Motilal Oswal Financial Services

Sector Update | 5 June 2025

Consumer

Technology

Our recent updates

Productivity gains and Indian IT – What is the value at risk?

Among generative AI's many use cases, its potential to disrupt the Software Development

Life Cycle (SDLC) stands out as the most immediate and tangible. While the debate rages

on around the revenue accretive potential of GenAI, it is clear that organizations are

already seeing clearly quantifiable productivity benefits in one area: SDLC or Application

Development and Maintenance (ADM), which accounts for 35-45% of the IT service

industry's revenues.

We took a two pronged approach to estimate the time savings delivered by tools such as

GitHub Copilot. First, we did some digging into the research available so far

—

rather than

relying on anecdotal evidence, there is now concrete research that documents time

savings across various IT services tasks (as listed in the appendix on page 8). We have also

been picking the brains of industry experts (we recently invited Mr. Saurabh Gupta from

HFSResearch for a fire-side chat on the same topic). Our key findings: 1) Across various

stages of the SDLC, there is a 10-50% productivity gain from GenAI tools, 2) The upper-end

of these estimates assumes enterprise-wide scale-up of Agentic AI.

Key takeaways from the report: For most IT companies, ADM accounts for 30-40% of total

revenue. Our research suggests a ~40% productivity gain from enterprise-wide

implementation of GenAI Copilot, putting ~10-15% of IT services revenues at risk.

Key components of SDLC and the impact of GenAI on each of them

We have broken down the SDLC into six operational buckets, each representing

a different share of the total engineering effort within ADM.



Low-level coding/routine feature work (20% of ADM):

GenAI tools like Copilot

drive ~55% efficiency in repetitive coding tasks, translating to ~11% of total

ADM hours saved.



Code review & test writing (20% of ADM):

Automated suggestions and AI-

generated test cases reduce effort by ~40%, resulting in an overall time saving of

8%.



Debugging & incident response (28% of ADM):

AI-driven root cause analysis

and auto-remediation enable ~35% savings, cutting ~9.8% of the total

engineering hours.



Security fixes (15% of ADM):

Copilot Autofix reduces remediation time by ~50%,

saving ~7.5% of ADM effort.



Documentation & deployment (17% of ADM):

AI-generated documentation and

scripted release pipelines deliver ~50% blended savings, accounting for ~8% of

total hours.

This totals 44% of ADM hours that are potentially automatable in the near term.

Since ADM accounts for ~30% of total IT services revenue, the revenue at risk from

GenAI-driven productivity shifts is ~12-13%.



Abhishek Pathak - Research Analyst

(Abhishek.Pathak@MotilalOswal.com)

Research analyst: Keval Bhagat

(Keval.Bhagat@MotilalOswal.com)

Tushar Dhonde

(

Tushar.Dhonde@MotilalOswal.com

)

Investors are advised to refer through important disclosures made at the last page of the Research Report.

Motilal Oswal research is available on www.motilaloswal.com/Institutional-Equities, Bloomberg, Thomson Reuters, Factset and S&P Capital.

Technology

Is this real? Yes—assuming Agentic AI is real



Our evidence stack for these figures includes research papers, GitHub product

demos, and public rollouts of GitHub Copilots to date (sources are mentioned

below).

However, these numbers assume an almost utopian rollout of Agentic

AI across enterprises—and herein lies the catch.

AI agents can perform tasks autonomously and are capable of project-level

orchestration; however, their implementation will require enterprises to

transform their IT stack.

This presents a chicken-and-egg situation—clients are reluctant to modernize

their tech stacks as they continue to cope with macro uncertainty, which may

result in a slower pace of change.

That said, we echo the views of Mr. Gupta (summary of our interaction below):

there is only so much cost saving in labour arbitrage and off shoring now, and

the industry needs to enter a new "S curve" of efficiency gains.

The 2018-21 digital vs. legacy revenue divergence offers some insight into

today's GenAI-led transition. As shown in Exhibits 2-4, digital revenues for TCS,

Infosys, and HCL surged—rising as high as 57% YoY for TCS—while core (legacy)

revenues remained flat or declined.

In the current GenAI phase, we're seeing early signs of a similar

nature:

ADM

and core IT services are facing productivity and pricing pressures,

however the

revenue uplift from scaled GenAI programs is still to materialize.

This is

exerting pressure on top-line growth, especially for large-cap companies. For

Indian IT firms, the key is whether they can again bridge this value shift— by

scaling GenAI from pilots to production.

Parallels between the ‘digital’ vs ‘legacy’ divide in 2018-21



Key takeaways from our conversation with Mr. Saurabh Gupta (President,

Research and Advisory Services, HFS Research)



The industry is entering a new S-curve of productivity, with GenAI—particularly

Agentic AI—driving faster-than-expected gains across development, testing, and

maintenance.

