Date: 22 August 2026
Primary Source: Reuters
Topic: Artificial Intelligence, Business Models, Professional Services, Productivity
For decades, much of the global service economy has relied on a remarkably simple commercial equation:
time × people × hourly rate = revenue.
Artificial intelligence is beginning to break that equation.
India's enormous IT services industry is becoming one of the clearest real-world laboratories for what may come next. Companies including Tata Consultancy Services, Infosys, Wipro, HCLTech and Cognizant are increasingly moving away from contracts based primarily on hours worked and towards agreements where payment depends on measurable outcomes, productivity improvements or cost savings.
This may look like a change in how technology consultants invoice their clients.
It could become something much bigger.
AI is beginning to separate the amount of work performed from the amount of value created.
And once that happens, charging for time becomes increasingly difficult to defend.
India's $315 Billion Warning Signal
India's technology services industry generates roughly $315 billion in annual revenue, according to industry body Nasscom. It was built partly around a labour-intensive outsourcing model in which large workforces were an important competitive advantage.
AI is changing that advantage.
TCS chief executive K. Krithivasan told Reuters that around 80% of contracts within the company's finance, human resources and other business-services operations are now tied to performance outcomes.
According to Reuters, that proportion has roughly doubled since generative AI moved into the mainstream in late 2023.
Other contracts are becoming even more explicit.
A Cognizant agreement with Daimler Truck reportedly includes a mechanism where AI-related cost savings are shared between customer and supplier.
In another example, an HCLTech cloud-management agreement with German utility E.ON was structured so that HCLTech receives no payment during the first year, with later payments linked to efficiency gains and specific business outcomes.
This is a fundamentally different commercial relationship.
The supplier is no longer simply saying:
We provided 10,000 hours of work.
It increasingly has to say:
We reduced your costs, accelerated your processes or produced a measurable result.
From Hours to Output to Outcomes
The transition can be understood as three economic stages.
Pay for hours → Pay for output → Pay for outcomes
Traditional consulting and outsourcing largely monetised input.
Software-as-a-Service shifted much of technology towards subscriptions and access.
AI could push parts of the economy further towards performance itself becoming the unit of sale.
Deloitte has already examined outcome-based pricing specifically for agentic AI software. Its June 2026 guidance describes models where customers pay for successful outcomes such as resolved customer-service cases, completed transactions or prevented errors rather than merely paying for access to the software.
That is important.
It means outcome pricing is no longer simply an experimental consulting concept.
The commercial infrastructure needed to support it is beginning to emerge around AI products themselves.
PwC similarly argues that traditional fixed-price and per-seat models may not adequately capture the economics of AI and that pricing metrics increasingly need to correspond directly with customer outcomes.
The implication is profound:
AI does not merely automate production. It can change the unit in which economic value is sold.
The Productivity Paradox
For businesses built around billable hours, AI creates an uncomfortable paradox.
Imagine a consulting assignment that previously required ten people working for four weeks.
If AI allows five people to complete the same assignment in two weeks, productivity has increased dramatically.
But under an hourly billing model, the supplier may also have destroyed much of its own billable revenue.
The customer will hardly volunteer to pay for hours that were never worked.
This creates a structural conflict between productivity and revenue.
The better the technology becomes, the weaker the traditional pricing model can become.
Reuters reports that clients of mid-sized Indian technology provider Persistent Systems are already asking for equivalent work at 25–30% lower prices, while simultaneously expecting faster delivery and higher productivity.
AI productivity is therefore not automatically profit.
The crucial question is:
Who captures the productivity dividend?
The customer?
The technology provider?
The AI platform?
Or some combination of all three?
That question could become one of the most important determinants of corporate margins during the next stage of AI adoption.
Scale Is Being Repriced Too
There is another signal hidden inside the Indian IT story.
For decades, having tens or hundreds of thousands of employees demonstrated scale.
Large outsourcing companies could take on enormous contracts because they possessed something smaller competitors could not easily replicate: labour capacity.
AI reduces the scarcity of that capacity.
Reuters reports that smaller competitors such as Persistent Systems and Coforge are increasingly winning business through faster pilot programmes, flexible pricing and rapid deployment of senior staff. Persistent's dollar revenue increased around 16% in the April-June quarter while Coforge grew by roughly one-third. Large competitors including TCS, Infosys, Wipro and HCLTech recorded growth closer to 1–3%.
That does not mean the giants will disappear.
They retain enormous advantages in client relationships, infrastructure, expertise, data, reputation and global delivery.
But AI may reduce the value of one traditional advantage:
headcount itself.
The business that can produce the most valuable outcome with the smallest effective resource base may increasingly outperform the business with the largest workforce.
A New Metric: AI Leverage per Employee
This suggests that investors may eventually need different ways of assessing service companies.
Revenue per employee has long been useful.
But AI may create a more revealing concept:
AI leverage per employee
In simple terms:
How much economically valuable output can each human employee produce when amplified by AI?
Two companies could employ 10,000 people.
One might use AI mainly to reduce administrative work.
The other might redesign workflows, automate production, accelerate decision-making and create products that operate continuously without proportional increases in staffing.
Those companies may have completely different economics despite looking similar through traditional workforce metrics.
