- Define a clear and standardised AI strategy to guide the process: A clear AI strategy is needed to guide the process in understanding the value that can potentially benefit the organisation and outline the steps needed to achieve the goal. A typical AI strategy should clearly state the problem statement, the impact of the solution and the reporting required by the stakeholders.
- Retire legacy systems and start improving interoperability internally: To redesign an existing business model, retiring legacy systems and improving interoperability across the technology infrastructure must remain a key priority in the endeavour. Only by overtaking the current IT applications with new systems, can businesses develop the right capabilities in using data, models, and software to address the problems detailed in the strategy. Project leaders will also be required to fundamentally prepare sufficient and quality data and start labelling them into useful assets to power the AI model. /li>
- Data quality, quantity, and labelling – Another critical element involves hiring and developing specialists in data engineering, data science, data and AI ethics, model security, and machine learning engineering. They should work as a team to optimise the complex navigation around the interplay of software and hardware. Before acquiring the right capabilities to operationalise AI, it is also important to set up internal processes for end-to-end and top-down management of the project.
- Ensure scalability in the project scope – Finally, as with all projects, the owners need to ensure scalability in the project scope from the onset and work towards achieving the complete purpose. Not realising scalability after the first phase will lead to poor reporting, resulting in difficulty in securing future investments.
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