While legacy models of offshoring and rate card-based FTEs continue to

dominate, their limitations are becoming increasingly apparent. Enterprise-wide

tech debts—including data debt, process debt, and skill debt—pose significant

roadblocks that must be addressed to successfully scale GenAI.

GenAI adoption faces bottlenecks from outdated procurement practices, weak

process maturity, and an underprepared talent ecosystem, despite evidence of

30%+ productivity gains in in-house use cases.

SDLC is a major impact zone, along with cybersecurity and commoditized BPO

tasks. Market dynamics are shifting rapidly, with competition intensifying from

startups and SaaS players; consulting firms crowding in; and traditional IT

vendors needing to rethink their delivery models, pricing strategies, and IP-led

differentiation.

While GenAI delivers the best performance in modern stacks like Java and C++,

significant challenges persist in complex legacy systems. The real opportunity

lies not in efficiency alone, but in building new value streams—requiring deep

production-grade capabilities, vertical specialization, and partner ecosystems to

scale AI meaningfully.

5 June 2025

Technology

Key components of SDLC and the impact of GenAI on

each of them

We have broken down the SDLC into six operational buckets, each representing a

different share of total engineering effort within ADM. These buckets reflect how

time is actually spent across delivery teams and serve as the basis for estimating

GenAI-driven efficiency gains:

Low-level coding / routine feature work:



GenAI tools like Copilot drive ~55% efficiency in repetitive coding tasks,

translating to ~11% of total ADM hours saved.

Code review & test writing:



Automated suggestions and AI-generated test cases reduce effort by ~40%,

resulting in an overall time saving of 8%.

Debugging & incident response:



AI-driven root cause analysis and auto-remediation enable ~35% savings, cutting

~9.8% of total engineering hours.

Security fixes



Copilot Autofix reduces remediation time by ~50%, saving ~7.5% of ADM effort.

Documentation & deployment



AI-generated documentation and scripted release pipelines deliver ~50%

blended savings, accounting for ~8% of total hours.

This totals 44% of ADM hours that are potentially automatable in the near term.

Since ADM accounts for ~30% of total IT services revenue, the revenue at risk from

GenAI-driven productivity shifts is ~12-13%.

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Technology

Exhibit 1:

GenAI could automate ~43% of ADM hours, putting ~13% of overall IT services’ value at risk

Source: Industry, MOFSL

Research referred to for the gains mentioned above:

1. Generative AI and Security Operations Center Productivity: Evidence from Live

Operations (Microsoft Security blog, 2025)

2. Copilot autofix announcement:

https://github.blog/news-insights/product-

news/secure-code-more-than-three-times-faster-with-copilot-autofix/

3. Security Copilot: Evidence of Productivity Gains in Live Operations (Microsoft

Security blog, Nov 2024)

Is Github Copilot worth it? Here is what the data says - Faros + Github study,

May 17, 2024

5. The Impact of AI on Developer Productivity: Evidence from GitHub Copilot

(2023, Sida Peng, Eirini Kalliamvakou, Peter Cihon, Mert Demirer)

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Technology

Parallels between the ‘digital’ vs ‘legacy’ divide in 2018-21



The 2018-21 digital vs. legacy revenue divergence offers some insight into

today's GenAI-led transition. As shown in Exhibits 2-4, digital revenues for TCS,

Infosys, and HCL surged—rising as high as 57% YoY for TCS—while core (legacy)

revenues remained flat or declined.

In the current GenAI phase, we are seeing early signs of a similar nature: ADM

and core IT services are facing productivity and pricing pressure, however the

revenue uplift from scaled GenAI programs is still to materialize. This is exerting

pressure on top-line growth, especially for large-cap companies. For Indian IT

firms, the key is whether they can again bridge this value shift— by scaling

GenAI from pilots to production.

Exhibit 2: TCS’s digital revenues accelerated in pre-COVID years as enterprises prioritized transformation

TCS (YoY, %)

57%

46%

Digital Revenue

49%

41%

Core Revenue

40%

25%

-1%

-2%

1QFY19

2QFY19

3QFY19

4QFY19

1QFY20

2QFY20

Source: MOFSL, Company

Exhibit 3: Infosys’s core business faced revenue pressure during the same period, as digital adoption cannibalized legacy

services

INFO (YoY, %)

38%

Digital Revenue

39%

40%

30%

24%

27%

Core Revenue

27%

32%

31%

36%

34%

39%

-1%

-2%

-6%

-8%

-14%

1QFY21

-12%

-9%

-6%

1QFY19

2QFY19

3QFY19

4QFY19

1QFY20

2QFY20

3QFY20

4QFY20

2QFY21

3QFY21

4QFY21

Source: MOFSL, Company

Exhibit 4: HCLT followed a similar trend, with the surge in digital revenues weighing on legacy services

HCLT (YoY, %)

39%

35%

23%

13%

11%

15%

14%

-8%

1QFY20

2QFY20

3QFY20

4QFY20

1QFY21

Source: MOFSL, Company

Digital Revenue

Core Revenue

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Technology

Appendix

This section provides a comprehensive overview of the key infographics and

reference sources that were utilized in the preparation of this report.

