AI-led services are projected to create up to USD 200 billion in net new revenue by 2030. Experts suggest technology firms can meet this growing demand by strategically reskilling their current workforce for specialized Applied AI roles.
AI-Led Services Market Growth
Artificial intelligence (AI)-led services are expected to generate between USD 100 billion and USD 200 billion in net new work by 2030, with technology services companies able to meet most of the demand by reskilling their existing employees, according to a report by Boston Consulting Group (BCG). The report, titled 'The Talent Is Already on Your Payroll. The Clock Isn't', said the global services market is expected to grow from around USD 1.6-1.7 trillion in 2025 to USD 2.2-2.4 trillion by 2030. AI-led services are projected to contribute USD 300 billion to USD 400 billion to this growth. However, the report estimates that around USD 200 billion in traditional services revenue will be lost, leaving a net market opportunity of USD 100 billion to USD 200 billion.
The Need for Applied AI Professionals
The report said technology companies need professionals skilled in Applied AI, who can deploy, operate and maintain AI-based solutions in real-world business environments. Applied AI professionals account for only 10-15 per cent of AI-related staff at traditional technology services companies. In comparison, they make up 90-95 per cent of the workforce at AI-native firms. The remaining 85-90 per cent of AI-related staff at traditional companies primarily use AI to improve the speed of existing tasks. The report also highlighted a gap in roles that work directly with clients to develop and deploy AI solutions. Such forward-deployed positions account for around 45 per cent of Applied AI staff at AI-native companies, compared with less than 5 per cent at traditional technology services firms.
Strategic Talent Decisions
“Technology services firms do not have an AI talent shortage; they have a talent decision to make,” said Sreyssha George, Managing Director and Partner at BCG and co-author of the study. “The people who can deliver Applied AI are largely already on the payroll, and clients are moving from paying for hours to paying for outcomes. That changes how work is staffed and rewarded,” George added. “Every quarter without defined roles is a quarter of real-engagement experience your people do not get.”
Reskilling and Role Architecture
BCG identified 40 Applied AI roles that can be developed from positions already available within technology services companies. For instance, full-stack developers and data scientists can move into forward-deployed engineering roles, while DevOps specialists can transition to AI Ops and data engineers can become semantic engineers. However, the report noted that the effort required for reskilling varies across roles. Upskilling a data scientist to become a forward-deployed engineer requires three times the effort needed to train a data engineer for an AI data engineer role. Building these skills internally could also help companies reduce costs. According to the report, organisations hiring externally for these specialised positions may have to pay a premium of up to 35 per cent compared with equivalent traditional roles.
The report grouped the 40 Applied AI roles into four categories: buying scarce anchor roles, building scale drivers, retaining the delivery core and watching early indicators. It stressed that companies need a clear framework to define these jobs, including skill requirements, proficiency levels, pay bands and a dedicated job category. “The decision that turns existing talent into supply is role architecture: a skill taxonomy, proficiency gates, bands and a dedicated job family, so that each Applied AI role has a title, a level and a band attached,” the report said. (ANI)
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