Global engineering services firm Coforge released a white paper on September 18, 2026, identifying a significant disconnect between artificial intelligence ambitions and realized value creation in the private equity sector.

The report, titled AI in Private Equity: Driving Value Through AI Execution, argues that while AI has become embedded in investment theses, many firms struggle with execution. This operational challenge is widening the gap between expected outcomes and measurable business results for investors seeking projected returns.

Strategic Focus on Execution

The publication outlines a structured playbook for operationalizing AI across the investment lifecycle. Key components include AI-based due diligence, repeatable execution programs across portfolio companies, and portfolio-level governance capabilities designed to improve visibility and accountability.

Preeti Singh, Executive Vice President and Head of North America at Coforge, stated that AI is no longer an optional upside but a core assumption underpinning revenue growth and margin expansion expectations in many PE investment models. She noted that winning firms are developing repeatable rollout models capable of scaling AI across their portfolios to turn projected value into real-world return on investment.

Business Unit Launch Context

The release follows the earlier launch of Coforge’s Private Equity Business Unit this year. The unit aims to help private equity firms move from experimentation to measurable impact by driving operational transformation, EBITDA improvement, and AI-enabled value creation across their portfolios.

What the Numbers Show

The report indicates a structural divergence in the PE sector where AI adoption rates are high, yet tangible financial outcomes lag. The data suggests that without a repeatable execution model, AI initiatives remain experimental rather than contributing to sustained margin growth or lower operating costs as intended by investment models.

Coforge unveiled its operational strategy for enterprise autonomy on September 17, 2026, highlighting a twelve-month executable order book of $2.23 billion as of Q1FY27. This represents a 44% year-on-year increase, underpinned by a business model where 86% of revenue now stems from AI-led engineering, data, and cloud services.

The company disclosed a standalone EBIT margin of 16.7% for Q1FY27 and noted a 21.9% compound annual growth rate (CAGR) in revenue over the past nine years. CEO Sudhir Singh outlined a shift from model access to industry autonomy, supported by $58 million invested in AI innovation during FY26.

Strategic Framework

Coforge’s approach centers on three layers: Nuuron for intelligence, Mod Squads for scale, and Momentum Blue for activation. The firm claims that AI agents now handle execution while humans govern outcomes. Examples include reducing vehicle inspection costs from $75 to $35 per transaction and increasing mortgage tax operations capacity threefold with the same headcount.

AgenticOps and Infrastructure

Ashish Kumar, Global Head of Cloud & AI Infra, stated that only 5% of enterprises can orchestrate unsupervised autonomous actions. Coforge reported 160+ clients on its AgenticOps journey, with 37 live deployments achieving 1.2 million autonomous resolutions at 98.2% accuracy.

Data Enablement

Deepak Manjarekar, Head of Data & AI, emphasized that 60% of AI projects without ready data will be abandoned. Coforge’s Data Cosmos platform includes 122+ assets and 68+ AI agents, deployed across 52+ client sites. The firm argues that data readiness is the primary prerequisite for successful autonomy programs.

What the Numbers Show

The concentration of 86% of revenue in AI-led services indicates a significant pivot from traditional IT outsourcing. Combined with the 44% surge in the order book, this suggests strong market adoption of Coforge’s proprietary IP assets like Nuuron and Data Cosmos, rather than pure headcount scaling.

Industry Applications

In travel, Erika Moore highlighted the shift to agent-driven retailing. In automotive, Niraj Patel described a workflow standardizing vehicle inspections across four systems for over 66,000 arrivals. Lalit Wadhwa, CTO, detailed the Momentum Blue model, where outcome-owned pods reduce human effort from 75% to 15% within 18 months as agents mature.