IndonesiaTelecomScale · $100K
Indosat Ooredoo Hutchison

Make the 'AI-first telco' real at the customer edge: agentic conversations for ~100M IM3 and Tri subscribers

Indosat has declared itself an AI TechCo; Gemini Enterprise Frontline turns that declaration into customer-facing proof — dual-brand retention and service conversations that showcase the strategy while cutting serve cost.

Entry use case
Dual-brand churn-risk retention conversations
Expected outcome
Measured save-rate lift on at-risk prepaid cohorts across IM3 and Tri at conversation costs prepaid ARPU can carry
Recommended next step
Propose a co-branded 'AI-first frontline' pilot with the consumer team: one at-risk IM3 cohort, holdout-measured saves, built on the existing Google Cloud relationship.
What we understand

Indosat Ooredoo Hutchison's operating reality

Indosat Ooredoo Hutchison, formed by the 2022 Ooredoo–Hutchison merger, serves on the order of 100M subscribers across the IM3 and Tri brands.

Public fact

Leadership has publicly positioned Indosat as an 'AI-first TechCo', with large-scale AI infrastructure initiatives and partnerships announced with global AI players.

Public fact

Indosat and Google Cloud announced a strategic partnership in 2023 spanning cloud and AI capabilities.

Public fact

A dual-brand prepaid base skews young and price-sensitive; churn between brands and to rivals is the core commercial leak.

Reasoned inference

Internal AI ambition likely outpaces deployed customer-facing agentic capability — the gap between narrative and frontline is the opening.

Seller hypothesis — validate

Indosat's strategic partnership with Google Cloud, announced in 2023, is public — this account has a stated cloud-AI alignment to build on.

Publicly reported Google Cloud relevance

Validate with the account team before outreach: Where the announced AI initiatives have already claimed the customer-care roadmap · Churn-model maturity and campaign-engine APIs for real-time triggers · Incumbent care chatbot vendor and contract status

Build vs buy

Why we have a right to win here

Partner-led target

Technically capable but needs industry workflows, integration, acceleration, or managed operations

Evidence: Indosat has the loudest AI ambitions in the market — a declared 'AI TechCo' strategy, sovereign-AI infrastructure initiatives with global partners, and a public Google Cloud partnership — but its build energy targets AI infrastructure and national platforms, not customer-workflow applications; partner-led with strong co-marketing potential.

Why they won't build the full stack: The AI-first narrative needs visible customer-facing proof faster than an internal application build can deliver; Indosat's differentiation play is AI infrastructure and distribution, so a partner-delivered agentic frontline is complementary to its build agenda, not competitive with it.

What management is signalling

Leadership has publicly committed to an AI TechCo strategy — including sovereign-AI infrastructure initiatives with global AI partners and local-language AI ambitions — alongside double-digit profit growth in FY2025.

Reported factFY2025 results commentary and public strategy announcements · Feb 2026

GTM implication: Sell the customer-facing proof point: an agentic frontline makes the AI-first story visible to ~100M subscribers, built on the Google Cloud relationship already announced.

Modest revenue growth against stronger profit growth implies efficiency, not headcount, must fund the AI ambition.

Inferencerecent investor communications (validate) · 2026

GTM implication: Frame serve-cost reduction as self-funding the AI narrative — the pilot pays for the story.

What already exists (don't pitch this)
  • myIM3 and bima+ apps
  • Existing care chatbots across IM3 and Tri
  • Publicly announced AI-infrastructure and sovereign-AI initiatives
What customers still can't do end-to-end (pitch this)
  • →Execution-grade customer-facing agentic workflows
  • →Reason-aware retention conversations beyond blast campaigns
  • →Voice automation over charging systems with in-channel payment
  • →Attributed save-rate measurement against holdouts
Opportunity map

