How Stella Saved The Farm

A young owner who just took over a farm in trouble invites an idea hunt (a Farmtastic Contest!). A Miss Stella comes up with a winning idea. Like any good idea, this one has lot of promise and everyone in the farm likes it. The story (and this book) is all about how this innovative idea takes shape and how everyone in the farm are impacted by it, both in good and not-good ways. The title of the book is a oxymoron as one of the important take-away’s is “Who really saved the farm?”. The idea generator? The execution team? The Marketing and Sales Team? The guy on the factory floor? Management? The Coach who gave right advice at the right time?
The book highlights some insights to use when executing an innovation within an organisation. When you read the insights themselves (right below), you might find them good … but do spend a couple of hours reading the book (that’s all it takes) as the story of the farm is likely to stay with you forever and these same insights will end up being more sticky, powerful and actionable!

Getting Started

  1. In any great innovation story, the idea is only the beginning.
  2. Asking one leader to “just go make it happen” is a woefully inadequate approach to moving an innovative idea forward.

Building The Team

  1. Assign to a dedicated team any activities that are beyond the narrow, specialised capabilities of your existing organisation.
  2. Build the dedicated team as though you are building a new and different company from the ground up.
  3. Conflicts between the dedicated team and the existing organisation are inevitable. Nonetheless, you must nurture a healthy partnership between the two.

Planning and Assessing Progress

  1. Put learning first — learning through disciplined experimentation. If you do, you’ll make better decisions and you’ll get to profitability sooner.
  2. Gather evidence to validate each major expenditure.
  3. Evaluate the innovation leader based on whether he or she executed a disciplined experiment.
The last three insights have had lot of value for me when I read them in the context of the story. Below are some snippets from the story that stitch these three together. Deirdre is the boss of the farm. Einstein in the smart guy .. a Mentor that Deirdre often looks up to. Mav is the guy who is leading the team that is executing the idea.

“We’ve already invested more in this business than we anticipated,” Deirdre countered. “How much longer until cash flow turns positive?”

Mav really didn’t know. He had been busy focusing on operations, working with the team, visiting customers, perfecting the manufacturing process, and trying to make peace with the sheep and alpaca. He hadn’t slept well in as many nights as he could remember. How could be possibly have found time to revise the plan?

“I have a question.” Einstein spoke up for the first time. Until then he had seemed absorbed in polishing his thick glasses. “When you two launched this experiment — ”

“This is no experiment, Einstein,” Deirdre interrupted. “The future of the farm depends on this business.”

Einstein looked at Dierdre with a mix of curiosity and exasperation. He silently estimated the difference in their IQs to be forty to fifty points. Perhaps the best tactic was to employ the Socratic method of guided questioning in order to inspire critical thinking.

“Is the luxury wool business new to us?” he asked, addressing both Dierdre and Mav.

The two nodded in unison.

“And when we launched, would you say that the outcome was certain or uncertain?” Einstein continued.

“Uncertain,” said Deirdre.

“Completely uncertain,” Mav emphasised.

“Sounds like an experiment to me,” Einstein stroked his wattle and grinned. He loved being right all the time.

“Okay, it’s an experiment,” said Mav. “So what?”

“What was your hypothesis?” Einstein asked.

[..]

“Hypothesis!” Einstein said. “Every experiment starts with a hypothesis. It’s a set of assumptions about what you expect to happen. It’s really just a story about how you expect the business to succeed.”

“Well, then we had one,” Mav said defensively. “We had a plan. It told the story of how we expected to succeed.”

“Did it turn that your hypothesis was correct?”

“Not exactly,” said Mav.

“What do you mean?”

“We’ve made some changes since then.”

“You changed the hypothesis?” asked Einstein.

“If you say so.”

This ruffled Einstein’s feathers. “Mav, you can’t just go around changing your hypothesis whenever you feel like it! That’s not disciplined. It’s just random. How do you expect to learn from your experiment?”

Mav sighed. “We’re not planning on writing up a lab report, Einstein. We’re trying to earn money, not win a Nobel prize.”

[…]

“Learning first, profits second,” Einstein announced.

Blank stares.

“Look,” he said, “it just means that if you put learning first — learning through disciplined experimentation — you’ll make better decisions and you’ll actually get to profitability sooner. Learning leads to profits.”

