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Justkul and AI in 2026

Posted on by jfhannon

From the very beginning, Justkul’s goal has been to provide the best data and insights possible. This year we are launching new AI and portal offerings to help our clients develop deeper understanding of their most important strategic issues and evaluative needs.

Why I started Justkul

Twelve years ago I founded Justkul under the belief that surveys done well are among the most powerful tools for understanding customers, companies and markets.

At the time, I had spent most of a decade designing well-regarded surveys for the top strategy consulting firms, leading private equity firms and CEOs of billion-dollar companies, but I always had felt that something was left on the table and we were not giving clients everything that we could. Sometimes it was because projects were rushed, but even when they were not rushed, it took so much time to manually review and code the data, or to constantly readjust PowerPoint charts as data was removed for quality issues or errors were found, that there just wasn’t time to develop a deep understanding of what was happening.

Furthermore, I felt that the general standard of surveys at the time was poor. The current orthodoxy was that designing a good survey only required someone to drop standardized, “unbiased” questions into a survey tool and obtain a large enough sample size. Surveys were seen as boring, mechanical tools that could only address a finite class of simple issues such as determining market size, brand awareness, or providing NPS scores. As a consequence, the design of a survey was often assigned to the lowest-level person on a consulting team.

Justkul 2013

Having experienced all this, I believed surveys could be done better, and founded a research company at the end of 2013 to make it happen. In the first few years of my company, we developed:

  • Automated systems for coding, proofing and reviewing data so there was more time for my team to focus on those parts of the survey process that really benefited from human insight.
  • We then built our own data science tools that enabled us to review the answers to vast numbers of questions simultaneously and find hidden trends and relations.
  • We focused on developing surveys with engaging narrative and logical flows, so that participants were pushed to reveal deeper insights about why they made the decisions that they did, leading to a more enjoyable survey experience and better data.
  • Finally, by bringing all of these systems together in a systematic way, we could use surveys to answer questions that were not normally thought answerable by online surveys.

By bringing all these tools together we provided novel strategic insights to companies, organizations, schools, and investors that went far beyond the status quo, and helped people see important issues in entirely new ways.

Justkul 2026

Now after 12 years, I still believe that no one does strategic surveys better than Justkul. However, with the advent of AI, some of the things that gave Justkul a competitive advantage have become mere table stakes.  We know in the age of AI we have to continually push our business model further. That’s why we’re happy to announce today that thanks to AI and new algorithms we have made a number of significant improvements to our research processes that will enable projects to be completed more efficiently and that will enable my team to develop even deeper insights.

First, while we continue to share dashboards, final reports and preliminary data in Excel as before, we have now directly tied our internal analysis tools to an online project portal where we can share the most-up-to-date version of our analyses and reports. This enables us to show how we are thinking about the market while we are writing our reports and while the surveys are still in the field. The portal works on laptops and mobile, and has interactive and downloadable visualizations. It also conveniently combines all the current deliverables of the project in one place, including current Excel files and downloadable PDF and docx drafts of the report. These instant reports are not as polished as our final reports, but they enable you to see what we are thinking about and how conclusions are evolving whenever you need to. This will allow us to more efficiently share data on longer-term longitudinal studies and projects with shorter timelines, and in general make our work more collaborative and insightful.

Second, we have enhanced our workflow with artificial intelligence that is focused primarily on research. Unlike generalist models, our AI is trained on over a decade of our best-in-class practices of survey design. Unlike our clients’ AI models, our AI is independent and not subject to the same internal biases and groupthink that can lead to surveys that merely confirm what a company already knows.

There are many specific ways AI has improved our work:

  • AI accelerates our survey design process by pulling relevant questions from our database of questions that serve as a starting point for our surveys, enabling us to produce first drafts of a survey more quickly and explore all the possibilities.
  • We use AI to review data quality and to help us better identify when bots and people are cheating or gaming our surveys. This means our human researchers need to spend less time finding inconsistencies and can spend more time determining which inconsistencies are truly significant.
  • Our AI assists with our analysis, as we can ask it to take free runs through the data, code open-ended responses, or look for its own correlations and connections and help us to determine if there are better ways to think about the data.
  • AI also helps review our conclusions for soundness and inconsistencies and thereby increase our confidence in our results.

