Gen Z on Corporate Innovation

Young woman (Gen Z) having her picture taken with a smartphone at outdoor cafe.

At the recent FEI 2024 conference, held this past June, All Things Innovation set the stage for a lively conversation at a special panel to gain intelligence on Generation Z, as we explored their thoughts on corporate innovation, their ideals and culture, what they see currently working and what is currently turning them off.

To explore this, FEI hosted a panel with three accomplished Gen Z leaders—Samantha Johnson, Founder & CEO, Tatum Robotics; Bodhi Patil, UN Ocean-Climate Solutionist; and Kyne Santos, TikTok Math Queen, a math communicator, influencer and author. We paired them with three corporate insights leaders: Cherie Leonard, Head North America, Insights, Colgate-Palmolive, NA; Michael Nevski, Director, Global Insights, Visa; and Oksana Sobol, Vice President Insights, The Clorox Company.

Check out the session’s video from FEI 2024 as the Gen Z panel dove deeper into embracing change, their motivations, aspirations and careers; what brands can do better to speak to and understand Gen Z; the innovations occurring in their respective fields; and their future goals and more.

Key Takeaways

  • Gen Z is accelerating innovation
  • Harness your superpowers
  • Find your passion
  • Democratization of knowledge—innovation is more accessible
  • Make an impact
  • Design and learn quickly
  • Gen Z cares!
  • Intergenerational collaboration is key

Finding the Voice of the Gen Z Consumer

All Things Innovation explored Generation Z further in, “Shaping the Next Generation of Innovation.” Generation Z is in a unique position as they are often viewed as one of the first to be shaped and influenced by technology such as the Internet, social media and smartphones. This generation, often labeled digital natives, has embraced change, adopted and interacted with tech from an early age. Gen Z comprises people born between roughly 1996 and 2010. This generation’s identity has been shaped by the digital age, climate anxiety, a shifting financial landscape, the war on terror and COVID-19, among other things. Yet, despite that, Gen Z is super comfortable with tech, giving them a unique impact on shaping technology trends, the future of work and play and the challenge of societal norms. In other words, Gen Z may be perfectly suited for innovation.

All Things Insights also looked at Gen Z in, “Connecting with Generation Z.” As brands grapple with the challenges and opportunities of marketing to Generation Z, it becomes crucial to learn more about the preferences of this tech-savvy demographic. With substantial buying power, which is only expected to grow in the future, Gen Z presents significant potential for marketers but it’s important to balance traditional strategies with fresh ideas to capture their interest. These are truly digital natives, who are mobile and tech-savvy, value-driven, and appreciate authenticity, who have grown up with technology, social media and connectivity.

Video courtesy of Streamly

Setting the Stage for Startups

A graphic that shows a kind of innovation and startups word cloud in the shape of a maze.

As an expert at early startups, can you just tell us a little bit more about that space, such as your thoughts on challenges and opportunities?

“It’s funny you say early. I’m interested in when a startup is brand new and so I distinguish that,” says Vladimer. “My expertise is on the nascent stage, which I define as when someone, a founder, has the kernel of an idea for a startup business, but they have no customers, no product, and no funding. I distinguish that from an early-stage startup that has initial customers, product and funding. In the past, it made a lot of sense for founders to kind of blend the two and blur those lines.”

He continues, “The reason that was so important was historically, if we wanted to do a startup, our limiting factor was funding. We could not buy servers for less than tens of thousands of dollars, for example. What that bought you in terms of technology 20 years ago is drastically different than what it buys you today. My argument is what you used to need $100,000 for just as table stakes to start your startup, you can now get for less than a $100 today.”

Vladimer observes, “The world of entrepreneurship, the world of innovation, everything changed on the magical date of April 23, 2015. Nobody knows what happened on that day. That was the day that Amazon first released standalone revenue results for AWS (Amazon Web Services). Technology is getting better and cheaper. But the reason I think that day is so special in my mind is that’s the inflection point. Of course, people were using cloud services before that. But if you look at all the headlines then, everyone’s losing their mind that it’s a billion-dollar industry. Now it’s like tens of billions just for Amazon alone, it’s like $80 billion, I think.”

An Innovator Mindset

“In present day, if we want to start a company, what do we need to focus on?” asks Vladimer. “Should we build a product, there’s no question. The answer is it used to be that we needed someone else’s money and now we don’t. Amazon and everything else that’s followed has completely changed the priorities of founders. Back when funding was the most important aspect, then you wanted to fake it until you make it. You don’t have to do that anymore. You don’t want to say that you just have the kernel of an idea. No founder would ever want to go to investors and say, I just started. I’ve got the kernel of an idea; would you fund me? It’s a different world now. You don’t have to rely on investors.”

Speaking of investors, venture capitalists or private equity investors, isn’t that part of the atmosphere nowadays for startups?

