The workforce implications of AI extend beyond simple narratives of machines replacing humans. Our research reveals a more nuanced reality: AI reshapes what skills matter, how work gets done, and what makes a valuable employee. This demands not just new technical capabilities but new ways of thinking about human potential.
"The reason people do offshoring and outsourcing is salaries are cheaper," observes Bram Pauwels. "The moment AI comes in, why would they outsource?" This existential question forces reimagining workforce value propositions. If AI handles routine tasks more efficiently than offshore labour, what role remains for humans?
The answer lies in developing uniquely human capabilities that complement AI's potential. This requires workforce transformation as significant as any technological change.
Ankur Saxena from Evalueserve provides insight: "The most challenging aspect of AI implementation has been the cultural shift required. We're fundamentally changing how things have worked until very recently, which demands adaptation from our teams."
He continues: "We're investing heavily in educating everyone not just on technical skills like prompt engineering and agent interaction, but more importantly on the mindset change needed to embrace AI effectively. This requires strong leadership across the organisation to champion the change and bring everyone along on the journey."
The skills evolution manifests across dimensions:
From processors to problem-solvers. Where traditional models valued efficient task execution, AI-first delivery rewards creative problem-solving. Workers who followed scripts must now handle exceptions and complex situations AI cannot resolve.
From specialists to orchestrators. Ability to coordinate between AI systems, human experts, and client needs becomes paramount. Workers need understanding beyond their domain.
From technical to interpersonal. As work becomes more technology-infused, human skills become more important. Empathy, communication, and relationship building differentiate humans in ways AI cannot replicate.
From static to continuous learning. Technical skill half-life shrinks. Successful workers embrace continuous learning, adapting as AI evolves.
The skills evolution manifests across dimensions:
From processors to problem-solvers. Where traditional models valued efficient task execution, AI-first delivery rewards creative problem-solving. Workers who followed scripts must now handle exceptions and complex situations AI cannot resolve.
From specialists to orchestrators. Ability to coordinate between AI systems, human experts, and client needs becomes paramount. Workers need understanding beyond their domain.
From technical to interpersonal. As work becomes more technology-infused, human skills become more important. Empathy, communication, and relationship building differentiate humans in ways AI cannot replicate.
From static to continuous learning. Technical skill half-life shrinks. Successful workers embrace continuous learning, adapting as AI evolves.
Kathiravan Udayakumar from Cognizant emphasises the shift: "I believe we're witnessing a fundamental shift towards outcome-driven services. Currently, the industry operates primarily on FTE, time-bound or scope-bound service models, but this is changing as clients increasingly measure value by outcomes rather than resources deployed."
He continues: "A second major evolution is the platform-based delivery of services. Just as we've seen software and infrastructure transition to platform models, similar changes are happening in service delivery. Service providers will increasingly rely on proprietary platforms where their accumulated organisational knowledge becomes a competitive advantage."
This platform shift creates new workforce requirements. Workers must understand how their work fits into broader platform capabilities and client outcomes.
Suri Babu Komma from EXL describes transformation in finance operations: "We're developing accelerators and platforms that integrate the entire software development lifecycle, weaving AI throughout to ensure seamless end-to-end management. Our solutions aim to impact productivity and user experience while reducing manual interventions."
The transformation creates challenges and opportunities:
Career path evolution.
Traditional pyramids with clear progression break down. New paths value expertise in human-AI collaboration and innovation.
Compensation model changes.
As value shifts from hours to outcomes, compensation must evolve accordingly.
Geographic advantages diminish.
Specific location advantages matter less when AI handles routine work. Locations providing skilled orchestrators gain advantage.
Training investment imperatives.
Organisations must invest in reskilling. Modern development must build adaptability and learning capability.
Industry leaders take varied approaches. Some hire new AI-native talent. Others transform existing workforces. Most successful pursue both simultaneously.
Vinti Mathur from Genpact describes their approach: "We are looking at our own solutions, including AI, and just making sure that we can adopt them within different areas." This internal transformation provides valuable lessons for client engagements.
Regulatory dimensions add complexity, particularly in financial services. As Bram Pauwels notes: "I work in banking and there are certain things that we simply cannot do yet from an AI point of view." This creates demand for workers navigating AI capabilities, business requirements, and regulatory constraints.
Timeline pressure makes workforce transformation challenging. Ankur Saxena's 12-month warning leaves little time for gradual development. Organisations must pursue aggressive programmes while maintaining service levels.
The required transformation is significant but achievable. Successful organisations recognise people remain their greatest asset, though the nature changes. Tomorrow's valuable employees won't process the most transactions or follow procedures most accurately. They'll think creatively, collaborate with AI, understand business context, and deliver outcomes automation cannot achieve.
This transformation requires investment, commitment, and difficult decisions about workforce composition. But organisations successfully navigating this gain powerful competitive advantage: a workforce amplifying AI's potential rather than competing with it. The intelligent workforce determines which providers thrive in the AI era.
As workforces evolve, so must the metrics used to measure success.
About Enate
Enate is the leading SaaS solution for business services. Enate orchestrates work from start to finish, giving clients the visibility and control needed to deliver better services. From email management and data analysis to intelligent document processing, Enate also offers a host of touch-button AI features designed to slash the time spent on manual work. Trusted by global service teams, Enate ensures smooth, consistent operations that help clients perform at their best.