McKinsey: GCC companies adopt AI at record rates – but scaling remains elusive
There has been a significant surge in AI adoption across the GCC in recent years. Yet despite this momentum, a new report from McKinsey & Company shows that many organisations are seeing ambitious projects stall – and continue to struggle with scaling AI across the business.
The report, which surveyed 39 senior executives and board directors in GCC organizations, found that fewer than a third of organizations have been able to move beyond pilot programs to further scale AI capabilities. And that is not for lack of initiatives – McKinsey & Company notes that there has been an enormous amount of investment in recent years into AI.
“Boards and executives are excited about AI, but many still don’t know how to convert intent into action. What they need is a blueprint on where to invest and how to prioritize,” is how one interviewee put it.
High rate of AI adoption
Many GCC organizations have put an enormous amount of funds and resources into AI development in recent years. For example, Saudi Aramco has built a proprietary generative AI model with 250 billion parameters drawn from decades of operational data. Other organizations have put millions of dollars towards partnerships that aim to leverage AI.
McKinsey found that a total of 84% of GCC companies have adopted AI in at least one business function, a significant increase from just 64% that said the same in a similar survey from 2023. Despite that, only 31% of respondents reported their organizations have successfully scaled AI or deployed it across the organization.
“Make no mistake – leaders in the GCC region are keen backers of AI,” said Tarik Alatovic, senior partner with McKinsey and co-author of the report.
“Yet leadership intent might not suffice. Translating intent into wide deployment with bottom-line impact hinges on three components: a business-led AI strategy that chases value, delivery capabilities (the organization’s technology and talent, for example), and a change management program that encourages adoption at scale. The value realizers in the survey outperform their peers on precisely these fronts.”
AI and data use cases
Designing and operating a successful AI deployment can be incredibly costly. In order to create real value, companies need to make the right choice in terms of technology and immense amounts of data need to be processed. An AI tool is only as good as the data that it runs on.
According to one executive interviewed: “Many firms lack the capital, which is one factor slowing down adoption, since implementing AI recommendations requires significant investment in automated equipment and infrastructure.”
Among the areas where AI has been used the most are service operations, marketing, and sales functions. The value of AI here is clear for many organizations, which have been successful in automating routine and tedious tasks, allowing employees to focus on more important work.
AI has also been huge in product and service development, with companies enhancing their existing products or launching new AI-based features. This is the business function that has seen the fastest uptick in AI use, up 23 percentage points since 2023.
Components of a successful deployment
Strategic AI deployment hinges on three core principles, according to McKinsey’s report: Accountability, AI-driven development, and workflow redesign.
Accountability ensures that all AI initiatives are tied to measurable business outcomes (KPIs) and are rigorously monitored for success before being scaled. Accelerating value capture involves using AI itself to build new solutions, rapidly generating code and workflows to maximize developer output and time-to-delivery.
Finally, realizing the full potential of AI requires organizations to fundamentally redesign existing workflows – a far cry from simply plugging AI into old processes and hoping for the best. This type of reinvention is the strongest driver of earnings improvement.
Risks and shortcomings
Despite all the promising advantages that AI tools offer to companies, there are still some lingering risks when it comes to adopting new AI technologies, especially in the case of a rapid and haphazard adoption.
The survey shows that cybersecurity was the most pressing concern to respondents, which increased in the past two years. Inaccuracies and privacy were the next most cited risks, which have also become more of a concern since 2023.
“Reliable data is the lifeblood of AI, and the lack of it is often a barrier to scaling AI,” said Alatovic. “Common issues include poor data quality, missing values, bias, and outliers, all of which can degrade output and expose an organization to risk.”
Since AI tools began to become mainstream, accuracy issues related to so-called ‘hallucinations’ have been a recurring criticism of products like ChatGPT and other LLMs. When it comes to privacy, many users of such tools are also concerned with data collection and the potential of that data to later be sold off, a long-time business practice of many large tech companies.
Alatovic concludes: “The pace of change is fast. Only a year ago, the focus was on generative AI, but technology has since leaped ahead; many organizations are now piloting AI agents, heralded as marking a major evolution in enterprise AI.”
“As technology continues to evolve, building the right capabilities and adoption strategies could prove key to keeping up with leaders in AI adoption when it comes to creating value.”
