Picasso and the Redefinition of Leadership in the Age of AI

Picasso and the Redefinition of Leadership in the Age of AI

Spain, 1896

At the tender age of 14, Pablo Ruiz Picasso painted a portrait of his Aunt Pepa a work of brilliant academic realism that would go on to be hailed as “without a doubt one of the greatest in the whole history of Spanish painting.”

In 1901, he abandoned his mastery of realism, painting only in shades blue and blue-green.

There’s debate over why Picasso’s Blue Period began. Some argue that it’s a reflection of the poverty and desperation he experienced as a starving artist in Paris. Others claim it was a response to the suicide of his friend, Carles Casagemas. But Bill Gurley, a longtime venture capitalist, has a different theory.

Picasso abandoned realism because of the Kodak Brownie.

Introduced on February 1, 1900, the Kodak Brownie made photography widely available, fulfilling George Eastman’s promise that “you press the button, we do the rest.”

An ocean away, Gurley argues, Picasso’s “move toward abstraction wasn’t a rejection of skill; it was a recognition that realism had stopped being the frontier….So Picasso moved on, not because realism was wrong, but because it was finished.”

 
 
 
Washington DC, 2004

Three years before Drive took the world by storm, Daniel Pink published his third book, A Whole New Mind: Why Right-Brainers Will Rule the Future.

In it, he argues that a combination of technological advancements, higher standards of living, and access to cheaper labor are pushing us from a world that values left brain skills like linear thought, analysis, and optimization towards one that requires right brain skills like artistry, empathy, and big picture thinking.

As a result, those who succeed in the future will be able to think like designers, tell stories with context and emotional impact, and combine disparate pieces into a whole greater than the sum of its parts. Leaders will need to be empathetic, able to create “a pathway to more intense creativity and inspiration,” and guide others in the pursuit of meaning and significance.

  

California, 2026

Barry O’Reilly, author of Unlearn, published his monthly blog post, “Six Counterintuitive Trends to Think about for 2026,” in which he outlines what he believes will be the human reactions to a world in which AI is everywhere.

Leadership, he asserts, will cease to be measured by the resources we control (and how well we control them to extract maximum value) but by judgment. Specifically, a leader’s ability to:

  • Ask better questions
  • Frame decisions clearly
  • Hold ambiguity without freezing
  • Know when not to use AI

 

The Price of Safety vs the Promise of Greatness

 Picasso walked away from a thriving and lucrative market where he was an emerging star to suffer the poverty, uncertainty, and desperation of finding what was next. It would take more than a decade for him to find international acclaim. He would spend the rest of his life as the most famous and financially successful artist in the world.

Are you willing to take that same risk?

You can cling to the safety of what you know, the markets, industries, business models, structures, incentives that have always worked. You can continue to demand immediate efficiency, obedience, and profit while experimenting with new tech and playing with creative ideas.

Or you can start to build what’s next. You don’t have to abandon what works, just as Picasso didn’t abandon paint. But you do have to start using your resources in new ways. You must build the characteristics and capabilities that Daniel Pink outlines.  You must become the “counterintuitive” leader that embraces ambiguity, role models critical thinking, and rewards creativity and risk-taking.

Do you have the courage to be counterintuitive?

Are you willing to embrace your inner Picasso?

5 Questions: Ellen DiResta on Why Best Practices Fail

5 Questions: Ellen DiResta on Why Best Practices Fail

For decades, we’ve faithfully followed innovation’s best practices. The brainstorming workshops, the customer interviews, and the validated frameworks that make innovation feel systematic and professional. Design thinking sessions, check. Lean startup methodology, check. It’s deeply satisfying, like solving a puzzle where all the pieces fit perfectly.

Problem is, we’re solving the wrong puzzle.

As Ellen Di Resta points out in this conversation, all the frameworks we worship, from brainstorming through business model mapping, are business-building tools, not idea creation tools.

Read on to learn why our failure to act on the fundamental distinction between value creation and value capture causes too  many disciplined, process-following teams to  create beautiful prototypes for products nobody wants.


Robyn: What’s the one piece of conventional wisdom about innovation that organizations need to unlearn?

Ellen: That the innovation best practices everyone’s obsessed with work for the early stages of innovation.

The early part of the innovation process is all about creating value for the customer.  What are their needs?  Why are their Jobs to be Done unsatisfied?  But very quickly we shift to coming up with an idea, prototyping it, and creating a business plan.  We shift to creating value for the business, before we assess whether or not we’ve successfully created value for the customer.

