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.
“Most AI pilots fail not because the models are weak, but because the data and journeys weren’t ready for them.”
From POCs To Production Agents: A 12–18 Month Execution Playbook For CXOs
A sophisticated architecture and a well‑chosen stack are necessary but insufficient. The enterprises that actually realise martech and AI value are those that execute with discipline: sequencing data, journeys, AI models, and agents in a way that builds organisational confidence step by step.
Phase 1: Stabilise The Data (Months 0–3)
Every agent decision is only as good as the data it consumes.
Key actions:
- Conduct a pragmatic data audit focused on a handful of high‑impact journeys: onboarding, activation, cross‑sell, and churn rescue.
- Clarify identity resolution across channels and systems; document where IDs diverge or fragment.
- Implement or tighten consent and preference management in line with DPDP and sectoral regulations.
- Choose or validate your primary customer data hub (CDP or equivalent) and define minimum acceptable freshness and completeness thresholds.
An Indian NBFC started by simply reconciling three different “truths” of a customer—loan system, collections app, and marketing database—before even touching AI. That three‑month data sprint prevented thousands of mis‑directed messages later.
“If you can’t trust your data enough to let an agent act on it, you have a data problem, not an AI problem.”
Phase 2: Instrument The Journeys (Months 3–6)
Rather than trying to “AI‑enable everything,” go narrow and deep.
Focus on:
- Selecting 3–5 critical journeys with clear value and measurable outcomes.
- Ensuring the journeys are fully instrumented: events, states, and transitions are captured with appropriate metadata.
- Designing initial rule‑based journeys in your engagement tools (CleverTap, MoEngage, Salesforce, Adobe) to create a solid benchmark.
- Defining KPIs and baselines for each journey: conversion rates, time‑to‑value, retention at key milestones, incremental revenue per cohort.
A large private bank mapped the “first 30 days” journey for new savings accounts end‑to‑end—branch, app, SMS, WhatsApp—and discovered that 40% of drop‑offs happened after the first login. That insight alone reprioritised where AI and agents should focus first.
Phase 3: Introduce Smart Autopilot (Months 6–12)
Introduce AI and early agentic decisioning—carefully and transparently.
Steps:
- Deploy predictive models for churn, propensity, or next best product, and integrate their outputs into your CDP and journey tools.
- Allow AI to influence low‑risk levers first: send time optimisation, channel selection, and content variations within brand‑guaranteed templates.
- Implement simple agents to manage campaign‑ops tasks: automatically pausing underperforming variants, escalating anomalies, or reallocating test splits.
- Design control groups and holdouts so that uplift from AI‑driven decisions is measurable and credible.
A Tier‑1 D2C brand allowed an agentic engine to control only send‑time and channel mix for abandoned‑cart journeys for three months. The result: higher recovery rates and a 20–25% reduction in spam complaints, with no change in offers.
Governance remains strict at this stage: limited scopes, explicit approvals, and close monitoring.
Phase 4: Scale Autonomous Agents (Year 2 Onwards)
Once data reliability, instrumentation, and early AI have proven their value, expand agent scope.
This can include:
- Allowing agents to design and run end‑to‑end experiments for specific segments and journeys.
- Delegating more complex decisions—budget reallocation across campaigns, prioritised treatment strategies for high‑value segments—within defined financial and risk limits.
- Introducing specialised agents: onboarding agent, churn‑protection agent, creative‑testing agent, each with clear KPIs and guardrails.
- Embedding agents into everyday tools: sales teams using Einstein/Agentforce, marketers using AI copilots in Adobe or Salesforce, call centre agents supported by AI‑assisted suggestions.
Only after a year of clean data and governed experimentation did one large consumer brand allow agents to autonomously re‑assign budgets between always‑on campaigns within pre‑set floors and ceilings.
“Treat agents like new team members: start with clear scopes, low‑risk tasks and tight supervision—then gradually give them more responsibility.”
Organizational Readiness 2.0
Update it for AI:
- Data readiness: quality, latency, and model readiness (feature stores, feedback loops, safe sandboxes).
- Team capability: AI literacy, prompt, and agent‑briefing skills, understanding of risk and bias, emerging roles like “agent‑ops” and “AI product owners.”
- Executive alignment: a shared AI vision that articulates why the organisation is investing in AI and agents, what success looks like, and what risks are acceptable.
- Technology infrastructure: logging, monitoring, and auditability of AI actions across systems; ability to quickly roll back or override agent decisions if needed.
Enterprises scoring high on these dimensions can responsibly deploy sophisticated agents across Salesforce, Adobe, and specialised engagement platforms. Those earlier on the curve should start with simpler, human‑in‑the‑loop use cases while they strengthen foundation.
