EDITOR ’ S QUESTION
CAROLINE CARRUTHERS , CEO , CARRUTHERS AND JACKSON
The key to successfully integrating AI into company operations is having a robust data strategy in place which aligns with business goals . Once you ’ ve got your data foundations right , you can focus on the efficiencies data and AI can unlock .
Before jumping into AI , it ’ s important to think about purpose . You need to know what problem you are trying to solve , where you are now and where you want to go . I would suggest completing a Data Maturity Assessment because data fuels AI , so high-quality data is essential , otherwise , organisations are likely to face the issue of ‘ rubbish in , rubbish out ’.
Organisations should experiment a little bit with AI to understand how it fits into operations and determine what types of AI will work . While Generative AI ( Gen AI ) has been the focus for many , as businesses rush to keep up with competition by deploying and exploiting new technologies , AI is not a one-size-fitsall solution . Organisations should explore a variety of AI technologies .
To get the most out of their data , companies need the right tools in place . This doesn ’ t mean jumping onto the latest IT trends , instead , for an organisation to get the most out of its assets , technology and company-wide data need to work together to unlock the full potential of the different tools .
Vitally , selecting the correct tools will only have an impact if existing employees are engaging with the technology , and understand what data can do for
Before jumping into AI , it ’ s important to think about purpose . You need to know what problem you are trying to solve , where you are now and where you want to go .
them . What really makes or breaks an organisation ’ s data strategy , or its rollout of AI is the people using it every day . For this reason , organisations ’ data and AI strategies should have a large focus on data literacy , enabling employees to understand and leverage data and AI within their roles . Clear policies and regular training are essential to facilitate this understanding .
Finally , organisations ’ data strategies must be constantly refined and tweaked to ensure teams remain compliant with quickly emerging AI regulations . The EU AI Act and the UK ’ s principles-based approach exemplify the diverse regulatory frameworks that organisations must navigate while balancing innovation with compliance . If data and AI strategies are found to be non-compliant , organisations face serious legal , reputation and financial consequences , significantly impacting business success .
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