While GenAI promises falter, WNS InfoTurf.ai makes productivity real

Enterprises are confronting a disquieting paradox: over 80% of GenAI deployments are chasing prediction and personalization, while the original promise of productivity is languishing as pilots scale to production (see Exhibit 1). For operations leaders in information services, this widening intent-to-impact gap sustains manual effort, inflates costs, and throttles insight. WNS InfoTurf.ai seeks to bridge this gap by translating generative intelligence into measurable throughput and tangible business impact.

Exhibit 1: The pilots-to-production gap continues to cripple productivity-focused AI for operations leaders

Sample: 979 GenAI and agentic AI use cases collected by HFS in the last 12 months
Source: HFS Research, 2025

Fighting inefficiency at its operational heart through InfoTurf.ai’s automation capabilities

Information businesses such as financial data providers and compliance platforms still depend on laborious, high-cost sourcing and integration models. Manual ingestion means costs are overshot, refresh cycles are slowed, and trade-offs are made between coverage and speed. InfoTurf.ai simplifies these workflows with unified automation for research, extraction, summarization, and publishing, effectively streamlining them under the framework through integrated automation modules (see Exhibit 2). Also, its modular nature allows customers to directly implement specific offerings into current ecosystems with custom features or use the platform end-to-end. For enterprise operations leaders, this means deploying specific capabilities or just adopting the complete platform. The InfoTurf.ai framework reconciles automation with accountability. Human-in-the-loop governance influences initial implementation, while validation agents control accuracy and reprocessing outcomes. Human-in-the-loop is not just limited to validation but also helps in training the AI engine, becoming more smarter with time. Bounded ingestion parameters and audit trails reinforce trust in all output, primarily through regulated domains. The platform’s configurator turns AI into a pre-, mid-, or post-processor based on areas that require some degree of automation.

Exhibit 2: WNS is positioning InfoTurf.ai as a productivity-as-an-outcome tool, aligning technology, process, and oversight within one operational continuum

Source: InfoTurf.AI functional capabilities depicted by WNS

Delivering measurable productivity at scale

InfoTurf.ai’s implementations across publishing, consulting, compliance, and investment management demonstrate consistent, verifiable impact:

• Leading US-based publisher: Automated extraction from more than 6,000 data points from 100 US court websites, which now refreshes 3 times daily, improving productivity by up to 50% and unlocking millions of dollars in annual revenue.
• Global management consulting firm: Interprets project briefs, estimates effort, and assigns work to 18,000+ consultants across 110 sites, reducing handling time by 30% and increasing throughput by 40%, resulting in much faster turnaround time.
• B2B provider of payments and compliance: Automated due diligence reviews across 40,000 banks and shortened cycles from 14 days to less than 24 hours, cutting KYC and AML risk.
• Investment management firm: Portfolio-tracking automation reduced data-extraction time by 98% and accelerated reporting.

Each example is in live production, with human supervision scaling back as accuracy exceeds the defined normal. At the same time, orchestrating agents, propagating accuracy across steps, and real-time monitoring are still maturing domains. The solution is still relevant and sensible but must be proven in every client context. Also, the aforementioned results should not be taken at face value.

Scaling GenAI with commercial and governance discipline

Enterprises are reluctant to scale GenAI due to economic insecurity and/or governance complexity. WNS offsets these in both cases through unit-transaction pricing, requiring clients to pay only for verified outputs. Productivity gains often pay for increased customization, generating self-financing growth models where incentives are tied directly to measurable benefits. Adoption is enhanced by deployment flexibility.

InfoTurf.ai supports Azure OpenAI, AWS, and Google Cloud, secure on-premises environments, and enterprise-grade authentication and compliance. Its microservices architecture facilitates selective adoption of specific accelerators (auto-publishing, data summarization, etc.), rather than in isolation, without impacting systems. This modularity makes AI an operational layer that supplements existing infrastructure instead of redesigning it.

Three steps to make your GenAI investment pay off

Real-time, provenance-rich information is now a baseline expectation. Business leaders implementing GenAI in information services should focus on three priorities:

1. Anchor GenAI to productivity KPIs: Establish baselines for handling time, refresh latency, and cost per verified output.
2. Reinforce governance discipline: To maintain reliability, apply bounded data configurations, validation-agent monitoring, and audit trails.
3. Scale with deliberate progression: Start with a high-friction dataset, validate accuracy and cost metrics, and then expand once performance thresholds are achieved.

The bottom line: Productivity will decide who owns the future in the information services space. Tools like InfoTurf.ai will help you stay on the winning side.

InfoTurf.ai’s client record shows that GenAI-driven productivity is a tangible, repeatable outcome when technology, process, and governance advance together. It provides a pragmatic path to realizing intent, combining modular automation, validation-driven accuracy, and transaction-based economics. The smart move for business and operations leaders is to test the platform’s efficacy before deploying it.