In an interaction with Adlin Pertishya Jebaraj, correspondent of Finance Outlook India Magazine, Rajeev Ranjan, Chief Operating Officer of Ness Digital Engineering, shares how today’s enterprise customer expects outcome-driven, AI-first, Pod-driven engagement frameworks which can be achieved through a data-led governance method and by collaborating to create new products using a combination of these methodologies.  

With over 30 years of extensive global experience, Rajeev has a proven track record in running large scale global delivery organizations as well as launching new business units in new markets. At Ness, Rajeev is responsible for leading the global delivery across Engineering as well as Industry Consulting, institutionalizing Engineering Excellence, implementing best in class Talent Supply Chain management and driving Digital Transformation within Ness.   

What emerging customer expectations are influencing enterprise deal structures today? 

In today’s market, enterprise deal structures are increasingly shaped by five evolving customer expectations: 

Outcome-Based Value over Effort-Based Billing 

• Customers are moving away from T&M models toward productivity, velocity, and business impact-based constructs. 
• They expect providers to commit to improving metrics rather than just capacity fulfillment. 
• Engineering is being treated as a strategic value driver, not as a cost center. 

AI-Infused Delivery with Demonstrable RoI 

• Enterprises expect AI-first solutions, not just AI adoption. 
• GenAI and Intelligent Engineering capabilities must be embedded into the engagement model from Day 1. 
• Deals now incorporate accelerator-led delivery, AI-readiness assessments, and co-developed AI use cases with measurable impact milestones (e.g., cognitive automation, decision augmentation). 

Flexible, Scalable, Pod-Based Engagements 

• Customers want ready-to-deploy engineering pods with cross-functional capabilities instead of rigid FTE-based models. 
• Composable team structures (product + design + engineering + data + automation) with elastic scalability based on product lifecycle and release velocity. 
• Expectation of “speed to squad” vs long ramp-up cycles. 

Industry-Relevant Governance & Transparency 

• Clients demand data-driven engineering governance, not just project updates. 
• Real-time intelligence on productivity, code quality, DevEx metrics, and platform maturity using tools like Ness Atonis. 
• Expect co-ownership of transformation goals (e.g., shift to product ops, DevSecOps maturity, Agile health). 

Co-Innovation & Capability Transfer 

• Deals increasingly include joint innovation constructs (e.g., Innovation Studios, Engineering Centers, GenAI Universities) and knowledge transfer mechanisms 
• Customers expect the partner to upskill their teams, build autonomous capabilities, and reduce long-term reliance on external partners. 
• A shift from delivery ownership to shared competency build. 

In short, enterprise customers no longer want “delivery partners”. They want competency partners that accelerate their engineering transformation, embed AI across lifecycle, improve productivity, and build future-ready capabilities inward while sharing risk and upside. 

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What methods do you use to forecast revenue and cost projections for multi-year enterprise contracts? 

While customer expectations are evolving and flexibility is now the norm, this means revenue forecasting must be more adaptive. 

For instance, at Ness, we use both bottom-up and top-down models. Bottom-up forecasting considers detailed assumptions like product line, delivery phases, FTE allocation, onboarding and infrastructure needs, and total resource consumption. 

Then apply top-down forecasting using historical patterns and similar customer profiles. We run scenario analyses (base, aggressive, conservative) and conduct sensitivity testing on key drivers such as adoption rate and cross-sell potential. 

This approach gives us a realistic, risk-adjusted revenue and cost forecast. 

How do you quantify and mitigate financial risks associated with single-client dependency? 

We assess financial risk from single-client dependency by tracking revenue share, contract duration, renewal likelihood, margins, and the client’s overall health. We also run stress tests such as reduced volumes or delayed ramp-ups to understand the impact on profitability and cash flow. 

To mitigate this, we build flexible deal structures, diversify services, and secure multi-year commitments linked to transformation goals. Internally, we reduce dependency by expanding across geographies, industries, and offerings, and by using delivery pods that can be redeployed. In some cases, we apply productivity-based pricing or risk-sharing models to protect margins. Strong account governance and early warning signals help us identify risks early and act before business is impacted. 

How do you calculate the break-even point for a long-term partnership with a high initial investment? 

To carry out long-term partnerships with high initial investment and calculate the break-even point, one can compare upfront and transition costs with projected cash inflows over the contract duration. This includes factors such as team ramp-up, expected productivity gains, automation-led efficiencies, and the transition from build to run phases. 

Assess multiple scenarios like conservative, baseline, and accelerated to estimate when cumulative returns are likely to surpass total investment. Projections are refined using inputs like resource optimization, throughput improvements, pricing shifts, and potential expansion. In some cases, break-even is aligned to value delivery milestones or capability maturity instead of purely financial makers as returns often increase over time in transformation-led engagements. This helps ensure balanced investment pacing and sustainable value realization. 

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What financial metrics or KPIs do you prioritize when assessing whether a large deal is strategically viable?  

When evaluating the strategic viability of a large deal, we should prioritize financial metrics that balance profitability, resilience, and long-term growth potential. Key KPIs include: 

• Gross margin and EBITDA impact across the deal lifecycle 
• Revenue concentration and dependency risk (typically measured as % of total portfolio) 
• Cost-to-serve and productivity improvement trajectory, especially AI-led efficiency gains 
• Time to break-even and long-term ROI, including upsell potential 
• Cash flow predictability and investment recovery curve 
• Engineering throughput and value realization milestones, treated as leading indicators of financial performance 

This allows assessing softer strategic value metrics such as platform expansion potential, co-innovation opportunities, and alignment with our AI-first engineering capabilities. A deal is considered viable when it demonstrates sustainable margin growth, manageable risk exposure, and a clear pathway to transformational impact, not just revenue scale. 

How do you foresee the financial modeling of large, multi-year deals evolving over the next decade?  

Over the next decade, financial modeling for multi-year deals will move from static, cost-based projections to dynamic, value-linked frameworks. Instead of pricing based on effort, clients will expect models tied to business outcomes, AI-driven productivity gains, and engineering velocity. These models will build efficiency curves, factoring in the impact of automation and generative AI, which gradually reduces manual effort. 

Forecasting will also become more adaptive with scenario-based models that adjust based on platform maturity, product roadmap changes, and real-time delivery performance. Deal structures will increasingly include risk–reward mechanisms linked to transformation of milestones and capability building, rather than just service delivery. 

For example, a five-year contract may start with traditional delivery pricing but gradually shifts to outcome-based fees like a bonus tied to a 30% reduction in release cycle time driven by AI. As automation scales, the cost base reduces, and a portion of the savings is shared with the client, balancing value creation and profitability. 

In short, financial modeling will become more data-driven, predictive, and value-focused, evolving alongside technological maturity and customer transformation goals.