Decision Making

One of the keys to Genentech’s success, in my opinion, was its decision making process.

Unlike many other companies, which make decisions by consensus, Genentech always had one final decision maker. Usually, the team leader had that responsibility, but in some cases, the portfolio committee’s head, such as Sue Hellman or Myrtle Potter, had the final say. In some cases, the committee vote would be 20 to 1, but if the 1 was the committee head, that single vote carried the day. Of course, the decision maker had the responsibility of listening to and weighing all arguments and data, but the final decision was his/hers.

The reason why consensus decision making doesn’t work is that in an innovation based industry where 90% – 95% of drugs fail, you must take calculated risks to be successful. There is an apocryphal story about a senior executive at Pfizer who would vote against every project because he knew that if he did so, he would  be right 95% of the time (because 95% of drugs fail), while if he voted for a project even 10% of the time, he would be wrong 50% of the time.

If you rely on consensus decisions, you usually end up with the decision that the most conservative person on the team is comfortable with. That is a problem. I can’t tell you where on the spectrum of risk any given decision should sit, but one thing I can tell you is that the most conservative decision is almost sure to be the wrong one.

In addition, in a consensus decision making system, what often happens is that the functions horse trade, so that the decision that is the least painful to every function, and pain of the decision is relatively equally distributed. The problem is that often, the right decision is one that is beneficial to one function at the cost of another one. A claim for the drug, for example, may be worth so much that the clinical group should conduct a difficult study. Or stability may be so fickle for a drug from a CMC standpoint that marketing needs to take a hit on the shelf life.

Left to consensus decision making, the decision often ends up such that it’s a compromise between two functions. Once again, I can’t tell you where on the spectrum the right decision is, but what I can tell you is that it’s rarely at the halfway point between what two functions want.

Of course, once a decision is made, all the functions need to line up behind the decision. In a healthy corporate culture, this happens. In an unhealthy one, there are a lot of foot dragging and passive aggressive behavior. This leads to consensus decision making solely to insure that foot dragging is avoided.

The second component of decision making that was done well at Genentech was that decisions were based on data. Opinion carried some, but not much, weight. The decisions were scientific and fact-based. In some cases, this might have been carried too far, since at a certain point, it becomes impossible to further de-risk projects with additional data (see my comment about complicated vs. complex decisions), but overall, the data-based decisions turned out to be high quality decisions.

The third component of decision making that Genentech excelled at was cutting losses. They didn’t fool themselves by performing secondary ad hoc analysis to data dredge for a positive signal. If the primary endpoint was missed, they moved on to the next molecule. Of course, this was easier to do for them than some other companies because they had 20 promising molecules waiting for every 1 that was dropped, but they avoided the futile efforts that many other companies exerted, chasing a phantom positive signal from post hoc analyses.

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