Agentic AI is about more than reacting to input; it’s about pursuing outcomes. At its core are intelligent agents: software entities that don’t just wait for instructions, but take initiative, make decisions, collaborate, and learn over time.
What’s Making Agentic AI Possible Now?
LLMs with reasoning capabilities not just generating text, but planning, reflecting, and making decisions.
Function calling enabling models to use APIs and tools to take real-world actions.
Retrieval-Augmented Generation (RAG) grounding outputs in relevant, up-to-date information.
Vector databases and embeddings supporting memory, recall, and contextual understanding.
Reinforcement learning allowing agents to learn and improve through feedback.
Orchestration frameworks platforms like AutoGen, LangGraph, and CrewAI that help build, coordinate, and manage multi-agent systems.
Getting Started with Agentic AI
If you’re exploring Agentic AI, the first step isn’t technical but strategic. Focus on use cases where autonomy creates clear value: high-volume tasks, cross-functional coordination, or real-time decision-making.
Then, build your foundation:
Start small, but design for scale. Begin with pilot use cases in areas like customer support or IT operations
Invest in enablers. APIs, clean data, observability tools, and a robust security posture are essential
Choose the orchestration frameworks. Tools that make it easier to build, deploy, and monitor agentic workflows
Prioritise governance. Define access control, ethical boundaries, and clear oversight mechanisms from day one
Ecosystm Opinion
Agentic AI doesn’t just execute tasks; it collaborates, learns, and adapts. It marks a fundamental change in how work flows across teams, systems, and decisions. As these technologies mature, we’ll see them embedded across industries – from finance to healthcare to manufacturing.
But they won’t replace people. They’ll amplify us; boosting judgement, creativity, speed, and impact. The future of work isn’t just faster or more automated. It’s agentic, and it’s already here.
Vector embeddings are numerical representations that capture the meaning behind data, not just the words. AI models convert inputs like text or images into vectors in a multidimensional space, where similar ideas cluster together. For example, “annual revenue report” and “yearly income summary” use different words but share the same intent, and their vectors land close together.
They are built for meaning, not just matching. Unlike traditional databases that depend on exact keywords, they use embeddings to find information based on semantic similarity, retrieving what you meant, not just what you typed.
Vector databases enable context-aware search across unstructured data, helping organisations uncover deeper insights, boost relevance, and make faster, smarter decisions at scale.
Why This Matters: Strategic Business Value
Vector databases aren’t just a backend innovation; they unlock real strategic value. By enabling smarter internal search, deeper customer insight, and more context-aware analytics, they help teams move faster, uncover hidden patterns, and make more informed decisions.
Smarter Search. Teams can find information using natural language, not exact keywords, making internal search faster and more intuitive across functions.
Clearer Customer Signals. Embedding unstructured data reveals recurring pain points and patterns, even when phrased differently, sharpening customer insight.
Stronger Decisions. Vector databases enable deeper, context-aware analysis, surfacing insights traditional systems miss and driving more informed decisions.
Kickstart Your Journey with Vector Databases
Getting started doesn’t mean overhauling your entire data stack. Identify high-impact unstructured data sources, choose a platform that fits your ecosystem, and begin with focused use cases where semantic understanding drives clear user value.
Identify High-Value Unstructured Data. Assess where unstructured data resides; these sources hold untapped insight and are ideal for vector embedding.
Select the Right Platform. Evaluate purpose-built solutions and prioritise compatibility with existing cloud environment and API ecosystem to ensure seamless integration.
Start with Targeted Use Cases. Begin with specific, high-impact applications – such as semantic search for knowledge retrieval, summarising large documents, or enhancing virtual assistants. Focus on measurable outcomes and user value.
Ecosystm Opinion
Vector embeddings and vector databases may sound technical, but their purpose is profoundly human, helping systems understand meaning, context, and intent. As AI adoption accelerates, competitive advantage will belong not to those with the most data, but to those who understand it best. This is how we move from information to insight – and from data to decisions.
CRMs are relationship systems, built to track sales conversations, account history, support interactions, and contact details. CDPs are behaviour systems designed to unify signals from web, mobile, ads, apps, and third-party tools.
They each solve different problems, but the same customer is at the centre.
Without integration, CRMs miss the behavioural context needed for real-time decisions, while CDPs lack structured data about customer relationships like deal history or support issues. Each system works in isolation, limiting the quality of insights and slowing down effective action.
