Traditional data processing is reaching its limits. In 2025, data creation will reach 175 zettabytes1 worldwide, with vast majority taking the form of unstructured information such as documents, video, audio and social media content. As data volumes grow, so does the pressure to turn that information into actionable intelligence.
For information services companies, the explosion of data poses a threat to their competitive edge. Slow manual research, high management costs, fragmented sources and long refresh cycles make it difficult to extract meaningful insights. To stay ahead, they need systems that can extract, contextualize and distill intelligence from data with precision, agility and efficiency.
Businesses are racing to generate real-time insights that enhance customer experiences and deliver continuous value in a volatile and hypercompetitive environment. The challenge is compounded by rising regulatory scrutiny over Environment, Social and Governance (ESG) disclosures, Anti-Money Laundering (AML) reporting and other compliance mandates. These intersect with hurdles like inconsistent formats, multiple languages and widely varying reporting standards across regions.
Next-gen Platforms Are Key to Powering Data Intelligence
Generative AI (Gen AI) combined with hyperautomation is transforming how organizations manage and extract value from data. Next-gen platforms go beyond simple data collection, enabling intelligent research, contextual analysis, automated summarization and publishing across a wide range of sources.
By unifying structured and unstructured data and applying domain-specific context, they turn raw information into harmonized datasets ready for analysis. Proprietary algorithms and AI-driven automation ensure insights are accurate, actionable and delivered at scale, supporting faster, smarter decision-making. Key highlights include:
How Data-intensive Sectors Are Unlocking Measurable Value
For research and compliance-intensive businesses, such as banking and financial services, consulting and research, legal intelligence, market intelligence and data aggregation, implementing next-gen platforms is already driving striking results.
Real-world Impact from WNS
Compliant Business Presentation Design
A top management consulting firm needed advanced support in designing and editing complex presentations without overloading consultants. Traditionally, their consultants struggled with interpreting information hierarchies, building graphics and ensuring strict brand compliance. Deploying AI-driven design intelligence helped automate slide formatting, compliance checks and access to a library of ready-to-use templates.
The result: streamlined workflows, 100 percent quality audit, faster turnaround and uncompromised protection of brand integrity and information security standards such as the EU’s General Data Protection Regulation (GDPR).
Accelerated Cross-Industry Research
One of the largest credit agencies in the US faced challenges in scaling research across industries, including infrastructure, mining, pharmaceuticals, and manufacturing. Manual sourcing and validation of information slowed cycles, raised credibility concerns, and introduced human bias. Leveraging Large Language Models (LLM) and enabling cognitive extraction on a unified platform, the firm automated the sourcing, extracting, processing and updating of industry-relevant information.
This ensured 1.5x faster data refresh cycles, 70 percent higher productivity and a substantial increase in both the breadth and depth of research coverage for more reliable insights – freeing analysts to focus on high-value operations.
Rapid Scaling of Legal Data Intelligence
In the legal sector, a leading US-based legal publisher struggled with slow, costly manual processes for collecting and updating court data. These inefficiencies hindered refresh cycles, increased operational costs and limited scalability.
By implementing a Gen AI-powered cognitive extraction solution with Application Programming Interface (API)-driven updates, the firm reduced refresh cycles from 6 months to 24 hours for thousands of data points. Furthermore, data extraction operations became near touchless after the initial configuration.
Maximizing Investments: A 5-step Approach
For businesses looking to harness the power of AI, the key is to move beyond piecemeal, reactive initiatives and point solutions. Engaging with strategic partners that combine AI-driven capabilities with deep domain expertise and take an enterprise-wide holistic approach, can dramatically improve outcomes and ROI.
Josh Gazes, SVP – Operations at ALM Media shares how a successful strategic partnership enabled their decades-long shift to a digital-first business.
Adopting a structured approach accelerates adoption and optimizes ROI:
Turning Data Complexity into a Growth Catalyst
Businesses are increasingly leveraging Gen AI and LLMs to turn data into actionable intelligence. McKinsey estimates that the strategic use of data analytics and AI could unlock as much as USD 5 Trillion2 in value globally over the next decade. By integrating text, audio, video and image data into a unified intelligence layer, AI supercharges data, providing context-aware insights, detecting emerging trends and surfacing risks before they materialize. This advanced capability allows organizations to recalibrate risk models, monitor compliance and extract deeper customer intelligence – transforming raw data into a driver of competitive differentiation and growth.
To discover how AI and hyperautomation can transform your data strategy and create powerful competitive advantage, connect with our experts today.
References
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https://www.networkworld.com/article/966746/idc-expect-175-zettabytes-of-data-worldwide-by-2025.html
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https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/upgrading-software-business-models-to-thrive-in-the-ai-era
FAQs
1. What are generative AI data intelligence platforms, and how do they help organizations transform unstructured data into actionable insights?
Generative AI data intelligence platforms combine GenAI, cognitive extraction, automation, and domain expertise to process large volumes of unstructured and structured information. They identify relevant data, validate and contextualize it, and transform fragmented content into standardized, decision-ready insights that help organizations accelerate research and improve business decisions.
2. How does GenAI-powered data extraction and insights improve research accuracy, productivity, and decision-making?
GenAI-powered data extraction and insights automate the sourcing, extraction, processing, validation, and summarization of information from diverse sources. By reducing repetitive manual activities and improving consistency, organizations can accelerate research, increase productivity, access refreshed information faster, and provide decision-makers with more accurate, contextual intelligence for timely business decisions.
3. What are the business benefits of implementing AI-driven data extraction and processing across research, compliance, and information services functions?
AI-driven data extraction and processing can improve productivity, reduce manual effort, accelerate data-refresh cycles, and enhance consistency across research, compliance, and information services. Organizations can process complex information at scale, strengthen data accuracy, support regulatory requirements, reduce operational bottlenecks, and deliver timely intelligence for better business outcomes.
4. How does real-time data intelligence with generative AI help enterprises respond faster to market, regulatory, and customer changes?
Real-time data intelligence with generative AI enables enterprises to continuously process and interpret information from diverse sources, helping them identify emerging trends, regulatory developments, and changing customer expectations. Faster access to contextual insights allows organizations to refresh intelligence more frequently, recognize risks and opportunities earlier, and respond with greater agility.
5. Why is enterprise data-to-insights automation becoming essential for organizations managing large volumes of structured and unstructured data?
Enterprise data-to-insights automation is becoming essential because traditional manual processes struggle to manage the growing volume, complexity, and velocity of structured and unstructured data. Automation can streamline extraction, validation, harmonization, analysis, and reporting, helping organizations reduce processing time, improve scalability, maintain data quality, and convert information into actionable intelligence faster.
6. How does WNS leverage generative AI data intelligence platforms, hyperautomation, and domain expertise to accelerate data extraction, refresh cycles, and insight generation?
WNS combines generative AI data intelligence platforms with hyperautomation and domain expertise to automate information sourcing, cognitive extraction, validation, processing, and insight generation. This approach helps organizations handle complex data at scale, accelerate refresh cycles, improve productivity, and transform fragmented information into contextual, decision-ready intelligence with greater speed and consistency.