Think about all those innovation best practices. We’ve got business model canvas. That’s about how you create value for the business. Right? We’ve got the incubators, accelerators, lean, lean startup. It’s about creating the startup, which is a business, right? These tools are about creating value for the business, not the customer.

 

R: You know that Jobs to be Done is a hill I will die on, so I am firmly in the camp that if it doesn’t create value for the customer, it can’t create value for the business.  So why do people rush through the process of creating ideas that create customer value?

E: We don’t really teach people how to develop ideas because our culture only values what’s tangible.  But an idea is not a tangible thing so it’s hard for people to get their minds around it.  What does it mean to work on it? What does it mean to develop it? We need to learn what motivates people’s decision-making.

Prototypes and solutions are much easier to sell to people because you have something tangible that you can show to them, explain, and answer questions about.  Then they either say yes or no, and you immediately know if you succeeded or failed.

 

R: Sounds like it all comes down to how quickly and accurately can I measure outcomes?   

E: Exactly.  But here’s the rub, they don’t even know they’re rushing because traditional innovation tools give them a sense of progress, even if the progress is wrong.

We’ve all been to a brainstorm session, right? Somebody calls the brainstorm session. Everybody goes. They say any idea is good. Nothing is bad. Come up with wild, crazy ideas. They plaster the walls with 300 ideas, and then everybody leaves, and they feel good and happy and creative, and the poor person who called the brainstorm is stuck.

Now what do they do? They look at these 300 ideas, and they sort them based on things they can measure like how long it’ll take to do or how much money it’ll cost to do it.  What happens?  They end up choosing the things that we already know how to do! So why have the brainstorm?”

 

R: This creates a real tension: leadership wants progress they can track, but the early work is inherently unmeasurable. How do you navigate that organizational reality?

E: Those tangible metrics are all about reliability. They make sure you’re doing things right. That you’re doing it the same way every time? And that’s appropriate when you know what you’re doing, know you’re creating value for the customer, and now you’re working to create value for the business.  Usually at scale

But the other side of it?  That’s where you’re creating new value and you are trying to figure things out.  You need validity metrics. Are we doing the right things? How will we know that we’re doing the right things.

 

R: What’s the most important insight leaders need to understand about early-stage innovation?

E: The one thing that the leader must do  is run cover. Their job is to protect the team who’s doing the actual idea development work because that work is fuzzy and doesn’t look like it’s getting anywhere until Ta-Da, it’s done!

They need to strategically communicate and make sure that the leadership hears what they need to hear, so that they know everything is in control, right? And so they’re running cover is the best way to describe it. And if you don’t have that person, it’s really hard to do the idea development work.”

But to do all of that, the leader also must really care about that problem and about understanding the customer.


We must create value for the customer before we can create value for the business. Ellen’s insight that most innovation best practices focus on the latter is devastating.  It’s also essential for all the leaders and teams who need results from their innovation investments.

Before your next innovation project touches a single framework, ask yourself Ellen’s fundamental question: “Are we at a stage where we’re creating value for the customer, or the business?” If you can’t answer that clearly, put down the canvas and start having deeper conversations with the people whose problems you think you’re solving.

To learn more about Ellen’s work, check out Pearl Partners.

To dive deeper into Ellen’s though leadership, visit her Substack – Idea Builders Guild.

To break the cycle of using the wrong idea tools, sign-up for her free one-hour workshop.

Managing Uncertainty: 3 Steps from Stuck to Success

Managing Uncertainty: 3 Steps from Stuck to Success

When a project is stuck and your team is trying to manage uncertainty, what do you hear most often:

  1. “We’re so afraid of making the wrong decision that we don’t make any decisions.”
  2. “We don’t have time to explore a bunch of stuff. We need to make decisions and go.”
  3. “The problem is so multi-faceted, and everything affects everything else that we don’t know where to start.”

I’ve heard all three this week, each spoken by teams leads who cared deeply about their projects and teams.

Differentiating between risk and uncertainty and accepting that uncertainty would never go away, just change focus helped relieve their overwhelm and self-doubt.

But without a way to resolve the fear, time-pressure, and complexity, the project would stay stuck with little change of progressing to success.

