Traditional consulting operates on a flawed assumption: that clients should pay for effort rather than results. This misalignment breeds inefficiency, as service providers profit from extended timelines while clients seek swift, effective solutions. Phaneesh Murthy identified this contradiction decades ago and pioneered results-oriented pricing structures that have since reshaped how the technology services industry approaches client partnerships.
The Financial Logic of Results-Driven Contracts
Murthy’s revelation about outcome-based pricing stemmed from observing persistent client dissatisfaction with hourly billing models. “Business outcomes means ‘I won’t charge you for effort, but I’ll charge you for the results,'” he explained during his transformation of iGATE’s business model. This approach fundamentally realigns provider incentives with client success.
The economic rationale becomes apparent through practical application. In 2002, Murthy’s team addressed mortgage processing inefficiencies that plagued American banks. Traditional processing cost institutions approximately $2,300-$2,400 per loan due to manual, paper-intensive workflows. By implementing technology-driven solutions and charging $1,000 per completed loan rather than hourly rates, his team demonstrated how results-based pricing benefits both parties.
This model naturally incentivizes efficiency improvements. Service providers must optimize their operations to maintain profitability, while clients pay exclusively for successful deliverables. The partnership dynamic emerges from this shared interest in outcomes rather than activities.
Consumer behavior supports this approach. “Would you ever go to an auto mechanic and show your car to him and be comfortable when he or she turns around and said, ‘I’ll just charge you so many bucks an hour until I figure out what the problem is?'” Murthy asks, highlighting how outcome-based pricing reflects natural consumer preferences for predictable, results-oriented transactions.
Execution Challenges and Strategic Solutions
Despite its logical appeal, implementing outcome-based pricing requires sophisticated risk management and precise outcome definitions. Murthy’s experience reveals several essential success factors for making this model function effectively.
The primary challenge involves identifying measurable business outcomes that both parties can influence and monitor. During the mortgage processing engagement, Murthy’s team discovered they could enhance “pull through rates”—the percentage of approved loan applications that actually close. By applying data analysis to understand that processing speed, not just product features, drove client selection, they increased pull through rates from 38-40% to 60%.
This improvement generated multiplicative value for both organizations. “We were doing all of the work anyway for 100 loans earlier, earlier we were getting paid for 40, now we’re getting paid for 60,” Murthy explained. Banks experienced similar benefits, extracting value from 60% of their processing efforts instead of 40%, demonstrating how properly structured outcome-based contracts can generate mutual gains.
Risk management becomes essential in results-based engagements. Service providers must possess sufficient confidence in their ability to influence outcomes and adequate financial resources to absorb potential losses during optimization periods. Murthy’s teams invested extensively in understanding client businesses, with one notable behavioral change being that new hires from the Indian IT industry initially struggled with the model.
Current Applications in Contemporary Advisory Work
Murthy’s current advisory work through Primentor continues emphasizing outcome-based relationships with clients. Rather than charging traditional consulting fees, he frequently takes equity positions in the companies he advises. “I work with these companies, and I take an equity position in these companies that I’m working with. And over the course of a few years, the company has changed its trajectory, has become a lot more valuable,” he states.
This approach has attracted attention from major technology firms. In June 2024, InfoBeans appointed Murthy as Advisor to the Board to help achieve their long-term growth objectives. The company specifically sought his expertise to position itself as a sought-after global presence in digital engineering and AI spheres. “We are super excited to leverage Phaneesh’s vast experience and expertise in transforming enterprises,” stated Mitesh Bohra, InfoBeans Co-founder.
Earlier in April 2024, CriticalRiver appointed Murthy as a Senior Executive Advisory Board Member, where his extensive experience in spearheading growth and operational excellence provides strategic direction and insights.
The evolution of outcome-based models extends beyond traditional IT services. Industrial applications demonstrate the principle’s versatility. Companies like GE have used outcome-based approaches in manufacturing settings, where they optimize clients’ fuel consumption and take a percentage of the savings achieved.
Technology Infrastructure and Market Evolution
The outcome-based pricing model that Murthy pioneered has evolved significantly as technology capabilities have advanced. Modern applications extend beyond process optimization to encompass digital transformation, artificial intelligence implementation, and comprehensive business strategy consulting.
Technology infrastructure has been essential for enabling these models. When Murthy started in 2002, cloud computing was nascent. The emergence of cloud platforms from Google, Microsoft, and Amazon has made outcome-based services more viable by reducing infrastructure investment requirements and enabling rapid scalability.
Current implementations often involve sophisticated integration of multiple technologies. “The idea is to integrate a whole bunch of services—AI, analytics, cloud, software, business processes—and offer it to the client,” Murthy explained at a recent conference. This integrated approach allows service providers to take responsibility for comprehensive business outcomes rather than discrete technical deliverables.
The shift requires different organizational thinking and talent management approaches. Incentive structures must change from resource management to outcome optimization. This fundamental reorientation from managing resources to optimizing results represents a significant cultural shift for traditional service organizations.
Recent analysis from Andreessen Horowitz indicates that AI is driving a dramatic pricing shift across software companies, with many transitioning from traditional models to outcome-based structures. This trend validates Murthy’s early insights about the economic logic of results-based pricing. Organizations like InfoBeans and CriticalRiver, where Murthy serves in advisory capacities, are positioning themselves to capitalize on this shift by integrating comprehensive technology solutions with measurable business outcomes.
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