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How will continuing developments in artificial intelligence (AI) and machine learning influence IT professionals? This article approaches this question by identifying the factors that influence the demand for software developers and IT professionals, describing how these factors relate to AI, and articulating the likely impact on IT professionals.
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Artificial Intelligence and IT
As “self-programming techniques” manifest in the form of
artificial intelligence (AI), many are wondering how AI
will affect IT professionals. For example, some predict that
AI could reduce the number of jobs for software develop-
ers by 70 percent in India, which accounts for 65 percent of
global IT offshore work and 40 percent of IT-enabled busi-
ness process work.1
However, such dire predictions are not new. It is helpful to
recall a similar prediction almost 60 years ago when Her-
bert Simon, a Nobel Prize winner sometimes called ‘the
founding father of AI,’ predicted that ‘self-programming
techniques’ would lead to the extinction of the computer
programming occupation by 1985. Simon noted:2
“…we can dismiss the notion that computer programers [sic] will become a powerful elite
in the automated corporation. It is far more likely that the programing occupation will be-
come extinct (through the further development of self-programing techniques) than that it
will become all powerful. More and more, computers will program themselves….”
While massive industrial and technical developments—including personal computers in the
1980s; the World Wide Web in the 1990s; outsourcing and offshoring in the 2000s; and social
media, mobile computing, and cloud computing in the 2010s—created some peaks and valleys,
the computer programming occupation has continued its inexorable growth, belying the initial
Rather than attempt a blunt prediction of future decades, we approach the question of how AI
will affect IT professionals by first identifying the factors that influence the demand for software
programmers, then discussing how these factors relate to AI, and finally articulating the likely
impact of AI on IT professionals.
We begin by delving into what software programmers do and how those activities are affected by
technical developments. To ‘program’ is to develop a series of instructions or operations to be
performed by a mechanism such as a computer. The personal computer revolution of the 1980s
and the advent of the World Wide Web in the 1990s greatly increased the information intensity
of industrial activity by allowing numerous occupational tasks to be codified and standardized.3
The ability to access world-class intra-firm efficiencies through the personal computer and inter-
Sunil Mithas
Muma College of Business at
the University of South
Thomas Kude
ESSEC Business School
Jonathan Whitaker
University of Richmond
Robins School of Business
IT Professional Published by the IEEE Computer Society
1520-9202/18/$33.00 ©2018 IEEE
September/October 2018
firm efficiencies through the World Wide Web drove demand for software such as enterprise re-
source planning (ERP) software. In turn, this demand for software accelerated the number of
computer science degrees during the mid-1980s and late 1990s, as shown in Figure 1. In this
way, the codification and standardization enabled by IT created significant new demand for IT
and the software programming profession during the 1980s and 1990s.
Figure 1. Degrees in computer and information sciences conferred by US postsecondary
institutions. (Source: Digest of Education Statistics, 2016, US National Center for Education
While the increase of outsourcing and the advent of offshoring during the 2000s might not have
changed the overall demand for software programming, it certainly shifted and reallocated that
demand across firms and geographies. Outsourcing and offshoring of software development and
other occupations during the past two decades was enabled by higher levels of modularization.
Modularization is the decomposition of a product or service into components, such that the com-
ponents can be performed independently by separate people in different firms or geographies,
and later be reintegrated. We find evidence of the impact of modularization on software pro-
grammers in developed economies by noting the relatively flat level of employment for software
programmers in the US (see Figure 2) and of information and communication technology (ICT)
specialists in the European Union during the 2000s. Meanwhile, India’s National Association of
Software and Service Companies (NASSCOM) reports that the number of IT and business pro-
cess outsourcing (BPO) professionals in India increased almost ten-fold from 430,000 in 2001 to
3,860,000 in 2017.
To place these figures in context, the research firm IDC estimates that the number of global soft-
ware development professionals was 11,000,000 in 2014. Combining this IDC estimate with the
US Census Bureau data in Figure 2 and the NASSCOM data above suggests that about 15 per-
cent of software development professionals are in the US, and about 30 percent of software de-
velopment professionals are in India.
Figure 1 shows that the number of bachelor’s degrees in computer and information science de-
clined by 26 percent from 2004-2005 to 2009-2010, but then increased sharply from 2009-2010
to reach an all-time high in 2014-2015. The sharp decline in computer and information science
degrees might have been due to concerns about offshoring because we do not see a decline in
two related fields (business and engineering/engineering technologies) from 2004-2005 to 2009-
2010, where the number of bachelor’s degrees in business increased by 15 percent, and the num-
ber of bachelor’s degrees in engineering and engineering technologies increased by 12 percent.
