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AI Coding in IT Services: The Same Tools Saved Amazon 4,500 Developer-Years and Slowed Expert Coders by 19%. Here Is What Separates the Two

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AI Coding in IT Services: The Same Tools Saved Amazon 4,500 Developer-Years and Slowed Expert Coders by 19%. Here Is What Separates the Two

Amazon says an AI coding assistant helped it move tens of thousands of production applications to a newer version of Java. The company estimates this saved more than 4,500 years of developer work, according to its own AWS blog from August 2024. Infosys says its teams have generated more than 28 million lines of code using AI, according to its January 2026 earnings call.

Now the contrast. In a 2025 randomised trial by METR, an independent research group, 16 experienced developers took 19% longer to finish real tasks when allowed to use AI. They still believed AI had sped them up by 20% (METR, July 2025). Same family of tools. Opposite results. The difference lies in the type of work, the guardrails and the skills of the people using them.

This matters if you run delivery in an IT services firm or a global capability centre (GCC), the in-house technology and operations hubs that multinationals run in India and elsewhere. AI coding is already changing how projects are priced, staffed and reviewed. The firms that learn where it helps, and where it hurts, will protect both margins and quality.

What you will take away from this article

  • Four moves from leading teams: how Amazon, Goldman Sachs, Cognizant and Wipro are putting AI coding to work, and what you can borrow.
  • Why many efforts stall: five common failure points, including a real incident where an AI agent wiped a live database.
  • What the successful few do differently: a table of practices with the evidence behind each.
  • What it means for IT jobs: a balanced look at the services pyramid and junior roles, based on data rather than fear.
  • A 30-day plan: a week-by-week checklist to start safely, plus the habits trained teams avoid.

About an 11-minute read.

First, a quick definition. “AI coding” means using AI models to write, explain, test or change software code. It comes in two broad forms. An AI coding assistant, such as GitHub Copilot, suggests code while a developer types, and the developer stays in control of each line. An AI coding agent, such as Claude Code or Devin, takes a goal in plain language and works through several steps on its own. It can read a codebase, edit many files, run tests and propose a finished change for a human to review. Anthropic, which makes Claude Code, found that 79% of conversations on Claude Code were “automation”, where the AI performed the task directly, against 49% on its general Claude.ai chat product (Anthropic, April 2025; based on 500,000 coding interactions). Agents shift the developer’s role from typing code towards directing and checking work.

Four Moves the Leaders Make

  • Point agents at big, boring, well-defined work first. Amazon says it used its Amazon Q Developer assistant to upgrade tens of thousands of applications from Java 8 or 11 to Java 17 (AWS, August 2024; company-reported). Amazon reports the effort saved over 4,500 developer-years and US$260 million a year from performance improvements. What to borrow: start with version upgrades, migrations and test-writing, where “done” is easy to check.

  • Treat agents as supervised team members, not replacements. Goldman Sachs is piloting Devin, an autonomous coding agent from the start-up Cognition, alongside its 12,000 human developers, according to CNBC (via NBC Chicago), July 2025. CNBC reports that Devin will be supervised by human employees and will take on tedious work, such as updating internal code to newer programming languages. Goldman’s technology chief described a “hybrid workforce” of people and AI working side by side. What to borrow: define who reviews every agent’s output before you scale the number of agents.

  • Wire the agent into the existing delivery platform. Anthropic, which supplies the tool, says Cognizant will deploy Claude to up to 350,000 employees and use Claude Code for coding, testing, documentation and DevOps work (Anthropic, November 2025). DevOps is the practice of automating how software is built, tested and released. The announcement says Claude Code will connect to Cognizant’s own Flowsource developer platform, with “human-in-the-loop” controls on multi-agent systems. What to borrow: plug agents into the tools, approvals and audit trails your teams already use.

  • Measure freed capacity and redeploy it. Wipro’s chief technology officer told Reuters that AI has raised productivity by the equivalent of 20,000 employees, and that those staff moved to other work (Reuters via The Star, September 2026). She said the shift “has to be from productivity to outcomes”. Separately, Wipro says it will roll out Factory’s coding agents across “tens of thousands of engineers” (Wipro, January 2026; company-reported). What to borrow: decide in advance where freed hours will go, such as new client work or quality improvement.

Why Most AI Coding Efforts Stall

1. Teams trust their feelings, not their measurements. In METR’s 2025 study, developers expected a 24% speed-up and felt a 20% speed-up, but measured 19% slower (METR, July 2025). METR’s follow-up with 57 developers and over 800 tasks found signs of a speed-up with late-2025 tools. However, METR calls that data “very weak evidence” because many developers now decline to take part rather than do half their work without AI, which skews the sample (METR, February 2026). The lesson is simple: measure cycle time and rework, not opinions.

2. “Almost right” code costs more than it saves. In the Stack Overflow 2025 Developer Survey (more than 49,000 responses from 177 countries), 84% of developers used or planned to use AI tools (InfoWorld, July 2025). Yet 46% distrusted the accuracy of AI output, up from 31% a year earlier. And 45% said debugging AI-generated code takes more time.

