Four foundations for MENA telecom operators to succeed with digital M&A

21 December 2017

Middle East telecom operators need to prepare for a new wave of merger and acquisitions (M&A) on the back of digital opportunities, writes Amr Goussous, a Partner with Strategy& in the Middle East and a member of the firm’s Communications, Media, and Technology Practice.

Global telecom M&A is robust but is changing in it composition. There were roughly 500 deals a year since 2010, worth around $100 billion per year on average. Telecom operators are seeking value in adjacencies in the rapidly growing ICT sector. Such deals constituted 44% of total telecom deals value in 2016, up from 4% in 2012.

Moreover, 76% of the acquired ICT targets were business-to-business (B2B) propositions compared to 12% for business-to-consumer (B2C) and 13% for B2B and B2C combined. The Middle East, however, accounted for $10.3 billion from 2010 to 2016, out of which 99% was telecom-to-telecom and less than 1% targeted ICT players.

Deals in telecom sector

Middle East telecom operators will need a different approach to value creation. In the recent past, telecom M&A was about building scale. A specialised team was in charge of M&A, collaborating with corporate leadership and the board. These deals had little effect on existing business. By contrast, moving into ICT and digital services means closely involving business and functional units in the specifics of the new M&A agenda because these deals will affect the future value proposition and capabilities of these units.

To succeed with digital M&A, Middle East telecom operators need an inorganic growth agenda based on four foundations:


First, operators must define precisely the strategic purpose of their deals with respect to the digital domains they want to enter, how they will enhance their digital ecosystem, and their investment approach. The value proposition in B2B ICT will derive probably from the internet of Things (connected devices and sensors) and analytics, managed IT, system integration, and cybersecurity.

There are also opportunities in ICT services for consumers. Operators must understand the requirements for success in different digital segments and verticals so that they can assess which capabilities they need to build, whether alone or through partnerships, and which capabilities they must acquire.

Four foundations for deals in telecom


Second, operators should reconsider how they source deals. This is particularly important in the Middle East where viable targets seem scarce and company information is hard to obtain. Operators should signal to the market their interest in acquisitions and expand their search to include international targets with strong financial performance and/or strong capabilities, whether for talent or product.

For example, telecom operators can acquire an ICT company operating outside the Middle East and bring its technological capabilities to the region. Alternatively, telecom operators could consider a minority stake in an international target and then establish a reverse JV to serve the Middle East where the telecom operator controls the majority share.

Operating models

Third, operators must ensure that their operating models evolve in tandem to ensure aspired value and synergy capture. The model usually consists of six main elements: supervisory governance of the target company, executive appointments, business plans and agreement on budgets, service level agreements and definition of performance indicators, alignment on reporting, and agreement on decision rights and processes. 

The model will depend upon how much integration is intended between the operator’s business units and those of the target. Ultimately, this demands more flexibility and agility from the operator as the model needs to accommodate potentially a series of acquisitions across multiple digital domains. 

Operating model elements


Fourth, operators must use different performance measurement metrics as traditional telecom indicators do not apply to ICT acquisitions. For example, earnings before interest, tax, depreciation, and amortisation margins (EBITDA) are usually above 35% for healthy operators while in ICT these tend to be around 15%. Similarly, while operators have an investment to total revenues ratio of around 15%, in technology firms it is some 7%. 

When measuring operational performance, operators typically monitor subscriber numbers and turnover, average revenue per user, and yield per usage type. However, as they start working with new business models and industries, operators should tailor their key performance indicators and consider metrics that measure digital value creation.

These include, for example, hours of usage, number of views, data centre utilisation, frequency of access to application programming interfaces, the number of connected devices managed, the billability of the professional services team, R&D spending, and intellectual property generation per year.

Digital services, the target of future telecom M&A, will challenge the division between industries. This will provide telecom operators with opportunities for growth in new business and consumer areas. Those operators which move first will steal a march on their competitors.


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EY launches advanced tool to assess trustworthiness of AI technology

12 April 2019

Global professional services firm Ernst & Young has announced the release of an advanced analytical tool to assess the trustworthiness of artificial intelligence.

Enabled by Microsoft Azure, the EY Trusted AI platform released by the global professional services firm Ernst & Young produces a technical score of an artificial intelligence system by leveraging advanced analytics to evaluate its technical design, measuring risk drivers including its “objective, underlying technologies, technical operating environment and level of autonomy compared with human oversight.”

Aimed at helping to resolve the issue of trust in technology, which the firm contends is the biggest barrier to wider AI adoption, the new tool’s risk scoring model is based on the ‘EY Trusted AI conceptual framework’ launched last year, which speaks to embedding trust mechanisms in an AI system at the earliest stages around the core pillars of ethics, social responsibility, accountability and explainability, and reliability.

“Trust must be a front-line consideration, rather than a box to check after an AI system goes live,” said Keith Strier, EY’s Global Advisory Leader for Artificial Intelligence. “Unlike traditional software, which can be fixed, tested and patched, if a neural network is trained on biased data, it may be impossible to fix, and the entire investment could be lost.”AI system overviewUsers of the new solution such as AI developers, executive sponsors, and risk professionals will be able to garner deeper insights into a given AI system to better identify and mitigate risks unique to artificial intelligence technology, with the platform score produced by the tool subject to a complex multiplier based on the impact on users – taking into account potential unintended consequences such as social and ethical implications.

According to the firm, it’s the first solution designed to help enterprises evaluate, monitor and quantify the impact and trustworthiness of AI, while an evaluation of governance and control maturity further serves to reduce residual risks and allow greater planning – helping to safeguard “products, brands, relationships and reputations” in the contemporary risk environment.

“If AI is to reach its full potential, we need a more granular view – the ability to predict conditions that amplify risks and then target mitigation strategies for risks that may undermine trust, while still considering traditional system risks such as reliability, performance and security,” said EY Global Trusted Artificial Intelligence Advisory Leader Cathy Cobey.

Offered as a standalone or managed service – which will be regularly updated with new AI risk metrics, measurement techniques and monitoring tools – the new solution will be available to clients globally this year, with further features including a guided interactive, web-based interface and a function to drill down for additional detail, as well as the ability to perform dynamic risk forecasting on when an AI component changes – such as an agent’s functional capabilities or level of autonomy.