years later, what we are discovering is that anything with patterns and some descriptive underlying logic, like a language, can be automated.  This does not mean the end of humans, or even copywriters, because the AI still requires interesting prompts.  In other words, it knows HOW to write, perhaps even more effectively and fluently than we do, but it does not know WHAT to write, and in many instances, it may miss the point about WHY to write at all.

Having a specific point of view is vital.  After all, would you rather read a piece about the English language and its uses from Orwell or Churchill, or Mick Jagger?  Orwell and Churchill understood the use of language, first to shape how people think (Orwell) or to move and motivate people (Churchill).  Jagger uses language as a way to evoke thoughts or emotions and to entertain people.  And, yes, you could create an AI that became expert at mimicking Orwell - what would he have thought of that?  A whole book could be written just about the speculation about how Orwell would have reacted to ChatGPT and its potential use or disuse.  But AI cannot replace Orwell, because Orwell had several characteristics that the AI will never have - lived experience, a point of view, ideas about the current state and visions about the future.

A single AI won't have all of these facets - at least not yet.  It would take several AIs working in tandem, all trained to Orwell's writing style, perspective, politics, life experienes and more, to adequately reflect how Orwell wrote, and to begin to try to fully replace Orwell.  Not saying it isn't possible, just very difficult right now, when AI could be put to better use.

Where is the creative spark?

A good friend of mine likes to say that all programming code is a derivative of the original line of code ever written.  Likewise, most music is derivative of original notes and beats that humans created thousands of years ago.  The beats per minute may change, the instruments may change, the different chords and keys may change, but eventually all music is derived from an original source.

So it is with most things.  I like Hemingway's books and find his style and usage and word choice interesting.  It is the combination of his choice of words, his characters and his settings, as well as his point of view that make his work interesting.  So, is the creative act the process of putting words on a page, words that have been used again and again for centuries?  Or is the creative act in the selection and choice of words, the short sentences and the long, run-ons?  Where does the actual creative spark lie, and what creates or generates that spark?  Because ultimate that's what will differentiate humans from our AI partners. Note that I say partners, not overlords, as some will suggest.  If and when multiple AIs are joined together, all of which can do very different things, and all the AIs band together to work against us, we may be in trouble, but fortunately that's a problem for my kids' generation to solve.

Weavers, whip makers and copy writers

So, yes, some jobs and roles will be threatened by the onset of AI in language processing.  Just as manual weavers gave way to automated looms and whip makers were out of business when the automobile became popular, creative destruction as envisioned by Schumpeter will continue.  Innovation and technology will create new capabilities that replace or in some instances destroy jobs and even industries.  We can stand athwart history, fighting the emergence of new technologies, or we can adjust and reframe capabilities to take advantage of new opportunities.  The Luddities tried to destroy the weaving machines.  Less is recorded about the demise of whip makers, although I am sure they did not go silently into that good night.  What will those of us whose occupations and incomes depend on developing great copy do?  Perhaps we'll learn to train machines and AI to do the bulk of the work for us and put the final touches on ourselves.  Is what AI is doing really so different from what James Patterson is doing - writing story outlines and having other authors flesh out the story?

One day, an AI may write this blog, but if it does, I'll provide the context and the prompts and the ideas and will probably rework the draft.  You'll be able to tell it's my own work when there are run-on sentences, misplaced metaphors and poor subject/verb agreement.  But hopefully the good ideas will shine through.

Why campfires are the perfect analogy for innovation

 I grew up in Scouting and really enjoyed my time as a scout.  What I think most of us enjoyed the most were the weekend campouts, where we could sleep in tents, roam in the local forest and, best of all, build fires.  I've never fully understood why boys are so attracted to fire.  Perhaps it is fire's potential destructive nature. Perhaps it's the concept of controlling such an awesome power.  Perhaps it's just cooking hot dogs and marshmallows over an open flame.  All I know is, we loved to build fires and cook over the open flame.

Campfire 101

Every scout knew that a good fire begins by preparing the ground, scraping away the dead leaves to expose bare soil, so that when the fire was burning, it was contained in a small spot.  We'd often gather rocks to contain the wood in a small circle.  These were simply good practices and safety measures.

