How telecom firms can maximise IT to drive cost efficiency

According to McKinsey companies must reach a higher level of tech maturity.

Operators face pressure to monetise infrastructure investments as the telecommunications industry grapples with the massive costs of deploying advanced network technologies, according to a McKinsey report.

McKinsey said operators that have achieved stronger information technology (IT) cost performance did so by reaching a higher level of tech maturity.

These high-performing operators boast an average IT cost-efficiency ratio of 3.7%, compared with 5.2% for their peers. One operator transformed its IT programmatically across all capabilities and reduced IT spending by as much as 40%.

Moreover, more than 60% of operators adopted agile methodologies, yet most struggled to reap the rewards. Whilst leading operators can industrialise a proof of concept into production in three to six months, their peers need 1.5 years on average.

McKinsey noted that top-performing operators have consistent delivery methodologies amongst all teams, a single and straightforward process for portfolio management based on objectives and key results and quarterly business reviews, and a common approach to talent management and sourcing.

The report also suggested insourcing IT talents as leading operators have strategically prioritised the growth of in-house, hands-on talent and the development of a strong internal engineering culture.

Engineering excellence leaders currently have 22 percentage points more insourced engineering roles than their peers.

Further, a centralised product catalogue allows operators to manage all products efficiently by eliminating the need for increasingly complex synchronisation logic across system domains and channels.

Additionally, telecom cloud leaders said cloud-native development is essential for success. On average, the industry uses such practices for only 5-10% of all apps, but those that outperform are already at a rate of 10-25%.

Lastly, all operators are working on numerous generative artificial intelligence use cases, with industry leaders expecting annual earnings before interest, taxes, depreciation, and amortisation impact of up to 10%.

However, most players are deploying specific gen AI use cases without the aid of a comprehensive, domain-based data and AI strategy which is essential for realising the full financial benefits of the new technology.