EDITOR ’ S QUESTION
SID BHATIA , GENERAL MANAGER AND REGIONAL VP ,
MIDDLE EAST , TÜRKIYE AND AFRICA , DATAIKU
Generative AI is an extraordinary milestone in the history of our technological development . It can automate the mundane , yet creative tasks that keep humans from innovating . With due care , it can take a place of honour among all the other innovations that power the modern , digital business and lead us into a new paradigm of Everyday AI .
Because everybody is creating AI projects , reuse becomes second nature .
The platform approach is the best of all worlds , reduced entry-barriers , lower costs , faster time to value , easier maintenance , more audit options , better governance , and more . But to fulfil its potential , an AI platform requires an organisation-wide shift in mindset .
Business leaders must be prepared to break down silos and move towards an Everyday AI culture where teams collaborate routinely , and value can be added repeatedly through brainstorming and rapid prototyping . A middle ground must be found between developmental autonomy and governance , so that employees feel empowered to add new value .
Because everybody is creating AI projects , reuse becomes second nature . Each project gets off the ground more efficiently and makes it to market more quickly . A common platform also allows for critical governance to be woven into the day-to-day use of AI and enforced , reducing the risk associated with AI scale-up .
To ensure value , the organisation must also identify use cases up front , allowing both for mundane applications that optimise and tweak , and moonshots that transform the business . A medium-term roadmap and budget should align with a specific adoption path .
You can buy off the shelf . Many modern AI applications cover a wide range of use cases for the augmentation of operations and processes . If the organisation opts for this route , it can accelerate time to value if its chosen use cases are well covered by the out-of-thebox functionality of the selected solution .
The downside of point solutions is their limited potential for scalability . They may cover the optimisation of one process but not be applicable to an associated process . Technical debt accumulates as many tools pile up and become integral to core business processes , leading to mounting costs in support and vendor dependencies .
The off-the-shelf option also fails to solve the skills-gap problem as it makes no significant moves towards Everyday AI , where everyone knows how to use these technologies . Additionally , point solutions bring the black-box problem , where even beneficiaries may not trust results because they have no idea how they arose .
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