“I’m at the nascent stage, and so the thing that I tell founders is there’s a lot of investors that want deal flow,” he says. “There’s a lot of cultural push within innovation, within entrepreneurship to go to demo days, to go to pitch days, to talk to angel investors, to try to pitch for funding. At least at the nascent stage when a founder has just the kernel of an idea, one of the most important pieces of advice that I offer the founders that I mentor is do not talk to investors right now. They’re a distraction. The most important investor for a founder with a nascent stage startup is the founder. It’s completely shifted from this external focus to an internal focus.”

Breakthrough Advice

What is your advice for that innovator or founder in terms of their new product or service?

He notes, “Innovation means a lot of things to a lot of people. As a founder, you’re worried about at least five things. You need a product that solves a problem. You need a market of customers that have that problem. You need a team to build the business. You need a way to access those customers. You need funding to pay for all of it. Funding used to be the most important, now it’s not. So what’s the most important? Trying to discover a new market of customers. You can try to create a new type of established business, or you can create a breakthrough business.

“What do I mean by established business? If we want to open a laundromat or a pizza shop or anything where most of the knowledge exists, you can go work at a pizza shop or laundromat, figure out how the business works, and then go start your own thing. That to me is not innovation. Innovation is much more like a breakthrough. There’s a breakthrough on the customer side or there’s a breakthrough on the technology side. A breakthrough on the customer side is there’s this new thing called a smartphone. Who would think that somebody would want to use that to get a taxi? That’s Uber.”

He adds, “The other type of innovation is what I would think of as technology innovation. Everybody agrees that there is a big expensive problem. We all desperately want a solution, and we just haven’t gotten there yet. A good example of that is Ozempic. Everyone knows that people want to lose weight. Everyone wants a magic solution to it. And Ozempic was this amazing technological breakthrough to solve this obvious market problem.”

Advancing Affordable Intelligence

What are your thoughts on the outlook for 2025 in in terms of startups or innovation, or whatever lens and filter you want to use?

Vladimer says, “We’re knee deep right now in the hype cycle for generative AI. It’s a great solution and everyone’s kind of rushing to find compelling problems that it solves. I’m sure it’s going to continue to grow but some startups are going to fail. It’s just the typical way. But the thing that I would say for 2025, it’s in some ways the same as 2015 or 2005. We live in an amazing time in human history.”

“What happened about 20 years ago was an inflection point very similar but instead of affordable energy now we’re in the age of affordable information. As entrepreneurs, as founders, as innovators, we went around with our feelers out trying to find compelling problems to solve where we said, that’s how you’re gathering all this information on paper and pen? That’s the silliest way to possibly do it, but it’s the best way you could do it so far. Let me tell you about these amazing things called computers. For the last 20 years, we have been looking for these opportunities in the world of innovation of information, of affordable information.”

Vladimer adds, “I think it’s going to continue into 2025. What’s interesting now with AI is that we’re on the cusp where we just started in this world of affordable intelligence. Affordable energy, affordable information, and now it’s affordable intelligence. It’s not just how do I gather data at scale but how do I now analyze information? There’s going to be a lot of entrepreneurs trying to find those problems and then apply AI against it.”

For more of the interview with Mike Vladimer, check out the video from FEI 2024. Editor’s note: Vladimer offers a blog and a newsletter which can found at https://www.nascentstartups.com/.

Applying Prompt Engineering: Inspiration to Innovation

A painter's palette and brushes with mixed up colors.

FEI 2024, held this past June, which focused its educational sessions on human and AI themes, leveraged this trend during the conference by holding an onsite workshop featuring prompt engineering.

The “Prompt Engineering Mindset Workshop” at FEI was a special event that highlighted and demonstrated this growing specialty field. But prompt engineering is not necessarily a tool used only by the experts. In fact, it is quite accessible for the novice user with a bit of experimentation, and it goes beyond text to other mediums such as audio and video. Imagine tapping into a technology that not only enhances productivity but also redefines the creative process. Generative AI is doing just that—reshaping storytelling, music, and visual arts by offering new tools for innovation.

You’ve likely tested Generative AI for your own productivity. But you’re missing out, if you’re simply focused on text. The FEI session provided key examples of completed projects from “first prompt” to “desired outcome.”

For example, Amal Irgashev who runs AudioVerse Lab, an innovation initiative at Berklee College of Music focused on music technology, provided a prompt engineering mindset case study. Irgashev took us behind the scenes of music production, using generative AI to create an entire song from conception to completion.

Other guests at the workshop included Donald High, Chief Data Scientist, Internal Revenue Service, who took a more text-based approach to creating a visual report presentation with AI. Paul Greenberg, CEO, Butter Works, demonstrated various ways text prompts can be transformed into versatile and useful videos. And Matt Marcus, CEO and founder of Rhombus Ventures, showcased how you can leverage real-time image-to-image AI generation to create usable storyboards and concept videos.

