WORLD ACADEMY OF INFORMATICS AND MANAGEMENT SCIENCES
Robots will do jobs we have been doing, and do them much
in 1962 as part of his
better. They will do jobs we can’t do at all. They will do
Innovations’ theory. He
jobs we never imagined even needed to be done. And they
innovation could be
will help us discover new jobs for ourselves, new tasks that
characteristics: innovators,
expand who we are. They will let us focus on becoming
majority and laggards.
more human than we were.
MODERNIZATION AND EXPONENTIAL TOOL
PATTERNS
Our interviews have revealed patterns of how these tools are
being used in combination by organizations to improve their
basic finance activities. Organizations that are already using
cloud and process robotics with their finance functions find
that the combination leads to unmanageable amounts of
data. Big data is nothing new, however, and can provide
new sources of insight that might be applied in performance
management.
But organizations have found themselves drowning in
what’s termed a ‘data lake’. At its most basic, a data lake is
a description of any large raw data pool. This includes
structured, semi-structured and unstructured data that is
stored in one place until required.
To enable mining of their new data lakes for maximum
insight, organizations we spoke with are having to invest in
exponential tools. They are investing in advanced analytics
and in-memory computing to assemble information more
quickly, while simultaneously using cognitive computing
solutions to query the data lake, to provide automated
insight.
It is only through a combination of core modernization and
exponential tools that finance functions are beginning to
change their organizational landscape. Influential and
impactful reporting is then achieved by combining the
technology with the technical expertise of the finance
professional. A 2016 World Economic Forum / Accenture
white paper forecasts that ‘these types of technologies will
reduce the costs of the finance function by 40 percent’.
TECHNOLOGY ADOPTION
Our research is uncovering patterns in the area of
technology adoption. It’s allowing us to understand which
types of organizations are already experiencing and
embracing the finance function of the future, and what’s
motivating their technological adoption:
1.
INNOVATORS AND LAGGARDS
The data reveals that the innovators and early adopters
implementing disruptive technologies into their finance
functions tend to be either:
a. Multinational or transnational organizations,
b. National organizations (normally in competition
with multinational or transnational organizations
c. Specific sectors such as:
i.
Financial services
ii.
Telecommunications
iii.
Private
iv.
FinTech (financial technology); or
d. Start-ups run by young entrepreneurs.
These types of organizations typically represent the first 16
percent of a technology adoption bell curve. Roger’s
adoption bell curve (Figure 2) was first published
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research into the ‘Diffusion of
proposed that the adopters of
categorized into five group
early adopters, early majority, late
Figure 2: Roger’s adoption bell curve
Of course, there are risks in being the first to implement
technology, just as there are risks associated with turning up
to the party too late and being left behind. It’s an age-old
dilemma, as Haskel and Westlake remind us, ‘People often
observe that while the early bird catches the worm, it is the
second mouse that gets the cheese’. And this must be
contrasted against McAFee and Brynjolfsson’s early 1900’s
electrification revolution example: We also know that not all
factories were able to electrify intelligently. Some companies
and their leaders saw the potential of unit drive and embraced
it, while others debated the matter for decades. For all these
reasons, it seems likely that the early-adopting factories
contributed directly to the deaths of many old industrial trusts.
Whether you are an innovator or part of the late majority,
knowing where you sit on the adoption bell curve will inform
your organization’s risk appetite around disruptive
technologies.
2. SOCIAL DEMAND IN SUPPLY CHAIN FACTOR
A 2017 McKinsey paper on automation, employment and
productivity highlights factors affecting the pace and extent of
automation. The report sets out five factors that are
influencing the extent and pace of automation around work
activities for organizations. These are:
a. Technical feasibility
b. Cost of developing and deploying solutions
c. Labor market dynamics
d. Economic benefits
e. Regulatory and social acceptance.
Our research brings to light a sixth factor that’s influencing
the pace of automation within organizations. The missing
factor is that of social demand in the ecosystem. When others
in an organization’s supply chain implement a disruptive
technology, this speeds up adoption of similar technologies
across the ecosystem. This missing component is linked to
what Haskel and Westlake term ‘dynamic clusters’. Dynamic
clusters are ‘places where innovative businesses and people
are more likely to come together and share ideas’.
In one interview with a representative from a FinTech
company, a discussion on the topic of blockchain arose. The
individual disclosed that their organization was investing in
blockchain technology primarily because the rest of the sector
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