Saturday, July 17, 2021

HBR's 10 Must Reads - Management Ideas 2021 - Review

 

HBR’s 10 Must Reads - The Definitive Management Ideas of the Year from Harvard Business Review – 2021

(Amazon)

HBR’s 10 Must Reads - The Definitive Management Ideas of the Year from Harvard Business Review – 2021 is a good collection of short articles covering diverse topics. Of all, however, The Hard Truth about Innovative Cultures, by Gary P. Pisano, is the most important, and also the best written, piece. 

It may seem harsh to use the saying – ‘Monkey See, Monkey Do’, but success begets imitators. Decades ago, there was the ‘HP Way’, then came Google’s ‘20% Project’ and Amazon’s ‘extreme tolerance for failure’. If HP was the original garage startup that became one of the most successful companies of Silicon Valley (before suffering the inevitable decline, terminal in many cases, that every company goes through; Jim Collins' 2009 book, How the Mighty Fall, is a good read on the subject), Google and Amazon have grown to become trillion-dollar industry leaders. It is unsurprising that leaders at companies look to these successful companies for best practices to emulate. However, a superficial adoption of these practices without an understanding of what makes them successful in the first place is a recipe for failure. The article brings out the truths about five of the best practices of these innovative corporate cultures. 

If you espouse a tolerance for failure, you, ipso-facto, also imply an ‘intolerance for incompetence.’ A Sachin Tendulkar attempting a paddle sweep or an M.S. Dhoni playing the ‘helicopter’ shot and getting out, as few times as that has happened, is tolerated because they are the best out there. ‘Rigorous discipline’ must go with a ‘willingness to experiment’. If an organization cannot practice ‘individual accountability’, then ‘collaboration’ is going to fail. A company like Google can encourage risk taking, have a high tolerance for failure, and reap its rewards because it is confident that its employees are competent. In the absence of competence, there is little to separate risk taking from undisciplined daydreaming. 

Consider another fact – even the most competent make mistakes. But ‘at what point does forgiveness slide into permissiveness? And at what point does setting high performance standards devolve into being cruel or failing to treat employees – regardless of their performance – with respect and dignity?’ Where is the line that divides giving frank, honest feedback from being an asshole? Prof. Robert Sutton’s 2010 book, ‘The No Asshole Rule’, is an instructive read on the subject of toxic workplaces and bosses. 

A corporate culture is primarily a function of its people, among other things. People have their biases, cognitive blinkers, and egos. To expect a corporate culture to translate from a CEO’s desk to execution on the ground with perfect fidelity and no loss in transmission is naïve. Some companies that proclaim a ‘willingness to experiment’ lack the discipline to establish clear ‘criteria at the outset for deciding whether to move forward with, modify, or kill an idea.’ An ego is a defining characteristic of humans. The higher one goes up the executive ladder, the more fragile these egos can get. People may remember how AOL CEO Tim Armstrong, in 2013, publicly fired an employee while on a call with 1,000 employees. Tim was incidentally a former senior vice president at Google. Criticism, even if constructive, is sometimes taken as a judgment on one’s entire career and professional competence. This poses a problem at companies that encourage candid feedback but forget that such feedback is a two-way street: ‘if it is safe for me to criticize your ideas, then it must also be safe for you to criticize mine – whether you’re higher or lower in the organization than I am.’ Employees have often suffered grievous professional harm where they forgot that feedback was implicitly assumed to be a one-way street; like water, it flowed only from top to bottom.

How about collaboration? How is it different from consensus? ‘Ultimately, someone has to make a decision and be accountable for it.’ Collaboration works only when there is accountability. Pisano cites the example of the development of web services at Amazon and how Andy Jassy, then head of its nascent cloud computing business and accountable for its success, used collaboration to drive its success. With the onus of accountability also came the rewards of success. In other words, if success is an individual’s, so should failure.

In conclusion, making an organization’s culture innovative introduces inherent instability, and it therefore becomes incumbent on the leader to be on the lookout for and to correct excesses arising from this instability. ‘Organizational cultures are like social contracts specifying the rules of membership. When leaders set out to change the culture of an organization, they are in a sense breaking a social contract.’ Any change is disruptive, to varying extents, and to expect change to happen without friction is no different from believing in perpetual motion machines. Both are fantasies that do great harm to organizations. Gradual change requires discipline and a focus on execution (I highly recommend Ram Charan’s 2011 book, ‘Execution: The Discipline of Getting Things Done’). Leaders who manage by headlines are most ill-suited to driving such change. 

This collection has several other good articles too. The Feedback Fallacy, the first article in the book, is a good piece on the fallacy in the belief of unalloyed feedback as a universal good. The authors point out the several fallacies underlying the belief in ‘radical candour’ as a way of improving employees. ‘When Data Creates Competitive Advantage’ is a readable piece but which ultimately provides little new insights. ‘Creating a Trans-Inclusive Workplace’ and ‘Towards a Racially Just Workplace’ both provide advice on making workplaces more equitable. ‘Building the AI-Powered Organization’ has a couple of truisms, like ‘siloed processes can inhibit the broad adoption of AI’, or that ‘Most AI transformations take 18 to 36 months to complete, with some taking as long as five years’. 


© 2021, Abhinav Agarwal (अभिनव अग्रवाल). All rights reserved.