Telco transformation case study: Indosat Ooredoo Hutchison
When Vikram Sinha stood before investors in 2024 to outline Indosat Ooredoo Hutchison’s (IOH) AI ambitions, the company was still emerging from a massive telecom merger. Integration work was ongoing, and investors were watching closely.
The pressure surrounding the merger was also difficult to ignore. Indonesia’s telecom market was intensely competitive: IOH faced a unique challenge, serving approximately 94 million customers across thousands of islands, with massive volumes of network, sales, and customer data flowing through disconnected systems.
Instead of playing it safe, Sinha bet big, describing a future in which IOH would become an AI-native telco and, eventually, reimagine itself as a broader technology company helping accelerate Indonesia’s digital future.
The ambition went well beyond deploying new technology to rethinking how the company made decisions, served customers, invested capital, developed talent, and operated day to day. Growth was also a North Star.
“The challenge with AI is that it is everywhere other than the P&L. If it is not showing up at the EBITDA level, then it is not really scaled,” Sinha says. “The biggest bold decision I made in front of my board was: Within the first year, I want to show positive impact on EBITDA.”
If AI was advancing rapidly, customer expectations were shifting just as quickly. Rather than asking how to preserve existing ways of working, a new question emerged: How could IOH turn the merger from an integration challenge into a transformation opportunity?
Together with McKinsey and Google Cloud, IOH launched a transformation built around a simple but powerful premise: business-led reinvention rather than a technology deployment.
McKinsey and its AI arm, QuantumBlack, worked with IOH leadership to identify where AI could most meaningfully move the business. Rather than organizing the transformation around isolated use cases, the team focused on enterprise domains tied directly to financial outcomes.
The largest opportunities emerged in four areas: hyper-personalized customer engagement, hyperlocalized sales and distribution, AI-driven network investment decisions, and productivity improvements across support functions.
“This was a test case for three organizations coming together: the customer, a world-class consulting organization, and a world-class technology organization,” says Karan Bajwa, president of Google Cloud APAC.
“Google Cloud brought the technological engine— industry-leading data analytics, and cutting-edge AI capabilities. McKinsey brought a deep understanding of where value sits in the business and the ability to drive end-to-end transformation and adoption.”
Nearly 40 colleagues across McKinsey and QuantumBlack worked across the organization to build 40+ models to provide the best possible offer to the right customers. This was coupled with an “empathy engine” that was able to come up with the right messaging and imagery based on each customer. This was operationalized through an engine that was running 400+ new campaigns a month without a single incremental full-time employee.
Three decisions proved foundational:
- Building a unified AI and data platform. IOH consolidated more than 30 disparate data sources into a unified cloud-based environment powered by Google Cloud and BigQuery. The platform processes trillions of data points every month and supports billions of AI predictions tied to customer experience, network optimization, and operational performance.
- Organizing around business domains instead of pilots. McKinsey helped IOH prioritize opportunities with the highest economic impact. Hyper-personalization enabled individualized customer offers and communications at scale, while AI-driven network planning improved capital allocation and operational precision.
- Treating culture change as a CEO priority. “My biggest learning is: You can’t be half-hearted,” Sinha says. “You have to go all in.” Quarterly AI immersions for executives, capability-building programs across employee groups, and AI-focused training programs helped shift the organization from siloed decision-making toward a more data-led culture.
The partnership model between IOH, McKinsey, and Google Cloud became an important differentiator. The three companies operated through integrated governance structures that included joint executive reviews, shared transformation scorecards, and cross-functional working teams.
“We were very deliberate about our working model,” says Vinayak HV, a McKinsey senior partner. All three organizations were working toward the same objectives.”
That alignment became especially important because the transformation unfolded during a period of rapid technological change. New capabilities emerged continuously, from increasingly sophisticated foundation models to agentic AI workflows.
Within the first year, the transformation began producing measurable financial and operational outcomes.
AI contributed directly to business performance. IOH reported a positive EBITDA impact in year one, with leadership targeting more than a six percentage-point lift as the transformation scaled. The company also achieved ARPU growth significantly above industry benchmarks.
Elsewhere, AI became embedded into enterprise operations. Today IOH processes more than 2.5 trillion data points monthly and runs billions of AI predictions across customer engagement, network optimization, and operational workflows.
Employee adoption accelerated across the organization. More than 2,000 employees, or approximately 75 percent, became AI-certified, while AI capability-building programs helped drive broad adoption across leadership teams, technical talent, and frontline employees.
Elsewhere in the organization, sales representatives gained AI-powered recommendations that improved productivity and reduced time spent on manual planning. Network investments became more predictive and targeted. Customer interactions became more tailored and localized across a highly diverse market.
Perhaps the clearest signal of progress came not from the technology stack, but from employee behavior. Functions such as HR, procurement, and finance—which had once viewed AI as peripheral to their work—began building and adopting their own AI-enabled solutions.
“When we started, there was a lot of fear,” Sinha recalls. “But now those same employees are standing up and saying, ‘I am doing it, and I am doing so much more.’ That is what I am extremely proud of.”
For many organizations, AI remains a promising experiment. For IOH, it became the operating system for the next phase of growth.