Deirdre was intrigued. “What are the implications, Einstein? How do we focus, as you say, on learning first?”

“One, state a hypothesis. Two, predict what will happen. Three, measure results. Four, assess lessons learned by comparing your predictions to actual outcomes.”

Max rolled his eyes. Deirdre didn’t.

“Einstein, I’d like you to help Mav figure out what we’ve learned so far about luxury wool. Help him articulate our current hypothesis. Help him make the best possible estimate of future cash flows.”

[…]

Together, Einstein and Mav put together a plan on paper and highlighted all the critical assumptions. Then Einstein made sure Mav has a disciplined process to follow. He urged Mav to plot every result as a trend so he could quickly identify new evidence and lessons learned.

[…]

“Inside a big experiment, there are little experiments,” Einstein explained.

“So, you are saying that we run this social networking initiative as a distinct experiment?” Mav asked.

“Yes. As one piece of the big experiment. We look for evidence that shows it’s either working or not working. It is critical that we gather evidence to validate each major expenditure.”

There is this discussion Deirdre has with Einstein about writing Mav’s performance report. I jump directly to Einstein’s advice.

“The innovation leader’s job is to execute a disciplined experiment”.

“That’s it?”

“That’s a lot. If you run a disciplined experiment, you learn quickly. If you learn quickly, you make better decisions. If you make better decisions, you win! Or at least you lose at the lowest possible cost.”

“Can’t I hold Mav accountable for the results?”. There’s frustration in her voice.

“To some degree,” Einstein conceded. “You can hold Mav accountable for the results from any activity, any portion of the experiment that is well understood and predictable. There are some aspects of Mav’s job that are like that. For example, you can hold him accountable for the costs of Alpaca care. It’s hardly any different from Sheep care. But most every other aspect of what he is doing is unknown to us and uncertain.”

“So, explain this to me. Exactly how do you hold someone accountable for executing a disciplined experiment?”

Deirdre spoke of these to Mav when they meet next for his performance review.
  • I’m going to be watching you very closely to evaluate how well you run a disciplined experiment
  • I’m going to assess your performance as an innovation leader
  • I expect you to have a clear hypothesis
  • I expect you to clearly identify the most critical unknowns
  • I expect you to invest a great deal of time and energy in planning, analyzing results, and deciphering lessons learned
  • I expect everyone on your team to understand the plan and every assumption in it. In fact, I am going to do spot-checks to see if everyone can articulate the same assumptions
  • I expect you to react quickly to new information
  • I’m going to question you more on your decisions
  • You need to clearly understand the evidence behind any changes in direction you make
  • I’m going to insist on frequent updates to your plan
Note to Self: Check “Other Side of Innovation: Solving the Execution Challenge: HBR Press” by the same authors … for in-depth recommendations, comprehensive analysis, and numerous real-world examples of innovation inside recognizable companies like IBM, BMW, and Deere & Company.
Note to Self: Think these:
  • What kind of leader is needed to run an innovation effort? One who usually does not have all that much respect for tradition? One who is young and not that experienced?
  • How would someone whose focus is “Faster, Stronger, More Efficient” run a innovation effort?
  • What is special about a Company’s history? How will this influence innovation? Does it have a history of innovation? How has innovation changed over the years?
  • What one can learn about innovation by reading books?
  • How will existing teams react to dedicated new teams? What motivations they usually want to see?
  • How can one bring the existing and new teams together?
  • Businesspeople typically put more energy into a Big Idea Hunt than they put into the effort to turn an idea into something real, like a new product or business. Why do you think this is?
  • In my experience, where do the best new ideas come from?
  • What are the motivations affecting people that create difficulties for a innovation leader?
  • Is it appropriate to take good teams out of core business to help a new business? What other options are there?
  • Deirdre insists that “We have to find a way to do two very different things at the same time. We have to sustain excellence in the existing business and build a new and uncertain one.” Is doing both simultaneously even possible for one company?
  • Is a new team likely to succeed if they operate in isolation from existing teams?
  • Is it possible to manage a new business like a disciplined scientific experiment?
  • Is it sufficient to test a possibility as quickly and cheaply as possible?
  • Who saved the farm?
  • In the real world, who do you think would get the most credit?
  • If given the opportunity to start over, should Deirdre do anything differently? If so, what specifically?