Algorithms Work Out Loud

Whether we like it or not, working out loud is coming as a work trend. The benefits for productivity, learning and effectiveness from working out loud make greater transparency and connection in our work inevitable. If we do not work out loud, it will be our tools that work out loud for us.

Algorithms Ascendant

If you have any interest in digital trends you will have noted the news that software beat the world’s best Go player 4-1. I’ve played a little Go and even at a much smaller scale than a competition board it is a mind-bendingly tricky game that relies on intuition as well as logic. Software being able to beat a Go master so comprehensively is a significant development because analysts had forecast it could be up to a decade before Go fell. Go is too complex for a simple brute force strategy of computation of possible paths. 

The breakthrough occurred because the Google team developing AlphaGo didn’t just rely on one source of technical expertise or one strategy to beat a Go Master. AlphaGo improved itself by testing multiple strategies in machine learning, specifically learning better models of play each time it watched or played a game of Go.  AlphaGo’s success reflected a key benefit of working out loud – learning through observation and experience of not just one’s own practice but also the practice of other Go algorithms and Go masters.

Algorithmic Insight

Whether we practice working out loud or not, the software around us is already beginning to leverage our work to learn and enhance its effectiveness. Social media sites are all moving to algorithmic display because they can leverage our behaviour and relationships to better meet our needs (& their own business models).  I remember my resistance when Yammer first implemented an algorithmic feed and moved away from following. I thought there was no way that I would value the algorithms choice of messages over my own curation of content through following strategies. These concerns passed quickly in use and it has been a long time since I reflected on the need for a better following model.  Incidentally, Yammer moved to this change as a result of analytics and A/B testing, leveraging the work of thousands of customers to find better ways to build its product.

These algorithms are coming deeper into our work. I recently had a demonstration of Microsoft Office’s Delve and Delve Analytics. My takeaway was here was that I was looking at the potential for algorithms and analytics to turbocharge the value by leveraging a form of passive automation of working out loud. Clearly tools like Delve can help by reducing search, however they can also deliver further benefits for learning, collaboration and business value by helping make working out loud a default practice in the future of work.

Delve offers a key way to address the concerns many critics of working out loud raise. Today working out loud requires an individual to push their work out visibly so that others can pull the work for the purpose of learning or collaboration. That first push upsets some critics as it is seen as contributing to noise, raising the possibility of unconstructive distraction or requiring incremental effort from the worker. My experience is that the benefits far outweigh this minor inconvenience.  However, algorithms and analytics like Delve, change this game by leveraging our working behaviours to pull information and insights from the work of others and make them available to enable us to better learn or to find better practice. 

Solutions like Delve enable all of our working out loud practice to rest on a pull model. If Delve can surface a document that I need to see or I can use from the work of my peers then it doesn’t rely on any more effort from my peer that to enable this sharing and configure privacy and security settings. If Delve Analytics can help me to learn how better to use Microsoft’s productivity tools by supplying insights on my use and that of my peers, then again it does not require my colleagues to measure, document and share their approaches. A similar example is that Swoop Analytics have now released Swoop personas to enable each user of an enterprise social network like Yammer to understand their personal style and effectiveness in the use of the platform. 

The trajectory of innovation is that these algorithms will be increasingly effective and increasingly deeply integrated into our products.

Is that it?

If algorithms are the answer, it that it? Do we no longer need the human practice of working out loud? Why don’t we just wait?

There is an adoption challenge of sorts with the coming algorithms. Algorithms can help with insight, but they cannot address the human side of openness to learn, willingness to experiment and ability to handle the social elements of working out loud.  We all need to learn to be able to manage new practices and to have mindsets to be able to benefit from the change.  These mindsets stretch from an attitude of generosity, desire for connection, a move from reliance on personal expertise and through to the ability to handle odd moment of embarrassment. If we do not get the mindsets right, then we will miss the benefits of new ways of working.

The value of the practice of working out loud now is that it enables each of us to learn important social skills in the network era: building connections, reciprocity, generosity and how to create and sustain the creation of value in networks.  The networks and the algorithms are not going away. The challenge for all those seeking to be ready for the future of work is to learn how best to leverage these new models.

Just like AlphaGo, those who are already working out loud are discovering new practices and approaches to work through their own work and through watching the practices of others. You can wait for an algorithm to arrive to make the change for you or you can get ahead of the curve and enhance your practice of working out loud.

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