Next, we'd spread out and gather as many small dry twigs as we could find for kindling.  You need something to catch fire quickly, that doesn't take too much effort to get started.  Then your kindling will burn hot enough and long enough to allow larger branches and eventually logs to catch fire.  Good fire-makers know that the wood - kindling and the larger logs - needs to be good and dry and ready to burn, not freshly cut green trees full of sap, but older, dead logs that have dried out.  The older, dead and dry wood catches fire more quickly and burns more cleanly.  Once you have a real fire going, you can burn green wood, but fresh, green wood kicks up a lot of smoke.

If you've built your fire correctly, preparing the ground, building up your dry kindling with good dry branches and logs ready, a few small sparks will be all it will take to get your fire going.  We used to challenge each other to see who could start a fire with the fewest matches.  Some more intrepid scouts had learned to create sparks by striking flints.  I've even once witnessed someone start a fire with a fire bow, but that takes a lot of work.

So, what's this interesting flashback to scouting days have to do with innovation?  A lot - and we forget far too much of the symmetry.

Innovation and campfires

There's a reason so many innovation tools and methods are associated with light or fire.  Ideas are often considered "sparks" that will catch flame to create new ideas.  When scouts are striking matches or striking flints, they are creating sparks directed to their kindling, in hopes that the spark will ignite the kindling and then the rest of the wood.

When you are generating ideas, through individual work, or brainstorming, or some other means, you are sparking ideas.  While idea generation can often seem challenging to introverts or people who don't think of themselves as "creative", it is the easiest activity in the innovation realm.  Anyone can do it, anytime, in any location, with little preparation.  And, often, that's what's wrong with idea generation - it can be too easy to do - just like striking a match.

As I noted above, we would never strike a match without first preparing a fire circle and building a good platform of kindling.  A lot of preparation went into building the right conditions for that spark to catch up a flame.  Yet in most businesses, we are apt to go into an idea generation activity with little preparation, and with little connection between ideas generated and how they will be realized.  There's often a big gap in companies between idea generation and the work necessary to develop an idea into a new product or service.

As scouts, we'd build a small mound of kindling, ready to catch the spark, with more wood prepared to feed the fire as it grew.  As innovators, we need to have the right conditions, the right preparation, but also the right next steps for a good idea.  Who will manage the idea?  What are the processes to develop and validate the idea?  Who will build a prototype and test it?  These concepts need to happen BEFORE the idea is generated, not afterwards.  Too often, we expect that simply generating good ideas will be enough - that ideas have value in and of themselves and will mature naturally with little help or guidance.  No, ideas are like matches that flare for a moment but will burn and die unless they become the spark for something else.  

Wet wood

Scouts know that dry wood matters when you build a fire.  It's far simpler to ignite dry wood than wood that is wet or green.  The same concept applies to innovation - using the right people and setting the right conditions and ingredients matter.  Working with people who are not ready to innovate, or who have doubts about the ideas, is like trying to catch wet wood on fire - it is possible, just not the best approach.  Here, we can also talk about corporate culture.  If the culture of the business is to focus on existing products, to avoid risk, then you are metaphorically working with wet wood.  It is as important to describe how ideas will evolve and mature as it is to find the right people with the best motivations and help them overcome any corporate culture or challenges to innovation or innovative thinking.

Practice

We scouts didn't get good at building fires by doing this occasionally, but by trial and error and by learning from our peers and our scout leaders.  We had our scout manuals to tell us how to build a good fire, and we built fires every time we went camping, because that was the source of heat, and how we cooked our food.  And yes, we did challenge each other to build fires using the fewest matches (or no matches).  To earn one specific merit badge, you were sent off into the woods by yourself to build your own shelter, to find and cook your own food, for a weekend with a knife, a blanket and two matches.  Believe me, you learn to build your fires carefully when you have a limited supply of matches.