Where agentic communications pays off first

WorkflowWhy it matters hereValueComplexitySpeedChannels
Churn-risk retention (IM3 + Tri)
Two-way, reason-aware retention conversations with matrix-bounded offers and attributed saves.
Retention economics drive the merged entity's revenue defense; an AI-first brand needs an AI-powered save motion.
Friction today: Blast SMS and capacity-limited outbound reach a fraction of at-risk subscribers with untargeted offers.
WhatsAppVoice
Digital care deflection with execution
Grounded resolution of top care intents in-channel, including payment completion.
Serve-cost per prepaid subscriber must fall for the AI-first story to show in the P&L.
Friction today: App and chatbot answer FAQs; package, network, and SIM issues still escalate to humans.
In-appWhatsAppVoice
Indosat Business SME desk
Qualified SME conversations with meeting booking for sellers and grounded support for existing lines.
B2B growth is a stated pillar; SME connectivity buyers need fast quotes and support without enterprise account-manager cost.
Friction today: SME leads and tickets queue behind consumer volume.
WhatsAppVoiceWeb
Watch the change

Dual-brand churn-risk retention conversations: today vs the agentic model

Scenario: A Tri user whose top-ups have stalled gets a WhatsApp conversation that surfaces her real issue — coverage at her new campus — and saves her with a targeted package, not a blanket discount.
Today
same interaction, two worlds
Agentic layer
At-risk base contacted
capacity-limited fraction
100% attempted
Platform capability
Cost per contact
$13.50 median assisted
$1.84 median self-service
Benchmark
QA coverage
1–2% sampled
100% scored
Platform capability
Sources & assumptions
  • · At-risk base contacted: Platform capability; save rate measured in pilot
  • · Cost per contact: Gartner customer service cost benchmarks, 2024
  • · QA coverage: Platform capability: every interaction logged and evaluated
  • · Gartner, customer service cost benchmarks (2024): $13.50 median assisted vs $1.84 self-service per contact
  • · McKinsey, digital-first collections research: 20–25% NPL reduction among leaders; up to 40% opex reduction with gen AI
  • · Baymard Institute: ~70% average cart abandonment (meta-analysis)
  • · IAMAI–Kantar via IBEF (2025): 900M+ Indian internet users; 98% consume Indic-language content
  • · LeadSquared and vendor funnel studies: 78% of students choose the first institution to respond (directional, vendor data)
  • · HDI / ITSM operator benchmarks: $15–25 per L1 ticket; 40–60% of L1 volume is resets/status (validate per customer)
  • · Conventional-flow wait times and volumes are typical operator patterns — assumptions to replace with the customer's own baseline
  • · Agentic-flow behaviors (context retention, 100% logging, in-line policy checks) are platform capabilities, not projections
Recommended solution

One integrated stack, opinionated for this account

Channels · Tilicho Labs
WhatsAppVoiceIn-appWeb

Voice & channel orchestration, telephony, conversational execution, session/state, routing, integration build. Capability coverage validated during implementation.

Intelligence · Google Cloud
Gemini reasoningEnterprise groundingWorkflow agentsMulti-agent orchestrationGoverned actionsEvaluation & analytics
Systems · Indosat Ooredoo Hutchison
Churn modelCharging systemCampaign engineNetwork status feedsPaymentsCRMOrder management

API access to these systems is the critical-path dependency.

Trust & languages
Bahasa IndonesiaEnglish

Identity-bound sessions, policy-bounded actions, 100% audit logging, human approvals at defined points, in-tenant intelligence.

Business case

The economics, with your numbers

Addressable monthly interactions6.0M
Seller assumption — replace in discovery
Current cost per interaction ($)$0.6
Industry benchmark scale — validate
Automation / assistance rate (%)55%
Seller assumption — pilot proves this
$3.6M
Current operating cost / mo
$9.9M
Modelled gross benefit / yr
0.1 mo
Payback on Scale
71%
3-yr ROI (modelled)
Automated/assisted interactions per month3.3M
Modelled AI run-cost per month (usage + cloud, system estimate)$1.2M
New monthly operating cost$2.8M

All figures are modelling estimates from the labeled inputs above — nothing here is customer-provided yet. The pilot's first job is replacing these assumptions with Indosat Ooredoo Hutchison's measured baseline. Package price covers implementation only; recurring usage billed separately.

