As India accelerates its adoption of artificial intelligence across technology, services and engineering sectors, expectations of rapid productivity growth are running high.
Yet, according to global economic analysis and industry‑level observations within India, the reality appears far more measured. Evidence suggests that while AI is undoubtedly boosting efficiency at a task level, its broader impact on national productivity, especially in the near term, may be modest at best.
These findings align with a new report from Oxford Economics dated March 18, 2026, which concludes that despite the global excitement surrounding AI, its transformative economic effect remains unproven. The report states that "the evidence doesn't show the impact has been substantial so far" and that AI is "unlikely to have a transformative impact, especially in the near term", according to Adam Slater, lead economist at Oxford Economics and author of the report.
India's IT Sector Finds Efficiency Gains, But Not Breakthrough
In India's technology services sector, one of the largest in the world, AI has quickly become a core part of engineering, testing, and delivery workflows. Companies are experimenting with generative AI tools to automate repetitive tasks, accelerate development cycles, and improve testing accuracy.
Neeti Sharma, CEO of TeamLease Digital, notes that Indian firms are largely in sync with their global peers when it comes to integrating AI into software engineering and IT workflows. She explains that Indian IT organizations are already observing material improvements: "AI adoption within Indian IT services is broadly aligned with their global peers... About 15-35% efficiency in coding and testing and about 15-25% in engineering cycles are seen."
However, Sharma stresses that these gains are not yet uniform or predictable. "We don't have any set trend or pattern to evaluate gains as yet," she says, pointing out that current variations arise from differences in enterprise data quality, the maturity of delivery frameworks, and how deeply AI has been embedded into operational models.
Her assessment is in alignment with the observations in the Oxford Economics report, which warns that even when AI tools generate noticeable task‑level efficiencies, these do not easily translate into immediate macroeconomic gains. The report emphasizes that correlations between recent productivity upticks and AI adoption in the US are statistically weak and not visible in other advanced economies.
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A Human‑in‑Loop Phase, Not Automation Revolution
The TeamLease CEO describes India's current AI environment as one where human expertise remains central. Engineers, she argues, are working more effectively because AI handles tedious tasks, not because AI replaces them. As she puts it, engineers perform better when AI handles routine tasks while humans bring judgment, context, system design and client accountability.
This aligns with the report's conclusion that technological revolutions take time to diffuse. Oxford Economics notes that in earlier eras, such as the electrification wave of the 1920s or the tech boom of the late 1990s, major productivity gains did not materialize immediately, and in many cases plateaued after a decade. "Historical evidence... shows there is potential for big productivity gains... but these gains can take decades to materialise," Slater writes.
In India, this human‑centred phase also means that AI's impact on employment is more evolutionary than disruptive. Sharma explains that job effects so far have been "about augmentation rather than displacement," with reskilling and natural attrition offsetting shifts in demand. However, she warns that demand for AI‑related skills far exceeds current supply, posing constraints on the sector's ability to scale deeper AI deployment.
Global Constraints Echo India's Challenges
The Oxford Economics report highlights structural barriers that limit AI-driven productivity growth:
- High upfront investment in computing infrastructure
- Energy supply bottlenecks
- Regulatory and organizational frictions
- Rising marginal costs as AI use scales
Many of these constraints apply directly to India. Companies here rely heavily on legacy delivery models and often lack high‑quality, centralized data repositories, conditions that hinder seamless automation. As Sharma notes, for AI to reshape hiring and organizational structures meaningfully, it must advance beyond assisting individual tasks to owning end‑to‑end delivery outcomes, something she argues India's data readiness and commercial models "don't support as much as we would expect them to."
Globally, the London Based Economist Slater also cautions against assuming that AI-driven cost efficiencies will scale rapidly: "Although the gains could be boosted if AI also raises the rate of technological progress... aside from a few tantalising examples, this is largely speculative."
This explains why, despite intense investment and hype, the near-term economic impact of AI remains uncertain.
Macro View: Gains Will Be Real, But Smaller, Slower
Based on current evidence, Oxford Economics forecasts that total factor productivity gains in the next decade will likely be around 3%, significantly lower than the dramatic improvements predicted in some of the more optimistic models.
In India's context, this projection suggests that the country's large and dynamic services sector may benefit from steady, incremental efficiency improvements, but not a productivity revolution.
AI, A Slow March, Not Leap
India's enthusiasm for AI is well‑founded. The country's IT workforce is absorbing new tools rapidly, efficiency gains are already showing up in engineering cycles, and the foundation for deeper AI adoption is firmly in place.
But as both Sharma and Slater highlight, one from the ground level, the other from a macroeconomic vantage point, AI's true economic impact will play out over decades, not months.
For now, India's AI journey remains a story of steady progress, clear but contained gains, and a long runway before transformative productivity shifts take hold.
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