Starter Wood

Those of us who had a little more experience would often carry a few shavings of what we called "fire starter".  Fire starter (or what some call fat wood) comes from the joints or roots of an old pine tree, filled with resin, which catches fire really easily and burns very hot and fast.  It acts as a catalyst for the other kindling and will burn even when other wood won't.  You may find that you need a catalyst as well - someone from outside your group, or perhaps outside your company, to spark ideas.  Or, you may want to have your team read magazine or web articles about what competitors are doing or new emerging technologies in order to spark creative juices or create a competitive challenge.  Smart innovators will use any catalyst they can to engage their audience and create heightened competitive challenge.

Campfires and Innovation

So, there are at least five ideas to take away from this reminiscence:

1. Ideas are sparks, and without the right preparation and good processes and responsibilities defined, you are wasting your time generating ideas.  Build your firepit, stack your kindling and know how to fuel a fire before you generate ideas.
2. Sparks don't burn wet wood.  Find the right people, prepare them, help overcome cultural objections.  Establish the best conditions possible, in the room and beyond the initial meeting.
3. Nobody does this right the first time.  It takes practice to learn to build a fire with a single match.  It takes practice to learn to generate and manage ideas.  If innovation is important in your company, it will be something that you practice and do regularly.
4. Have a clear purpose.  While this discussion may sound like it borders on pyromania, we also knew that our leaders would send us home if we started fires that did not have a purpose.  Fire is a tool, useful and occasionally dangerous, and not to be invoked lightly or carelessly.  When we started a fire, we had to have a purpose - warmth or cooking.  When you do innovation work, you need to have a clearly defined purpose, that everyone understands and agrees on.
5. Identify a catalyst or create a challenge.  We'd use or find fat wood whenever we could, because it made starting the fire easier.  You may need to introduce a catalyst who can generate ideas that your team would not normally consider or produce information or a competitive challenge to push your team to be more creative.

Will you shape innovation, or will it shape you?

 There's a famous quote about tools we use, which says that we first shape our tools, and then they shape us.  Meaning, I think, that we are natural creators, meant to solve problems, to create new things.  When we create new things, they become valuable and in demand, and to a great extent, at least initially, unattainable until produced in mass quantities.  Once attainable and proven useful, those tools change the way we live and work.  Thus, the tools we created and shaped will eventually change and shape the way we live and work.  If you doubt this is true, look no further than the iPhone.  What was an incredible innovation is now changing how we work and live.  And to some degree, creating new humans with more flexible and useful thumbs!

Through creativity and innovation, from the earliest origin of man, we have been creating and shaping tools - fire, sharpened sticks for tools and weapons, the wheel and thousands upon thousands of other tools and devices.  Now, we are surrounded by the evidence of centuries of innovation:  indoor air conditioning on a hot summer day.  Automobiles that can travel hundreds of miles on a single electrical charge.  The ability to sit and write a blog that others may read (or ignore) at their leisure.  And all of these tools and services are built on dozens of other innovations, that we all too casually accept and ignore.

The question I'd like pose today is:  when do we stop shaping innovation and when does it start shaping us?  One of my biggest concerns is that we are losing sight of the power of innovation, and its ability to constantly change (and often improve) our lives.  To a great extent, we are becoming complacent, waiting for others to change or improve or modify our lives, rather than dreaming up big ideas and developing them ourselves.  Too many times we've outsourced the development of ideas to others, waiting for them to take the risks, to scale the ideas, rather than get involved and participate in the learning, experimenting and yes, sometimes failing process.  This lack of larger engagement in innovation and creativity means that the development of interesting new products and services is left more and more to people who are willing to take risks, and those people, unfortunately, may include people who don't care about risk or who don't care about you.  Facebook, as an example, has proven to be an exceptionally valuable innovation that generates a lot of social interaction, for good and for bad, driven by Mark Zuckerberg, who, it seems, is happy to share your data with anyone for payment.  And Facebook is just one example.

When people become passive, and when we fail to innovate, we risk turning over innovation and creativity to people who don't have our best interests at heart, but more importantly we risk abdicating the act itself to others, and eventually to machines.  While I recognize that this sounds futuristic, good innovation often involves the combination of unexpected features or solutions in new or novel way.  Artificial intelligence can spot and discard thousands of connections that would take humans years to even consider.  A significant amount of drug discovery is already working in this way.  