Ultimately, the session’s focus on music, film, text and image case studies highlighted that the prompt itself is simply a tool, and your mindset going into the prompt is actually the first step to ensuring your objectives and outcomes are met. Taking a holistic approach to your prompt engineering mindset is key. With that mindset, you can simply optimize your text-based GenAI libraries, strategies and projects while expanding your outcomes to include audio, image and film. In addition, in this way humans can add another layer of value to their collaborations with AI.

Prompting Progress

Want to learn more about prompt engineering? All Things Innovation’s “Positioning the Role of Prompt Engineering,” explores the subject in more detail. Prompt engineering is one of the latest buzzwords when it comes to artificial intelligence, but what is this evolving and complex job function? Some may think it’s just another form of data engineering, but ultimately prompt engineering is a primary cog in the AI and machine learning world, one in which it plays a crucial role in AI progression. According to Giskard, an AI and data science practice, it “consists of the generation and refinement of prompts, steering language models towards the intended responses.” Despite appearing as a fundamental foundational tool on the surface, prompt engineering is an intricate undertaking that intertwines the creativity of the human element with AI expertise.

Editor’s note: All three “Prompt Hacks” demonstrated at the FEI workshop are available at https://streamly.video/category/innovation.

Video courtesy of Streamly

Creating a Venture Capital Mindset

Creator or entrepreneur sitting with laptop, business graphics swirling around him like thought bubbles.

Weigh the Risks of Innovation

Large companies may be reluctant to take on the risk of innovation projects, they allocate resources inefficiently and are slow to kill innovation projects that are not working. We looked at Manifold Group’s article, “What corporate innovation can learn from venture capital,” to see just why these mistakes are often made, including:

  • Comfortable with big swings: Large organizations are used to taking big swings. After all, it’s much more efficient to put your muscle behind a few key initiatives than focus on lots of small ones.
  • Misunderstanding the risk curve: They tend to choose a very small number of innovation initiatives they think are good bets and put significant resources behind them. But that logic breaks down in the context of innovation, where failure is the norm, and only a small set of projects actually work out.
  • Throttling effects of risk mitigation systems: Most companies end up with a long list of checklists and approvals required for any new initiative. That friction greatly reduces the number of initiatives that can realistically launch in any given time period.
  • Maladapted resource allocation models: Resource allocation models at large organizations naturally evolve to accommodate evolutionary change, but tend to break down for more innovative projects. Finance teams employ repeatable tests for ROI (e.g., hurdle rates) to ensure capital is being allocated efficiently. But real innovation involves significant uncertainty—after all, by definition you’re doing something new.

Partner Up to Fuel Innovation

All Things Innovation’s “The Partnership Playbook: Opening Internal & External Innovation,” looks further at the world of innovation partnerships. Developing innovation can be a challenging task on many fronts for corporate enterprises. Innovation might not be embedded into the culture or leadership of an organization, making it difficult to move projects forward, for example. Innovation management systems and methodologies can help streamline the process. The role of balancing both internal and external innovation systems and partnerships can also help maximize successful innovation initiatives.

In “Creating the Right Innovation Partnership,” we also explored startup partnerships. Corporations often face many challenges when it comes to developing innovation in a fast, evolving market. They can face barriers such as inertia, the silo mentality, a large bureaucracy, and even risk aversion can permeate the company culture. That’s where partnerships with startups can come into play, as these firms tend to be smaller and more flexible, creative, agile and disruptive. The partnership between an established corporation and startup can benefit both parties, as they leverage shared resources and a singular vision, whether it be rolling out an innovation or expanding into new markets.

Become an Investor in Innovation

Corporations often aim for disruptive innovation by using startups as a model for the way to innovate. But Manifold points out that it’s common to forget about the benefits of the venture capital put into the system, which often goes hand-in-hand with startups and entrepreneurs. “By injecting the concepts of venture capital into the mix, large organizations can begin to realize the extraordinary potential offered by innovation,” says Manifold.

Indeed, suggests Manifold, startups exist “in symbiosis with venture capitalists who assess, fund, and guide them towards maximum value creation.” Executives often fail to understand the distinction between being a boss and being an investor. Playing a key role in the startup ecosystem, venture investing is in essence different from traditional investing, meaning that applying a traditional portfolio approach often fails in the context of disruptive innovation. Venture capital is more than just investing, as Manifold notes, it is also a way to approach asset allocation, portfolio management and optimizing value.