At Justkul, while we use AI as a tool to enhance our human skills, it does not replace them. We use AI so that experienced researchers have more time to do what they do best: deciding on the best way to address strategic issues, tweaking questions so they address the issues without revealing a client’s identity or importing unhelpful biases, improving the narrative flow to make it more engaging, judging when a conclusion is warranted and when it is not, and uncovering insights that do not just depend on local contexts, but on the holistic results or trends across industries. One of the greatest moments working on a survey is that magical moment when you are reviewing data that seems bad, nonsensical or wrong, and then suddenly see the principle that makes everything make sense. We strive for those moments.

Using Independent Research to Avoid the Bullseye of Bias

Posted on by jfhannon

A bullseyeOver the past ten years the Justkul research team has worked on hundreds of customer surveys for investment deals and strategic initiatives. Many of our clients could execute research themselves, but they have come to value the quality of our work, our expertise in knowing what questions to ask, our skill in avoiding common mistakes, and the way we free up their team’s time to concentrate on other aspects of a deal or strategic initiative. However, there is perhaps one factor more important than all of these: the independence of our research. Unlike most other parties in a particular deal or initiative, Justkul never has significant vested interest in any particular outcome or conclusion: Justkul is paid the same amount whether a deal or strategic initiative goes forward or whether it does not. We believe this type of independence is essential for minimizing bias.

Bias, or an inherent prejudice for or against a given outcome, can have a significant impact on the conclusions of a research study. It is often underestimated how easy it is for bias to enter a research project. Bias can creep into a survey at almost any stage in the process:

  • In the decisions of which questions to include,
  • In who is invited to take part in the survey (digital redlining, etc.),
  • In the phrasing of particular questions and the order and phrasing of answer choices,
  • In adopting a narrative flow that favors certain answers over others,
  • In decisions as to which data points are included or removed from the final data set,
  • In how the data is interpreted or summarized.

A lack of independence at any of these stages can produce misleading results, which lead to poorer and more risky decisions.

Hence, in order to make sound decisions, it is important to understand the potential causes of bias so that conscious decisions can be made about how to minimize the impact of these causes.

The Bullseye of Bias

The causes of bias are various, but one way to organize these causes is as the bullseye at the top of this article with intentionally malicious causes of bias in the center, intentionally non-malicious causes of bias in the next concentric circle, and unintentional causes of bias in the outer circle.

In this schema the most nefarious type of bias is in the center, but as its relative area suggests, in our experience it is fortunately also the rarest type. Far more commonly, biases arise from the causes in the outer circles. Because of the importance of avoiding this bias, it is worth looking at each of these types of causes in more detail.

Intentional and Malicious Causes of Bias

Intentionally-malicious causes of bias occur when decision-makers make a conscious decision to either lie or omit important details that would be likely to change the overall outcome of a project or decision. “Intentionality” is a complicated concept (See Anscombe, Intention), but in this case, we only mean that it is the result of explicit choices.

Justkul team members have encountered this situation on more than one project in the past. For instance, in one project we worked on the buy-side of a private equity deal and were asked to gather customer data on a high-end consumer product company. The potential investment looked great from its own presentations: its products were attractive, prior survey work showed that clients liked the products when they initially bought them, the management team seemed experienced, and the short-term financial data looked solid. Nevertheless, at the time the potential investment had a dark secret it was actively seeking to repress: its products were prone to breaking down. In an apparently deliberate effort to conceal this fact, none of the prior survey work or reports mentioned issues about repair and maintenance, and the company deliberately went out of its way to delay sharing customer contacts with the Justkul research team. The malice of this strategy was made explicit when a leader at the company asked if they could just wait a few more days to share customer contacts, which would mean that we would only get the contacts after the deal closed. This raised red flags and after we communicated it to our private equity client, the deal was eventually called off.

