Accenture outlines $400 billion AI boon to Saudi and UAE production

04 June 2018

Saudi Arabia and the UAE could combine for over $400 billion in GVA by 2035 through AI technologies according to an analysis from professional services firm Accenture, with the technology delivering a range of other possible social benefits in addition.

In its ‘Pivoting with AI’ evaluation report on the Middle East, the tech-minded professional services firm Accenture has concluded a potential $400 billion bonus in combined revenues for the Saudi Arabian and UAE economies by 2035 through the adoption of artificial intelligence technologies in local production.

The firm’s economic modelling, conducted by Accenture Consulting in conjunction with economic consultancy Frontier Economics, assumes a 50 percent uptake of AI’s full technological potential over the analysed timeframe, and considers three channels of AI growth – intelligent automation, labour & capital, and innovation diffusion.Growth scenarios for Saudi Arabia and United Arab Emirates with AIThe conclusion; $215 billion in gross value added (GVA) to the Saudi Arabian economy, representing 1.1 percentage points added to its growth rate against a business-as-usual baseline, and a figure of $182 billion GVA for the Emirates for a 1.6 percentage-point boon. Numbers, the firm says, that would be transformative – perhaps especially so for the studied states, driven, both, by the imperative to diversify their economies away from a historical reliance on oil.

Further to the overall figures, the analysis looked into potential sector-specific gains with AI as a factor of production, focusing in on a number of industries (13 in Saudi Arabia and 15 in the UAE), including manufacturing, wholesale & retail, utilities, healthcare, construction, transportation & storage, and professional and financial services, with the most impacted sectors for each nation demonstrating a divergence in overall economic make-up.AI's impact on industries in Saudi ArabiaFor Saudi Arabia, in absolute dollar terms, the industry given the biggest boost with AI factored into production is public services, with a GVA of $67 billion, followed by manufacturing and professional services contributing $37 billion and $26 billion in turn to the $215 billion total GVA projection. Transportation, financial services, and wholesale & retail would together add a further $50 billion plus in GVA output. 

As to the UAE, the highest benefitting sectors from AI implementation would be financial services, healthcare, and transport & storage, adding an annual $37 billion, $22 billion and $19 billion in GVA respectively. The Emirates however sees a more even spread across industry, with the utilities ($18 billion), public services, wholesale & retail ($17 billion apiece) and manufacturing ($16 billion) sectors all geared for significant AI-aided growth.AI's impact on industries in the UAE

Further to the purely monetary gains, and the socioeconomic benefits stemming from such, the consulting firm highlights the additional social dividends that AI could deliver to the region, termed in the report as the Super Seven. These include predictive machine learning aids around regional issues such as food insecurity, water scarcity, oil price fluctuations, and growing urbanisation, as well as improved production efficiencies to help curb the effects of climate change.

“While AI-led growth will be felt across a wide variety of industries, the financial services sector has the most to gain – which isn’t surprising, given that many of its jobs can be significantly augmented with AI and machine learning. In addition, Accenture reports have already shown that banking executives globally are taking action to transform their businesses through the use of AI,” Accenture’s managing director of Financial Services for the Middle East and Turkey, Amr Elsaadani, said in conclusion.

AI and Fintech in the GCC

If there’s any further convincing needed on the potential of artificial intelligence in the region, and with particular regard to financial services, one just has to follow the trail of consultancies. Both The Boston Consulting Group and Sia Partners have hosted expert forums on AI for CEOs and business leaders in recent weeks, while A.T. Keaney and its National Transformations Institute in Dubai have declared a SAR $1 trillion potential for Saudi Arabia from emerging advanced technologies.

Meanwhile, the Saudi Arabian Monetary Authority (SAMA) has just tapped Big Four firm Deloitte, which has recently established a Digital Delivery Centre in Riyadh, to help develop the Kingdom’s fintech ecosystem, and Alvarez & Marsal Managing Director in the Middle East, Saeeda Jaffar, has been appointed as an independent director of the Executive Board of Bahrain FinTech Bay, which is supported by Roland Berger.

Accenture, itself, has just welcomed a new Managing Director for the Saudi Arabia, Khaled Al-Dhaher, an experienced senior IT executive who has undertaken business studies on AI with the Sloan School at M.I.T, while the firm’s UAE branch will be partnering with the Dubai International Financial Centre on a world-class fintech accelerator programme. The firm is also a premium technology partner for Dubai Expo 2020. 

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.