The critical measures could therefore increasingly become:
output per employee
gross profit per employee
AI-assisted revenue per employee
cost savings delivered to customers
and ultimately:
the share of AI productivity gains that the company is able to retain as profit.
The companies with the most AI may not necessarily win.
The winners may be those with the best mechanism for capturing the value created by AI.
The Risk of Promising Too Much
Outcome-based pricing also transfers risk.
A company charging by the hour gets paid for performing work.
A company charging for an outcome gets paid when the outcome occurs.
That distinction can become dangerous when competitors begin making aggressive assumptions about future AI productivity.
Tech Mahindra chief executive Mohit Joshi warned that some competitors were building contracts around expected productivity improvements of 70–80% over five to seven years, sometimes while guaranteeing pricing despite uncertainty around future technology costs.
Tech Mahindra has declined to accept some of those risks, while Infosys has also said it walked away from contracts that were no longer economically viable.
Outcome pricing therefore creates a new competitive discipline.
Companies need to know what their technology can actually deliver.
Overestimate AI productivity and margins can collapse.
Underestimate it and competitors may take the contract.
The ability to measure AI productivity accurately could therefore become almost as important as the AI technology itself.
The Service Economy Could Be Next
India's technology-outsourcing sector is unusually exposed because its traditional economics are so directly connected to billable labour.
But the underlying tension exists elsewhere.
Legal services provide a good example.
AI can conduct research, analyse documents, draft contracts and summarise large volumes of information far more quickly than traditional workflows. Thomson Reuters has described AI as directly challenging the economics of the billable hour, although the model remains deeply embedded within the legal industry and is unlikely to disappear overnight.
Accounting, marketing, recruitment, design, financial analysis and management consulting face variations of the same question.
If an AI-assisted professional can complete a task in one hour that previously required ten, what exactly should the customer pay for?
One hour?
Ten hours?
Access to the AI?
The completed task?
Or the economic value generated by the result?
There is no universal answer yet.
But the direction of travel is becoming clearer.
AI Could Reprice Knowledge Work
Much of the discussion around artificial intelligence still centres on whether AI will replace jobs.
That may be too narrow.
The larger economic transformation could be that AI changes how knowledge work itself is measured, purchased and valued.
Industrial automation changed the economics of manufacturing by reducing the labour required for each unit of production.
AI may do something similar to intellectual production.
But services have an additional complication.
Many professional businesses historically monetised the labour itself.
If labour ceases to be the best measure of production, the commercial model must eventually adapt.
That is why the developments inside India's IT industry matter far beyond India.
They provide an early glimpse of a service economy where businesses may increasingly compete on:
results rather than resources
capability rather than headcount
speed rather than hours
and
value created rather than effort expended.
The Hikari Nova View
The biggest AI story may ultimately not be another chatbot, model release or coding assistant.
It may be the gradual rewiring of business economics underneath them.
The first phase of enterprise AI adoption was about experimentation.
The second was about productivity.
The next phase may be about value capture.
Who gets the benefit when AI makes work dramatically cheaper?
That is where pricing strategy becomes strategic.
Companies able to convert AI productivity into scalable margins, new revenue structures and defensible outcome-based business models could gain an advantage far greater than the efficiency improvements alone.
Conversely, companies that automate their work while continuing to sell the same old unit of labour may discover an uncomfortable truth:
AI can make a company more productive while simultaneously making its existing business model less valuable.
The billable hour is not disappearing tomorrow.
But its economic logic is beginning to weaken.
And that may be one of the most important business signals of the AI era.
AI-Powered Sentiment Analysis
Sentiment Score: 8.4 / 10 — Strongly Transformative
The development is positive for productivity and innovative service providers, but disruptive for businesses whose economics remain dependent on labour volume.
Financial Sentiment: Mixed Positive
AI creates major opportunities for margin expansion and operating leverage, while simultaneously creating pricing pressure and revenue deflation for traditional service providers.
Polarity Score: +0.48
The long-term opportunity outweighs the disruption, although competitive redistribution between companies could be significant.
Subjectivity Score: 0.36
The core shift is supported by observable changes in major commercial contracts, while the extent to which outcome-based pricing spreads across the broader service economy remains an analytical projection.
Strategic / Market Structure Perspective
Signal Strength: ★★★★★
The important investment signal is not simply increased AI adoption.
It is the emergence of a new competitive variable:
AI leverage × value capture.
Investors may increasingly need to distinguish between companies that merely use AI to reduce costs and companies capable of converting AI productivity into superior economics.
The strongest businesses may not be those employing the most people.
They may be those capable of creating the greatest measurable customer value with the least incremental human effort.
That would represent a fundamental change in the economics of professional services.
Read More
Reuters: AI reshapes India's IT services sector contracts as clients demand more for less
Deloitte: Accounting for Outcome-Based Pricing in an Agentic AI Software Product
PwC: The Future of Monetization: Redefining Customer Value with AI and Consumption
Thomson Reuters: From Billable Hours to Value-Driven Legal Services
Disclaimer
This article is for informational and educational purposes only and does not constitute investment, financial or trading advice. Market conditions, technologies and business models can change rapidly, and past or projected performance does not guarantee future results.