In this case, the explicit request from the executive made it fairly easy to identify the bias in the information the company shared. However, in our experience such obviously malicious cases are fortunately rare. More commonly, biases are introduced for non-malicious reasons, either intentionally or unintentionally.

Intentional and Non-Malicious Causes of Bias

In intentional and non-malicious causes of bias, a conscious decision is made to provide incomplete or potentially misleading data, but unlike the malicious case, decision-makers have grounds to justify their decision. Decision-makers might even convince themselves that they are making the right decision under the circumstances, such as when a company gives into the temptation to ignore survey results that do not fit in with their overall narrative. For instance, a company might nearly always receive a very customer satisfaction score in their internal survey results, but one independently-conducted survey might show a far lower score. Perhaps the company could provide a fairly good argument that because 95% of surveys showed a different result, this result is merely an outlier and can be ignored.

However, it might also be the case that there is something wrong with the other 95% of surveys. This suggestion may seem radical, but it’s actually not unusual: perhaps the company’s survey tool was inherently biased to begin with by making respondents only focus on positive experiences, or perhaps there were factors in the recruiting process that encouraged only respondents who were more enthusiastic about the company to complete the survey, or perhaps survey respondents were not assured the anonymity of their results. The importance of anonymity has been underscored for us many times on telephone interviews — ­especially in B2B industries where customer-sales rep relationships are key. In such interviews, a respondent will often ask for reassurance that the interview is truly anonymous before proceeding to give critical feedback. This is the reason Justkul generally insists that our survey respondents be assured anonymity in addition to confidentiality.

Unintentional and Non-Malicious Causes of Bias

Finally, the most common case is non-malicious and unintentional causes of bias. Examples are numerous. An investor who has an interest in having a deal go forward might overlook some key hypotheses that would suggest it should not go forward. A consultant who thinks a company should focus on countering the strategies of a specific competitor may overlook the challenges posed by other competitors. A clothing company that believes the quality of its fabrics are obviously better than competitors might be less inclined to put that hypothesis to the test, even though customers may actually perceive the fabric as lower in quality. All of these assumptions can have a significant impact on the outcome of research, and thus produce data that is inferior to research that is conducted independently.

Finally, even if data is conducted well—using surveys that are designed not to favor any particular outcome and recruiting participants in an un-biased manner so that the resulting data is truly representative of the underlying population—there is still considerable possibility for bias in the interpretation of those results. Negative data points can be ignored, other positive data points may be artificially promoted, and almost any survey result is potentially manipulated through spin or by ignoring important qualifications. The solution to all of this is to take steps to ensure the interpretation of the research is also independent.

Conclusion

Companies often have to rely on self-collected data to make routine business decisions, and this is good and entirely appropriate, provided the company take steps to limit bias in the data set. However, if an unusually important or difficult strategic decision needs to be made, for the reasons outlined here, we believe there is no substitute for data from an independent third-party research partner.

Why Would A Private Equity Firm Hire An Outside Customer Research Company?

Posted on by John Hannon
One could argue that no component is more important for an investment's long-term success than understanding the relationship between a company and its current and potential customers.

