FeatureWith digital transformation accelerating rapidly, du supports clients by helping them to build and manage digital platforms that bridge technology innovation boundaries and scale up their respective transformations. These platforms support artificial intelligence and machine learning applications and use cases, enabling organisations to adopt these technologies, integrate them with internal processes, and benefit from their capabilities.
Additionally, du’s digital infrastructure solutions also support clients where artificial intelligence is concerned. With a portfolio including infrastructure services, cloud management, and connectivity solutions, clients harness artificial intelligence capabilities to realise their development objectives.
du’s product portfolio also considers organisations of all maturity levels across the artificial intelligence spectrum. For organisations beginning their digital transformation journeys, du provides demystified services that comprise data insights, offer digital assistance and computer vision capabilities, and can be easily embedded into their current application stack.
du’s portfolio considers organisations of all maturity levels across the artificial intelligence spectrum
In terms of companies already artificial intelligence native, du facilitates their respective infrastructure needs, providing the necessary platforms that manage full artificial intelligence model development and delivery workflows.
Several vertical markets are well-suited for early artificial intelligence adoption and ROI. Government, finance, transportation, and education are all markets to which this applies, as are the manufacturing and technology, media, and communication fields. du’s solutions and platforms for supporting artificial intelligence applications and use cases were previously mentioned – and these are available to partners in these sectors.
Demands for calculating real-time data for artificial intelligence applications are increasing
From a telecommunications standpoint, network optimisation, preventative maintenance, virtual assistants, and robotic process automation are among the AI use cases that have already solidified sector suitability, with ML also supporting anomaly detection, managed services delivery, and root cause analysis.
Several vertical markets are well-suited for early artificial intelligence adoption and ROI
As enterprises place a greater emphasis on utilising artificial intelligence to their advantage, their success in implementing applications and delivering use cases has facilitated innovation and digital transformation in several ways. Expanded organisational market presence and greater profitability are being made possible through artificial intelligence.
While artificial intelligence is accompanied by enormous potential, there also several challenges that have recently arisen when implementing these technologies. In terms of datacenters, successfully meeting evolving performance requirements is not without its difficulties, especially as demands for calculating real-time data for artificial intelligence applications increase.
While artificial intelligence is accompanied by enormous potential, there also several challenges
Moreover, another pressing datacenter challenge is effectively implementing new applications or solutions into existing environments. Regarding the obstacles that enterprises must overcome, they are required to boast the necessary computing power, remain compliant with data privacy and security regulations, and consolidate data that is often located across several different databases.
Datacenters meeting evolving performance requirements is not without its difficulties
As businesses implement artificial intelligence solutions, they should do so in accordance with several best practices. Business objectives should be clearly defined, backed by a comprehensive roadmap for delivering on these objectives, as should the roles and responsibilities of individuals and teams.
The necessary data should also be gathered for the project in question, while the implementation of solutions should be continuously monitored to measure success and identify areas where further progress in required. As for verifying returns on investment, companies can measure the total project costs, refer to pre-project objectives, and evaluate outcomes through data and analytics.
Thanks to AI capabilities, enterprises are benefiting from automated processes, streamlined service delivery, and heightened productivity across geographies.