 

Turn uncertainty into an asset

 

It’s a truism in the field of innovation that you must fall in love with the problem, not the solution. Falling in love with the problem ensures that you remain focused on creating value and agnostic about the solution.

While this sounds great and logically makes sense, most struggle to do it. As a result, it takes incredible strength and leadership to wrestle with the problem long enough to find a solution.

Uncertainty requires the same strength and leadership because the only way out of it is through it. And, research shows, the process of getting through it, turns it into an asset.

3 Steps to turn uncertainty into an asset

 

Research in the music and pharmaceutical industries reveals that teams that embraced uncertainty engaged in three specific practices:

  1. Embrace It: Start by acknowledging the uncertainty and that things will change, go wrong, and maybe even fail. Then stay open to surprise and unpredictability, delving into the unknown “by being playful, explorative, and purposefully engaging in ventures with indeterminate outcome.”
  2. Fix It: Especially when dealing with Unknowable Uncertainty, which occurs when more info supports several different meanings rather than pointing to one conclusion, teams that succeed make provisional decisions to “fix” an uncertain dimension so they can move forward while also documenting the rationale for the fix, setting a date to revisit it, and criteria for changing it.
  3. Ignore It: It’s impossible to embrace every uncertainty at once and unwise to fix too many uncertainties at the same time. As a result, some uncertainties, you just need to ignore. Successful teams adopt “strategic ignorance” “not primarily for purposes of avoiding responsibility [but to] allow postponing decisions until better ideas emerge during the collaborative process.

This practice is iterative, often leading to new knowledge, re-examined fixes, and fresh uncertainties. It sounds overwhelming but the teams that are explicit and intentional about what they’re embracing, fixing, and ignoring are not only more likely to be successful, but they also tend to move faster.

 

Put it into practice

Let’s return to NatureComp, a pharmaceutical company developing natural treatments for heart disease.

Throughout the drug development process, they oscillated between addressing What, Who, How, and Where Uncertainties. They did that by changing whether they embraced, fixed, or ignored each type of uncertainty at a given point:

As you can see, they embraced only one type of uncertainty to ensure focus and rapid progress. To avoid the fear of making mistakes, they fixed uncertainties throughout the process and returned to them as more information came available, either changing or reaffirming the fix. Ignoring uncertainties helped relieve feelings of being overwhelmed because the team had a plan and timeframe for when they would shift from ignoring to embracing or fixing.

Uncertainty is dynamic. You need to be dynamic, too.

 

You’ll never eliminate uncertainty. It’s too dynamic to every fully resolve. But by dynamically embracing, fixing, and ignore it in all its dimensions, you can accelerate your path to success.

The Design Squiggle is a Lie.  This is Why.

The Design Squiggle is a Lie. This is Why.

Last night, I lied to a room full of MBA students. I showed them the Design Squiggle, and explained that innovation starts with (what feels like) chaos and ends with certainty.

The chaos part? Absolutely true.

The certainty part? A complete lie.

 

Nothing is ever Certain (including death and taxes)

Last week I wrote about the different between risk and uncertainty.  Uncertainty occurs when we cannot predict what will happen when acting or not acting.  It can also be broken down into Unknown uncertainty (resolved with more data) and Unknowable uncertainty (which persists despite more data).

But no matter how we slice, dice, and define uncertainty, it never goes away.

It may be higher or lower at different times,

More importantly, it changes focus.

4 Dimensions of Uncertainty

Something new that creates value (i.e. an innovation) is multi-faceted and dynamic. Treating uncertainty as a single “thing”  therefore clouds our understanding and ability to find and addresses root causes.

That’s why we need to look at different dimensions of uncertainty.

Thankfully, the ivory tower gives us a starting point.

WHAT: Content uncertainty relates to the outcome or goal of the innovation process. To minimize it, we must address what we want to make, what we want the results to be, and what our goals are for the endeavor.

WHO: Participation uncertainty relates to the people, partners, and relationships active at various points in the process. It requires constant re-assessment of expertise and capabilities required and the people who need to be involved.

HOW: Procedure uncertainty focuses on the process, methods, and tools required to make progress. Again, it requires constant re-assessment of how we progress towards our goals.

WHERE: Time-space uncertainty focuses on the fact that the work may need to occur in different locations and on different timelines, requiring us to figure out when to start and where to work.

It’s tempting to think each of these are resolved in an orderly fashion, by clear decisions made at the start of a project, but when has a decision made on Day 1 ever held to launch day?