For corporate innovation executives to act more like venture capitalists, Manifold advises:

  • Volume beats volatility: Corporate executives, like entrepreneurs, tend to have a few active areas of focus at any given time. Venture capitalists, by contrast, seek to have a lot of active portfolio companies at any given time. They know it’s very hard to predict outcomes and resource needs, so they smooth out the volatility with a large portfolio.
  • Don’t act like a boss: Corporate executives participate in execution—hence the name. They run teams and are by their very nature bosses. Venture capitalists know better than to try to operate their portfolio companies. That’s why good VCs act like board members instead of bosses. They engage periodically with their startup teams for updates and to provide guidance, but they don’t try to manage them.
  • Your product is the portfolio: Corporate executives are accustomed to modeling each project, and justifying each individually to external parties (typically finance). Venture capitalists know that they make money from the overall success of their portfolio, and act accordingly. Act like a VC and raise a large pool of capital from your organization designed to be allocated over an extended period of time, and without having to go back each time to explain your needs to the finance department. Justify it based on a portfolio of returns instead of trying to prove that any particular initiative is likely to be successful.
  • Focus on resource allocation, not individual outcomes: Corporate innovation executives tend to focus on ensuring that each project performs well. When an initiative struggles, it’s very tempting for people with an operator mindset to jump in and try to fix it. But that’s a great way to waste a bunch of time and resources on something that’s destined to fail, and meanwhile starve more deserving projects. Investors know that some of their investments are going to fail. Instead of focusing on fixing troubled projects, they focus on doing a good job of allocating resources.
  • Milestones and stage-gate processes: Corporate innovators tend to focus on the viability of each individual initiative, using normative models derived from their corporate parent. Venture capitalists use carefully considered—and startup appropriate—milestones and stage-gate processes to understand the risk, potential, and value of each of their portfolio companies. They use these assessments to efficiently allocate their capital resources. This is perhaps the most challenging aspect of applying venture paradigms to corporate innovation, because it involves very different skills and approaches than are typically used in corporate environments.
  • Treat the portfolio like a division: Tight organization and integration are the norm, with clear reporting structures and aligned strategies. But it’s really hard to act like a venture capitalist if you’re being managed like an executive. Venture capital partners are called “General Partners” and their investors are called “Limited Partners.” The “Limited” part refers to the limited control and oversight investors have over the day-to-day operations of the general partners. By keeping innovation teams and systems separate, you can avoid some systemic and procedural limitations that might otherwise constrain innovation efforts.
  • Founders are the key: Corporate executives understand how important people are to success. Venture capitalists know that the most important ingredient for startup success is the founding team. Even the best idea with a mediocre team is very likely to fail. VCs also understand that the inherent complexity of innovation means it’s more about learning than execution, at least until the company finds product-market fit and begins sustainably scaling. Small teams are more effective in the early phases requiring adaptability and learning.

Organize Like a Venture Capitalist

All of these venture initiatives, of course, take time. A venture capitalist mindset, notes Manifold, focuses more on the long-term success of the startup as opposed to short-term gains. That has implications for resource allocation, team organization, and management.

Innovation tends to be a risky bet. But taking some of these lessons learned from the venture capital industry could create best practices for the corporate innovator. “From a high level, this means taking a portfolio approach to innovation, and acting as an investor (resource allocator) instead of an operator,” says Manifold. “This has the benefit of enabling a significantly higher volume of innovation projects, which increases expected ROI. It also enables organizational behavior that is much more conducive to success for each initiative.”

Video courtesy of INSEAD

Is Transformational Innovation Possible?

A colorful galaxy-like image with a lot of circles, stars and effects.

Seth Adler, Head of IMI Media at Informa, brought together an impressive group of nine innovation experts from diverse fields to explore these issues. All Things Innovation would like to thank them for their innovation and insights expertise:

  • Tammy Butterworth, Product Innovation Director at Welch’s
  • Lisa Costello, Director, Head of Platform, Prologis Ventures at Prologis
  • Milan Ivosevic, VP of R&D and Innovations at CooperSurgical
  • Prapti Jha, former Design Strategy & Research, Design Thinking & Innovation at Harvard University
  • Cherie Leonard, Head of North America Insights at Colgate-Palmolive
  • Nevada Sanchez, Co-Founder and Vice President of Core Technology at Butterfly Network, Inc.
  • Michele Sandoval, Director of Innovation at E&J Gallo Winery
  • Leslie Shannon, Head of Trend and Innovation Scouting at Nokia
  • Harsh Wardhan, Innovation Lead, Design Strategist at Google

Exploring Transformational Innovation

Seth Adler: Here we go again. We have many of the same faces from last year’s roundtable that we did at FEI 2023. This is FEI 2024, and we’ve added some new voices and faces, so thank you for doing this, all of you. So, transformational innovation, we talked about it last year. We’re going to kind of attack this again, because we need to back up and ask the question, is this even possible? Milan, you have the microphone in your hand, so we will ask you first, is it even possible? And if so, how?

