Learning Organization: Strengthening Superpowers

Earlier this year, a few folks from sparks & honey were interviewed about the story of s&h. We have taken excerpts from that interview and translated it into a 5 part “Story of Us.” A special thank you to Kendra Clarke, VP Data Sciences; Paul Butler, Chief Operating Officer; Camilo LaCruz, Chief Strategy Officer; Kristin Cohen, Head of Marketing and Business Development; Jared Alessandroni, Chief Technology Officer; Rob Gaige, Managing Partner Q™ and Laura Chiavonne, Managing Partner Transformation for your support and being part of the interview.

Strengthening our Superpowers

“There’s an old maxim,” offered Terry, “that human analysis trades speed for depth, whereas machine computation trades depth for speed. We think of AI as a source of augmented intelligence that shrinks those implicit trade offs. Within s&h, we are a living example of that evolution. As we experience the growing symbiosis between people and machines, we are seeing how AI elevates human superpowers.”

“Just who are s&h’s humans?” laughed Camilo. “We have 50+ full-timers, and nearly 20+ part-time collaborators. We also have a 66-person Advisory Board who provide special expertise across the full spectrum of areas crucial to our business. Our Advisory Board is high touch and high engagement – they frequently share updates on their work through our Briefing series and join as participants in our regular Briefings (which have been reformatted and repurposed a bit since we scaled Q™ and are now four days per week). We also engage them to provide insights on relevant consulting projects. Plus, they join us for an annual summit where we discuss many of our business-critical decisions.”

“In addition to our advisors, we have enrolled over 250+ scouts around the world. The primary criterion to be selected as a scout is to be an individual curious about culture. Some scouts are s&h alumni, but most are former undergraduate and graduate students from outside the US who came to one of our Briefings and wanted to remain involved with us when they returned home. They scan their localities for signals and pattern identification, and we often engage them to provide local data on selected assignments.”

“Q™ was built primarily for our strategists and consultants,” remarked Laura, “so we have seen them make the most progress as ‘augmented humans.’ By making research and discovery much more efficient, Q™ has freed up about a third of the time they formerly spent on projects. And by augmenting their intelligence, Q™ has significantly changed the nature of what they do and the value they add.” 

Bias Confirmation? 

“Now, the team must draw on different talents,” continued Camilo, “both upstream and downstream. Downstream, the abundance of data requires us to be much more skilled at honing the logic behind our queries and refining how we zoom in and zoom out of the information in front of us. It has become really important to be able to move seamlessly back and forth between grasping a trend map with twenty EOCs and then deep diving to mine specific signals supporting a particular view of the future.”

“That has required some of us to adjust our egos,” smiled Camilo, “from being singular oracles of insights we then validate with research to humans working in partnership with machines to conceive ideas. There are many more instances today where the machine generates the clues that become a strategy and much of the human value is building the story around it. So storytelling is a muscle we are definitely exercising a bit more.”

“That, in turn, opens new opportunities. For instance, leveraging the knowledge of our Advisory Board has always been a very important part of our methodology. Q™’s added downstream contributions afford us the chance to draw more deeply on our expert practitioners to enhance the insights and solutions we provide upstream. So another evolving skill of our consultants is improved expertise in formulating higher-order questions and in human-to-human collaboration to enrich the thinking behind our deliverables.” 

Hunch in a Lunch? 

“To me, the most important talent Q™ brings to light,” asserted Kristin, “is the speed at which we make connections. It’s one thing to have a hunch in a lunch discussion with a client and go back to the office to research it and another thing to survey a wide assortment of dots on a map and be expert at connecting them, almost in real-time.”

“The idea of flexibility in formulating strategy also takes on a new meaning. Traditionally, the process of answering a brief moves from general/flexible to tight/rigid over six weeks as you invest time exploring hypotheses and narrowing down to a point of view. When you shorten the time required and simultaneously widen the aperture of valid input, you must remain flexible in terms of being able to change your mind, to quickly process different concepts, and to even rethink the question. It’s a much more agile way of finding strategy than the traditional linear waterfall process of the past.” 

“Being ‘quick on your feet’ is more important than ever,” insisted Kristin. “Rather than being very polished on a certain narrative you want to sell, you’ve got to find that narrative swiftly, sometimes with clients in the room. That’s very stimulating from a creative point of view – our value centers much more on the art of connecting the dots and imagining possible futures.” 

Adapting to Augmentation 

“As an example of the advanced value our ‘augmented humans’ now deliver,” suggested Laura, “we have a client in the cosmetics and personal care industry with a massive workforce. They asked us how to bring that workforce into the 21st century and future-proof it. We started by using Q™ to decode the cultural forces intersecting with beauty, skin care, and new direct to consumer business models.”

“Q™ turned up EOCs like Aspiration, Camera Culture, and Clean that you would expect to find in developing strategy in the beauty industry. But it also pushed us to consider seemingly disparate EOCs like Circular Economy (economic systems aimed at eliminating waste and the continual use of resources), Microbiome Sciences (choosing foods, products, environments, and activities that are good for our microbial profile), and Genomics (customizing products for our specific genetic profiles).”