Exhibit 5: Early results from the ‘Copilot Autofix’

—

a beta feature built on AI agents

—

showed 3x faster fixes than manual

efforts, while code scanning helped detect vulnerabilities, thereby improving efficiency and delivering better security

outcomes

Source: GitHub blog: “Found means fixed: Secure code more than three times faster with Copilot Autofix”

21 January 2025

Exhibit 6: Estimated productivity gains from the adoption of Generative AI

Source: Security Copilot

–

Evidence of Productivity Gains in Live Operations (Microsoft Security, Mar’25)

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Technology

Exhibit 7: GitHub Copilot was generating more Pull Requests (PRs)

—

i.e., submitting more changes or contributions to the

codebase

—

suggesting greater productivity

Source: Faros.ai Blog: “Is GitHub Copilot Worth It? Real-world Data Reveals the Answer”, 17

May, 2024

Exhibit 8: Lead time to production decreases ~55% for PRs generated by GitHub Copilot, with most savings generated in the

development and code review stages

Source: Faros.ai Blog: “Is GitHub Copilot Worth It? Real-world Data Reveals the Answer”, 17

May, 2024

5 June 2025

Technology

Key takeaways from our conversation with Mr. Saurabh Gupta (President of

Research and Advisory Services, HFS Research)

We hosted an expert session with Mr. Saurabh Gupta, President of Research and

Advisory Services at HFS Research, to understand Gen AI driven productivity gains

to Indian IT companies and its broader impact. Below are the detailed key

takeaways:



The industry is entering a new S-curve of productivity, with GenAI—particularly

Agentic AI—driving faster-than-expected gains across development, testing, and

maintenance.



Large enterprises are encountering the law of diminishing returns. The limits of

offshoring have been reached, and jumping into a new "S-curve" of value

creation is increasing.



Agentic AI, with its autonomous capabilities, holds greater promise to drive

meaningful impact.



Most enterprises continue to operate with outdated procurement models

—

such

as buying services based on FTEs and rate cards. New tools are often deployed

using the old playbook, with 80 percent of services still driven by FTE-based

models. To fully realize the potential of GenAI, new delivery models are

required.



External pressures, such as tariffs, have added urgency, prompting enterprises

to invest in AI as a means to absorb the shocks. Enterprise-wide tech debts—

including data debt, process debt, and skill debt—present significant roadblocks

that must be addressed to scale GenAI.



GenAI adoption faces bottlenecks from outdated procurement practices, weak

process maturity, and an underprepared talent ecosystem, despite evidence of

30%+ productivity gains in in-house use cases.



SDLC is a major impact zone, along with cybersecurity and commoditized BPO

tasks.



The competitive landscape has now leveled, not only between large and mid-

tier IT players but also between traditional Indian IT companies and emerging

startups.



Consulting firms (like KPMG/PwC) and software/tech providers (like Salesforce)

are entering the space.



Market dynamics are shifting rapidly, with competition intensifying from

startups and SaaS players; consulting firms crowding in; and traditional IT

vendors needing to rethink their delivery models, pricing strategies, and IP-led

differentiation.



Most IT firms now offer GenAI implementation services; however, the true

differentiator lies in the ability to scale these solutions and deploy them into

production. Much of the IT industry remains focused on maintenance rather

than transformation, with the DNA of organizations not inherently geared

toward innovation.



Partner ecosystems are becoming a hygiene factor. Vendors will need to align

with hyperscalers like Nvidia and Salesforce as a minimum requirement, but

true differentiation will arise from vertical-specific partnerships and IP.

5 June 2025

Technology



Agentic AI could absorb much of the gains from GenAI when implemented at the

production level. The key is not only to automate for productivity/efficiency but

also to create new value streams.

While GenAI delivers the best performance in modern stacks like Java and C++,

significant challenges persist in complex legacy systems. The true opportunity

lies not in efficiency alone, but in building new value streams—requiring deep

production-grade capabilities, vertical specialization, and partner ecosystems to

scale AI meaningfully.

Investment in securities market are subject to market risks. Read all the related documents carefully before investing.

5 June 2025

Technology

NOTES

5 June 2025

Technology

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Rating may undergo a change

We have forward looking estimates for the stock but we refrain from assigning recommendation

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30 days take appropriate measures to make the recommendation consistent with the investment rating legend.

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5 June 2025