Milan Ivosevic: If it’s not, I’m completely lost. It’s an interesting topic that I touched on in my FEI session, and how it’s possible. I believe it is. There are many examples in the industry that have happened throughout history, so it’s possible. Is it more difficult these days? Can we really push for a transformational level, that’s the question right now. We have new tools. Is it AI? Is it still the human element? What is it? But it’s been possible up to now, and I hope it will remain in the future.

Adler: Michele, you’re coming from a different type of organization. How is transformational innovation possible?

Michele Sandoval: Absolutely. It’s how you think about transformational. You can take an existing business and reinvent it and transform it and how you navigate that business, or you can truly come up with something that’s new, innovative and new to the world. But I think both are considered transformational if they significantly change how you work and what you do.

Tammy Butterworth: Yes, it’s possible and much needed. I think what I learned from the panel that we started FEI with is the importance of collaboration. We’re not going to get to it on our own. It has to be across the business and maybe across businesses. What I’m seeing more and more is to make that big difference and reach true transformation. You need lots of input and lots of people working together.

Cherie Leonard: Yes, transformation innovation is possible. I’d actually love to build on something just said around the fact that collaboration is key and it’s like one plus one equals three or exponentially in my mind. But, thinking about this question, I was trying to think about an analogy. Is it push and pull? What is it? I turned to Gen AI to see what kind of analogy they would use. And I think it was interesting. It was a little bit about the city and the outskirts. And the city is the core, the heart and soul, the business, the commercial, the P&L, giving lending resources to the outskirts, which really drives the creativity, the provocative thinking, the longer-term vision, and how those two have to work symbiotically together.

Adler: AI is this tool that we can use to help innovators and maybe go leaps and bounds forward with. This is the leapfrog type situation. Harsh, what would you add here about AI as a tool for transformational innovation?

Harsh Wardhan: I think, specific to AI, it is. The way that people are thinking about AI is that it’s going to change the world, and that’s where our head goes to. But we should be thinking about how it supercharges our today and how it supercharges our work, and that’s how it’s going to help with transformational innovation. Because again, with transformational innovation, people are thinking it’s like a light bulb moment. It’s disruptive, it changes. But there’s a lot of work that goes behind that. And it’s months of work, if not years. For those who have a good research background, you know how much research you are doing to get to that transformational element. Just an example, using AI, you can cut short that time. That’s how you are supercharging innovation, transformational innovation with AI.

Leslie Shannon: I want to throw a little wrench into the concept of transformational innovation. In my experience, it takes a crisis, it takes some kind of existential threat before people go, this is serious. Okay, now we really need to do it. I just wanted to get that out there, that charming, optimistic take. But on the AI side, it’s important to understand that all AI is not the same. We’ve got machine learning, which is using structured data, and that has been around forever. It’s all the information in your databases, and it’s things that we’ve been using. In my industry, telecommunications, we’ve been using that for the basis of automation. Decisions can be made in millisecond speeds, and machines can figure out what to do next based on the information that’s coming from the structured data. That is unchanged, and that is carrying on.

Generative AI, on the other hand, is looking at unstructured data. It’s looking at the information that you have in your user manuals, in your PowerPoints, in your marketing information, coming from the corporate point of view. It takes seconds, not milliseconds. And so that means that it’s not fast enough for machines. The output of generative AI is specifically designed to be looked at by a human. So generative AI assumes a human in the mix to first ask the question and then to interpret the answer. Yes, it’s a tool. I think what you were saying is exactly right about the role. It doesn’t do everything, but it speeds things up. You still have to have a good question in and you still have to have good interpretation of what comes out.

Adler: Obviously all AI is not created equal, and we can use it differently and there are different iterations.

Prapti Jha: Great points. I think what we’re all referring to is when we talk about transformational innovation in a corporate setting, two things that become super crucial is what are the structures in that corporate setting, because that inhibits any kind of transformation or any kind of disruption to happen.

The second is the culture. I know it’s something we all talk about. It’s hard to change, but that’s also connected to Leslie’s point that it needs a crisis because when that crisis happens, you’re open to changing those structures that you’re comfortable with and also changing the culture when it comes to AI and those decisions around structures and culture.

Humans will have to take the initiative, the leadership and the vision. AI can help you with what those structures might look like. There’s the tool aspect of it. I believe that and also because I work in this human centered innovation world so much, asking the right questions that we talked about and the aspect of empathy would be something that humans would have to bring into the equation and that with help from AI can do that.

Lisa Costello: I used to have a boss that used to say, never waste a good crisis. He was a military guy and I’ve always kind of taken that to heart. On the transformational AI and innovation front, I would add that a lot of times we think we have to do this alone in our own silo as a corporation. What AI is allowing us to do to achieve that transformational innovation is to go outside of our organization, be able to collect a lot more data that we haven’t had access to before. We get a lot more of that external innovation that we can bring in. That’s the role that venture capital plays in the area that I work in. We can take advantage of a lot more startups and resources from outside in.

