On what resources do management strategists need to focus, if they want to leverage Artificial Intelligence and
create Competitive Advantage? To provide an answer from business academia, a framework for leveraging Artificial
Intelligence in a business context is presented in this 5-part article series. In Part 4, the relevance of skilled
labor is elaborated, especially for the role of the Data Scientist. The first part of this series can be found
AI is changing our work environments and the needs of employers
The third type of resources you need for applying AI in a business context are AI-enabling human capital
concretely analytically, managerially and technically skilled labor. The rise of AI is resulting in a so-called
Technical Change” (SBTC), defined by Bresnahan as a “technical progress that shifts demand toward more
workers relative to the less skilled”. He states three arguments why AI or IT
in general causes SBTC:
- Complementation Effect: IT-related skilled labor is complementary to IT infrastructure, because IT
infrastructure always requires people for management, operation, maintenance, etc. Since IT hardware prices are
declining rapidly, the demand for complementary goods are rising by basic economic principles.
- Substitution Effect: IT, and especially AI, is often used to automate routines and well-defined tasks,
clerical or blue-collar workforce. On the other hand, more complex work (like those of managers and
professionals) is hard to substitute, so there is only “limited substitution”.
- Information Overload Effect: There is an effect of information overload, meaning that computerized
business processes accelerate data generation, which in turn
causes more demand for skilled labor.
High Demand + Low Supply = High Price
This high demand for skilled workers caused by SBTC contrasts the shortage of supply in the labor market. While the
most immediate need remains critical technical skills, companies are also encountering a great lack of people at
interface between business and AI as well as system and data engineers.
A study by
McKinsey&Company differentiates between three key types of skilled workers:
- deep analytical talents for
statistical modeling and knowledge discovery,
- data-savvy managers and analysts for interpretation and
- supporting technology personnel for maintenance and data processing.
They forecasted that
demand for deep analytical talents in the USA would exceed the projected supply by 50%
to 60%, as well as a need for 1.5 million additional data-savvy managers and analysts. This
disequilibrium between demand and supply of skilled labor results in enormously high salary offers by tech
companies. The New York Times reported that typical AI experts (including PhDs and less educated people with work
experience) can earn between $300,000 to $500,000 a year
in salary and equity.
The "Sexiest Job of the 21st Century" – or not?
This general role of the analytical specialist is widely known as “Data Scientist”. Even though it has been
the “sexiest job of the 21st century”, there is no precise definition of what a Data
Scientist is, or what skills this role comprises. Many definitions from business as
well as from academia describe an all-round talent, incorporating knowledge in Data Science, statistics, Machine
Learning, programming, data
visualization, the specific business domain, and at the same time being a team player and excellent communicator.
However, to put it in a nutshell:
“Data Scientists discover new patterns in data, using sophisticated statistical methods
Data Mining or Machine Learning, to realize business value by supporting or automating decision-making”.
Thus, the discovery of new, business-relevant knowledge from data is the key purpose of Data Scientists. This
especially hard in a business context, since real world problems are rarely aligned for applying AI techniques
directly. The Data Scientist needs time to exploratively experiment with the data and the freedom to build and test
hypotheses, prototypes or proofs-of-concept.
Unfortunately, this challenging, creative work is often overshadowed by
rather simple, time-consuming data processing work. The reason for this is that the role of the Data Scientist
still not well understood. Like chemistry in the mid-19th century, the field of Data Science is not yet well
a good Data Scientist must not only be proficient on the scientific level, but also be a good “lab technician”,
doing most of the preparatory work himself. This lack of understanding might be one of the key issues in hindering
organizations from realizing the full potential of (big) data. To tackle this issue, business and academia should
define the required knowledge, roles and skill sets across the organization.
Skilled Labor as a Source of Competitive Advantage?
To summarize, since analytically, managerially as well as technically skilled-labor is very rare, but a necessary
complement to make use of AI at the same time, it indeed can be a source of Competitive Advantage. This
point stands in contrast to Mata, who argue that technical skills cannot be a source of Sustainable Competitive
since they are not heterogeneously distributed and highly mobile. On the other hand, they point out that managerial
IT skills can be a source of Sustainable Competitive Advantage, due to their long-term development and social
more than 20 years later, it is debatable if this argument still holds for AI-related skills, since they actually
are very rare and heterogeneously distributed. Anyway, the good news is that AI-skills are spreading quickly, thanks
to the increasing amount of online educational resources as well as novel curricula at universities. But for the
time being, managers need to balance out the benefits of gaining more business value from
their data, against the high cost of skilled labor.
In the last part of this 5-part series, the relevance of AI Knowledge is elaborated, especially the role of open
source Machine Learning algorithms. Stay tuned!
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