The advent of innovation by AI does not preclude people from working in innovation and certainly won't prescribe the activity, but as long as we continue to focus our attention in the educational system on rote memorization and de-emphasize risk taking and creativity, the more and more these acts become acts of rebellion or resistance against a system, by people who do not fit into the mainstream.  This is not to suggest that there will be an "us vs them" mentality, but most good innovators are somewhat outside of the mainstream, and the current environment and movement to more automated methods of innovation may make it even more so.  

Plus, there are some innovations and some breakthroughs that simply require human involvement and innovation.  While AI can combine existing capabilities and molecules to decide if a new combination has value, AI (at least not now) can't imagine new possibilities, new services and business models, new experiences that consumers value.  There are entire vistas of innovation possibility out there, waiting to be explored and, yes, monetized, if only businesses would be more active in seeking out the possibilities.  Yet, most companies stick within a very narrow range of new product development options, unable or unwilling to shift the frame and think differently.  Little wonder, since the population seems to be moving in that direction as well.

The dystopian future of Terminator is bleak but unrealistic.  Machines and AI will become more powerful, but the relationship will be symbiotic, just not in the way of Terminator or The Matrix.  We will become more dependent on machines and AI for more of our thinking and even our creativity if we aren't careful.  True creativity and innovation are almost purely human characteristics, exceptionally difficult for machines to create, but these acts aren't always useful or beneficial.  However, in the right conditions they lead to new learning and new discovery.

The question is:  are we willing to relegate our lives, our thinking and our innovations to a small group of people who continue to work and innovate, and to machines and AI that won't explore all the innovation possibilities and options?  Will we be happy with a continuous supply of incremental and safe ideas that aren't all that interesting and fail to explore new opportunities?  If what I am saying as a warning seems to reflect the current reality, then get ready for more of it.

If you want more, if you think people and society is capable of more, then start by training your kids and students in creative thinking and innovation.  Encourage them to explore beyond the rigid academic boundaries, to combine new thoughts and new experiences.  As Heinlein said - specialization is for insects.  We need people who are open to new ideas and new experiences, who will push further into creativity and innovation, to re-invigorate innovation in society, in government and in business.

And it cannot come soon enough.  Otherwise, complacency, lethargy and satisfaction with the current state will settle in, and we'll create an entire generation of people who are completely satisfied with staring at photos and short video clips on a handheld device.  Oh, maybe we've failed this generation.  Maybe they'll recognize that life and experiences and risk beyond the handheld are out there and reject the trap of social media to go well beyond.

It's time to move beyond relying on Zuckerberg, Elon Musk, Sam Bankman-Fried and other "innovators" to create new things.  SBF didn't create, he merely masked a Ponzi scheme and bamboozled a willing press.  We, all of us, are responsible for what we want to buy, consume and experience.  Stop waiting for others to innovate, stop waiting for the next band leader to bring the music to town.  Start innovating on your own, in small groups, with your friends, with teams at work.

Start defining and shaping your own innovations, rather than waiting for someone to sell you one of theirs.

Innovation Portfolio Gymnastics

 There's been a significant number of posts on LinkedIn and other social media about innovation lately, especially focused on the type of innovation that companies perform.  The traditional way of thinking about this, which is really just a loose rule of thumb, is to suggest that companies SHOULD plan for 70% of their innovation efforts (and hopefully, results) to be focused on incremental innovation, and 20% on transformative innovation and 10% on disruptive innovation.  The 70/20/10 allocation was originally created by one of the big strategic consulting firms and seems reasonable on its face, but was never intended to be a fixed proportion, but a signal that every company should be innovating across all three horizons. 

The idea of diversification across the three horizons was the key point, not the amount of innovation in each horizon.  There may be companies or industries where the appropriate division is 70/20/10.  More than likely, there are a few companies where this is the correct investment across the portfolio.  However, there are several issues with this allocation.  First, it isn't gospel.  Clayton Christensen didn't carve it on stone and hand it down to the rest of us.  It is a rule of thumb that should be understood as such, and then modified by every company based on competition, the pace of change in the marketplace, the amount of impact or disruption the company wants to create and so on.  It is lazy thinking to believe that your innovation investment should be the same as every other company.

