This five‑part series is designed as a practical field guide for CXOs, CMOs, CDOs and digital leaders who know their martech stack is under‑leveraged but are unsure where to start fixing it. Across the series, the articles move from strategy to architecture, then into ROI, platform choices and execution, so leadership teams can see the whole chessboard rather than isolated tools or features. For Indian organisations navigating agentic AI, evolving data regulations and an increasingly complex vendor landscape, the series offers a structured way to understand what truly matters, what can wait, and how to turn martech from a cost line into an intelligent growth system.
Choosing Martech Platforms In 2026: A Buy‑Guide For Indian CX Leaders
Platform selection has always been political, technical, financial and emotional. In 2026, it is also existential. As AI agents become embedded in martech, leaders are no longer choosing only a CRM or a journey tool; they are choosing the operating environment for autonomy and intelligence.
Start With Strategic Priorities, Not Vendor Shortlists
Your original decision matrix anchored platform choice in strategic priorities—revenue growth, complex B2B sales, enterprise data integration, regulated industry needs, mid‑market cost efficiency, and high‑volume transactional requirements. That framing remains essential.
Clarify:
- What are the one or two non‑negotiable outcomes over the next 24 months?
- Where does your organisation sit on the readiness spectrum in data, team, and infrastructure?
- Where is the biggest friction today—journey orchestration, lead management, analytics, or compliance?
Once this is clear, introduce two new filters tailored for 2026: agentic readiness and India stack alignment.
Filter 1: Agentic Readiness
This lens focuses on how well each platform supports AI agents today and in its roadmap.
Key questions:
- Does the platform provide embedded AI assistants or copilots that can act—not just generate content?
- Are there robust, well‑documented APIs, events, and security models for external agents to safely read and write data?
- What governance features exist—role‑based permissions, audit logs, policy enforcement, sandbox environments?
- How mature is the vendor’s AI roadmap in your geography, and how frequently are features shipped and localised?
Salesforce and Adobe are investing heavily in Einstein, Agentforce, RT‑CDP, and AI‑driven journey optimisation. Zoho is embedding Zia across workflows for mid‑market customers. Indian vendors like Netcore, MoEngage, and others are incorporating AI and agent‑friendly features into orchestration and infra.
Filter 2: India Stack Alignment
This lens reflects the realities of Indian enterprises: regulatory complexity, digital public infrastructure, and market diversity.
Evaluate:
- Data residency and DPDP compliance tooling for Indian PII.
- Integration depth with India’s communication and payment rails—WhatsApp, SMS, UPI, local gateways.
- Vernacular and localisation support: interfaces, templates, and AI that can handle Indian languages.
- Availability of strong local implementation partners and support teams who understand BFSI, telecom, retail, and other local verticals.
Regional players like Zoho, LeadSquared, MoEngage, CleverTap, and Netcore typically score high on this dimension; global suites like Salesforce and Adobe bring enterprise depth but require deliberate localisation and partner strategies.
“In 2026, you aren’t buying a CRM or CDP; you are choosing the operating environment your AI agents will grow up in.”
Illustrative Platform Patterns For India
1. BFSI / Fintech India
Priorities: regulatory compliance, transaction reliability, lifecycle orchestration, fraud and risk awareness.
Typical pattern:
- Salesforce or equivalent CRM for account‑level workflows and compliance‑ready audit trails.
- Netcore Cloud as the regulated messaging backbone for OTPs, statements, and alerts.
- MoEngage or CleverTap for app and WhatsApp engagement across onboarding, activation, and servicing.
- Early‑stage agents orchestrating across these layers to manage missed‑call leads in LeadSquared‑type CRMs, prioritise service queues, and fine‑tune repayment nudges.
2. Mid-Market India
Priorities: speed to value, TCO control, sales productivity, basic AI assistance.
Typical pattern:
- Zoho CRM as core system of record, leveraging Zia for predictive scoring and automation.
- LeadSquared where structured lead management and field force coordination are central.
- MoEngage or CleverTap as the single engagement platform for multi‑channel campaigns.
- Narrow‑scope agents improving lead prioritisation, email drafting, and light campaign optimisation rather than deep architectural AI.
3. Enterprise, Multi-Market / Multi-Brand
Priorities: global consistency, local flexibility, complex B2B/B2C orchestration, content scale.
Typical pattern:
- Adobe Experience Cloud or Salesforce as the primary CX platform.
- RT‑CDP or Data Cloud as the unified customer and feature hub.
- Agents focusing on cross‑brand experimentation, journey optimisation, budget allocation, and content selection across markets.
“The best martech platform doesn’t exist; only the platform best aligned to your strategic priority and organisational reality does.”
Running The Decision As A Design Exercise
Encourage leaders to turn RFPs into design sessions.
Ask service partners to:
- Demonstrate how three or four of your critical journeys would be built, monitored, and optimised—including where AI and agents would sit.
- Show governance screens: audit logs, permission models, and how an AI action can be traced and reversed.
In 2026, the “best” platform is not an abstract winner; it is the platform (or combination) that best aligns with your strategic priority, supports agents safely, and fits the Indian digital and regulatory environment you operate in.
