Gary Pisano’s recent Harvard Business Review piece, The Hard Truth About Innovative Cultures, beautifully frames up how innovative corporate environments are frequently misunderstood. Innovative startups aren’t just about being cool and nimble, having beer taps in the kitchen, or an endless bounty of swag. Pisano sums up the harder, harsher reality of truly innovative environments: “These cultures are not all fun and games.”
Pisano’s piece struck a huge chord with my own experience working with both large pharma and small startups over the past 20 years. And many of the observations are in line with my post from July 2017 on Distinctive Biotech Corporate Cultures.
While reading Pisano’s piece, my brain kept saying “Yes!” and “So true!” every few sentences. I tweeted out my endorsement of the piece back in January but have finally gotten around to blogging on it; in this post, I’d like to add some “practitioner” color-commentary to Pisano’s observations.
Here are his five major attributes about innovative cultures:
I’m going to share some reflections on each from my vantage point as a biotech investor.
Tolerance for failure requires an intolerance for incompetence.
We work in a high risk field: less than 5% of drug discovery projects ever make it to market. We have to embrace failure. When projects or investments are killed, we should openly acknowledge it in the light of day, not bury the dead in the night, and celebrate the learnings that can be gleaned: as we did with both the Quartet Medicine and OnQity write-offs. Innovative cultures have to convey that its “ok to fail” and foster quality learning loops.
But it’s not ok to fail due to sloppy science or poor decision-making. Not asking the tough killer questions, or using the right controls and comparators, can lead to chasing false positives for too long – which is not acceptable. It’s just too easy to keep drug programs going, overhype the data to get the next dollop of funding, or play around but never truly test the science – simply because we can rather than we should. It’s incompetent not to do the killer experiments, and do them as well as possible. If you are waving your hands too much, maybe the science is too early. Try to invalidate the central biological hypothesis early, before mountains of capital put too much weight on the scales of judgement – leading to low stringency and poor decisions biased by the sunk cost fallacy and the practicalities of being “too big to fail”.
All of this requires not only competence, but a shared view that the collective time of truly competent people is the scarcest and most valuable resource in an innovative culture: don’t waste that resource on ideas that are passed their sell-by-date (or never should have had a date!). Chalk up a failure and spend your time on better things. Move onto the next project, or the next startup, or back to big pharma. But move on nonetheless. Competence, by definition, can’t tolerate complacency around the decision to move on.
There’s another corollary of this observation. We all fail at hiring sometimes, as it’s impossible to hire only truly competent people when you are scaling an organization beyond more than a couple dozen employees. Mistakes in hiring happen, and that’s ok. But there can be no tolerance for keeping underperforming hires around just because we’ve got a “nice” culture. Robust performance management systems need to be in place to weed out those folks who aren’t delivering with high performance. I’m not suggesting it has to be the Jack Welch “fire the bottom 10%” rule – but if you aren’t transitioning some people out of a startup over time than you are almost certainly tolerating too much incompetence. It’s just the practical reality that we all make mistakes in hiring over time. Sadly, I’ve been involved with companies that have tolerated mediocrity for too long. It enables weaker researchers to keep hand-waving around their data, when all the competent folks around them cringe. It feels benign and “nice” not to fire people, but honestly it is enormously corrosive over time.
Nothing reeks worse in a company than tolerance for incompetence: everyone smells it, knows it’s there. And knows the leadership are allowing it. That stinks for everyone, and negatively affects the overall corporate culture.
Willingness to experiment requires rigorous discipline.
Comfort with ambiguity and a lack of structure, and often process, is frequently key to finding innovative ways of doing things. But in biotech startups this loose structure doesn’t condone doing willy-nilly science – or crazy “Friday afternoon experiments” every day of the week.
In a world where a researcher can do only a few of many possible experiments, which ones should be chosen? The only way to prioritize the experiments for a program is to understand the questions you are asking – and the answers you are likely to get from the data. Once chosen, laying out the goals and milestones of the work is key. And then measure up the outcomes against those. Applying the age-old maxim – “Measure twice, cut once” – is a great rule for scientific exploration.
Experimentation works best in applied R&D when discipline is applied to the thought-process and the decision-tree before actually doing the work: defined a priori, specific go/no-go criteria need to be in place for where to take the project or initiative next.
Further, as Pisano notes, experiments should be selected for their learning value, and designed “rigorously to yield as much information as possible relative to the costs.” Essentially, it’s applying the logic of capital efficiency to innovative experimentation.
Don’t confuse free-ranging scientific exploration of a new modality, or new drug target, with a lack of rigor. Even experiments done with “early hit” molecules, designed in the spirit of learning, need to be done as rigorously as possible. I’ve seen programs get derailed by early negative signals caused by “Hail Mary pass” in vivo experiments done with suboptimal hit series; hope and prayer studies are occasionally ok to do over time, but full transparency around the decisions, criteria, and outcomes needs to be in place.
Lastly, just because a biotech doesn’t have formal committees to designate “stage gate transitions” doesn’t mean this lack of “process” or “structure” should lack discipline. I’m very confident that we have Development Candidate criteria in our startups that look as robust as Big Pharma’s, maybe without Lipinsky Rule biases – we just deploy them without cumbersome governance committees, associated box-checking exercises, or a strict “cover-your-ass” mentality to every one of them.
Fundamentally, scientific discipline has to be intrinsic to a biotech startup’s culture or they will inevitably drink their own Koolaid and lose their way.
Psychological safety requires comfort with brutal candor.
Organizational “safety” is hugely important: a culture where individuals can discuss the truth without fear of vengeful consequences. Almost every biotech has values up on the wall that reflect transparency, openness, and challenge; but the big question is do companies and their executives “walk the talk” and live these values with both safety and candor.
