Algorithmic technology revolutionized chess. It’s about to do the same for work allocation

William Dougherty

May 10

On a table in front of you is a chessboard, its pieces frozen midway through a game. It’s white’s turn to play. How long would it take you to find white’s best move? Not a good move or even a great move, but white’s very best move.

Before you answer, consider what you’re up against. Five moves into a game, the number of possible board configurations already sits at over 69 trillion. Mathematicians believe the total number of possible chess games exceeds the number of atoms in the known universe. Somewhere inside this cosmic haystack lies white’s best move.

To find it, you’ll need to know the rules of the game, the relative value of each piece, and how they should be positioned to maximise their utility. You’ll need to consider how each possible move contributes to your overall strategy. And finding the best move, not merely a strong one, means comparing their merits by accounting for dozens of strategic factors, and then finally selecting the one that seems the strongest.

All this is to say that, when you’re searching for white’s best move, there really is no shame in concluding that you may never find it.

From the board to the boardroom

Making choices in chess is a lot like allocating work. When law firms distribute tasks and staff matters, they’re also making strategic decisions. The aim, as in chess, is to marshal all available resources in such a way that their utility is greater than the sum of their parts.

Like finding white’s best move, finding the best person for a task is a monumental challenge. It involves weighing each lawyer’s relative availability, experience, preferences, proficiencies, and development needs, passing these through the filter of your firm’s strategic objectives, and then somehow comparing the results for each person to find the strongest candidate.

We humans do possess a wonderful capacity for finding shortcuts – for discarding the vast majority of possible options to concentrate on the handful that appear most promising. What undermines this capacity, as the psychologist Daniel Kahneman has convincingly explained, are the very human crosswinds of fatigue, emotion, bias, and false confidence that we’re exposed to when we use mental shortcuts.

Thinking fast, we overlook too much; thinking slow, we look over too much. And as former World Chess Champion Garry Kasparov put it, “the worst enemy of the strategist is the clock.”

The strategist’s best friend

There’s a punchline to the question posed at the beginning of this article. I never said you couldn’t use your phone. If you did, you could find white’s best move in seconds. Widely available chess programmes will instantly show you how white should best proceed.

Chess engines, as they’re known, don’t think like us. They can calculate 80 moves ahead and can consider up to 1 billion moves per second, feverishly comparing and discarding options until only the very best move remains. They think fast, extremely fast, but without any of the shortcomings of a human doing the same.

Moreover, they don’t see the board in terms of pawns, knights and rooks, but in centipawns – a positional score equivalent to a hundredth of a pawn. This enables the engines to peer deeper into the intricacies of chess than a human ever could. In doing so, they have revealed many “unknown unknowns” of the game, reaching far beyond the horizon set by our cognitive capacity.

Needless to say, these programmes don’t only wipe the floor with today’s grandmasters – they do it in seconds.

Work allocation’s magic button

Today’s technology provides everything we need to make an engine for work allocation. Working digitally, every employee possesses a data profile – the raw information processed by the engine. Cloud computing gives us access to the processing power required to make sense of that data. And algorithms, which do the sense-making and suggesting, are now far easier to design, thanks in no small part to pioneering early chess engines.

Brought together, these technologies will mean that the best work allocation decisions are no longer hidden behind dizzying layers of complexity, but behind a button. Such an engine would instantly produce recommendations that a human allocator could never have spotted on their own.

Still, there’s one crucial difference between the board and the boardroom. Chess strategies are fixed by the rules that govern the board. Firm strategies are not – they differ considerably based on an organisation’s size, specialisms, objectives, and challenges. That means one firm’s best decision is another firm’s dud.

As such, work allocation algorithms will need to be bespoke to each firm. That’s not as challenging as it sounds. In the back-end of any engine, it’s easy to tweak the weighting of the factors that are considered by the algorithm. The aim of every allocation algorithm should always be to nudge firms towards their unique strategic objectives, whatever they may be.

Adoption and acceleration

Garry Kasparov became the first chess grandmaster to lose to a computer in competitive chess, in a momentous 1997 match with IBM’s Deep Blue. Reflecting on the rise of the chess engine, he writes that “smarter computers are one key to success, but doing a smarter job of humans and machines working together turns out to be far more important.”

Chess engines didn’t automate players out of existence. The game is more popular now than at any point in history, and never before have there been so many top-level chess players. Their impact, in fact, has been to accelerate innovation. Working alongside machines, man is learning a new mastery of the game.

Allocation algorithms won’t erase the human touch when work is delegated. The final staffing decision will still rest with the allocator. Executed well, an allocation engine will empower them, conserve their cognitive resources, and help them forge new and deeper relationships with their colleagues.

Chess engines are now an integral part of the game, considered invaluable in teaching, punditry, preparation, and analysis. I’m confident that the same will one day be said of allocation engines. They’ll be fundamental to the resourcing strategy at every firm. They’ll make grandmasters of anyone who distributes work. And, as the engine did for chess, they’ll elevate the entire process of work allocation to heights that are simply unimaginable today.



William Dougherty


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