 “We used Q™ to quantify all those areas and explore their interrelationships,” continued Laura. “Then we identified priorities and added texture to how each might play out in the future, and we aggregated all our thinking into an incisive discourse with our client. Q™ made the breadth and depth of our field of study much wider and freed us to leave no rock unturned in helping our client to point their compass to disrupt their industry.” 

Two-Way Trust 

“Augmentation is a two-way street,” stressed Kendra, “and human input remains vital to advancing Q™’s development, especially in these early stages. We do have multiple patents pending, but at its core, what’s really proprietary about Q™ is that it’s built around s&h’s unique methodologies and cultural taxonomy. Its algorithms aren’t perfect, so we need the humans who understand those methodologies best to intervene when it’s wrong and train it.” 

“And we do,” nodded Camilo. “We have always had an intense feedback loop among our tech and Data Science teams and our consultants. We get together weekly to review our work and talk about the challenges we have encountered, and they provide answers and solicit our input on their development plans.”

“The reason I used the word ‘intense’ to describe our feedback loop is that we set it up to be purposely challenging so that we could address issues directly. That required respect and trust among everyone involved. Coincidently, one of the first hurdles we encountered was simply trusting the data produced by Q™. At some level, machine learning and AI will always represent mysterious algorithms locked inside a black box.” 

“In some cases, our mistrust of Q™ was warranted. It got things wrong – improperly tagged signals or translation glitches, for example. In other cases, we had to learn to examine ourselves. Sometimes we had to be more thoughtful about our queries, and at other times we might have looked askance at perfectly sound outputs from Q™. Maybe they didn’t fit within our existing paradigms or they didn’t support an argument we advocated, or perhaps we didn’t like the fact that AI was starting to make choices we used to.” 

Learning as a System (and a Superpower)

“We needed a learning model that prepares people for disruption and strengthens our capacity to thrive in a VUCA world,” said Paul. “Accelerating change, the growing intersectionality of diversity (visible, invisible, and cognitive), and the uncertainty of our world required us to restructure and rebuild current models of learning. We needed a systematic way to inquire and expand the questions we are asking. We needed an approach to learning that was  always on and constantly updating. Learning organizations needed open and curious minds; agile, novel and adaptive thinkers; a mindset of growth; future-makers and change catalysts.”

Meet The s&h Model of Learning

“We’ve been building a Learning Organization over the last eight years,” said Paul. “Grounded in our Laws of Culture—these are the principles of our learning organization at sparks & honey and are especially critical in times like this.” 

1. Curiosity is not an attitude - it’s a skill  In a learning organization curiosity is not just an attitude. It’s a fundamental skill at the core of any company dedicated to learning. It’s a practice that should be developed and mastered. Our Culture Briefing—now streamed live from our living rooms—is just one place where we build the muscle of curiosity. In these discussions, we don’t present conclusions, but we explore fringe ideas. We look for patterns. We pose radical questions to open minds and identify innovation.  

2. Apply knowledge and skills across categories and industries: We value specific expertise with subject matter experts and the mastery of skills, but culture isn’t siloed—it’s horizontal. So, we’re developing people and machines to make connections and identify patterns across categories and industries. We have staffed cultural anthropologists to work on technology builds. We have brought the expertise of our data sciences team to produce work on brand messaging.  

3. Prioritize experimentation and experiential learning: Of course, we are looking for a highly skilled workforce. But we don’t believe that the most valuable skills come from formal classrooms or Ivy League degrees. They come from real-world experiences that feed a passion to experiment. We promote learning that is self-directed, experiential and built on experimentation. Instead of a training reimbursement for a preselected list of online management courses, we offer “Curiosity Cash.” With this program, we let staff members choose their own courses and experiences where they can explore their passions and expand their skills. Our staff ran with it. One staff member took a class in fiction writing to help tell more compelling narratives and another took stand-up comedy classes to help with presentation skills. That spirit of experimentation and experiential learning drives adaptability and resilience. Strive for bias balance, not bias removal We challenge our perspectives by bringing the outside in, whether in expert opinions, thoughts leaders or any diversity of thought. 

4. Traditional methods of learning look to remove bias. But humans are complex and biased. And so are machines.  Biases are data points about what humans believe and understand and also about how machines behave and operate. They uncover gaps in the way we collect, interpret and use information. To close those gaps and normalize those biases, we need more data not less. As a learning organization, we bring in as many perspectives as we can to check our biases. Our Advisory Board, along with guests from the outside, join us daily in our culture briefings. They bring different perspectives and expertise. Online viewers of the briefing are encouraged to challenge ideas being discussed. The inclusion of many voices strengthens the output of our work.

5. Humans + Machines learn together: Augmented and accelerated learning requires a system of both humans and machines (AI) that is always-on. Q™, our AI-powered cultural intelligence platform, combines the power of human and machine intelligence to decode culture. Human analysis, empathy and insight will always be necessary. We must learn and hone those skills. But machine learning and artificial Intelligence can amplify the learning process – they make teams better, smarter and faster. It’s almost like giving them learning superpowers.