A Non-Technical Person’s Guide to Commercializing Hardtech

You've developed an industry-changing innovation. And you want to start selling it to your clients yesterday. But it will take a whole lot of dollars and resources to get your unique solution to market.

Sound familiar?

Julia Byrd worked closely with a smart business to figure out if the investment would be worth it. And if so, how.

There was only one tiny hitch: she knew very little about their industry.

The time to start is now. Enjoy.

By Elke Boogert, Mach49 Managing Editor

A Non-Technical Person’s Guide to Commercializing Hardtech

By Julia Byrd , Operating Partner at Mach49 

I know nothing about soybean processing. Yet, I just finished a project advising a client on whether their technology would be a good fit for the soybean processing market. Impossible? No. In fact, I would argue that having zero soybean processing experience made me a better candidate to evaluate the given technology than the large corporate client who invented it.

It started with a request from a processor to our client to get their process more into spec - they believed a lack of optimization, in particular, part of the process, was leading them to miss out on extracting as much oil as possible from each bean. A solution to this was critical and, according to the client, could generate millions in additional revenue for soybean processors.

Large Original Equipment Manufacturers (OEM) and processing companies previously tried to develop solutions before, but nothing worked, mostly due to the dusty, sticky, rattly environment taking its toll on sensitive measurement systems. This company, however, with their engineering strength, was able to succeed where others had not. 

Our client, an electrical and automation integrator, had incredible technical expertise, but building a product is not something they typically do. Normally, they respond to RFQs for specific jobs already defined by their own customers, so they were not sure how to evaluate the potential of this new business where they would be the ones reaching out to customers with a product offering. Before they committed a lot of resources to figuring out how to productize this innovation, they first wanted to make sure it was worth it. Very smart. They brought Mach49 in for Impact Framing – defining what success for the initiative should look like – as well as to conduct a venture assessment using primary and secondary research to determine if the technology would be able to reach those goals.

This assignment made me think of my time back at Columbia University. The university researchers would often lust over the resources of the corporates and believe that if they just had that kind of funding and access to customers, they would no doubt be successful. The grass is always greener. Here are some of the problems the client encountered:

Engineers often fine-tune before testing core value propositions

The client set up complicated, cost-intensive pilots with fully functional units instead of simple experiments to see if accurate measurements change behaviors and yields.

The challenge was that the technology improved while the business case for it did not. The experiments were only designed to test feasibility, not desirability or viability of their core value propositions.

Confirmation Bias Hides Real Risks 

Internal interviews revealed unsolved risks, but participants still estimated a 70%+ chance of success. People tend to spotlight supporting evidence and overlook conflicting signals. Everyone wants to believe the tech will work and no one wants to be the public doomsayer minimizing the hard work of their colleagues.

Secondary Research Incorrectly Trumps Primary Data

Even though they had strong customer relationships and had done some interviews about the solution, the client relied more on papers and market stats to confirm core assumptions. For example, when we asked if they had talked to their customers about how often their process ran out of spec, they replied, “We haven’t. That’s bad, isn’t it?” Instead, they were relying on data from a paper published 25 years prior that looked at only two plants – certainly an important data point, but not incontrovertible evidence.

Losing Sight of the Big Picture: Value to Customer is Paramount

Technologists and entrepreneurs alike often concentrate on the problem being solved without pausing to think about its importance in the bigger system. In this case, the ability to increase measurement accuracy was useful, but it was less crucial to customers than issues like labor shortages, optimizing utility costs, and maximizing throughput. Many customers were willing to sacrifice the oil yields for being able to process a greater volume of beans.

After interviewing plant operators and industry experts, we realized that the millions of dollars in anticipated additional oil revenue from this particular optimization was not likely to materialize. The client thought that simply providing accurate measurements was all it had to do, but they quickly realized that they needed to understand what the client was going to do with those measurements to understand the true value of the innovation. The real value of the technology was not in the oil revenues. It was in the labor savings.

Defaulting to Build without Considering Other Growth Avenues

The integrator did what good engineers do: they saw a problem and built a solution. For building a prototype to prove the concept works, that’s often fine, but continuing to build out new manufacturing, sales, and service capabilities to support the venture is a big lift. 

After conducting a build, buy, partner, invest (BBPI) analysis, the Mach49 team ultimately recommended that they find a partner who can complement their capabilities.

How do you solve for these stumbling blocks? Here are some ideas:

• Brainstorm "killer hypotheses" by asking, “What outside of the technology not working would kill this venture?”
• Sort assumptions into buckets: Desirability, Feasibility, Viability, and Suitability
• Draw conclusions from primary customer research and use select secondary facts for backup
• Spotlight only the most relevant data to guide recommendations to avoid becoming overwhelmed by market data and endless facts
• Record complete customer quotes to make synthesis easier and the evidence more believable
• Build a strong viewpoint but keep it flexible so you can adjust as new information emerges – create recommendations, not just options!
• Highlight remaining unknowns during check-ins and outline how to tackle them
• Beating engineering blind spots, biased thinking, and vague strategy to get to validated recommendations based on customer reality is the secret ingredient for most accomplished tech projects.

I had no biases and had to ask basic questions to build my understanding from the ground up rather instead of skipping over assumptions that seem like givens. This venture has a strong foundation because of what we heard directly from experts and potential customers because we stayed open-minded, listened, and steered towards evidence-backed decisions. This is the process that they now want to learn how to do for themselves.

I was being a bit facetious at the start. I should have said that I knew nothing about soybean processing. I have certainly learned a lot in a short amount of time that has made me a lot of fun at parties lately as I share my newfound insights into the world of soybean, canola, and cereals. I have no idea what is next on the agenda – maybe population health services or virtual power plants or lithium phosphate batteries – but I look forward to not knowing anything about them as well.

Operating Partner JULIA BYRD brings her deep background in customer discovery and program development to bear identifying the most significant problems for Mach49 clients to solve. By helping businesses craft challenge statements with more initial validation behind the problem, she helps fill the Incubate pipeline with higher quality ventures from the start.

Previously, Julia worked at Columbia University as the Associate Director of NYSERDA’s Entrepreneur-in-Residence program and PowerBridgeNY — both programs designed to help cleantech startups in New York access the capital, expertise, technology, and talent needed to maturate from ideate to scale. On the back of her experience building and evolving PowerBridgeNY — a first-of-its-kind cleantech proof-of-concept center — she founded Byrd Consulting LLC, through which she helped national clients design, launch, and iterate on entrepreneurial programs based on lean startup methodology.

In addition to her work at Mach49, Julia serves as an Adjunct Professor at Columbia University, an Instructor with the National Science Foundation’s I-Corps Program, a mentor for Cleantech Open, an Advisory Board member for V1 Studio, and an Accountability Board member for Scale for ClimateTech. She received an MS in sustainability management from Columbia University and a BA in environmental studies and Russian language and literature from George Washington University. In her free time, Julia is a passionate aerialist, experienced on apparatuses including pole, lyra, and trapeze.

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