“Marketing runs on signals: clicks, visits, scrolls, app drops. If that data doesn’t talk to our CRM, our campaigns feel completely disconnected.” – VP, Growth Marketing
When CRM and CDP are Integrated
Sales gains visibility into customer behaviour, not just who clicked a proposal, but how often they return, what products they browse, and when interest peaks. This helps reps prioritise high-intent leads and time their outreach perfectly.
Marketing stops shooting in the dark. Integrated data enables them to segment audiences precisely, trigger campaigns in real time, and ensure compliance with consent and privacy settings.
Customer Experience teams can connect the dots across touchpoints. If a high-value customer reduces app usage, flags an issue in chat, and has an upcoming renewal, the team can step in proactively.
IT and Analytics benefit from a single source of truth. Fewer silos mean reduced data duplication, easier governance, and more reliable AI models. Clean, contextual data reduces alert fatigue and increases trust across teams.
Why It Matters Now
Fragmented Journeys Are the Norm. Customers interact across websites, mobile apps, social DMs, emails, chatbots, and in-store visits – often within the same day. No single platform captures this complexity unless CRM and CDP data are aligned.
Real-Time Expectations Are Rising. A customer abandons a cart or posts a complaint – and expects a relevant response within minutes, not days. Teams need integrated systems to recognise these moments and act instantly, not wait for weekly dashboards or manual pulls.
Privacy & Compliance Can’t Be Retrofitted. With stricter regulations (like India’s DPDP Act, GDPR, and industry-specific norms), disconnected systems mean scattered consent records, inconsistent data handling, and increased risk of non-compliance or customer mistrust.
“It’s not about choosing CRM or CDP. It’s about making sure they work together so our AI tools don’t go rogue.” – CTO, Retail Platform
The AI Layer Makes This Urgent
Agentic AI is no longer a concept on the horizon. It’s already reshaping how teams engage customers, automate responses, and make decisions on the fly. But it’s only as good as the data it draws from.
For example, when an AI assistant is trained to spot churn risk or recommend offers, it needs both:
Without the full picture, it either overlooks critical risks, or worse, responds in ways that feel tone-deaf or irrelevant.
A Smarter Stack for Customer-Centric Growth
The CRM vs CDP debate is outdated – both are essential parts of a unified data strategy. Integration goes beyond syncing contacts; it requires real-time data flow, clear governance, and aligned teams. As AI-driven growth accelerates, this integrated data backbone is no longer just a technical task but a leadership imperative. Companies that master it won’t just automate, they’ll truly understand their customers, gaining a decisive competitive edge.
As GCCs prototype cutting-edge tools for global banks, retailers, and healthcare systems, their proximity to India’s broader business ecosystem is creating powerful ripple effects. Local startups, mid-sized firms, and even traditional industries are gaining early access to best-in-class practices, technologies, and talent. For example, an AI-driven analytics platform developed for a US-based insurer may be adapted by a healthtech startup in Chennai within months, compressing tech adoption cycles and raising the digital maturity bar across sectors.
This fusion of global exposure and local relevance is accelerating India’s journey toward becoming a product and innovation powerhouse. As GCCs take on more strategic roles, their impact is no longer confined to their parent companies; they’re catalysing a wave of tech-enabled transformation across India’s broader economy.
2. The DPI Effect: Building Smarter, Scaling Faster in India’s Digital Economy
India’s Digital Public Infrastructure (DPI) is quietly powering one of the most inclusive and large-scale technology transformations in the world. While many countries depend on private platforms to deliver digital services, India has taken a distinctly public-first approach – building an open, interoperable digital stack designed for accessibility and scale. With Aadhaar (for identity), UPI (for payments), DigiLocker (for documentation), and the Account Aggregator framework (for secure data sharing) forming its backbone, DPI is not just a convenience but a catalyst for financial inclusion, health access, and rural entrepreneurship.
What sets India’s DPI apart is its dual impact. It empowers citizens while simultaneously accelerating tech adoption across Indian organisations. Enterprises – public and private – are reimagining service delivery, modernising workflows, and launching new offerings by plugging directly into these digital rails. MSMEs use UPI to streamline payments; insurers tap into Account Aggregator for personalised risk assessment; banks leverage Aadhaar for instant customer onboarding. As a result, digital-first operations are no longer limited to tech companies but extends to more traditional businesses.
The shift is as much cultural as it is technical. With trusted public infrastructure in place, startups and enterprises are building with greater confidence and speed, shortening go-to-market cycles and expanding reach. The Open Network for Digital Commerce (ONDC), for example, is enabling kirana stores and small businesses to participate in e-commerce without relying on proprietary platforms, levelling the playing field and accelerating digital inclusion from the ground up.