Recommended package

Scale — $100K implementation

Scale · $100K · A multi-channel production deployment10–14 weeks to production across priority workflows

Why this package for Indosat Ooredoo Hutchison: Two consumer brands and two workflows (retention plus service deflection) with an AI-forward buyer fit the Scale package; transform-scale ambition can follow the first attributed wins.

Included
  • 4–6 channels
  • 2–3 priority workflows
  • Multiple enterprise integrations
  • API credential & security setup
  • Advanced orchestration
  • Multilingual support
  • Agent Assist / human escalation
  • Production analytics
  • Expansion roadmap
Not included
  • ✕Usage & consumption (billed separately)
  • ✕Enterprise-wide governance build-out
  • ✕Multi-BU rollout
Recurring costs (separate from the package)

Packages cover implementation and integration only. Recurring costs are billed separately: Tilicho Labs platform usage (~$0.15/call-min indicative, usage only), Google Cloud consumption, telephony/carrier charges, managed operations, and support & optimization. No package includes unlimited usage.

Customer resources required
  • · API access + credentials for: Churn model, Charging system, Campaign engine
  • · A named business owner for dual-brand churn-risk retention conversations
  • · Security review counterpart and policy sign-off (Churn model scope)
  • · Baseline metrics for the pilot's success thresholds
Executive messages

What to say to whom

CEO

“You have told the market Indosat is AI-first. Customer-facing agentic conversations on Google Cloud are the fastest, most visible proof point of that strategy.”

CIO / CTO

“This composes with the Google Cloud partnership you already announced — Gemini agents grounded in your charging and CRM stack, deployed inside your existing cloud relationship.”

COO

“Retention outbound today reaches a fraction of the at-risk base. Full-coverage conversational outreach turns your churn model's scores into actual saved revenue.”

Head of Consumer Marketing (IM3/Tri)

“Untargeted offers burn margin. Reason-aware conversations retain with the smallest sufficient offer — and tell you why subscribers actually leave.”

Chief Risk / Compliance Officer

“Consent-governed outreach with enforced contact policies and complete logs keeps an aggressive retention motion inside Kominfo and consumer-protection lines.”

CFO / Procurement

“Save-rate lift is attributed per conversation against a holdout — you will see incremental revenue per rupiah of usage spend, not a modeled claim.”

Outreach

Pre-built offer emails for Indosat Ooredoo Hutchison

Written from this account's own research — the strategic signal, the capability gap, the entry workflow, and the modelled economics — not a mail-merge template. Pick the moment and the persona, edit anything, then copy or open in your mail client.

Moment in the deal
Cold outreach — no prior conversation
Who it's addressed to
Cares about: The workflow itself and its daily failure modes
Register
Length
Draft — edit freely before sending
Open in mail client

Customer-safe by construction: drafts are composed only from customer-facing fields. Account tier, build-vs-buy classification, priority score, internal routing, and partner-commercial detail are not inputs to the composer, so they cannot appear in a draft. Money figures are always framed as modelled from the customer's own volumes. Read before sending — you own what goes out.

The pursuit

Tier 2 — high-potential incubation

Why this tier

Indonesia's ~100M-subscriber #2 with the loudest AI-first strategy in the market and a publicly announced Google Cloud partnership — high receptivity, strong strategic fit, fierce vendor competition.

Recommended next step

Propose a co-branded 'AI-first frontline' pilot with the consumer team: one at-risk IM3 cohort, holdout-measured saves, built on the existing Google Cloud relationship.

Entry: Dual-brand churn-risk retention conversations · Scale package · 10–14 weeks to production across priority workflows. Human fallback throughout; success thresholds agreed before build.

Start the pursuit

Research-based priority-account universe assembled from public information, market scale, communication volume, and solution fit. This is NOT an authoritative list of top Google Cloud customers; existing Google Cloud relationships are noted only where publicly reported. Validate every account with the account team before outreach.