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HE EMERGENCE OF GenAI
T has sparked significant excitement over the last two years . The output of GenAI tools is so impressive that investment in the technology increased fivefold in 2023 – with 36 GenAI companies hitting unicorn status . According to Bloomberg Intelligence , the market is now expected to grow rapidly and be worth US $ 1.3 trillion by 2032 .
The potential is clear . But , there is also growing awareness around the practical realities of applying the technology . As businesses have scrambled to implement GenAI in various ways , they have also realised that this is not a simple plug and play solution .
For instance , we know the content creation capabilities of GenAI can be remarkable , but , in truth , the results can only ever be as good as the data it is based on . GenAI can be deployed to generate personalised customer experiences at scale – but it can only do this if brands hold accurate , comprehensive information on each individual and their preferences .
So , before any investment in GenAI can pay off , organisations must lay down an underlying data infrastructure to deliver the right information to these applications . This is one of the key reasons why almost half of business leaders say they are now actively driving forward data modernisation programmes – and this is likely to include an investment in other forms of AI .
Tackling the data challenge
While many organisations are awash with data , it ’ s often unstructured and siloed . It ’ s now more critical than ever for businesses to make this data usable . As businesses invest in their data infrastructure , we ’ re starting to see a seismic shift in the data management landscape , with many major vendors now adopting a ‘ lakehouse ’ architecture approach . This combines data lakes , which are repositories for raw data , with data warehouses , where more structured data is stored .
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WHILE MANY ORGANISATIONS ARE AWASH WITH DATA , IT ’ S OFTEN UNSTRUCTURED AND SILOED .
Intelligent SME . tech
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