Coforge Limited introduced the Value Gates Framework on September 19, 2026, to help enterprises turn AI investments into measurable business outcomes. The methodology aims to shift focus from technical delivery to tangible value realization across the AI project lifecycle.

The global AI-native engineering services leader stated that many organizations struggle to move beyond pilots and proofs of concept. The new framework addresses this challenge by establishing evidence-based criteria for evaluating AI projects.

Structured Approach to AI Value

The Value Gates Framework assesses initiatives against specific criteria including business impact, user adoption, governance, data readiness, operational feasibility, and return on investment. Coforge emphasized that deployment should not be the primary measure of success.

Instead, the framework helps enterprises validate whether an AI initiative creates demonstrable value at every stage of development. This approach allows organizations to make better investment decisions and reduce the risk of scaling low-impact projects.

Leadership Perspective

Anup Nair, Chief AI Commercial Officer at Coforge , highlighted the distinction between delivery and value. He noted that technically successful solutions often fail to deliver meaningful business impact without continuous validation.

"Too many organizations confuse AI delivery with value delivery," Nair said. "By continuously validating value, enterprises can improve adoption, strengthen governance, and concentrate investment on initiatives that create lasting business results."

Supporting Resources

To support adoption of the methodology, Coforge published a thought leadership paper titled Value-First AI Project Management: A Value Gates Framework. The document outlines principles for improving AI governance, investment discipline, and value realization.

The company described itself as an outcome-led entity that combines AI agents with specialized workforce units to engineer autonomous enterprises. The focus remains on delivering secure, governed, and enterprise-grade solutions.

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.