Adler: Nevada, you really exist because you’re part of an ecosystem, and that external innovation. Can you describe the way that you’re making transformational innovation happen through a network of partners?

Nevada Sanchez: This is actually a big focus for our company right now, and we’ve found tremendous success in being able to see an explosion of innovative applications by engaging with external parties, startups, researchers that saw what we had built, and realized they could use that to build something even more innovative and interesting. I never would have thought about building a brain implant, an ultrasound machine, but someone’s doing that right now, we are working with them to do that. It’s been pretty incredible what it’s done to the team and the organization, giving people the opportunity to see all these perspectives, new markets, new applications. It’s very motivating. It has carry-on effects beyond just the partnership itself. I think it’s improving the innovation spirit within the organization as well.

Stay tuned in the upcoming weeks for more posts featuring All Things Innovation’s roundtable discussion on transformational innovation.

The Symbiotic Relationship Between Corporations & Startups

Strands of DNA.

Hart held the session, “Corporate Innovation: Internal & External Best Practice,” at FEI. One of the reasons that good corporate startup partnerships succeed is due to a clear division of responsibilities based on what each party brings to the table. Corporates traditionally don’t move quickly due to refined and efficient processes. Startups, by definition, need to focus on finding scale.

If you could just tell us a little bit more about the session you conducted at FEI 2024?

“I’ve been working in corporate partnerships for the past seven years in a lot of different capacities, and I’ve just seen all the issues that can happen from both the corporate side and from the startup side,” says Hart. “Back maybe five years ago, I was facilitating relationships between corporations and startups. Now I work at Cornell Tech and really help corporations understand how to engage with academic startups, which is a little bit different sometimes than later stage startups. That’s kind of the focus now.”

In your role, you probably see a lot of what we term innovation roadblocks, especially when it comes to startups and partnerships. In your experience, what have you observed between the corporate world and these academics and startups?

“Regarding the four pillars that I’m talking about in my presentation, a key one is culture. Startups are small. Corporations, multinational corporations, usually can employ tens of thousands of people while startups can be a handful, at least under fifty employees. That’s why culture matching is so important. Both parties need to be able to speak the same language,” she says.

Hart continues, “Two other buckets are internal and external kinds of innovation. The ability to build teams internally within a corporation to be set up to work with startups and to be able to scout startups is significant to the formation of the venture, as well as having the right process and tools. It’s about making sure that companies have good processes for working with really small companies and making sure that their contracts are friendly to small companies, and their procurement processes are friendly to small companies, and so on. That’s something that’s hard for large corporations to do. That’s another pillar that we look at.”

FEI conducted several roundtables, and a lot of the discussion was about this kind of cross collaboration, interdisciplinary activity, and partnerships. For the corporate world, and for startups, would you say there’s a democratization of innovation happening? There are conversations about the democratization of data but also of innovation, of organizations being flatter. Should that help the corporate world with these startups?

“I think I’ve seen a lot of that broader accessibility at least within corporations,” says Hart. “It used to just be this siloed innovation team, they’re the only ones that are allowed to do the function. But that’s actually not very successful because then they’re not really able to pull in new innovations and send them to the right team. Using the phrase democratization is the ability to make sure that anybody within a company is able and empowered both from a cultural perspective in the organization and from a process perspective to be able to start new relationships with startups and bring in new technologies.”

Speaking of technology, artificial intelligence has been on everyone’s minds. What is your outlook in terms of innovation, partnerships and the future? How do you feel the future is shaping up?

“I am really curious to see what happens,” says Hart. “I think that startups have always been the kind of trope in that they move faster than corporations. I think it’s going to become exponential and corporations are looking to see what startups are going to overtake their industry or to overtake their business model. I think it’s just going to move faster. There’s that old statistic from a few years ago that the length of time a company stays on the S&P 500 has dropped one-third from 50 years ago. It’s going to get shorter, shorter, and shorter as AI becomes a technology to help companies move faster.”

For more of the interview with Carley Hart, check out the video from FEI 2024.

The Art of Asking: A Data Scientist’s Guide to Stakeholder Interactions

A stressed out businessman surrounded by stacks of papers.

The Dashboard Dilemma

Picture this: You’re in a meeting, armed with your laptop and a strong coffee, ready to dive into some serious data analysis. Suddenly, a stakeholder interjects:

“Hey, data wizard! Quick question—what’s our 3-year Average Revenue Per User (ARPU)?”

You resist the urge to sigh audibly. Instead, you take a deep breath and prepare to explain why this question, while seemingly straightforward, might not be as useful as they think.

The Art of Asking (and Why It Matters)

Here’s the deal. When you ask a data scientist for raw metrics like “How much traffic do we get from each marketing channel?” or “What’s our 3-year ARPU?”, you’re essentially saying one of two things:

  1. You believe you can interpret the data better than a trained professional (spoiler alert: that’s unlikely).
  2. You’re not entirely clear on the business question you’re trying to answer.

