# The End of the Billable Hour: How AI Is Changing Software Services URL: https://zippiai.com/blog/the-end-of-the-billable-hour-how-ai-is-changing-software-services Author: ZippiAi Team Published: 2026-08-25T15:09:25.768Z Picture a mid-sized retailer that decides to rebuild its inventory system. A few years ago, this was a familiar kind of project: a services firm would assign 30 or 40 developers, testers and project managers, and the work would run for nine months or more. The client would receive a monthly invoice based on the number of people on the team and the hours they logged. Today, the same project looks different. AI coding assistants can draft large parts of the code, generate test cases, write documentation and suggest fixes for bugs. A smaller team, working with these tools, can often deliver a comparable result in a fraction of the time. That is good news for the client. But if the work takes half as long and the invoice is based on time, the provider earns half as much for the same value. This is why the billable hour, the industry’s foundation for decades, is now under real pressure. ## How Software Services Traditionally Made Money The classic model is simple to describe: Developers → Hours → Projects → Client Billing. A services company hires engineers, assigns them to client projects, and bills for their time. The most common arrangement, “time and materials”, means the client pays an agreed rate per hour or day. Even fixed-price contracts were usually estimated the same way: how many people, for how long. This worked for decades because it was easy to measure and audit, revenue grew in step with headcount, and for offshore providers, especially in India, the cost gap between a developer in Bengaluru and one in Boston made hourly rates very attractive to clients. The model also encouraged the “pyramid”: a large base of junior engineers doing routine work under fewer senior people. More juniors meant more billable hours. ## What AI Is Changing AI tools now assist with most of the day-to-day work of a software team: • **Coding:** assistants generate boilerplate, suggest functions and translate between languages. • **Testing:** tools write unit tests and generate test data automatically. • **Debugging:** AI can read error logs, locate likely causes and propose fixes. • **Documentation:** code comments, API references and user guides can be drafted in minutes. • **Customer support:** chatbots and agents handle first-line tickets and route the rest. • **Research:** engineers can quickly explore unfamiliar libraries, standards or codebases. • **Maintenance:** routine upgrades, dependency updates and monitoring can be partly automated. None of these removes the need for a human engineer. Together, they change how much a team can finish in a week. Wipro’s CEO, Srini Pallia, said in January 2026 that AI-assisted software development would cost about 25% less, with the biggest gains in coding and testing. Persistent Systems CEO Sandeep Kalra told Reuters in August that clients are asking for the same work at 25% to 30% lower cost, delivered faster. When the same team can do 25% to 30% more, hours stop being a good measure of value delivered. ## The Problem With Hourly Billing Here is the problem in one sentence: if a team finishes a six-month project in three months because of AI, should the client still pay for six months of work? Under time-and-materials billing, the answer is no, and the provider’s revenue falls. Under a fixed price built on old estimates, the provider keeps the gain but the client feels overcharged. Neither is stable, so contracts are moving toward three alternatives: • **Fixed-price projects:** the client pays an agreed amount for a defined scope, regardless of hours. The provider carries the delivery risk but keeps any efficiency gains. • **Outcome-based pricing:** fees are tied to measurable results, such as tickets resolved, cost reduced or systems migrated on time. • **Value-based pricing:** the price reflects the business value created, for example a share of the savings the new system produces. This shift is already visible in real contracts. A Reuters report published on 20–21 August 2026 and widely carried by Indian business media found that TCS, Infosys, Wipro, HCLTech and Cognizant are increasingly tying fees to performance outcomes rather than hours worked. According to the report, TCS CEO K. Krithivasan said about 80% of contracts in the company’s business-services segment are now based on outcome measures, roughly double the share before generative AI went mainstream in late 2023. [](https://medium.com/choose?source=promotion_paragraph---post_body_banner_no_hate--9b9d5ffdfcb8---------------------------------------) The same report described a Cognizant deal with Daimler Truck in which AI-related savings are split between vendor and client, and an HCLTech agreement with utility E.ON in which the provider is unpaid in year one, with later payments linked to efficiency gains. This is what “paying for outcomes” looks like in practice. ## What This Means for IT Companies