Search, Seek, or Stalk – How Do You Find Growth?

Search, Seek, or Stalk – How Do You Find Growth?

Growth is the lifeblood of any organization, and the quest for growth opportunities is not just a strategic imperative. It is a fundamental necessity because the ability to identify and capitalize on opportunities is a game-changer for companies wanting to achieve sustainable success and stay ahead of the competition. 

The challenge, however, is that not all opportunities are the same – some are head-smackingly obvious, while others are like trying to nail down JELL-O.  Yet companies take a “one size fits all” approach to finding, developing, and capitalizing on them.

SEARCH when need to transform

What do you do when you need information but don’t know precisely what you need and certainly don’t know where to find it? You Google it or, in less-branded terms, you search for it. 

When searching for growth opportunities, you’re looking for something but don’t know exactly what you need or where you’ll find it.  Finding opportunities requires you to go beyond traditional market analysis and adopt a learner’s mindset to see ways to disrupt the status quo, challenge existing paradigms, and create new value propositions for your customers.

Searching is a creative process that entails investing in R&D, fostering a culture of intrapreneurship, and experimenting with new technologies. It requires a culture of creativity, experimentation, and agility to adapt to changing market dynamics.  You have to be willing to be wrong on your way to being right, to move slowly so you can act quickly, and to throw out the timeline to harness the game-changing opportunity.

SEEK when you need to innovate

What do you do when you know what you need and generally where to find it?  You seek it out – you go to where you think it will be, and, on the off-chance it’s not there, you pivot to Option B.

When you’re seeking growth opportunities, you have a target in mind but are not 100% sure how to hit it.  Maybe you know you want to enter a new geography, but you need to figure out how to do it successfully and avoid the mistakes of previous entrants.  Maybe it’s a new industry or category, but you must understand if and how to do it without disrupting your existing business model.

Seeking is both creative and analytical.  You look for data and market intelligence, interview experts and individuals, analyze industry trends and explore untapped segments. It also requires you to stay open to surprises and new possibilities and take calculated risks to capitalize on emerging trends or consumer preferences.  Like searching, it requires patience.  Unlike searching, it respects a deadline.

STALK when you need to improve

Just like a lioness stalking a wildebeest, you do this when you see an opportunity and know exactly how to capture it. Yes, there will be zigs and zags along the way, and an unexpected competitor may pop up. But this is who you are and what you do. 

When stalking opportunities, you bring the full value and power of your experience, expertise, resources, and capabilities to bear on an opportunity.  This may happen when you’re operating and improving your core business.  It may also occur after you’ve searched (and found) an opportunity, sought (and decided on) a strategy, and now you have the confidence to launch and scale.

Do Your Approaches Align with Your Goals?

Most companies say that they want to transform. Still, very few have the patience or intestinal fortitude to search because there is no Google for Transformation that produces the exact plan you need to transform successfully.

Companies also tend to stalk when they want to innovate, leaving opportunities to change the game and build sustainable competitive advantage on the sideline because they’re too uncertain or take too long.

Growth requires all three approaches – search, seek, and stalk – but only happens when your chosen approach aligns with your goals.

5 Lessons from the Death of the Apple Car

5 Lessons from the Death of the Apple Car

In 2014, rumors started to circulate that Apple was developing a self-driving autonomous car to compete with Tesla.  At the end of February 2024, rumors circulated that Apple was shutting down “Project Titan,” its car program. According to multiple media outlets, the only logical conclusion from the project’s death is that this decision signals the beginning of the end of Apple.

As much as I enjoy hyperbole and unnecessary drama, the truth is far more mundane.

The decision was just another day in the life of an innovation.

As always, there is a silver lining to this car-shaped cloud: the lessons we can learn from Apple’s efforts.

Lesson 1: Innovation isn’t all rainbows and unicorns

People think innovation is fun.  It is.  It is also gut-wrenching, frustrating, and infuriating.  Doing something new requires taking risks, which is uncomfortable for most people.  Even more challenging is that, more often than not, when you take a risk, you “fail.” (if you learned something, you didn’t fail, but that’s another article). 

What you can do: Focus on the good stuff – moments of discovery, adventures when experimenting, signs that you’re making life better for others – but don’t forget that you’re defying the odds.

Lesson 2: More does not mean success

It’s been reported that Apple spent over ten billion dollars on Project Titan and that over 2000 people were working on it before it was canceled. With a market cap of over two trillion dollars, a billion dollars a year isn’t even a rounding error. But it’s still an eye-popping number, which makes Apple’s decision to cut its losses downright courageous.