September/October 2018
Note that the number of computer and information science bachelor’s degrees surged by 50 per-
cent from 2009-2010 to 2014-2015 as concerns about offshoring subsided, an increase far greater
than the 2 percent increase in business degrees and 30 percent increase in engineering/engineer-
ing technologies degrees during the same timeframe.
It is important to note that the impacts of offshoring vary for different types of IT occupations.
For example, in our related research we found that information-intensive and high-skill occupa-
tions experienced higher employment growth, despite a slight decline in salary growth in the US
from 2000-2004, suggesting that many information-intensive service occupations have a tacit
component that make them more difficult to relocate offshore. In one of our research papers, we
note that total employment for computer and information systems managers (a more tacit and
less codifiable occupation) increased 20 percent from 283,480 in 2000 to 341,250 in 2015, while
wages increased 76 percent from $80,250 to $141,000 during the same timeframe. In contrast,
employment for computer programmers (a more codifiable occupation) declined 45 percent from
2000 to 2015, and wages for computer programmers increased 38 percent from 2000 to 2015,
only half the rate of increase for manager positions. More recently, firms are using crowdsourc-
ing or micro-sourcing through platforms such as Amazon Mechanical Turk or to
outsource or offshore some activities. However, the extent to which these platforms will nega-
tively impact the work of software programmers that involves complex workflows remains de-
Figure 2. Computer programmer employment and wages in the US. (Sources: 1970 data from US
Census Bureau supplementary report number PC(S1)-32,; 1980 data from US
Census Bureau supplementary report number PC80(S1)-8,; 1987-
1996 data from US General Accounting Office report GAO/HEHS-98-159R,; 1997-2017 data from US Bureau of Labor Statistics
Occupational Employment Statistics,
In addition to the offshoring of information-intensive activities, another factor that facilitated the
disaggregation of business processes is the global movement of labor. The cross-border move-
ment of IT professionals continues to attract significant debate on immigration, visa issues, and
employment and wages of IT professionals in developed economies.
Even firms that offshored
and outsourced realized the limits of outsourcing, and progressive firms kept at least some criti-
cal IT capabilities and programmers onshore and in-house for strategic reasons.
The above findings in the context of technological and organizational developments during the
1980s, 1990s, and 2000s can inform the discussion of ‘extinction’ or ‘substitution’ for the soft-
September/October 2018
ware programming occupation, because activities that can be codified, standardized, and modu-
larized are also more likely to be automated through AI.
While the modularization of software development has reduced the complexity of individual ac-
tivities, complexity is increased by the need to coordinate work across software teams and inte-
grate individual modules to create a product that is Apple-simple, Google-fast, and SAP-reliable
at the same time.6 The complexity of software programming jobs has also increased due to
changes in system development methodologies and the rise of agile methods that call for closer
collaboration among software developers and customers integrating
design thinking and related approaches.7
For example, given the difficulty to elicit precise requirements from
clients upfront, agile software development insists on constant cus-
tomer feedback and collaboration, which might be difficult to achieve
in a disaggregated work mode.8 Because of the need for innovation
and closer collaboration between software developers and users, firms
are realizing the value of investing in their internal technical capabili-
ties and digital transformation by bringing more software development
in-house, sometimes moving toward a hybrid model that includes both
on-premise and cloud computing.9
The foregoing discussion suggests that Simon’s 1960 prediction about
computer programmers becoming extinct needs to be seen in the con-
text of major industrial and technical developments. Simon made his
prediction before the advent of the personal computer and the World
Wide Web, changes in software development methodologies and the
role of IT departments in firms, and many other wider trends in tech-
nology, business, and society.10 As part of these developments, the de-
mand for computer programmers has increased (see Figure 2) and the
IT profession, which consisted mostly of code development in the
1960s, has now diversified into many different job descriptions and re-
sponsibilities.11 For example, in addition to computer programmers,
software developers, and web developers, current US Bureau of Labor
Statistics computer occupations include information security analysts, network and computer
systems administrators, computer network architects, computer user support specialists, and
computer network support specialists.