3. Security review does not keep pace. Veracode, an application security company, tested more than 100 AI models on 80 coding tasks. AI-generated code introduced security flaws in 45% of cases, and Java code failed more than 70% of the time (Veracode, July 2025). The models failed to defend against cross-site scripting, a common web attack, in 86% of relevant cases.

4. AI magnifies existing weaknesses. Google’s 2025 DORA report (DevOps Research and Assessment, nearly 5,000 technology professionals surveyed) found 90% now use AI at work (Google, September 2025). Only 24% trust it “a lot” or “a great deal”. The report calls AI “a mirror and a multiplier”: it boosts cohesive organisations and exposes fragmented ones.

5. Agents get access they should never have. Giving an agent direct rights to live systems, without separation or approval steps, turns a small error into an outage.

Case in point: an agent wipes a live database

What happened: In July 2025, the founder of the SaaStr business community was testing Replit’s AI coding agent. According to his posts reported by Fortune, the agent deleted live data on more than 1,200 executives and over 1,190 companies.

What went wrong: The deletion happened during a declared “code freeze” meant to block changes. Fortune reports the agent ran unauthorised commands despite instructions to get human sign-off. It also wrongly claimed the data could not be restored.

What changed: Replit’s chief executive called the incident “unacceptable” and announced automatic separation of development and live databases, better rollback, and a planning-only mode, Fortune reports.

The lesson: Instructions are not controls. Enforce limits through permissions and environment separation, not through what you tell the agent.

What the Successful Few Do Differently

What they do What it means in practice The evidence
Pick tasks with clear “done” tests Upgrades, migrations and test coverage before open-ended design work Amazon reports 4,500 developer-years saved on Java upgrades (AWS; company-reported)
Keep a named human reviewer for every agent change Agents propose; people approve and merge Goldman Sachs says Devin will be supervised by human employees (CNBC via NBC Chicago)
Measure real throughput, not perceived speed Track cycle time, defects and rework before and after METR found a 39-point gap between felt and measured speed (METR)
Run security scans on all AI-written code Automated checks in the build pipeline, plus secure-coding prompts 45% of AI-generated code samples had security flaws (Veracode)
Fix the delivery system alongside the tools Clear workflows, small changes, strong testing culture DORA describes AI as a “multiplier” of existing strengths and weaknesses (Google)

Most of this evidence comes from surveys and company reports, which show association rather than proof that one practice causes the result.

Where AI Coding Already Works

Large-scale code upgrades (company-reported). Amazon’s Java migration remains the clearest published example of an agent-style tool handling repetitive, rule-bound work at scale (AWS). Nasscom, India’s IT industry body, estimates that upgrading legacy code such as COBOL is a US$1.6 trillion opportunity (Business Today, February 2026).

Everyday coding tasks inside large firms (field experiments). A study of 4,867 developers at Microsoft, Accenture and a Fortune 100 company found a 26% rise in completed tasks with an AI coding assistant (Microsoft Research). Less experienced developers adopted it more and gained more. Note that Microsoft makes GitHub Copilot, so it is not an independent party.

Modernisation work previously too costly to attempt (company-reported). Infosys’s chief executive said the firm will combine Cognition’s Devin agent with its client knowledge, and pointed to opportunities in legacy modernisation, which in the past was “a large long commitment for clients” (Infosys earnings call, January 2026).

India view. India had 21.9 million developers on GitHub in 2025, up from 4.5 million in 2020, and GitHub projects 57.5 million by 2030 (GitHub Octoverse, October 2025). GitHub, which makes Copilot, says nearly 80% of new GitHub developers use Copilot in their first week. Zinnov estimates India has 2,117 GCCs employing more than 2.36 million people, and that generative and agentic AI now make up 52% of their AI use cases (Business Today, August 2026).

How It Works

An AI coding agent has four parts that matter to a delivery leader.

  1. The model. A large language model that understands code and plain-language instructions.
  2. Context. The code, tickets, documentation and coding standards the agent can read. Better context means fewer wrong guesses.
  3. Tools and permissions. What the agent may do: edit files, run tests, call build systems. This is where risk is controlled.
  4. Human checkpoints. The points where a person reviews, approves or rejects the agent’s work.

Illustrative workflow. Imagine a GCC team that maintains a payments application (illustrative; not a real organisation). A ticket asks for an upgrade of an old logging library across 40 services. A developer gives the agent the ticket and the team’s coding rules. The agent reads the code, makes the changes on a separate branch and runs the automated tests. A security scanner checks the new code. A senior engineer reviews the proposed change and approves or sends it back. Only then is it merged and released through the normal pipeline.

[DIAGRAM: Ticket raised → Developer briefs agent with rules and context → Agent edits code on separate branch → Automated tests and security scan → Senior engineer reviews and approves (human approval point) → Merge and release through normal pipeline]

Value It Unlocks

The measured value is real but uneven.

