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Choosing Your First Generative AI Use Cases
To get started with generative AI, first focus on areas that can improve human experiences with information.
Europe is behind the curve compared with other regions but there are opportunities to be had
Generative AI has the potential to add $575 billion to Europe’s economy over the next five years – but only if the region is prepared to grasp the opportunities presented by this wave of innovation.
That’s just one of the takeaways from an in-depth article published recently by McKinsey Global Institute which seeks to gain insight into the whole of the AI value chain across Europe.
It suggests that most of the value generated by generative AI “will stem from organizations’ adoption and scaling of generative AI solutions – an important consideration in Europe, where labor productivity has been slowing.”
The report, Time to place our bets: Europe’s AI opportunity, predicts that generative AI “could help Europe achieve an annual productivity growth rate of up to 3% through 2030.” This includes retail, banking, finance and insurance, transport, manufacturing and construction industries, plus breakthrough innovations that transform daily life such as accelerated drug discovery and personalized education.
Europe Needs To Stake Its Claim to the New Technology
But there’s a problem. While the report rightly points out the potential benefits of generative AI, it warns that Europe is behind the curve compared with other regions and that European corporations are “moving much more slowly than those in other countries”.
But things are moving – if you know where to look. In retail, for example, generative AI powered intelligent virtual assistants provide hyper-personalised customer service and product recommendations resulting in a more streamlined and customer-centric shopping experience.
These intelligent virtual assistants – which are increasingly more life-like and less robotic – can also tap into inventory management systems, browse purchase history and help process orders making the entire process smoother and more user-friendly.
In finance, banks and other lenders are using generative AI to make precise risk assessments when lending to high-risk individuals. It’s also taking algorithmic trading to the next level opening the door to split-second buy and sell decisions in response to changing market conditions.
These smart algorithms can process massive datasets, identify market trends, and execute trades at speeds beyond human capability. The result is more efficient, data-driven trading strategies that aim to maximize returns and minimize risks.
For example, a global bank is using retrieval augmented generation (RAG) powered search capabilities to help wealth advisors offer a quick analysis of reports from 100,000 documents. This then provides a summarised recommendation/answer to their queries, saving on average 20 to 30 minutes for them and giving more informed recommendations to customers.
While in healthcare, generative AI is being lined up to assist doctors in diagnosing complex medical conditions. For instance, when faced with a challenging medical condition, generative AI would be able to swiftly scan through vast databases of medical literature and case studies, drawing upon collective medical knowledge to help assist in diagnosis.
Much of Generative AI’s Potential Has Yet To Be Explored
Make no mistake – we’ve barely scratched the surface of the opportunities and potential that stem from this technology. And while there are plenty of challenges ahead, many of these can be addressed with strong leadership.
That means political leadership to ensure that a regulatory framework exists that protects individual rights while allowing innovation to flourish. It means taking a long-term strategic view around vital infrastructure such as energy and digital connectivity to ensure that any progress is fully supported.
While in the C-suite, business leaders need to have an appetite to learn – and be flexible about – the technology as they seek to roll it out at scale, as well as adopt self-driven responsible AI best practices. As the McKinsey report rightly points out, the opportunities are there to be had. It’s up to us to make them happen.
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