Both scenarios are about as productive as trying to paint a masterpiece with a sledgehammer. So let’s talk about the art of asking the right questions and why it matters for your bottom line. After all, that bottom line is probably why you brought a data scientist on board in the first place.

The “What” vs. The “How”

Here’s a paradigm shift that could revolutionize your business: Do your job, and I’ll do mine. As a stakeholder, your job is to handle the “what.” What are the business objectives? What are the key challenges we’re facing? What insights do we need to drive growth?

For example, instead of asking “What’s our customer churn rate?”, try framing it as “What factors are driving customer churn?”

My job, as your friendly neighborhood data scientist, is to handle the “how.” How can we analyze the data to answer these questions? How can we leverage advanced statistical techniques to uncover hidden patterns? How can we translate raw numbers into actionable insights?

Together, we’ll tackle the “why.” Why are certain strategies working (or not)? Why are customers behaving in specific ways? Why should we invest in one area over another?

From Metrics to Insights

So, instead of asking for raw metrics, try these on for size:

  • “How can we boost customer retention?”
  • “Which product features drive the most value?”
  • “What factors are most influential in predicting customer lifetime value?”

Now we’re cooking! These questions provide the context I need to work effectively. I’m not just pulling numbers out of a database; I’m using my expertise to dive deep into the data, uncover meaningful patterns, and provide insights that can drive the business forward.

The Stakeholder’s Guide to Data Science Interaction

To help you navigate the complex waters of data science interaction, here’s a handy guide:

  1. Define the business problem: Before you even think about asking for data, clearly articulate the business challenge you’re trying to solve.
  2. Ask open-ended questions: Instead of requesting specific metrics, ask questions that allow the data scientist to explore and analyze.
  3. Provide context: Share relevant background information and any hypotheses you might have.
  4. Be open to surprises: The data might tell a different story than you expected. Be prepared to challenge your assumptions.
  5. Collaborate, don’t dictate: Work with your data scientist to refine the questions and approach as you go along.

I can almost hear some of you protesting: “That’s all well and good, but the data scientists I’ve worked with didn’t have the domain knowledge to contribute this way. They couldn’t see the forest for the trees!”

First off, ouch. But also, fair point. Let’s unpack this a bit.

The Domain Knowledge Dilemma

If your data scientists seem to be operating in a vacuum (and not the kind that cleans your floors), unable to connect the dots between data and business impact, you’re facing one of two problems:

  1. The Hiring Hiccup: You might not have hired the right data scientists for your needs. A good data scientist isn’t just a number cruncher or a code monkey— they’re more like a Swiss Army knife with a penchant for statistics. Let’s break this down further:
    • Technical skills aren’t enough: While proficiency in Python, R, or machine learning algorithms is crucial, it’s not sufficient. It’s like hiring a chef based solely on their ability to chop onions really, really fast.
    • The curiosity factor: Look for data scientists who ask probing questions about your business model, industry trends, and specific challenges. If they’re not as curious as a cat in a room full of laser pointers, keep looking.
    • Adaptability is key: The business world changes rapidly. Your ideal data scientist should be able to pivot quicker than a politician during election season.
  2. The Silo Syndrome: Your organizational structure might be keeping your data scientists too far removed from the business. If they’re tucked away in a corner, only interacting with stakeholders through JIRA tickets and the occasional email, they might as well be on a deserted island, sending SOS messages in bottles. Here’s why this is problematic:
    • Context is king: Data doesn’t exist in a vacuum. Without understanding the business context, data scientists might focus on statistically significant results that have little practical impact—like discovering a strong correlation between ice cream sales and sunburn cases.
    • Missed opportunities: When data scientists are isolated, they miss out on casual conversations and impromptu meetings where valuable business insights are often shared. It’s like being the only person who missed the memo about casual Friday.
    • Lack of buy-in: If data scientists aren’t integrated into the business, their recommendations might be viewed with skepticism or misunderstood, leading to poor implementation of potentially valuable insights. It’s the corporate equivalent of bringing a chess set to a football game.

Consider this scenario: Your data scientist discovers a strong correlation between customer churn and the frequency of product updates. Without proper business context, they might recommend increasing update frequency across the board—about as helpful as suggesting everyone should eat more kale to solve world hunger. However, if they were more integrated into the product team, they’d understand that frequent updates are costly and might annoy certain customer segments.

The goal isn’t to turn your data scientists into business executives, nor to make your business leaders into statisticians. We need a shared language and understanding that allows the business to deploy both groups’ unique strengths effectively. Think of it as a corporate version of the Avengers—but with more spreadsheets and fewer Chitauri invaders.