While Simon wrote that “more and more, computers will program themselves” during a time of
great anticipation for AI, this anticipation did not come to fruition at that time. Now that we are
again at a time of enthusiasm based on recent advances in AI and machine learning, there is a
need to take a more thoughtful perspective on how the factors discussed above fit into AI, and on
how AI is likely to influence the software development profession.
We consider two different roles of AI for software development: (1) AI as a tool to program soft-
ware, and (2) AI as the software itself—sometimes referred to as Software 2.0.12 Making some
cautious but informed predictions, it is likely that both of these roles will be relevant for software
development in the future, and that there will be a place for human software developers in both
of these AI roles.
The first role of AI as a tool to program software means that AI directly writes program code or
indirectly helps human programmers to write program code—in the sense of instructions for
computers. Consequently, the tasks of human software developers will follow the general trajec-
tory of automation and outsourcing, where high-level tasks carried out by human programmers
will move to even higher levels of value creation, while lower-level programming tasks will in-
creasingly be performed by AI.13 This is in line with earlier conjectures that emerging technolo-
gies can destroy some jobs, and in this case, it will replace jobs that involve lower-level
There is a need to
take a more
perspective on how
AI is likely to
influence the
September/October 2018
programming. Thus, AI will substitute for humans by simplifying the entire job (robots replacing
workers) or substituting some activities within a job that are amenable to rule-based logic (simi-
lar to automated teller machines taking over some functions of a human teller). These trends
have been underway for some time and are already visible across industries.
However, technologies also create new jobs (for example, data scientists), change the mix of jobs
in the economy, and alter the nature of activities within jobs.14 For example, AI might comple-
ment humans in jobs that require pattern recognition or case-based reasoning. In the case of soft-
ware development, recent discussions on the role of AI suggest that AI assistance might help
human programmers avoid errors and strategic mistakes when coding.15 For example, AI could
act as a pair programming partner, reducing the resource needs for the established agile practice
of pair programming. Agile practices could also be useful in test-driven development, where hu-
mans could focus on writing test cases and AI would create code that satisfies the test.15 In this
way, software development would be conducted through human–machine interaction.16 Going
beyond software development, other occupations that are menial and/or prone to error, and there-
fore good candidates for AI-enabled displacement, include cashiers, laboratory technicians, ac-
countants, auditors, and tax preparers.
The second role of AI as the software itself suggests that we would not use traditional program-
ming code—in the sense of instructions as to what the computer should do—but would replace
program code with AI (Software 2.0).12 However, we believe that traditional program code will
continue to be relevant. For example, Brynjolfsson and Mitchell17 suggest that AI/Software 2.0 is
particularly useful for stable tasks. Thus, traditional programming might still be needed in more
dynamic environments such as the case of frequently changing customer requests or the context
of more exploratory research projects.
It seems feasible that some code will be replaced by AI and that new problems will be addressed
through AI instead of traditional code. For example, traditional instructions could be replaced by
the weights in a neural network. We see this already in the context of translation services, speech
recognition, and video gaming.12 But even in such a context, we would likely still need software
developers. The work of software developers might shift away from traditional coding and to-
ward designing and developing the architecture that brings together AI modules to solve a prob-
lem, to development tasks related to data governance, and/or to activities requiring judgment
rather than activities requiring rule-based decisions. Furthermore, the omnipresence of AI in
ubiquitous or experiential computing18 will create a continuing need for software developers. Re-
latedly, recent efforts to create explainable AI (XAI) or to address ethical questions related to AI,
such as bias and discrimination, will likely continue to require software programmers.19
To further explore the potential impacts of AI on various IT occupations, Table 1 shows US Bu-
reau of Labor Statistics 2016-2017 data for the average wage and current number of positions,
and 2016-2026 projections for the growth of each occupation. These seven IT occupations each
include at least 100,000 employees, and reasonably represent the range of average wages among
IT professionals.
The Bureau of Labor Statistics classifies the growth of each occupation into one of four catego-
ries: decline, average growth (5 to 9 percent), faster than average growth (10 to 14 percent), and
much faster than average growth (15 percent and up). The projected growth for each IT occupa-
tion provides some clues for how AI could affect various IT occupations, considering these Bu-
reau of Labor Statistics projections at face value.
