EDITOR ’ S QUESTION
ANIL INAMDAR , THE GLOBAL HEAD OF DATA SERVICES AT INSTACLUSTR
( PART OF NETAPP )
CIOs leading AI adoption are now spending much of their time vetting AI / ML platforms to revamp legacy environments and using AI to fast-track broader business goals . But this shift is introducing new focal points that reshape CIO workloads – especially as they embrace AI-powered data analytics , application development and platform engineering strategies .
That advantageous flexibility allows CIOs to select the most opportune infrastructures based on their piecemeal benefits when it comes to availability , scalability , reliability and their ability to support AI / ML initiatives ( while commanding singular systems ).
Today ( and increasingly going forward ), CIOs are pursuing integrated AI / ML processing platforms that can serve as the foundational keystone for harnessing AI advantages now and into the future . Integrated AI / ML processing platforms encompass data-layer technologies , storage , AI / ML frameworks , and infrastructure orchestration , offering a centralized and comprehensive environment for AI / ML utilization across a CIO ’ s organizations .
Backed by this platform , a CIO ’ s developer , DevOps , and data scientist teams gain a much more collaborative playing field for creating , continually iterating , and optimizing AI / ML models and applications . But getting this right naturally is streamlining the CIO ’ s workload as well . Ultimately , integrated AI / ML processing platforms simplify and accelerate AI / ML development , training , deployment and management , making them essential to CIO strategies right now .
AI implementations are also transforming CIO workloads around application development , as AI / ML-powered assistance arrives to change coding as we know it .
AI coding assistants and platform engineering internal developer platforms ( IDPs ) are eliminating the busywork of development , clearing developer bandwidth to zero in on innovation and removing barriers of entry when it comes to harnessing powerful advanced technologies ( of which AI itself is an example ).
This serves to democratize , streamline and accelerate application development and iteration that optimizes operations and CX .
In the same way , it streamlines and accelerates CIO decision-making , allowing them to concentrate on more forward-thinking and needle-moving strategies as well .
Many CIOs accustomed to dividing their attention across multiple on-prem and cloud infrastructure deployments – or that devote a portion of their workloads to investigating what they might be missing out on – are now simplifying their workloads by harnessing hybrid multi-cloud data platforms .
Doing so enables enterprises to better utilize AI across their operations and analytics functions . A hybrid data platform strategy allows CIOs and their organizations to manage , store , replicate and process data in a unified manner across the on-prem , private cloud and public cloud infrastructures of their choice .
Speaking of decision-making , data analytics enhanced by AI / ML , generative AI and breakthrough data science techniques now offer CIOs greater insights and clarity – delivered near-instantly – than have ever been available . CIOs with the foresight to implement these advantages will make data-driven decisions that outmaneuver competitors , both in terms of internal operational capabilities and in the marketplace .
CIOs a step behind in enlisting AI / ML advantages will struggle with limitations and relatively inefficient workloads – and that ’ ll be an increasingly costly disadvantage .
30 INTELLIGENTCIO LATAM www . intelligentcio . com