Breaking Down the Barriers

What’s the solution? It’s time to break down those barriers and bridge the gap between data science and domain knowledge. Here’s how:

  1. Hire Holistically: When bringing data scientists on board, look beyond technical skills. Seek out candidates who demonstrate curiosity about your industry, ask insightful questions during interviews, and show a willingness to learn.
  2. Integrate, Don’t Isolate: Embed your data scientists within business teams. Let them attend strategy meetings, customer calls, and product demos. The more they understand about your business, the more valuable their insights will be.
  3. Encourage Cross-Pollination: Set up regular knowledge-sharing sessions where business experts can educate data scientists about industry trends, and data scientists can explain the potential of different analytical techniques.
  4. Invest in Onboarding: Develop a comprehensive onboarding program for new data scientists that includes deep dives into your business model, key challenges, and industry dynamics.
  5. Foster a Learning Culture: Encourage continuous learning and development. Support your data scientists in attending industry conferences, taking relevant courses, or even shadowing colleagues in different departments.

Remember, a data scientist who understands your business is worth his or her weight in gold (or bitcoin, if that’s more your style). They’re not just there to answer your questions, but to ask the right ones, challenge assumptions, and uncover insights you might never have considered.

The Bottom Line

Remember, data scientists are not human calculators or walking dashboards. We’re problem solvers, pattern finders, and insight generators. When you treat us as such, that’s when the real magic happens.

So the next time you’re tempted to ask your data scientist for a quick metric, pause. Take a moment to think about the real business question you’re trying to answer. But also, reflect on whether you’ve set your data scientist up for success. Have you given them the context they need? Have you invited them into the heart of your business, or left them on the periphery?

By bridging the gap between data science and domain knowledge, you’re not just improving the quality of your analytics. You’re creating a powerful synergy that can drive real, tangible business value.

Together, we can turn data into decisions, metrics into meaningful insights, and questions into quantum leaps for your business. Now, isn’t that a lot more exciting than just asking for the 3-year ARPU or complaining that your data scientist doesn’t “get it”?

The right question is worth a thousand metrics. It’s time to upgrade our stakeholder-scientist operating system for greater interoperability.

Ready for a reboot?

For more columns from Michael Bagalman’s Data Science for Decision Makers series, click here.

Energizing Sustainable Solutions

A large spinning wind turbine.

Hamsho presented a session at the conference, “Next Generation Technologies: Putting the Talent Puzzle Together,” which focused on the transformative disruption taking place in the wind energy industry. When new technologies are utilized within burgeoning markets by inventive changemakers, business model transformation can take place through dynamic change led by entrepreneurs.

“The American Offshore Wind Academy business model is really transformative,” says Hamsho. “It’s a model that we would like to share with other industries, given that the area of corporate training is a place which very much needs disruptive innovation.”

She continues, “One of the major focuses of the FEI session is how the academy was able to connect the experts within the industry together. Over the past couple of years, there has been a lot of focus on workforce training. In the wind industry, there’s really a need to get the experts together to share their knowledge and advance their careers together. One of the industry CEOs used to call us the Uber for offshore wind knowledge because we’re basically just connecting these experts together and bridging the gap of knowledge in this industry.”

Offshore wind energy is certainly a transformative industry that is focused on developing sustainable energy for this country. The academy is focused in part on partnerships and training. What are your thoughts on this field from an innovation perspective?

Hamsho relates, “In the offshore wind industry, we must really differentiate between innovation and invention. Invention is turning money into ideas. This is basically our team members who are trying to think about the future. Innovation on the other side is turning ideas into money. This is exactly what we’re doing here in the offshore wind industry. Why did Thomas Edison take all the credit for the light bulb? Even though he’s not the one who invented the light bulb, he made it affordable and accessible for every single person in the world to have it, and that’s why he took the credit. This is really what we need to focus on in the offshore wind industry.”

In some ways, the offshore wind industry is just starting out. There’s a lot to strategize upon, in terms of the impact on the planet regarding climate change, developing sustainable solutions, and creating better energy storage and usage. What are your thoughts on the outlook for the offshore wind industry?

“Do you want the good news or the bad news?” says Hamsho. “Let’s start by the fact that in the offshore wind industry, we are 20 years behind Europe. The good news is we’re expected to lead the world by 2050. The bad news also slated for 2050, is that the world population is expected to reach ten billion. One billion of them won’t be able to access any source of energy or they won’t be able to afford it. The energy needed by 2050 will be tripled from the demand that we have today.”

“When we talk about wind energy in general, offshore or onshore, we have enough wind today in the U.S. to energize the nation three years ahead. The challenge though is to make these sources reliable to use by 2050, and that’s really where the innovation takes place. Storage, for example, green hydrogen, other sources of energy that we need to think about by 2050,” she says.

For more of the interview with Serene Hamsho, check out the video from FEI 2024.