The projection that software developer (applications) and web developer occupations are ex-
pected to grow much faster than average from 2016-2026 suggests that AI is expected to comple-
ment (not displace) traditional programming over the next decade. Similarly, the much faster
than average growth projection for information security analysts suggests that AI could create
demand for IT professionals who can address both cybersecurity and privacy considerations
when bad actors use AI capabilities to design more sophisticated attacks.20
September/October 2018
Table 1. 10-year projected growth for various IT occupations. Source: Digest of Education
Statistics, 2016, US National Center for Education Statistics, available at
Position 2016-2017
projected employment
Software developers (appli-
$101,790 831,000 Much faster than average
Web developers $67,990 163,000 Much faster than average
Information security analysts $95,510 100,000 Much faster than average
Computer user support spe-
$50,210 637,000 Faster than average
Database administrators $87,020 120,000 Faster than average
Network and computer sys-
tems administrators
$81,100 391,000 Average
Computer network support
$62,340 199,000 Average
Computer network architects $104,650 163,000 Average
The projection that the computer user support specialist occupation is expected to grow faster
than average also suggests a complementary role for AI over the next decade, as additional appli-
cations and functionality will draw additional users. Similarly, the need for effective manage-
ment of additional data that results from additional applications and functionality is consistent
with the projection that the database administrator occupation is expected to grow faster than av-
erage, reinforcing a complementary role for AI over the next decade. However, the projection
that the occupations of network and computer systems administration, computer network support
specialists, and computer network architects will grow at only an average rate suggests that AI is
expected to automate computation-intensive tasks such as managing the flow of network traffic.
It will be interesting to examine the extent to which these projections map to reality as capabili-
ties of AI unfold over time and reveal the extent to which AI complements or substitutes activi-
ties in IT occupations.
AI will bring many changes to the IT profession. While it is difficult to predict precisely how
these changes will unfold, just as it was difficult for Simon to predict six decades ago, there are
reasons to believe that software development will change and that with appropriate investments
in human capital, software programmers should be able to respond to the changes in technolo-
gies and customer needs.21
For computer science students, we do not expect any major short-term changes in curricula as
students still need to learn the basics of computer programming. However, over time we expect
that more entry-level computer programming concepts will trickle down into high school curric-
ula and coding boot camps, and more advanced concepts such as AI and machine learning will
extend beyond computer information and science degrees into other majors such as business and
September/October 2018
the natural sciences. On the whole, there are reasons to be optimistic about the future of software
programmers and IT professionals because the seamless integration of “human and computer in-
telligence to solve interesting and important problems that impact the future of work, organiza-
tions, and broader society” will continue the high demand for their talents and creativity.22
1. V. Ganesh, “Automation to Kill 70% of IT Jobs,” Hindu BusinessLine, blog,
November 2017;
2. H.A. Simon, “The Corporation: Will It Be Managed by Machines?,” Management and
the Corporations, M.L. Anshen and G.L. Bach, McGraw Hill, 1960.
3. S. Mithas and J. Whitaker, “Is the World Flat or Spiky? Information Intensity, Skills
and Global Service Disaggregation,” Information Systems Research, vol. 18, no. 3,
2007, pp. 237–259.
4. D. Retelny, M.S. Bernstein, and M.A. Velentine, “No Workflow Can Ever Be Enough:
How Crowdsourcing Workflows Constrain Complex Work,” Proc. ACM Human-
Computer Interaction, 2017.
5. S. Mithas and H.C. Lucas, “Are Foreign IT Workers Cheaper? U.S. Visa Policies and
Compensation of Information Technology Professionals,” Management Science, vol.
56, no. 5, 2010, pp. 745–765.
6. S. Earley et al., “From BYOD to BYOA, Phishing, and Botnets,” IT Professional, vol.
16, no. 5, 2014, pp. 16–18.
7. C.T. Schmidt et al., “How Agile Practices Influence the Performance of Software
Development Teams: The Role of Shared Mental Models and Backup,” Proceedings of
the 34th International Conference on Information Systems, 2014.
8. K. Schwaber and M. Beedle, Agile Software Development with Scrum, Prentice Hall,
9. J. Bennett, “Why GM Hired 8,000 Programmers,” The Wall Street Journal, blog,
February 2015;
10. S. Mithas and F.W. McFarlan, “What Is Digital Intelligence?,” IT Professional, vol.
19, no. 4, 2017, pp. 3–6.
11. R. Moncarz, “Training for Techies: Career Preparation in Information Technology,”
Occupational Outlook Quarterly, vol. 46, no. 3, 2002, pp. 38–45.