India offers the clearest view of this transition, because its IT services industry, worth roughly $315 billion a year, was built on the hourly model. Investors have noticed: the Nifty IT index fell by about a fifth in 2026, with its ten members losing a combined $73 billion in market value, according to Reuters. The pressure shows up in deal economics. HCLTech CEO C. Vijayakumar told analysts earlier this year that a contract once worth $100 million might now be worth around $80 million, while requiring 25% to 30% more effort to win. Infosys has said it walked away from contracts that were no longer economically viable. The large firms are repositioning around AI-led managed services. On 18 August 2026, Infosys announced a long-term partnership with German manufacturer Knorr-Bremse to run its enterprise applications using generative and agentic AI, with an explicit focus on measurable outcomes. TCS is embedding more engineers with clients and looking at AI acquisitions. Mid-sized firms have gained ground. Persistent Systems and Coforge have grown revenue by double digits for at least eight consecutive quarters, while the four largest Indian firms grew 1% to 3% in the June 2026 quarter. When AI automates routine work, a large employee base is no longer the advantage it once was. None of this is unique to India. Global providers face the same client questions; India is simply where the hourly model was most concentrated, so the change is most visible there. ## What Happens to Developers? It is tempting to read this as “AI means fewer developers.” The reality is more nuanced. Some reduction in entry-level hiring is real: TCS cut more than 12,000 roles in 2025, and former Infosys CFO V. Balakrishnan has said the pyramid model of large junior cohorts is ending because coding agents now handle basic coding. But the nature of the work is changing more than the total need for skilled people. Developers are spending: • Less time on repetitive coding, boilerplate and manual testing • More time on system design, architecture and integration decisions • More time directing and reviewing AI-generated code, rather than writing every line • More time on problem-solving: understanding what the client actually needs • More time building business and domain knowledge, because that is what AI cannot supply The engineer who understands insurance claims or retail supply chains, and can guide AI tools to build the right thing, becomes more valuable, not less. ## A New Software Services Model Put simply, the industry is moving from selling “100 developers for 12 months” to selling “we will deliver this specific business outcome.” A few realistic examples: • **Claims processing modernisation.** Instead of billing hours to rebuild an insurer’s claims system, the provider commits to reducing average processing time from ten days to three, with fees partly linked to hitting that target. • **Legacy migration at a fixed price with shared savings.** A bank pays a fixed fee to move a core system to the cloud, and the provider receives a percentage of the infrastructure savings for the next three years. • **AI-run application support.** A manufacturer pays a flat annual fee for its enterprise applications to be maintained, with AI handling routine tickets and the provider committing to defined service levels and year-on-year cost reductions. Each requires the provider to understand the client’s business well enough to define and measure the outcome. That is a different skill from staffing a project. ## The Bigger Opportunity There is another side to this story. When something becomes cheaper and faster to build, people build more of it. Many software projects never happened because they cost too much or took too long: a hospital chain’s custom scheduling tool, a logistics company’s bespoke routing system. At the old cost, the business case did not work. At a lower cost, it does. Wipro’s Pallia made this point in January: cheaper AI-assisted development will translate into new and more projects, which is why he does not expect IT budgets to shrink over the long run. Persistent’s Kalra has said AI is helping his firm win larger deals than before. So total demand for software may grow even as the price of any single project falls. The revenue moves to whoever can deliver outcomes efficiently. ## Conclusion AI is putting real pressure on the billable-hour model. When a team can deliver more in less time, charging by the hour stops making sense for either side, and contracts are already shifting toward fixed prices, outcomes and shared value. The Indian IT majors are living through this adjustment in public, with slower growth, deflating deal sizes and a rethink of their hiring pyramids. At the same time, cheaper software creates more demand for software, and clients still need people who understand their business and can turn AI capability into working systems. The firms that combine AI tools, experienced engineers and deep domain knowledge are well placed to grow. The ones that keep selling hours are the ones with the most to worry about.