What you can do: Be on guard for the sunk-cost fallacy.  It’s easy to believe that you’ll eventually succeed if you keep working and pouring resources into a project.  That’s not true, as Apple experienced.  And in the rare cases when it is, executives are often left wondering if the success was worth the cost.

Lesson 3: Pivot based on data, not opinions

At least four different executives led Project Titan during its decade in development, and each leader brought their own vision for what the Apple Car should be.  First, it was an electric vehicle with driver assistance that would compete with Tesla.  Next, it was a self-driving car to compete with Google’s WayMo.  Then, plans for fully autonomous driving were canceled. Finally, the team returned to its original target of matching Tesla’s Level 2 automation.  

Changes in project objectives, strategies, and execution plans are necessary for innovation, so there’s nothing obviously wrong with these pivots.  But the fact that they tended to happen when a new leader was appointed (and that Jony Ive caused an 18-month hiring freeze simply by expressing “displeasure”) makes me question how data-based these pivots actually were

What you can do: Be willing to change but have a high standard for what is required to cause a change.  Data, even qualitative and anecdotal data, should be seriously considered.  The opinion of a single executive, not so much.

Lesson 4: Dream big, build small

Apple certainly dreamed big with its aspirations to build a fully semi-autonomous vehicle and it poured billions into developing and testing the sensors, batteries, and partnership required to make it a reality.  But it was never all-or-nothing in its pursuit of the automotive industry.  Apple introduced CarPlay the same year it kicked off Project Titan, and it continues to offer regular updates to the system.  Car Key was announced in 2020 and is now offered by BMW, Genesis, Hyundai, and Kia.

What you can do: Take a portfolio approach towards your overall innovation portfolio (Apple kept working on the iPhone, iPad, Apple Watch, and Vision Pro) and within each project.  It’s not unusual that a part of the project turns out to be more valuable than the whole project.

Lesson 5: ___________________________

Yes, that is a fill-in-the-blank because I want to hear from you. What lesson are you taking away from Project Titan’s demise, and how will it make you a better innovator?

How I Use AI to Understand Humans (and Cut Research Time by 80%)

How I Use AI to Understand Humans (and Cut Research Time by 80%)

AI is NOT a substitute for person-to-person discovery conversations or Jobs to be Done interviews.

But it is a freakin’ fantastic place to start…if you do the work before you start.

Get smart about what’s possible

When ChatGPT debuted, I had a lot of fun playing with it, but never once worried that it would replace qualitative research.  Deep insights, social and emotional Jobs to be Done, and game-changing surprises only ever emerge through personal conversation.  No matter how good the Large Language Model (LLM) is, it can’t tell you how feelings, aspirations, and motivations drive their decisions.

Then I watched JTBD Untangled’s video with Evan Shore, WalMart’s Senior Director of Product for Health & Wellness, sharing the tests, prompts, and results his team used to compare insights from AI and traditional research approaches.

In a few hours, he generated 80% of the insights that took nine months to gather using traditional methods.

Get clear about what you want and need.

Before getting sucked into the latest shiny AI tools, get clear about what you expect the tool to do for you.  For example:

  • Provide a starting point for research: I used the free version of ChatGPT to build JTBD Canvas 2.0 for four distinct consumer personas.  The results weren’t great, but they provided a helpful starting point.  I also like Perplexity because even the free version links to sources.
  • Conduct qualitative research for me: I haven’t used it yet, but a trusted colleague recommended Outset.ai, a service that promises to get to the Why behind the What because of its ability to “conduct and synthesize video, audio, and text conversations.”
  • Synthesize my research and identify insights: An AI platform built explicitly for Jobs to be Done Research?  Yes, please!  That’s precisely what JobLens claims to be, and while I haven’t used it in a live research project, I’ve been impressed by the results of my experiments.  For non-JTBD research, Otter.ai is the original and still my favorite tool for recording, live transcription, and AI-generated summaries and key takeaways.
  • Visualize insights:  Mural, Miro, and FigJam are the most widely known and used collaborative whiteboards, all offering hundreds of pre-formatted templates for personas, journey maps, and other consumer research templates.  Another colleague recently sang the praises of theydo, an AI tool designed specifically for customer journey mapping.

Practice your prompts

“Garbage in.  Garbage out.” Has never been truer than with AI.  Your prompts determine the accuracy and richness of the insights you’ll get, so don’t wait until you’ve started researching to hone them.  If you want to start from scratch, you can learn how to write super-effective prompts here and here.  If you’d rather build on someone else’s work, Brian at JobsLens has great prompt resources. 

