Only one in three Indian organisations can prove learning drives business results: Why India Inc’s biggest learning push is struggling to show impact
Indian organisations are investing heavily in learning and development, but the business returns remain uneven. According to KNOLSKAPE’s L&D Predictions Report 2026, released on February 9, only 30–35 percent of organisations link learning outcomes directly to business OKRs. Most continue to measure participation and completion rather than whether learning improves performance, decision-making, or execution.
The report is based on inputs from more than 100 organisations across India, covering over 0.5 million respondents, with strong representation from large enterprises. It signals a shift in how learning is being discussed, even as practice lags behind intent.
The appetite for digital and AI-led transformation is unmistakable. Nearly 70–80 percent of organisations surveyed said they prioritise digital, AI, and future-ready workforce capabilities. However, this ambition has not yet translated into scale.
Studies by KNOLSKAPE have revealed that while a good majority, comprised of 75 to 85 percent, of Indian enterprises lie in the exploration or proof-of-concept/pilot phase, only a small fraction, under 10 percent, have successfully adopted AI at an enterprise-wide level.
While there has been a recognition of the value of data, the usage of analytics in L&D still remains limited. Despite widespread acknowledgement of the importance of data, analytics usage within L&D remains limited and inconsistent. The report notes that learning data is rarely integrated with business metrics, making it difficult to assess readiness or predict performance outcomes.
As a result, learning is still treated as a volume exercise in many organisations, rather than as a lever for improving judgment, productivity, and execution.
The report also highlights weaknesses in internal talent movement. Around 50–60 per cent of organisations lack an internal talent marketplace, and fewer than 20 per cent have scaled one at the enterprise level. This also constrains the possibility for personnel to utilize their skills across different posts and for organizations to utilize their personnel efficiently.
Without internal mobility systems to supplement learning, the results often stop at the classroom or platform level.
The report highlights new risks to early-stage employees in an increasingly AI-saturated work environment. Newcomers to the workforce are benefiting from fewer problem-solving opportunities and becoming increasingly dependent on AI tools; both factors contribute to skill atrophy; large datasets with junior employees exacerbate the issue.
This trend prompts broader future capability depth and leadership development issues if learning systems are not changed to support judgment, practice, and decision-making.
This trend raises long-term concerns about capability depth and leadership pipelines if learning systems are not redesigned to support judgment, practice, and real-world decision-making.
KNOLSKAPE’s India L&D Predictions Report 2026 concludes that while Indian organisations retain a strong advantage in execution muscle and talent scale, sustained competitiveness will depend on a fundamental shift in how learning is designed and measured.
The report calls for simulation-led, practice-based learning, stronger AI usage frameworks for early-career populations, and enterprise-grade talent intelligence platforms. As AI reshapes how work is done, learning must move beyond training programmes to directly support readiness, execution, and measurable business outcomes at scale.
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AI readiness is high, enterprise-scale adoption is not
Studies by KNOLSKAPE have revealed that while a good majority, comprised of 75 to 85 percent, of Indian enterprises lie in the exploration or proof-of-concept/pilot phase, only a small fraction, under 10 percent, have successfully adopted AI at an enterprise-wide level.
Analytics maturity lags learning investment
While there has been a recognition of the value of data, the usage of analytics in L&D still remains limited. Despite widespread acknowledgement of the importance of data, analytics usage within L&D remains limited and inconsistent. The report notes that learning data is rarely integrated with business metrics, making it difficult to assess readiness or predict performance outcomes.
As a result, learning is still treated as a volume exercise in many organisations, rather than as a lever for improving judgment, productivity, and execution.
Internal mobility remains a structural constraint
The report also highlights weaknesses in internal talent movement. Around 50–60 per cent of organisations lack an internal talent marketplace, and fewer than 20 per cent have scaled one at the enterprise level. This also constrains the possibility for personnel to utilize their skills across different posts and for organizations to utilize their personnel efficiently.
Without internal mobility systems to supplement learning, the results often stop at the classroom or platform level.
Early career capability risks are increasing
The report highlights new risks to early-stage employees in an increasingly AI-saturated work environment. Newcomers to the workforce are benefiting from fewer problem-solving opportunities and becoming increasingly dependent on AI tools; both factors contribute to skill atrophy; large datasets with junior employees exacerbate the issue.
This trend prompts broader future capability depth and leadership development issues if learning systems are not changed to support judgment, practice, and decision-making.
This trend raises long-term concerns about capability depth and leadership pipelines if learning systems are not redesigned to support judgment, practice, and real-world decision-making.
From learning volume to capability depth
KNOLSKAPE’s India L&D Predictions Report 2026 concludes that while Indian organisations retain a strong advantage in execution muscle and talent scale, sustained competitiveness will depend on a fundamental shift in how learning is designed and measured.
The report calls for simulation-led, practice-based learning, stronger AI usage frameworks for early-career populations, and enterprise-grade talent intelligence platforms. As AI reshapes how work is done, learning must move beyond training programmes to directly support readiness, execution, and measurable business outcomes at scale.
Ready to navigate global policies? Secure your overseas future. Get expert guidance now!
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