Global institutions like the World Bank and G20 are now taking note, studying how India’s model blends inclusion, scale, and innovation. In a fragmented digital world, India is showing that public infrastructure can enable private-sector agility and act as a force multiplier for enterprise tech adoption, from startups to state utilities.
3.From Pilots to Performance: India’s Shift Toward Scalable AI Impact
India’s AI journey is entering a critical inflection point. While 76% of organisations now view AI as essential to business success, only 23% have a clear roadmap to implement it, according to Ecosystm research. The gap is no longer about awareness; it’s about execution.
Many Indian enterprises are discovering that without defined outcomes, leadership commitment, and long-term investment in infrastructure and talent, AI efforts stall at the pilot stage. This realisation is shifting focus from experimentation to impact. Forward-looking organisations are starting to anchor AI to core business goals, measuring outcomes in terms of time saved, cost avoided, and revenue generated.
This strategic shift has major implications for India’s tech ecosystem. AI maturity will demand stronger collaboration across product, data, and operations teams; alongside an ecosystem of partners offering open, interoperable, and scalable solutions. It also presents a significant opportunity for Indian tech providers, startups, and systems integrators to support enterprise AI with domain-specific solutions, responsible AI practices, and robust infrastructure capabilities.
4. More Than Translation: India’s AI Accessibility Challenge
While much of the world debates whether AI can code, drive, or write like a human, India is asking a different question: can AI understand and respond like an Indian? With 22 official languages and hundreds of dialects, language is fundamental to access. That’s why India is placing early bets on vernacular AI. Government-led initiatives like Bhashini, under MeitY, are building an open language stack comprising Indic NLP models, regionally sourced datasets, and speech tools optimised for rural and low-literacy users.
This is starting to reshape how Indian organisations design digital experiences. Government services like MyGov and ONDC Saarthi now offer voice-first, multilingual interfaces. Startups such as Reverie, Lokal, and Sarvam are experimenting with models in languages like Hindi, Tamil, and Bengali to power regional content and customer support. The Krutrim LLM, launched recently, represents a step forward, an LLM model trained on Indian data and designed with cultural nuance in mind.
Yet the road ahead isn’t without hurdles. Data sparsity in low-resource languages, the complexity of dialectal variation, and limited commercial incentives for deep vernacular support remain real challenges.
India’s AI journey is being shaped by local priorities, where the next 500 million users coming online will rely more on voice, video, and vernacular than on text or English. That shift is forcing organisations to rethink how they train and deploy AI. The promise is real, but unlocking its full potential will require sustained investment, collaboration, and a deep understanding of linguistic diversity at scale.
5.Building India’s Digital Backbone: The Rise of Local Data Centres
Behind every AI application, mobile transaction, or video stream is a critical but often invisible layer: digital infrastructure. India is currently experiencing one of its largest data centre expansions, with capacity expected to nearly double from around 950 MW today to 1.8 GW by 2026. This growth is driven by rising cloud adoption, data localisation mandates, and increasing demand for AI training and inference within the country.
For organisations, this expanding infrastructure promises faster access to cloud and AI capabilities, improved latency for critical applications, and compliance with data localisation rules. For organisations, this expanding infrastructure promises faster access to cloud and AI capabilities, improved latency for critical applications, and easier compliance with data localisation rules. However, many face challenges in fully leveraging this growth – navigating the complexity of integrating new infrastructure with legacy systems, managing higher operational costs, and building the in-house expertise required to optimise AI workloads locally.
As India pushes for greater digital sovereignty and infrastructure resilience, the expansion of local data centres will be a crucial factor shaping how organisations innovate and compete in the AI era. Yet realising this potential will require addressing operational challenges alongside building scale.
Designing for Complexity, Delivering for Scale
The future of India’s digital landscape hinges on its ability to convert scale and innovation into sustained, inclusive impact. This requires organisations to move beyond experimentation, integrating new technologies deeply, overcoming legacy constraints, and building local expertise at pace. The race is no longer just about access or capability; it’s about agility, resilience, and leadership in a rapidly evolving global tech environment. How India navigates these challenges will determine whether it merely participates in the digital era or defines it.
RAG allows LLMs to draw on knowledge sources, such as company documents, databases, or regulatory content, that fall outside their original training data. By referencing this information in real time, LLMs can generate more accurate, relevant, and context-aware responses.
Why RAG Matters for Business Leaders
LLMs generate responses based solely on information contained within their training data. Each user query is addressed using this fixed dataset, which may be limited or outdated.
RAG enhances LLM capabilities by enabling them to access external, up-to-date knowledge bases beyond their original training scope. While not infallible, RAG makes responses more accurate and contextually relevant.