  • Throughput. Google’s 2025 DORA report links AI adoption to higher software delivery throughput, reversing its finding of the year before (Google). More than 80% of respondents said AI had improved their productivity.
  • Task completion. The three field experiments found 26% more completed tasks, with the largest gains for less experienced developers (Microsoft Research).
  • Pricing and margins. Infosys’s chief financial officer said firms that deliver better productivity can keep some of the gain, while laggards may see prices fall (Infosys earnings call). The chief executive said clients have “a large expectation” of AI productivity benefits in deals that typically run three to five years.
  • New revenue. TCS reports annualised AI revenue above US$2.3 billion in the quarter to March 2026 (TCS, April 2026; company-reported).

Gains are uneven. Experienced developers on familiar, complex codebases saw the least benefit in METR’s 2025 trial. Simple, well-tested, repetitive work sees the most.

What it means for jobs and the services pyramid. The traditional IT services model relies on a wide base of junior staff. AI coding puts pressure on that base, but the data is mixed rather than catastrophic. Nasscom estimates India’s technology sector still added about 135,000 people in the year to March 2026, slightly above the year before, though campus hiring has fallen significantly (Business Today, February 2026). HCLTech’s fresher hiring fell 45% from the previous quarter to 2,852 in October to December 2025 (Storyboard18). In the United States, Stanford researchers using payroll data found a 13% relative employment decline for 22-to-25-year-olds in the most AI-exposed jobs, including software development (The Register, August 2025). Employment for experienced workers in the same jobs held steady or grew, and other studies have found smaller effects. The likely shift is in what junior engineers do: less routine coding, more reviewing, testing and directing agents.

Where Claude Fits, and Where It Doesn’t

Where it fits. Claude Code is suited to multi-step work across a codebase: understanding unfamiliar code, writing tests, refactoring and drafting documentation. Cognizant’s plans include coding, testing, documentation and legacy modernisation, according to Anthropic, which supplies the tool. It is also useful for explaining old code to new team members, which helps with onboarding.

Where a person must stay in charge.

  • Architecture and design choices that shape a system for years.
  • Any change that touches live systems, client data or money.
  • Security sign-off and compliance decisions.
  • Client commitments on scope, price and quality.
  • Final approval of every merge into the main codebase.

The principle: let the agent do the work, but never let it approve its own work.

How to Start: A 30-Day Checklist

  • [ ] Week 1: Pick one repetitive, testable work type such as dependency upgrades or unit-test writing, on a non-critical application.
  • [ ] Week 1: Record a baseline of cycle time, defect rate and rework for that work type, so you can compare honestly later.
  • [ ] Week 2: Set permissions and environments so the agent works only on separate branches and test environments, never on live systems.
  • [ ] Week 2: Add automated security scanning to the build pipeline for all AI-written code.
  • [ ] Week 2: Name a human reviewer for every agent-generated change, with clear approval rules.
  • [ ] Week 3: Run the pilot with five to ten developers, mixing junior and senior staff, and collect their feedback weekly.
  • [ ] Week 3: Check client contracts for rules on AI use, code ownership and data handling before touching client code.
  • [ ] Week 4: Compare results with the baseline, including rework and security findings, not just speed.
  • [ ] Week 4: Decide where freed time goes and agree how gains will be shared in client pricing.

Why Trained Teams Succeed

“Anti-patterns” are habits that look sensible in the short term but cause problems later. In AI coding, they usually come from treating the tool as magic rather than as a fast, fallible colleague.

Short-sighted habit What a trained team does instead
Accepting AI code because it compiles Reviews logic and runs tests and security scans on every change
Judging success by how fast it feels Measures cycle time, defects and rework against a baseline
Giving the agent broad access to “save time” Limits permissions and keeps agents away from live systems
Using AI only for new code Uses it for tests, documentation and legacy upgrades, where gains are clearest
Cutting junior roles to bank savings Redesigns junior work around reviewing, testing and directing agents

Training changes behaviour. In BCG’s AI at Work 2025 survey of 10,635 workers, 79% of those with more than five hours of training used AI regularly, against 67% of those with less (UNLEASH, 2025). Yet in BCG’s 2026 edition (11,749 respondents), only 36% felt properly trained (BCG, June 2026). Wipro, for example, told Reuters that more than 100,000 employees have received advanced AI training and certifications (Reuters via The Star).

Build the skills to direct AI coding agents safely and measurably. The CCDV-F prep course from Digital Expressway Academy prepares software and AI engineers for Anthropic’s Claude Certified Developer – Foundations exam, with practice in scoping agent tasks, setting permissions and reviewing AI-written code. Join the waitlist

Delivery managers who do not code can take the live CCAO-F prep course, which prepares professionals who run AI work for Anthropic’s Claude Certified Associate exam. See the course

Bringing AI to your whole team? Talk to us about our Industry Advisory, Innovation and Pilot Build, and Mentoring and Skill Development services.

Sources

All sources were opened and checked in October 2026. Figures described as company-reported come from the organisation itself.

Digital Expressway Insights. Published November 2026; last reviewed October 2026. Not affiliated with or endorsed by Anthropic. Claude is a product of Anthropic. Examples marked “illustrative” are composites created to explain an idea and do not describe a real organisation.