 

Uncertainty in Pharmaceutical Development

 Let’s take the case of NatureComp, a mid-sized company pharmaceutical company and the uncertainties they navigated while working to replicate, develop, and commercialize a natural substance to target and treat heart disease.

  1. What molecule should the biochemists research?
  2. How should the molecule be produced?
  3. Who has the expertise and capability to synthetically poduce the selected molecule because NatureComp doesn’t have the experience required internally?
  4. Where to produce that meets the synthesization criteria and could produce cost-effectively at low volume?
  5. What target disease specifically should the molecule target so that initial clincial trials can be developed and run?
  6. Who will finance the initial trials and, hopefully, become a commercialization partner?
  7. Where would the final commercial entity exist (e.g. stay in NatureComp, move to partner, stand-alone startup) and the molecule produced?

 And those are just the highlights.

 

It’s all a bit squiggly

The knotty, scribbly mess at the start of the Design Squiggle is true. The line at the end is a lie because uncertainty never goes away. Instead, we learn and adapt until it feels manageable.

Next week, you’ll learn how.

This AI Creativity Trap is Gutting Your Growth Capabilities

This AI Creativity Trap is Gutting Your Growth Capabilities

“We have to do more with less” has become an inescapable mantra, and goodness, are you trying.  You’ve slashed projects and budgets, “right-sized” teams, and tried any technology that promised efficiency and a free trial.  Now, all that’s left is to replace the people you still have with AI creativity tools.  Welcome to the era of the AI Innovation Team.

It sounds like a great idea.  Now, everyone can be an innovator with access to an LLM.  Heck, even innovation firms are “outsourcing” their traditional work to AI, promising the same radical results with less time and for far less money.

It sounds almost too good to be true.

Because it is too good to be true.

 

AI is eliminating the very brain processes that produce breakthrough innovations.

This isn’t hyperbole, and it’s not just one study.

MIT researchers split 54 people into three groups (ChatGPT users, search engine users, and no online/AI tools using ChatGPT) and asked them to write a series of essays.  Using EEG brain monitoring, they found that the brain connectivity in networks crucial for creativity and analogous thinking dropped by 55%.

Even worse? When people stopped using AI, their brains stayed stuck in this diminished state.

University of Arkansas researchers tested AI against 3,562 humans on a series of four challenges involving finding new uses for everyday objects, like a brick or paperclip.   While AI scored slightly higher on standard tests, when researchers introduced a new context, constraint, or modification to the object, AI’s performance “collapsed.” Humans stayed strong.

Why? AI relies on pattern matching and is unable to transfer its “creativity” to unexpected scenarios. Humans use analogical reasoning so are able to flex quickly and adapt.

University of Strasbourg researchers analyzed 15,000 studies of COVID-19 infections and found that teams that relied heavily on AI experts produced research that got fewer citations and less media attention. However, papers that drew from diverse knowledge sources across multiple fields became widely cited and influential.

The lesson? Breakthroughs require cross-domain thinking, which is precisely what diverse human teams provide, and, according to the MIT study, AI is unable to produce.

How to optimize for efficiency AND impact (and beat your competition)

While this seems like bad news if you’ve already cut your innovation team, the silver lining is that your competition is probably making the same mistake.

Now that you know better, you can do better, and that creates a massive opportunity.

Use AI for what it does well:

  • Data analysis and synthesis
  • Rapid testing and iteration to refine an advanced prototype
  • Process optimization

Use humans for what we do well:

  • Make meaningful connections across unrelated domains
  • Recognize when discoveries from one field apply to another
  • Generate the “aha moments” that redefine industries

 

Three Questions to Ask This Week
  1. Where did your most recent breakthroughs come from? How many came from connecting insights across different domains? If most of your innovations require analogical leaps, cutting creative teams could kill your pipeline.
  2. How are teams currently using AI tools? Are they using AI for data synthesis and rapid iteration? Good. Are they replacing human ideation entirely? Problem.
  3. How can you see it to believe it? Run a simple experiment: Give two teams an hour to solve a breakthrough challenge. Have one solve it with AI assistance and one without.  Which solution is more surprising and potentially breakthrough?

 

The Hidden Competitive Advantage

As AI commoditizes pattern recognition, human analogical thinking and creativity become a competitive advantage.

The companies that figure out the right balance will eat everyone else’s lunch.