Beyond internal data, RAG can harness internet-based information for real-time market intelligence, customer sentiment analysis, regulatory updates, supply chain risk monitoring, and talent insights, amplifying AI’s business impact.
Getting Started with RAG
Organisations can unlock RAG’s potential through Custom GPTs; tailored LLM instances enhanced with external data sources. This enables specialised responses grounded in specific databases or documents.
Key use cases include:
• Analytics & Intelligence. Combine internal reports and market data for richer insights.
• Executive Briefings. Automate strategy summaries from live data feeds.
• Compliance & Risk. Query regulatory documents to mitigate risks.
• Training & Onboarding. Streamline new hire familiarisation with company policies.
Ecosystm Opinion
RAG enhances LLMs but has inherent limitations. Its effectiveness depends on the quality and organisation of external data; poorly maintained sources can lead to inaccurate outputs. Additionally, LLMs do not inherently manage privacy or security concerns, so measures such as role-based access controls and compliance audits are necessary to protect sensitive information.
For organisations, adopting RAG involves balancing innovation with governance. Effective implementation requires integrating RAG’s capabilities with structured data management and security practices to support reliable, compliant, and efficient AI-driven outcomes.
When done right, AI benefits every part of the organisation; not just data teams.
“Our AI-powered screening for insurance agents fast-tracks candidate selection by analysing resumes and applications to pinpoint top talent.” – HR Leader
“Conversational AI delivers 24/7 customer engagement, instantly resolving queries, easing team workload, and boosting CX.” – CX Leader
“AI transforms work by streamlining workflows and optimising transport routes, making operations faster and smarter.” – Operations Leader
“Using AI to streamline our sales pipeline has cut down the time it takes to qualify leads, enabling our team to focus on closing more deals with greater precision.” – Sales Leader
“We’re unlocking data value: AI agents personalise customer support at scale, while AI-driven network optimisation ensures seamless IT operations.” – Data Science Leader
In the short term, most businesses are focusing on operational efficiency, but the real wins will be in longer-term innovation and financial value.
For tech teams, this means delivering robust, scalable AI systems while supporting responsible experimentation by business teams – all in a fast-moving, high-stakes environment.
However, that’s not easy.
• High Costs. AI requires substantial upfront and operational spend. Without measurable outcomes, it’s hard to justify scaling.
• Security & Governance Risks. AI heightens exposure to bias, misuse, and compliance gaps. Most organisations lack mature guardrails to manage this.
• Regulatory Uncertainty. Shifting global policies make it difficult to design AI systems that are both future-proof and compliant.
• Skills Shortage. There’s a growing gap in AI and data expertise. Without the right talent, even promising use cases falter.
• Data Challenges. AI needs vast, high-quality data, but many organisations struggle with silos, poor lineage, and inconsistent standards.
Yet the toughest obstacles aren’t technical.
• Limited AI Fluency at the Top. Many leaders lack a practical understanding of AI’s capabilities and constraints, slowing decisions and making cross-functional alignment difficult.
• No Clear Ownership or Strategy. Without clear ownership, AI efforts remain scattered across IT, innovation, and business teams, leading to fragmentation, misalignment, and stalled progress.
• Unclear ROI and Benefits. AI’s value isn’t always immediate or financial. Without clear metrics for success, it’s hard to prioritise initiatives or secure sustained investment.
• Short-Term Pressure. The push for quick wins and fast ROI often comes at the expense of long-term thinking and foundational investments in AI capabilities.
• Rigid Business Models. AI demands adaptability in processes, structures, and mindsets. But legacy workflows, technical debt, and organisational silos frequently stand in the way.
• Change Management is an Afterthought. Many AI efforts are tech-first, people-later. Without early engagement and capability building, adoption struggles to gain traction.
Bridging the Innovation-AI Gap: The Power of Ecosystems
Bridging this gap between AI ambitions and success requires more than technology; it needs a coordinated ecosystem of vendors, enterprises, startups, investors, and regulators working together to turn innovation into real-world impact.
Public-private partnerships are key. In Singapore, initiatives like IMDA’s Spark and Accreditation programmes tackle this head-on by spotting high-potential startups, rigorously validating solutions, and opening doors to enterprise and government procurement. This approach de-risks adoption and speeds impact.
• For Enterprises. It means quicker access to trusted, local solutions that meet strict performance and compliance standards.
• For Startups. It unlocks scale, credibility, and funding.
• For the Economy. It creates a future-ready digital ecosystem where innovation moves beyond the lab to drive national competitiveness and growth.
