HFS Research has highlighted that WNS InfoTurf.ai, a Generative AI (Gen AI) automation platform for information processing, delivers on the long-promised productivity gains, an area where most enterprise Gen AI deployments continue to fall short. HFS notes that that InfoTurf.ai bridges the Gen AI productivity gap by converting generative intelligence into measurable throughput, operational efficiency and business impact.
The Gen AI Productivity Gap in Enterprise Operations
According to HFS, more than 80 percent of Gen AI deployments focus on personalization and insight generation, while the core productivity premise remains unrealized, as pilots fail to translate into production-grade improvements.
Figure 1: Distribution of Gen AI & Agentic AI Outcomes by Deployment
Information services firms still depend on manual research, data extraction and publishing workflows, which increase costs, slow updates and limit coverage. HFS identifies InfoTurf.ai as a differentiator in addressing this gap.
Why HFS Research Recognized WNS InfoTurf.ai
HFS notes that InfoTurf.ai addresses Gen AI productivity challenges through:
What is InfoTurf.ai? A Gen AI-powered Productivity Platform
Among InfoTurf.ai’s core capabilities are Gen AI-led research automation, AI-powered data extraction and workflow orchestration, to name a few. Its microservices-based architecture enables enterprises to adopt specific accelerators or deploy the platform end-to-end to modernize information operations.
“Productivity will decide who owns the future in the information services space. Tools like InfoTurf.ai will help you stay on the winning side.”
- HFS Research
What’s Inside the Report
- How InfoTurf.ai is being applied in live enterprise environments, with insights into the business outcomes achieved
- A detailed analysis, including frameworks and recommendations to scale Gen AI productivity effectively
To read the full HFS highlight paper, click here
FAQs
1. What is generative AI productivity in enterprises, and why is it critical for modern business operations?
Generative AI productivity in enterprises refers to using GenAI to improve the speed, quality, and efficiency of knowledge-intensive business processes. It helps organizations automate information processing, generate insights, extract data, and streamline workflows, enabling employees to handle higher volumes of work while improving operational performance and measurable business outcomes.
2. How do enterprise GenAI productivity solutions help bridge the gap between pilot projects and production-scale outcomes?
Enterprise GenAI productivity solutions help organizations move beyond isolated GenAI pilots by integrating AI capabilities into real-world business processes and workflows. They provide scalable, reusable solutions that can automate information-intensive activities, improve throughput, and connect AI initiatives with measurable productivity improvements, helping enterprises translate experimentation into sustainable production-scale outcomes.
3. What makes AI-powered productivity platforms more effective than traditional automation tools in improving operational efficiency?
AI-powered productivity platforms combine generative AI, intelligent data processing, workflow orchestration, and automation to handle complex information-driven tasks. Unlike traditional rule-based automation, these platforms can interpret unstructured information, generate insights, and support dynamic workflows, helping enterprises improve productivity, reduce manual effort, accelerate processes, and respond more effectively to changing business requirements.
4. How is generative AI in business operations transforming research, data extraction, and workflow efficiency at scale?
Generative AI in business operations is transforming information-intensive activities by accelerating research, extracting relevant information from diverse sources, summarizing findings, and supporting workflow execution. At scale, these capabilities reduce repetitive manual work, improve processing speed, enhance consistency, and allow employees to focus on higher-value analytical and decision-making activities.
5. What is GenAI-driven workflow automation, and how does it improve throughput, accuracy, and cost efficiency in enterprises?
GenAI-driven workflow automation uses generative AI to automate and coordinate multiple steps within information-intensive business processes. By combining intelligent processing with workflow orchestration, it can reduce manual intervention, accelerate task completion, improve consistency and accuracy, increase throughput, and lower operational costs while enabling employees to concentrate on more strategic activities.
6. How does WNS InfoTurf.ai help enterprises achieve measurable productivity gains through generative AI-powered information processing?
WNS InfoTurf.ai helps enterprises achieve measurable productivity gains by applying generative AI to information processing, research automation, data extraction, and workflow orchestration. Its microservices-based approach enables organizations to adopt individual AI-powered capabilities or deploy broader solutions, helping convert GenAI investments from experimentation into scalable operational improvements and tangible productivity outcomes.