As the CEO of a customer research firm that works primarily with private equity, I am often asked what value customer research can bring to a private equity deal. People outside the private equity industry sometimes find it surprising that customer research can have a role in transactions that are so often portrayed as driven solely by deal-making and hard numbers. Even some people within the industry undervalue customer research, thinking that a few telephone calls or general market analysis is all that is required. However, the private equity industry has changed significantly over the last few decades. Whereas a simple LBO model with minimal customer research might have been sufficient for good returns two decades ago, high valuations and increased competition among firms is leading the industry to more sophisticated and strategic thinking. This is reflected in who is involved in a deal: whereas in the early days an investment might be largely driven by bankers or financial officers, now strategic consultants – whether in-house or hired from external strategy houses – are playing an increasingly important role in investment decisions. This rise in strategic thinking coincides with an increased focus on the “softer” sides of a deal, such as customer research. Despite often being classified as a “softer” component, one could argue that no component is more important for an investment’s long-term success than understanding the relationship between a company and its current and potential customers. In the end, the success of every business depends on this crucial relationship: it is where growth comes from, and it is what gives a company an advantage over its competition. Why would a private equity firm want to conduct customer research? In my experience there are at least five reasons why private equity firms conduct detailed customer research before a deal closes:

  • Good customer research can help investors identify unexpected strengths or weaknesses of an investment.  This is perhaps the most traditional reason for conducting due diligence research. Knowing what risks investors may incur and learning how the investment’s products and services compare to its competitors can be important not only for adjusting valuations, but also for deciding whether the deal is even worth pursuing to begin with.
  • Good customer research can help investors develop and validate future growth strategies. Often an investment hinges on a number of very specific strategic growth hypotheses. By testing each of these hypotheses in detail with the current and potential client base, a private equity firm can get a sense of whether their assumptions are correct and what strategic changes will have the biggest impact on a deal’s success.
  • Good customer research can reduce uncertainty. Good research can also help answer a multitude of smaller questions and thereby reduce the uncertainty inherent in a deal. What effect will relocating a company headquarters have on sales? Will a potential acquisition be an asset to the brand? Will customers be open to purchasing an adjacent product or service? All these questions can be answered through good customer research.
  • Providing an acquisition with the results of good research can help set future expectations. Companies often initially succeed with a great idea, a superior product, and funding from angel and VC investors. At these early stages, a company may perform A/B testing and benchmark performance against its competition, but detailed market research is seen as an expensive distraction. However, when a firm reaches sufficient growth to enter the middle market and become a potential private equity investment, the role of research can change dramatically. Now, not only will research be important for setting wider brand strategies, but it is also necessary to fulfill board reporting requirements and for ensuring a firm is sufficiently transparent with investors. Conducting good customer research before a deal closes can help set expectations for what these future reports should look like.
  • Good research can create a useful dialogue between a company’s management and investors. Good dialogue between investors and a company’s leadership is essential for the success of an investment. Research helps a private equity firm gauge their ability to work with a company’s leaders. A management team that engages with unexpected insights rather than seeking to merely dismiss them is a good first sign that a successful partnership based on mutual trust will develop. I know of deals that were called off because such dialogue failed to be productive or management was unusually defensive. More than this, by talking through a research study’s findings and developing strategies together, both investors and the company will begin to see a deal as not just a transaction, but as the first step in a long-term partnership together.
By talking through a research study's findings & developing strategies together, both investors & the company will begin to see a deal as not just a transaction, but as the first step in a long-term partnership together.

As these five reasons suggest, customer research can be an important tool in the due diligence process. It not only provides investors with better information at the time of an investment decision, but it also can be useful for helping set future expectations and strategy. I emphasize above that “good” customer research does all these things. However, research that is performed without sufficient care and attention can lead investors seriously astray. Examples of these types of errors include insufficient care in phrasing or in ordering questions, paying insufficient attention to the UX of survey participants, not appropriately stratifying sample, not sufficiently pilot-testing tools to avoid technical errors, or not sufficiently exploring unexpected findings. These kinds of mistakes can often provide misleading data that makes bad investments look good or conversely can lead an investment team to pass on a very good investment. Despite the ubiquity of fast and inexpensive survey tools, getting research right can often require considerable thought, time and expertise. Why would a private equity firm want to work with a third-party research firm? Some private equity firms may have research expertise in house, and others may obtain the required expertise through strategy consulting firms. Even in these cases, though, considerable advantages can be obtained by partnering with a specialty customer research firm. A third-party research firm can provide four additional advantages to the process:

  • A third-party research firm can provide experience and expertise. Good business research requires skills that can be very different from those that make for a good investor or management consultant. A company with a culture that values and promotes employees who have these skills will often produce more solid results. In addition, third-party research firms often conduct research in a variety of industries and geographies, some of which may be outside of a private equity firm’s or consultant’s expertise. A third-party firm may therefore better know how to properly adjust research methodologies for these different markets and recognize unexpected questions that have cross-market application. For instance, a question that produces fruitful results in consumer packaged goods may also have applications in B2B contexts, and a research approach that makes sense in the U.S. may not make sense in Singapore.
  • A third-party research firm can help provide an additional level of discretion. Deals are complicated transactions that can have significant impact on different stakeholders. It is often useful for a board and potential investors to investigate a potential deal or partnership without causing unnecessary disruption if the parties involved in the deal decide to go in different directions. A third-party research firm can serve as a firewall between the research and the larger public, thereby preventing a potential deal from garnering unnecessary attention until it reaches a stage at which it makes sense to do so.
  • A third-party research firm can help reduce bias. There sometimes can be intentional biases in a deal: a company seeking to be acquired may only report positive data or fail to report potential risks adequately. In this case, independent research can ensure important questions are asked before a deal closes. However, more often biases enter unintentionally: a company that has a strong culture may be blind to its own weaknesses, and a company that only relies on A/B testing of current customers may have unrealistic expectations of the potential in the larger market. In addition, even research conducted internally by a private equity firm may carry some bias: team members who are personally invested in the success of a deal may be unable to step back sufficiently to recognize risks. Research from a third party that has no stake in whether a deal closes or not can significantly reduce this bias.
  • A third-party research firm can be a valuable resource for an acquisition after a deal closes. As I mentioned earlier, many companies that PE firms invest in are in the process of making a transition between a startup/VC-fueled business model and a business model that is focused on expansion and growth. An independent research firm can serve as a bridge, providing research guidance until those capabilities can be built up in-house. Furthermore, even after a company develops extensive internal research capabilities, the company’s executives may nevertheless find it useful to bring in the external company that conducted the due diligence research in order to verify assumptions before making important strategic decisions.

As company multiples steadily increase, and as it becomes consequently less likely that a private equity firm will find an amazing company at a great price, good customer research is becoming an increasingly important component of successful private equity deals. Given that good customer research adds little to the overall cost of a deal, and given the significant benefits and insight independent research can provide, we think good customer research should be an important part of any private equity deal. By @jfhannon, CEO at Justkul Inc., a research firm focused on the needs of strategy and private equity.

Steve Jobs and Market Research in Contexts of Uncertainty

Posted on by John Hannon
Steve Jobs in 1990

According to some, market research is pretty useless in contexts of uncertainty. These contexts include disrupted markets, the introduction of radical new innovations, or the launch of new products and services. Steve Jobs explained this view in an interview:

“The problem is that market research can tell you what your customers think of something you show them, or it can tell you what your customers want as an incremental improvement on what you have, but very rarely can your customers predict something that they don’t even quite know they want yet. As an example, no market research could have led to the development of the Macintosh or the personal computer in the first place. So there are these sorts of non-incremental jumps that need to take place where it is very difficult for market research to really contribute much in the early phases of the thinking about, you know, what those should be. However, once you have made that jump, possibly before the product is on the market or even after, it’s a great time to go check your instincts with the marketplace and verify that you’re on the right track.” (PBS/Nova Interview,1990)

Steve Jobs could recognize the usefulness of market research, but he suggests it is not useful when the market is going through these non-incremental jumps. A similar mindset is found in an even stronger form in certain parts of the present-day startup community: it is often thought that it’s better to just bring a product to market and see what happens, rather than rely on misleading market research that is incapable of envisioning the future.

We have no idea how many failed startups would have succeeded if they had paid more attention to the findings of market research.