What if the Emperor has no clothes? Can scientists challenge the CSO’s conclusions, or the CSO’s proposed experimental next steps? Can researchers critically (respectfully) evaluate other team members’ data? Can the CEO’s positioning of the status of a lead program be questioned by the team? These are the types of situations that reveal a lot about the culture in practice inside a biotech startup.
If the CSO or CEO is hand-waving around the scientific strengths of the story, and no one feels able to challenge them, what happens? Real innovative thinking suffers. And Boards are often fed this pablum rather than something of substance.
Having multiple mediums for communicating can create the right cultural outlets for this type of safe “champion and challenge” culture, everything from town hall style company discussions, open dialogues within working teams, cross-functional employee focus groups, discussions with the HR lead, or even anonymous suggestion boxes. Yelling a lot in the big company meeting about a concern isn’t always the right forum, so folks should be encouraged to find the right one. But good companies don’t let intellectual dissension fester quietly without an outlet – it needs to be shared in some format or another. As Pisano writes, “Unvarnished candor is critical to innovation because it is the means by which ideas evolve and improve.”
Our own investment model at Atlas is aimed to fostering this “champion and challenge” culture. We’re a flat, equal set of investing partners. There is no emperor (with or without clothes). There is no one boss that squelches discussion. We openly and respectfully challenge each other, and base a lot of our decisions on mutual trust in the partnership.
Collaboration but with individual accountability.
This fourth observation from Pisano is exactly in line with what I described previously as: “There is no ‘I’ in team, but know who has the “D” (#6 in this post).
Team work, across different disciplines, is critical to success in biotech, probably more so than some other innovative sectors; integrating biology, chemistry, tox, pharmacology, clinical, manufacturing and many other functions over the course of many years. Collaboration is the lifeblood of the drug R&D endeavor.
But, as Pisano notes, “too often, collaboration gets confused with consensus.” This is spot-on. Consensus in a world of biological ambiguity is hard. Further, consensus can be a huge bottleneck on the timelines, as energy is spent trying to get all the stakeholders on the same page. Eventually, an individual just has to make the decision. I called this the “D” in my post on culture. Someone needs to integrate the inputs but make a decision. And then be held accountable to that decision.
Biopharma R&D timelines often make real accountability difficult, however. Take decision like selecting one path or another, such as nominating a specific Development Candidate vs waiting for another to emerge, or picking an initial clinical setting vs another. These decisions take years to manifest into outcomes, positive or negative, to be able to reflect on whether they were the “right” decision. Sometimes one never really knows whether it was the best decision or not as science doesn’t always reveal itself that way. With timelines this long, ensuring the proper information, discussion, and challenge were integrated into key decisions is a big part of the accountability equation.
Endless, often headless, committee meetings in big organizations can crush accountability by de facto forcing consensus decisions. The flip side of accountability in these larger organizations is the “CYA” culture (“cover your a—“): if I have to put my name on this decision, I’m going to make sure 99.9% of the ambiguity and uncertainty is wrung out before making it. Innovative cultures can’t be run with 99%+ confidence intervals around decisions. Risk is essential, and working under 60-75% confidence is much more in line with nimble, smart risk-taking. Decisions need to be informed better than coin toss, but often the risks and unknowns remain significant. Understanding how to make innovation-enhancing decisions as individuals and teams in this environment is crucial.
Lastly, consensus frequently kills the outliers. Within venture firms, there are countless examples of firms passing on seed-stage deals because they were crazy ideas and consensus didn’t exist, only then to watch them grow into unicorns. Nearly a decade ago, Atlas changed its approach to seed investing in that any individual partner could back a “seed deal” (size delimited) without consensus support. This empowered partners to take some risk. As those investments mature, we generally move towards a team consensus as capital intensity increases. But at the earliest phases of the decision process, when uncertainties are high and getting to consensus could prevent leaning forward into a “risky” seed, we think this approach works. For Atlas, mutual trust and respect often gets you to implicit consensus in the end.
Flat but strong leadership.
Organizational charts are fine to capture the structured layers of a company, which is important. Who is reporting to who, and who is responsible for what functions. How big are certain functions. And using them to forecast how the company will look in 1-3 years is a useful tool. All this is valuable.
But the org chart only tells you about structure. It does not reveal “cultural flatness”, as Pisano calls it, that reflects the level of empowerment around ownership. Culturally flat organizations often push decision-making closer to those with first-hand knowledge of the critical information.
It also means that C-level executives can’t sit in their corner office and make decisions in a vacuum. To quote Pisano, “For senior leaders, it requires the capacity to articulate compelling visions and strategies (big-picture stuff) while simultaneously being adept and competent with technical and operational issues.” This is the essence of a great biotech science leader in my mind: able to frame the big vision of a 60K foot view while also being able to engage at the 6 inches view of a critical experiment or study design when appropriate.
In short, cultural flatness goes both ways: C-level executives being versatile with the details of the programs and platform, with researchers being able to engage their leadership with contributions beyond the hierarchy.
Innovative cultures don’t just emanate from cool Patagonia vests.
Building and leading this kind of culture in a startup biotech isn’t easy. It requires a mix of seemingly contradictory behaviors, like the tolerance for failure but not incompetence. And the pieces of culture outlined by Pisano aren’t really an a la carte menu – they all need to be integrated into the DNA of a startup in order to encode for a high performing culture.
Summed up, and repeating the phrase from the beginning of the blog, Pisano nails it: “These cultures are not all fun and games.”
This content was originally published here.