Spend time testing and refining your prompts by using a previous project as a starting point.  Because you know what the output should be (or at least the output you got), you can keep refining until you get a prompt that returns what you expect.    It can take hours, days, or even weeks to craft effective prompts, but once you have them, you can re-use them for future projects.

Defend your budget

Using AI for customer research will save you time and money, but it is not free. It’s also not just the cost of the subscription or license for your chosen tool(s).  

Remember the 80% of insights that AI surfaced in the JTBD Untangled video?  The other 20% of insights came solely from in-person conversations but comprised almost 100% of the insights that inspired innovative products and services.

AI can only tell you what everyone already knows. You need to discover what no one knows, but everyone feels.  That still takes time, money, and the ability to connect with humans.

Run small experiments before making big promises

People react to change differently.  Some will love the idea of using AI for customer research, while others will resist with.  Everyone, however, will pounce on any evidence that they’re right.  So be prepared.  Take advantage of free trials to play with tools.  Test tools on friends, family, and colleagues.  Then underpromise and overdeliver.

AI is a starting point.  It is not the ending point. 

I’m curious, have you tried using AI for customer research?  What tools have you tried? Which ones do you recommend?

The 93% Rule: How to Predict Unintended Consequences

The 93% Rule: How to Predict Unintended Consequences

Unintended consequences often catch us off guard despite their predictability.  The moment they occur, we gasp in shock, shake our heads, and look at each other in wide-eyed horror at this thing that just happened that we could never ever ever have anticipated. 

Yet, when (if) we do an After-Action Review, we often realize that these consequences were not entirely unforeseeable. In fact, had we anticipated them, we might have made different decisions.

The Unintended Consequences of Spreadsheets

In 1800 BCE, ancient Babylonians started recording data by scratching grids and columns onto clay tablets, and the spreadsheet was born.  Over the millennia, we went from clay tablets to papyrus to parchment and then paper. 

Fast forward to 1963 when R. Brian Walsh of Marquette University ported the Business Computer Language (BCL) program to an IBM 7040, and electronic spreadsheets became a reality.  The introduction of VisiCalc by Apple in 1979 revolutionized spreadsheet capabilities, followed by Lotus 123 and Microsoft Excel. Today, spreadsheets are ubiquitous in education, business operations, financial markets, budgeting, and even personal inventories.

Unintended yet predictable consequences

While spreadsheets have undoubtedly enhanced efficiency and accuracy compared to traditional methods like clay tablets or hand-drawn tables on parchment, their ease of use has inadvertently led to complacency.

We stopped engaging in a multi-millennial habit of discussing, debating, and deciding before making a spreadsheet. We started flippantly asking people to create spreadsheets and providing little, if any, guidance because “it’s easy to make changes and run scenarios.”

This shift resulted in a reliance on automated models and a lack of shared assumptions or analytical rigor in decision-making processes.

Of course, these behaviors were never intended.  They were, however, very predictable.

93% of Human Behavior is predictable.

Research spanning disciplines as varied as network scientists, anthropology, neuropsychology, and paleontology shines a light on how truly predictable we are.

Here are some examples:

Emotions before Reason: Ask someone if they make decisions based on their motivations, aspirations, and fears and use data to justify the decisions, and they’ll tell you no. Ask them the last time someone else made a decision that “made no sense,” and you’ll listen to a long list of examples.

Small gains now are better than big gains later: Thoughtfully planning before using solutions like spreadsheets, word processing, email, and instant messaging could save us time at work and help us get home 30 minutes earlier or work a few hours less on the weekend.  But saving a few seconds now by brain-dumping into Word, setting up a “flexible” spreadsheet, and firing off a text feels much better.

Confidence > Realism: We’ve all been in meetings where the loudest voice or the most senior person’s opinion carried the day.  As we follow their lead, we ignore signs that we’re wrong and explain away unexpected and foreboding outcomes until we either wake up to our mistakes or adjust to our new circumstances.

Predict the 93%. Create for the 7%

Acknowledging the predictability of human behavior is not an endorsement of stereotypes but a recognition of our innate cognitive processes. By incorporating this understanding into design, innovation, and decision-making processes, we better anticipate potential outcomes and mitigate unintended consequences.