There is no doubt that many companies succeed by adopting approaches that do not involve much market research. However, this in itself is not a sound proof for ruling out the usefulness of market research, because we have no idea how many failed startups would have succeeded if they had paid more attention to the findings of market research. Instead of being based on a sound empirical argument, the plausibility of this argument seems to rest, in part, on a number of assumptions about what market research is, and how it works.

Standard Market Research and the Principle of Continuity

When Steve Jobs spoke about market research, he was likely speaking about a particular kind of research. These are the surveys asking you to rate services on a scale of 1 to 10, asking you whether you prefer red cars or blue cars, or giving you complicated conjoint trees to determine if it is more important for you to get more product or lower prices. All of these studies depend implicitly on a principle of continuity: that your present opinions can be used to reliably predict your future actions.
As Jobs appears to recognize, this principle can break down in disrupted markets. If we look at this principle in more detail, it typically implies at least four sub-principles, all of which can fail when one is conducting research:
1.   The overall market will be the same in the future as it is now. This principle is reasonable in many situations, but Jobs proved it false when his teams introduced the Macintosh, the iPod, the iPhone and the iPad, each of which changed the markets in fundamental ways. Furthermore, it fails all the time in ordinary business practice: financial bubbles burst, companies that are on the top of the world go bankrupt a few years later, entire industries disappear.
2.   A respondent’s beliefs will be the same in the future. Closely aligned with the preceding principle, a respondent’s views can change even if the other components of the market do not. Someone might be against sharing credit card numbers or personal information online when they take a survey, but come to accept the practice a few months later.
3.   Respondents are accurately conveying their present beliefs when they take a survey. It’s common knowledge that people sometimes lie on surveys, or under-report activities they are ashamed of. However, even when people are trying to accurately convey their present beliefs, they may fail to do so. In particular, we tend to think of our personalities as uniform over time and place, but many of us adopt very different personas when we spend time with our family, or with our friends, or with our colleagues, or when we are on our own. A view we reveal on a survey may not reflect the view we act on later while partying with friends.
4.   What someone says accurately reflects what they will do. This belief is questioned frequently today as big data enables us to actually track the difference between saying and doing. In certain cases, it may turn out that basing predictions on past actions rather than opinions produces more accurate results.

In markets that remain constant over a long period of time, it may be unnecessary to question any of these principles. For instance, a mere correlation between what people say and an increase in sales might be all that is needed for an incremental gain, and no one may care to think any more deeply about why it’s the case.

Market Research in Uncertain Times

However, as soon as contexts arise when the principle of continuity does not hold, companies and whole markets can be led badly astray if they merely follow past correlations. In these situations, as Jobs notes, respondents may very well not know what they will want.

Yet, even in the most disrupted markets, where there are many discontinuities at the surface, there may be underlying factors that do not change, and therefore at deeper levels the principle of continuity can still hold, and research still has something to say.
In these cases, instead of mechanically asking respondents, “How likely is it that you would buy this new product?,” one should begin by thinking hard about the context of purchasing and using the product and ask, “What things have to be true if people are going to buy this product?” Moving from the general question to a hypothesis about its components in this way does at least four things:

  • First, it forces us to articulate and address the most important assumptions we are making. Often, we are implicitly assuming things that will prove to be false. Identifying the assumptions before conducting research allows us to explicitly test them.
  • Second, once we have identified a broader range of assumptions, we may be led to reconceive the range of possibilities we need to take into account in our questions and in the subsequent analysis. For instance, we may determine that a particular assumption does not hold in general and thereby identify new segments. These different segments may have different needs and decision-criteria.
  • Third, it can lead us to identify important outliers that do not necessarily conform to our expectations. If we think that a factor is essential, and it turns out to be false for a small number of respondents, this may be a cue to talk further with those individuals. Perhaps they are anticipating the next big trend in the market.
  • Fourth, it makes us think more deeply about the enterprise we are undertaking. Part of this comes from the fact that good research leads us to ask “Why?” questions that we may not have even thought about before.

