CIO OPINION documentation , you can enable the seamless integration of AI tools and methodologies necessary for a powerful AI deployment .
3 . Establish accountability and alignment : Successful AI implementation requires delegation of ownership and alignment of vision . Process owners must have comprehensive knowledge , display robust stewardship and be free to appoint and assemble stakeholders . This collective approach ensures that all stakeholders can contribute to the effective application of machine intelligence across the organisation .
4 . Prioritise impact factors : The roadmap to AIreadiness is replete with factors that range from complexity and scale of effort required to risk and questions of ethics . Tackled as a whole , these factors can become unmanageable and even derail how machine intelligence is implemented within each process . Accordingly , it is necessary to establish a hierarchy of priorities based on the type of implementation desired and the resources available , with an understanding that this hierarchy may be subject to change periodically , depending on shifting circumstances . 5 . Introduce automation : Automation is key to creating a context pipeline for AI integration that can be connected to various endpoints like mobile devices , tablets and servers . Moreover , by eliminating manual processes , efficiency skyrockets while risks are mitigated , complexities are reduced and space for limitless scalability is created . 6 . Focus on data : Data is the lifeblood of AI and
AI ’ s power to turn data into actionable information and outcomes is unprecedented . However , this means that access to relevant , high-quality data , including information system records , metadata , master data , reference data , labels and logs , is crucial . True AI-readiness that can produce accurate insights requires a record of where data is stored , who owns it , why it is valuable and a knowledge of how it ’ s generated , mined , refined , secured and governed .
AI-readiness is a systemic transformation
The most profound business transformations rarely involve fundamental changes in just one area of the organisation . Becoming AI-ready is no exception . It requires the contribution of and collaboration between skilled and non-technical personnel who are able and empowered to harness AI insights and apply them to various roles . It relies on nurturing a culture based on curiosity , openness and learning , with strong partners and vendors ready and willing to provide support .
It requires processes to be mapped , evaluated and automated where possible , with ethical considerations respected and robust data governance maintained . It requires platforms equipped to handle data in real-time , in the right form and in a secure manner .
But most of all , it requires a substantial shift in mindset powered by constant learning and adaptation . p
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