IT services company Mphasis stated that as the number of artificial intelligence (AI)-based deals increases, clients are becoming more receptive to outcome-based contracts rather than solely relying on time and materials models. Nevertheless, such agreements currently constitute only a small portion of the company's revenue.
The mid-sized IT services firm Mphasis reported that clients are showing a greater willingness to pay for results, not just for time and resources spent, due to the rise in AI-managed deals. These deals focus on improving productivity, achieving efficiency, and enhancing overall metrics.
Nitin Rakesh, CEO and Managing Director, told Business Standard after the release of the first quarter results that convincing clients to switch to outcome-based payment in the traditional procurement cycle is difficult because it is hard to define, and buyers are unaccustomed to this format. However, in certain cases related to business outcomes, it becomes more acceptable because the buyer is not a procurement or IT procurement specialist, but the business specialist itself.
When asked about the pace of outcome-based pricing, he noted that the next few quarters will allow for a better understanding of the situation's development. He emphasized that the general direction is obvious even to clients: 'If possible, I would like to pay for the result, not for the input or output capacity.'
In May of this year, Mphasis launched its Mphasis Tria platform, which will operate exclusively on an outcome-oriented pricing model. Rakesh had previously stated at a media briefing that the 2027 fiscal year will be foundational for implementing this system among clients, and starting from the 2028 fiscal year, the company aims to secure recurring annual revenue.
As of June 30, time and materials contracts accounted for 46.5 percent of the company's projects, down from 72 percent at the end of March 2020. Fixed-price projects increased to 44.3 percent, compared to 28 percent in the same period. Transactional projects account for 9.2 percent, typically fluctuating between 8 and 13 percent.
Rakesh added that outcome-based pricing differs from output-based pricing, which includes, for example, cost reduction, reduced exception handling time, or achieving specific accuracy levels across all processed transactions. He explained that when measuring the result, regardless of whether 100 or 10 people are required to perform the work, the company strives to create appropriate commercial structures.
His remarks align with the view of Salil Parekh, CEO of Infosys, who also noted increased client interest in similar pricing models, though this has not yet become a significant part of the company's business. He mentioned last week that discussions on this topic are becoming more active, especially when large investments are required for transformation, but the company is not adopting this model en masse.
Mphasis' total value of won contracts for the quarter was $461 million, which is 13 percent higher compared to the previous quarter, but 39 percent lower compared to the same year last year, as the company secured some large deals last year. Furthermore, 63 percent of these wins were related to AI.
Mphasis clarified that the AI-related wins concern not only cost savings but also specific improvements. The company noted that even if a financial benefit is provided, the conversation quickly shifts to how exactly it will be implemented: 'It's good if you give me a 20-30 percent discount, but can I reduce the number of cyber incidents by 50 percent or the volume of requests by 50 percent?'
In the first quarter, the company's net profit grew by 10.8 percent, reaching 4,895 million rupees, and revenue increased by 17.5 percent, totaling 43,841 million rupees. The growth was driven by banking, financial services, and insurance businesses, which grew by 8.5 percent and 17.6 percent, respectively, in constant currency. The operating margin decreased by 50 basis points to 14.8 percent.
Insurance companies are actively expanding the use of artificial intelligence (AI) at all stages of the insurance cycle. The goal of this step is to automate workflows, strengthen underwriting and claims management, increase customer engagement, and detect fraud. According to annual reports, the implementation of AI contributes to increased operational efficiency, improved productivity, and reduced task completion times.
ICICI Prudential Life uses AI throughout the entire customer journey—from lead generation and underwriting to policy issuance and servicing. During the financial year 26, the company utilized artificial intelligence, machine learning, and deep learning to drive cross-selling, improve consultant activation, reduce campaign costs, strengthen the onboarding process, detect fraud, enhance payment collection, prevent incorrect sales, and accelerate claims processing. In that year, over 96.8 percent of customer interactions were completed through digital self-service channels.
HDFC Life moved beyond traditional automation by creating a corporate generative AI platform, enabling a shift to an operations model based on data, cloud technologies, and AI. The company integrated AI into underwriting, claims settlement, fraud detection, and customer service processes to automate checks, improve decision-making, and reduce investigation time. In its annual report, the company noted that 'AI-based tools and automation have improved service accuracy, reduced turnaround times, and enhanced the effectiveness of resolving issues in service and claims processes.'
An AI-powered chatbot allows frontline employees to resolve operational and underwriting queries in real-time, while a digital sales assistant offers personalized product recommendations, instant calculations, and application tracking. Furthermore, HDFC Life uses AI for automatic cross-verification of data from multiple sources, which enhances underwriting efficiency and fraud detection.
The state-owned insurer Life Insurance Corporation of India (LIC) is also expanding the use of AI in customer service and operational activities. LIC has implemented AI-supported voice communication systems for notifying timely claim payouts and communications related to National Electronic Funds Transfer (NEFT) registration, and is extending this solution to reminders for premium payments. The company is also implementing an AI-based data extraction system from application forms to improve processing accuracy, reduce manual intervention, and speed up policy issuance. Additionally, LIC is launching an AI-powered email analytics platform for intelligent classification of customer emails, prioritizing complaints, and improving response efficiency. In its annual report, the insurer stated that further investments in AI-based analytics, personalization, and ecosystem integration will facilitate the next stage of its digital transformation.
The general insurer ICICI Lombard has also increased the adoption of AI in policy issuance, underwriting, claims settlement, and customer service processes. The company reported that initiatives such as One IL One Call Centre utilize AI, voice bots, and propensity modeling to provide personalized customer interactions, while Project Orion helps restructure business processes using a digital technology-oriented approach. The insurer added that Project Orion has implemented AI-based solutions to improve turnaround times and decision-making across the entire value chain.
During the financial year 26, the company expanded the use of generative AI from pilot projects to daily operations in underwriting, claims settlement, servicing, marketing, and overall enterprise performance. Generative AI was deployed in medical insurance processing and operations to reduce turnaround times, while maintaining human oversight. In the auto insurance sector, the technology is applied in specific functions of underwriting, claims settlement, and servicing.
Sanjiv Mantri, Managing Director and CEO of ICICI Lombard, stated in the annual report: 'Our query resolution using technology has improved from 30 percent previously to nearly 70 percent of total service requests. Our technology operations have shifted to a more predictive model... The principle governing all of this remains unchanged: Technology must augment human judgment, not replace it.'