Analyzing decisions into components makes it possible to find continuities in conditions of uncertainty. Moreover, when we have the whole framework of assumptions and possibilities available, it then becomes possible to track underlying factors that do change through proxies and analogical trend analyses.

An online survey constructed to test hypotheses about decision factors can tell you not only why your customers make the decisions they do, but also help you make better predictions in uncertain contexts.

Implementing this approach well requires a different mindset and a broader range of skills than most standard market research companies offer. Identifying assumptions requires being able to break things down into their components like an engineer or analytical philosopher. Exploring possibilities and outliers requires a certain openness to possibility characteristic of design-thinking approaches. Thinking more deeply may require additional skills such as big-data analysis, statistics, expert interviews, or ethnographic studies. Yet, all of these approaches can be incorporated into a research strategy.

Many skills may be required, but great consultants and entrepreneurs often bring together many of these research skills. I’m thinking of when Orit Gadiesh talked to customers and metallurgists when she introduced a Bain client to continuous casting techniques in steel manufacturing. Or, when survey data led Howard Moskowitz to discover that there was no perfect recipe for Prego tomato sauce. Steve Jobs, especially, shows the passion of a good researcher. This is quite apparent in his interviews in which he enthusiastically discusses quantifying data, visiting 80 automated manufacturers in Japan to understand automation, or revising products after observing people using them. People can bring about the future through research, albeit of a non-standard type.

This much may be obvious. However, what might not be obvious is that even the most humble research tools can benefit from this approach. An online survey constructed to test hypotheses about decision factors can tell you not only why your customers make the decisions they do, but also can help you make better predictions in uncertain contexts.

Follow Jobs’ Advice: Ask the Right Questions

In another interview five years later, Steve Jobs was asked about his transition from being a hobbyist to being the executive of a multimillion dollar company. “How do you learn to run a company?,” he was asked. He observed, “You know, throughout the years in business I found something, which is, I always asked why you do things. And the answer you invariably get is that it’s just the way it’s done. Nobody knows why they do what they do. Nobody thinks about things very deeply in business, that’s what I found.” (Steve Jobs, The Lost Interview, 1995)

It turns out Steve Jobs didn’t like market research when introducing non-incremental changes, because he was a great researcher himself.  According to his own account, the secret to his success in business was asking the right questions and not settling for status quo responses. Unless you have the luxury of being in an industry without innovation or disruption, you and your research company shouldn’t settle for the status quo either.

By @jfhannon, CEO at Justkul Inc., a research firm focused on the needs of strategy and private equity.

Key to Innovation: Hire a Philosophy Major

Posted on by John Hannon

philosophy majors score amont the highest verbal, quantitative and analytical GRE scores in 2011-2012In a recent article in the New York Times (September 13, 2013), Susan Dominus described how the Office of Personal and Career Development at Wake Forest University is transforming the liberal arts by emphasizing the importance of job placement. Although the discipline of philosophy was only mentioned off-hand at the end of the article, the New York Times decided to entitle the article, “How to Get a Job With a Philosophy Degree.” I am not sure exactly why the New York Times decided to give this article this particular title, but given the rhetorical build up at the end, one surmises that it is because philosophy is thought to be obviously one of the most impractical subjects one can study. And when Andy Chan, who is quoted throughout the article, tells parents to “hold their tongue” if their child expresses a desire to be a philosophy major, because they may “decide they love math anyway,” and that he will help turn “academic risks into résumé-ready experiences,” it sounds like Chan agrees. Philosophy is a risk, but a good college placement office will teach your child to be successful anyway.

I have sympathy with the idea that there is something “impractical” about philosophy. The discipline has a long tradition of taking pride in this fact, and Plato famously argued that one would have to use force to get a philosopher to become involved in the common affairs of the world. In addition, many of the problems that philosophers think about have very little direct impact on day-to-day business. However, many of the problems they think about do have direct impact, especially in the ambiguous area of contemporary ethics. This is one of the reasons why Google has hired a philosopher.