12. A. Karpathy, “Software 2.0,” Medium, blog, November 2017;
13. P. Smith, “SAP Founder and CEO Say Governments Must Act on AI Challenge as
Google Lays Out Core Principles,” The Australian Financial Review, June 2018;
14. F. Levy and R.J. Murnane, The New Division of Labor: How Computers are Creating
The Next Job Market, Russell Sage Foundation, 2004.
15. I. Huston, “AI Is Not the End of Software Developers: A Data Scientist’s Take on
Software 2.0.,” Built to Adapt, blog, January 2018;
16. B. McDermott, “Machines Can’t Dream,” SAP, January 2018;
17. E. Brynjolfsson and T. Mitchell, “What Can Machine Learning Do? Workforce
Implications,” Science, vol. 358, no. 6370, 2017, pp. 1530–1534.
18. Y. Yoo, “Computing in Everyday Life: A Call for Research on Experiential
Computing,” MIS Quarterly, vol. 34, no. 2, 2010, pp. 213–231.
19. G. Nott, “‘Explainable Artificial Intelligence’: Cracking Open the Black Box of AI,”
Computer World, April 2017.
20. I. Bojanova et al., “Cybersecurity or Privacy,” IT Professional, vol. 18, no. 5, 2016,
pp. 16–17.
21. S. Murugesan, “Stay Professionally Fit, Always,” IT Professional, vol. 19, no. 6, 2017,
pp. 4–7.
September/October 2018
22. H. Jain et al., “Special Issue of Information Systems Research-Humans, Algorithms,
and Augmented Intelligence: The Future of Work, Organizations, and Society,”
Information Systems Research, vol. 29, no. 1, 2018, pp. 250–251.
Sunil Mithas is a world-class scholar and professor at the Muma College of Business at the
University of South Florida. His research interests include strategies for managing innova-
tion and excellence for corporate transformation, focusing on the role of technology and
other intangibles. Mithas is the author of the books Digital Intelligence: What Every Smart
Manager Must Have for Success in an Information Age (Finerplanet, 2016) and Dancing
Elephants and Leaping Jaguars: How to Excel, Innovate, and Transform Your Organiza-
tion the Tata Way (2014). He is a member of IT Professional’s editorial board. Contact him
Thomas Kude is an associate professor at ESSEC Business School in France. His current
research focuses on digital ecosystems, agile software development, and IT management. In
his research, Kude regularly works with companies in the software industry and beyond.
Kude received a PhD from the University of Mannheim in Germany, and his work has been
published in renowned academic journals and presented at international conferences. Con-
tact him at
Jonathan Whitaker is an associate professor at the University of Richmond Robins School
of Business. Prior to his academic career, he worked as a technology consultant with Price
Waterhouse and A.T. Kearney. He earned an MBA from the University of Chicago and a
PhD from the University of Michigan, and his research has been published in leading aca-
demic journals and profiled in the Wall Street Journal and MIT Sloan Management Review.
Contact him at
September/October 2018
... 14 Governments have a role in reducing risks that AI may create by bringing about significant changes in the nature of jobs and skill sets; Herbert Simon, a Nobel Prize winner and "the founding father of AI," even predicted the potential extinction of the computer programming occupation by 1985. 15 Innovating AI can help firms pursue AI-embodied or AIenabled innovations by making R&D more effective and scalable, as well as by using innovation from outside the firm. For example, Aravind Eye Hospital in Madurai (in India) is collaborating with Google on developing an AI-based algorithm to screen diabetic retinopathy and detect the early onset of blindness. ...
The rise of AI raises new questions about AI strategy. How should firms formulate and execute their digital or information technology (IT) strategies and business strategies embracing opportunities that AI present? Should firms formulate a separate AI strategy, or should AI strategy be part of their overarching digital strategy? In many ways, these questions are similar to those in the past when newer technologies came onto the horizon.3–6 In this article, we outline some fundamentals of strategy and discuss how organizations can harness AI for their advantage, illustrated with a few examples of business applications of AI. Then, we discuss how AI strategy relates to an overall digital or IT strategy and how to develop a digital strategy that also encompasses and supports the enterprise’s AI strategy. Finally, we examine strategy implications for corporate leaders, IT professionals, and researchers.
... Mithas, S.,Kude, T., & Whitaker, J. (2018). Artificial intelligence and IT professionals. ...
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