Second, the original 70/20/10 split really only addresses one dimension - that is what new products should we create.  I think more than ever, it's reasonable to ask what the portfolio of new services, new customer experiences and business models we should be creating, and across all three horizons.  We need to free ourselves from a limited way of thinking about innovation outcomes.

Third, we need to recognize that few firms will ever fully embrace or realize the third horizon.  While some firms may espouse the goal of innovating to disrupt their market or other markets, the near-term incentives to protect what exists, and the risks associated with disrupting a home market are too significant for the vast majority of companies.  There's a reason most if not all disruptive innovation happens from outside an industry.  The upstarts and external actors don't have an investment in the status quo.  So, it's generally been my rule, let's call it the Phillips rule, that all disruptive innovation will be realized by companies innovating in an industry they hope to enter or to create.  This does not mean you should not plan for third horizon innovation, but if you do, you must ask how the innovation will be realized.

Building a real innovation portfolio

If you want to truly understand the power and depth of an innovation portfolio across these horizons, then we need to examine what criteria are important.  

First, it is clearly important to sustain and extend existing products and services.  Thus, the incremental segment of the three horizons is important.  What is critical here is knowing which products and services are worth extending or adding on to, and which should be culled from the portfolio.  Most companies are loath to cull products and services, so they spread too few innovation dollars across the entire portfolio, making meager changes that do not thrill customers.  The best action in the existing portfolio is first to go on a zombie hunt, to remove the weakest products and services to free up more innovation dollars for the best remaining products.  All incremental innovation should first begin with subtraction, to free up room and funds for future growth.

Second, you need to consider what new products and services and business models you'd like to create to add to your existing portfolio.  This will represent the second horizon of innovation, the transformative horizon.  It's in this segment that you create new products, introduce new technologies or target new customers.  In all of these cases, learning is involved.  You must invest in exploration, in learning and in engaging customers and prospects.  You should also do a good amount of work in trend spotting and scenario planning.  This segment is where you make the most important bets on your mid-term future, so the more you can learn, the better off you'll be.

Third, you will want to understand what disruptive innovation looks like, while recognizing that disruptive innovation can and will happen in your industry (likely by an upstart or outsider) AND that you can be a disrupter to other companies or industries.  It helps to understand how your industry might be disrupted, so you can anticipate the entrants and significant changes that could occur.  It's also valuable to see how your capabilities, products and services could disrupt another industry, opening up new markets and new customers.  It is important that you do both - understand how your market or company could be disrupted, so you can defend against it or prepare to move if necessary and examine where your capabilities could allow you to enter or disrupt other markets.  As Eisenhower said, plans are nothing, but planning is everything.

Who should do this work

The challenge with all I've identified above is that there is no one person or even one function that can do all of this work.  Few companies have competent planning departments that can spot future trends and build scenarios or spot important competitive movements.  Most innovation happens in R&D or in isolated product groups, so forming a true innovation portfolio means cobbling together different innovation pipelines from vastly different organizations, and most of those will be product innovations since customer service or business model innovation is rarely considered.

What this points out is that while innovation seems important, it is not well-organized, often not strategic and not carefully planned or executed, and does not have a single point of responsibility or accountability.  I've been in the innovation space for over 20 years, and I've seen a lot of improvement in corporate innovation but compared to the way many other business processes operate, there's another 20 years' worth of work before innovation gets the kind of attention it deserves.

What to do now

Where to start?  Define a corporate-wide innovation portfolio, to get a sense of all the innovation work underway.  Classify it as incremental, transformative or disruptive.  Determine the ratio of the three.  Gasp when you realize that the vast amount of innovation underway is incremental and unlike to move the needle.  Examine your markets, your competitors and your customers to determine just how much innovation you should be doing.  Cull your innovation portfolio and enforce the right ratios across the three horizons.  Invest in learning, discovery, customer engagement to gain more insight for transformative innovation (horizon two). Research disruptive innovations that you can create to attack other industries.  Research the disruption that is going to happen in your industry.  Learn, adapt and then change your innovation portfolio.  Make sure to track not just product innovation, but services, customer experience and business model innovation as well.