10 The European Business Review July - August 2019
engineers and marketeers – which I have
called Intelligent Artificiality.The current lingua franca of business is a
significant part of the problem. The prolifer-
ation of economists in business school faculties
since the 1960’s has contributed to the production
of a common language system (‘cost benefit
analysis’, ‘competitive mapping and simulation of
competitors’ responses, marginal cost and rates
of substitution analysis, portfolio planning, ... ) in
which executives plan their actions and justify their
decisions. In the age of fast algorithms working
on distributed data sets, this language system is
outclassed and thus dated. It needs to be replaced.
By what?Communication Codes and Protocols for
the AI-competent Organisation As Stephen
Wolfram and Jeanette Wing have argued,
computational thinking needs to be proac-
tively expanded beyond the current reaches of
computer science departments and technical
teams. Wolfram points out that, for any field
of human endeavour X (from linguistics to
architecture, from logic to music and from
plasma physics to dance ethnography) there is
now a specialised field of computational X: e.g.
computational analysis of discourse, computa-
tional historical research, etc. Businesses have
been too slow to get with the computational
wave, and are paying the price.Whatto do?- Computational modeling should complement
causal (physics, thermodynamics) and teleo-
logical (economics, parts of psychology)
modeling in everyday business discourse – as
well as in business. Computational strategy,
computational marketing, computational
logistics and computational hiring should
come next. The language of business needs
to meet the language of computer science on
an equal playing field; - Intelligent artificiality – the discipline of speci-
fying business problems and challenges at the
algorithmic and computational levels including
the procedures, routines, data sets, objective
functions and tolerable error rates for possible
solutions – should complement and in some
cases replace the standard conversational
capital imprinted from economics, finance
andaccounting. - The basic operations of defining and struc-
turing problems, enumerating and evaluating
solutions, designing solution algorithms,
iterating on a solution of the requisite
accuracy and reliability, and evaluating the
complexity of a problem before trying to
solve it – should become the lingua franca
of executive dialogue and of design – and
solution-oriented conversations between
executivesandtheirtechnicalteams; - Practice-guided training – helping executives
turn ‘business problems’ into ‘computational
problems’ intelligible to coders and scien-
tists – should be deployed at scale to help
executives appropriate and master language
fordesigningsolutions; - Those trained in talking to machines –
researchers and developers – must be helped
to develop ways of broadening the domain
to which their current patterns of speaking
and referring apply: not just sterilised data
sets already parsed into the requisite variable
fields stored in the right database formats,
but real organisational entities like people,
tasks, roles, decision rights, incentives,
expertise, product features and the architec-
tural topologies or products, services, value
linked activity chains and organisational
influence networks;
We need to bridge the
skills gap that leads to
this self-perpetuating
communication
debacle. Organisations
need people who can
talk to both people
and machines and
people who can talk
to people who talk to
machines to inhabit
their upper echelons.Artificial Intelligence