However, the case for hiring a philosophy major is much stronger than this. Ms. Dominus’ article quotes the CEO Alan Naumann saying that he hopes graduates have “analytical skills.” It is difficult to find a subject area that provides better analytical skills than philosophy. Students intending to be philosophy majors consistently score among the top verbal, quantitative and analytical writing scores of the GRE, and in many years they score higher than all other majors. Philosophy majors do better on LSATs than any other majors, and easily outperform business students on GMATs. This should not be in the least surprising: contemporary analytical philosophy teaches logical reasoning, and philosophy majors have to deal with some of the most difficult texts and issues human beings have ever come up with, whether it be the foundations of morality, Kant’s transcendental dialectic, Aristotle’s metaphysics, or defining concepts like rotation in relativistic physics.

It is not an exaggeration to say that philosophy is the art of innovation.In the age of business innovation, however, the argument for hiring a philosophy major is even stronger than this. Philosophy has been many things, but since the time of Socrates, the art of questioning assumptions, asking, “Why?,” and drawing out the implications of alternative assumptions are the most fundamental activities of the discipline. Businesses now call this activity, “thinking outside the box,” but in the ancient world it was called dialectic, and it was the very core of the discipline. Even today, it is not an exaggeration to say that philosophy is the art of innovation, because it is the discipline most focused on questioning the status quo. If a company is not good at developing innovations, or is facing a situation in which existing frameworks are failing, then they may seriously want to add a philosopher to their payrolls. In addition, many philosophy majors have been extremely successful in business: George Soros, Reid Hoffman, Don Brownstein, Carly Fiorina, Larry Sanger, and Patrick Byrne among others.

In my own experience in business consulting, I find myself constantly drawing on the skills I acquired as a philosophy major. My experience learning philosophy is what enables me to reframe client challenges at the very beginning of a project, to take difficult problems and reformulate them into simple examples, to seek out the underlying reason for a correlation, or to identify analogous situations to a problem at hand. Talking to a philosophy major who is a successful consultant at one of the largest consulting firms, he said that he often finds himself asking the question “Why?,” when no one else in the room is. I find that it is this move from the how to the why that enables me to avoid errors others are prone to make, or to obtain a fresh perspective on an ambiguous problem.  Having been a philosophy major I can ask these questions and explore the underlying logic of ambiguous and complex situations with more confidence than other more technically-focused majors. (Why-questions are also important for leadership too, see Simon Sinek’s Ted talk.)

Of course, not all philosophy majors will be at home in a business context. The very same skepticism that allows great innovations to happen also can lead philosophers to challenge the underlying assumptions of a business culture, capitalism in general, or to choose to pursue a life of the mind. In addition, contemporary business is often driven by mathematical models that some philosophers are ill-equipped to deal with. But philosophers do come in all stripes: in my own sub-discipline of philosophy of science many philosophers are experienced mathematicians, physicists, psychiatrists and biologists and quite comfortable with statistical concepts. Because these individuals have both mastered a science and philosophy, they can both do the math and understand deeply why the math works. It is curious that this subdiscipline is not on the radar of most corporations.

According to many business leaders today, they want employees with analytical skills who can questions assumptions and develop entirely new approaches for the 21st century. People who not only can handle complex or ambiguous problems, but also truly thrive in such environments. Yet, at the same time, HR departments in these very same companies routinely select for mere technical proficiency or for people who have only proven they can operate well within already existing frameworks. It’s about time that companies start realizing that in an age driven by innovation, hiring a philosophy major is not a risk, but not hiring one may be.

By @jfhannon, CEO at Justkul Inc., a research firm focused on the needs of strategy and private equity.

Image by Ty Fagan, courtesy of Katrina Sifferd.
See also the additional charts here.