While 93% of human behavior may follow predictable patterns rooted in evolutionary instincts, focusing on the remaining 7% allows for the exploration of unique behaviors and novel solutions.  By embracing both aspects of human nature, we can navigate challenges more effectively and anticipate a broader range of outcomes in our endeavors, leading to informed decision-making and value creation.

Now, if I could only get Excel to stop auto-converting numbers into date/time format.

New Data Shows that Good Intentions Pave the Way to Innovation Hell

New Data Shows that Good Intentions Pave the Way to Innovation Hell

The road to hell is paved with good intentions, and nowhere is that more true than in innovation.

That’s one of the insights I took away from InnoLead’s Q1 report on corporate innovation priorities.  The report is an eye-opening look at the impact of AI on corporate innovation as experienced by corporate entrepreneurs themselves.  But before deep diving into that topic, the report’s authors shared intriguing data about member companies’ innovation structure, leadership engagement, organizational connections, and results. Nestled amongst the charts were several that, when taken together, got my Spidey senses tingling.

61.0% of innovation teams are “directly under a high-visibility leader with a broad company focus.”

This is great because innovation needs senior leaders’ support and active engagement to survive, let alone survive for long enough to produce meaningful results. Add this to the fact that 45% of senior leadership teams frequently discuss the “progress and value of the innovation program,” and all signs point to innovation as a strategic priority.

But (you knew there was a but, didn’t you)…

If “broad company focus” means “no P&L responsibility,” we have a problem.  In every for-profit company I’ve worked for and with, people with P&L responsibility have greater power, influence, and access to resources than people without a P&L.  This division may not feel fair, but it makes sense – the people who bring in profit and revenue will always be more influential than people who represent “cost centers.”

You can see the impact of P&L owners who are, understandably, focused entirely on delivering short-term results throughout the report – 75% of companies have shifted their focus more towards near-term priorities, and 61% shifted their innovation portfolio away from Horizon 3 (also known as radical, breakthrough, or disruptive innovation).

As for all those discussions, it’d be great if they focused on walking the talk of innovation. But suppose it’s only innovation platitudes or, worse, questioning innovation’s ROI. That doesn’t bode well for the “high-visibility leader with broad company focus,” the innovation team, or the company’s culture.

71.2% of innovation teams’ customers or business partners are unaware of the team’s existence, don’t engage, or engage only occasionally.

Welcome to Innovation Island!  Where the cool people work on cool things in cool offices while all you drones slave away doing the same thing you’ve always done and making the money that pays for the cool people to do cool things in their cool offices.

I’m sure this isn’t the message the innovation team intends to send, but it’s the one received by most organizations.

When arguing for Innovation Island, managers often point to the organizational antibodies likely to swarm and kill H3/radical/breakthrough innovation and even some H2/adjacent innovations.  They’re right, and those innovations must be “protected.” But not every innovation needs protection.  H2 and certainly H1 innovations, where most portfolios are now, should be shared with the core business because the core business will eventually run them.

The bigger problem, in my opinion, is that innovation teams don’t seem to be reaching out to others in the organization.  Like the P&L owners they report to, people in the core business are busy running the business and generating revenue.  Very few have the time or energy to seek out the innovation team to discuss and explore innovation.  Companies that want to build a culture of innovation need to turn their innovators into evangelists, not residents of an island connected to the mainland by a single drawbridge.

23.4% of innovation teams are considered outsiders or actively undermined by other functions and business units.

This may not sound bad, but add to it the 55.0% that are “somewhat integrated with occasional collaboration” with other departments and business units, and you may be tempted to believe that Innovation Island would be wise to invest in a surface-to-air missile defense system.

Sadly, this perception of the innovation team as “The Others” isn’t surprising when considering that the most important tactic for building a relationship between innovation and the functions or business units is already having strong relationships and interpersonal trust (75.3% of respondents).  The least effective (4.7% of respondents) is “writing down shared objectives and expectations.”  So, no, the email you sent is not enough to win friends and influence people.

Bottom line

Well-intended companies appoint a senior executive to lead the innovation team because they’ve been told that doing so is powerful proof that innovation is a strategic priority.  They hire outsiders to inject new thinking into the organization because they know that “what got you here won’t get you there.”  They cordon the team and their work off from the rest of the organization because they read that separation is essential to preserving innovation’s disruptive nature. 

But if the senior executive doesn’t have the organizational power and influence that comes with P&L ownership, the team doesn’t have strong personal relationships with others in the business, and other functions and business units don’t know the team exists or how to interact with it, innovation will go nowhere.

But that’s better than where it could go.