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.