Industry
Communications, Media and Information Services
Connect. Curate. Comprehend: Unlocking Value Beyond Boundaries
Highlights
- 2026 marks a pivotal year for the information services industry as it shifts from simply collecting data to enabling smarter, faster decisions.
- Generative AI is now embedded in every stage of the software development lifecycle (SDLC) helping with requirements, coding, testing, documentation, and operations.
- Five themes - agentic AI, modernization, talent transformation, M&A, and GCC work together to drive the industry forward.
On this page
Overview
2026 marks a pivotal year for the information services industry – spanning credit bureaus, ratings, market data, and professional publishing – as it shifts from simply collecting data to enabling smarter, faster decisions.
Five forces are driving this transformation: Agentic AI, tech modernization, GenAI-powered talent evolution, strategic mergers and acquisitions for new capabilities, and captive/global capability center (GCC) models for efficiency and IP protection. Building on 2025’s customer experience trends, the focus now is on speed, autonomy, and proactive engagement.
Agentic AI: From assist to autonomy.
Agentic AI is moving beyond simple assistance to systems that can reason, decide, and act across enterprise tools and data - delivering real outcomes instead of just suggestions.
Open standards like the model context protocol are accelerating adoption and by 2026, many enterprise applications will feature specialized AI agents.
What this looks like in practice:
In customer service, agentic systems can autonomously triage, resolve issues, and coordinate back-office actions shifting the focus from call deflection to complete case resolution. In security operations, multi-agent systems have cut incident response times from 30 minutes to about 30 seconds, and in commerce, they enable scalable personalization. Best practices now emphasize multi-agent orchestration for building complex agent networks, which is critical for high-stakes environments like credit, ratings, and research delivery.
Trust remains a top priority. The industry is doubling down on safeguards: regulatory compliance, model validation, auditability, bias mitigation, and human-in-the-loop oversight.
Modernization: Cloud-native, event-driven, and data-centric.
Legacy, monolithic systems slow down innovation and make compliance harder.
To overcome this, providers are moving to domain-driven microservices, event-driven architectures (EDA), and data-centric platforms that deliver real-time insights and resilient workflows.
Modernization in action:
- Event-driven microservices allow flexible, reliable operations—like sending client alerts when ratings change.
- Data APIs and triggers connect different data sources, reducing complexity and ensuring secure, consistent access.
- Cloud-native security and observability (e.g., distributed tracing, policy as code) help meet strict audit requirements.
- Personalization stacks combining retrieval-augmented generation (RAG) and customer data platforms (CDPs) create hyper-personalized customer journeys while maintaining consent and data integrity.
All of this must align with evolving regulations like GDPR and Digital Services Act (DSA) and Digital Markets Act (DMA), ensuring transparency and fairness in data handling.
Talent transformation- GenAI in the SDLC.
Generative AI is now embedded in every stage of the software development lifecycle (SDLC) helping with requirements, coding, testing, documentation, and operations.
Organizations are seeing significant productivity gains, especially when AI-powered testing and code generation are integrated into continuous integration (CI)/continuous deployment (CD) pipelines and quality checks.
The biggest benefits come from moving beyond standalone AI tools to orchestrated workflows with strong governance. Teams using GenAI for automated code reviews and test generation have accelerated release cycles and improved code quality.
However, challenges remain such as ensuring codebase awareness, disciplined prompting, and automated evaluation. These can be addressed through platform engineering and SDLC governance. A practical framework is emerging: Govern–Map–Measure–Manage (based on NIST (National Institute of Standards and Technology, Department of Commerce, US Government) AI Risk Management Framework) to manage risks and controls, embed AI across all phases, and maintain human oversight for high-risk changes with clear model usage policies.
M&A: Acquiring capabilities, not just scale.
M&A activity is rebounding, focused on acquiring AI, data analytics, cybersecurity, and cloud capabilities to speed up delivery of agentic workflows and data products.
BNPLRecent moves illustrate this trend:
- Cision acquired Trajaan to boost AI-driven consumer and media insights.
- IBM acquired Confluent to build smarter data platforms for GenAI.
- Demand for RegTech and trust solutions—like model risk management and compliance automation—is rising fast.
While many firms are building AI capabilities in-house, strategic acquisitions remain key to filling gaps quickly.
GCCs - Cost centers to hubs.
Global capability centers (GCCs) have evolved from cost-saving units to AI and product engineering hubs.
India leads the way, hosting 1,700–1,800 GCCs and nearly 1.9 million professionals. GCCs enable 24x7 decision operations, protect IP, and concentrate scarce skills in ML Ops, data governance, and agent orchestration.
What’s changing:
- Captive centers are being repositioned as full-fledged GCCs driving innovation, R&D, and Agentic AI centers of excellence.
- Mid-sized firms are adopting this model to reduce risk and enable continuous global operations through a follow-the-sun approach.
GCCs now play a central role in agent monitoring, incident response, governance, and large-scale data trust operations aligned with EU digital regulations.
Integrated approach and actionable roadmap.
The five themes - agentic AI, modernization, talent transformation, M&A, and GCC work together to drive the industry forward. Agentic AI relies on modern, event-driven systems and trusted data.
Talent transformation turns pilots into production through GenAI and strong governance. M&A fills capability gaps quickly, while GCCs scale delivery and safeguard IP. Regulatory frameworks like the EU AI Act and GDPR ensure trust across ratings, market data, and research services.
What leaders should do:
Implement agentic AI safely: Start with progressive autonomy tiers, embed identity and rationale in decisions, adopt open standards, and align governance with NIST AI RMF and EU AI Act timelines.
Modernize tech estates: Break monoliths using EDA and DDD, build governed data APIs, and integrate CDP + RAG for personalized experiences under consent.
Transform talent and SDLC: Shift to AI-native SDLC for 15–30% productivity gains; invest in prompt literacy, secure coding, and model-risk skills.
Calibrate M&A: Target data, trust assets, and niche partners that accelerate platform speed.
Leverage GCCs: Establish Agentic Centers of Excellence, scale data quality and privacy engineering, and enable global operations with strong governance.
Conclusion
Information services are moving from data collection to decision-driven engagement, powered by autonomy, modernization, talent, and scale.
Segment-specific innovations such as autonomous agents in credit and ratings, real-time risk dashboards for financial platforms, AI-driven research assistants, and proactive fraud detection show how these themes translate into impact.
To stay competitive:
- Compete on autonomy, but build trust through strong governance.
- Modernize technology for agility and compliance.
- Invest in talent and integrate GenAI across the SDLC.
- Use M&A to close capability gaps quickly.
- Leverage captives/GCCs to scale delivery and safeguard IP.
The winners will be those who combine these strategies into a cohesive roadmap delivering faster, smarter, and more trusted outcomes for customers.