TALKING
‘‘ business required , not just a technological one . Market dynamics show more decision platforms integrating AI capabilities , reports Forrester , as well as AI-based applications capable of using models for decision-making . The integration of traditional decisioning into AI is starting to hit its stride . When it comes to AI-based decisionmaking , the biggest challenge is understanding AI as a major cultural shift instead of an isolated tool in a kit . AI , say the analysts , is very much a custom-fit for the work being done . Therefore , to apply it in as many places as it can to provide value will need that cultural change , as technological tolerance can vary across an organisation . For example , HR might be more willing to deploy AI-assisted decision-making than might a risk management function for cybersecurity . Both functions could benefit from AI assisted decision-making , but only if the underlying culture can see and accept the benefits .
Issues and concerns
However , there are also challenges when making this cultural shift . An industry survey found that among senior IT leaders , 79 % believe Generative AI has the potential to be a security risk , 73 % are concerned it could be biased and 59 % believe its outputs are inaccurate . This is in addition to legal concerns especially if externally used Generative AI-created content is to be considered factual and accurate , content copyrighted , or comes from a competitor .
( DCIM ) and environmental management systems ( EMS ) can not only manage and optimise , but can also derive insights for wholesale improvement , which will be vital as sustainability targets and deadlines loom . Embedded AI can also extend to predictive maintenance , where pattern analysis can reveal potential failures before an outage . These wins can help develop that trust for AI and ML within digital infrastructure as they are applied at ever more strategic levels , such as business decision support , strategy validation and implementation .
AI-assisted design and management
AI-powered management and orchestration systems will be critical in allowing organisations to meet their sustainability goals . This capability will be of particular importance in complex cases such as Edge Computing deployments , where architectural complexity will be an issue . Architectural complexity threatens to be multiplied in Edge deployments , as the relative ease with which such infrastructure can be deployed means that unless great care is taken , their proliferation could be a problem . AI assisted design and modelling , such as through our extensive design tool portfolio , will allow optimal deployments to be made with efficiency and effectiveness uppermost , while also ensuring sustainability concerns are met .
People , amplified
AI promise
And yet the promise of AI , combined with the other emerging technologies , in the hands of well-informed business leaders , seems clear . According to one study by MIT Sloane , those businesses that are led by the digitally savvy championing emerging technologies such as AI , outperform other like-sized businesses by 48 % on valuation and revenue growth .
Building trust
Trust is key in facilitating the cultural change necessary to employ and implement AI-assisted decision making in enterprise . CIOs must gradually introduce the features and facilities of AI . Extracting the value of AI requires gaining quick wins , even while developing at enterprise scale . Research in these areas found that the majority ( 71 %) of organisations state they would trust insights from AI and ML platforms , despite the concerns for security , bias , and transparency . That trust can be developed and built through the reliable and helpful use of AI elsewhere . In our use of AI / ML in the EcoStruxure sphere , we have already shown its utility in managing complex , hybrid environments , but also its vital future role in addressing sustainability challenges . AI embedded in systems such as Data Centre Infrastructure Management
With the increasing integration of AI into more and more applications and services , but specifically decision platforms , the technology has the potential to narrow down myriad options , more easily perform due diligence from data and sources , and significantly reduce complexity . The CIO can initiate the introduction of technical decision support facilities , learning and optimising as they progress , gradually building trust . With incremental trust-building , the power of AI to amplify human decision-making and discernment can propel businesses towards faster , better and more informed decision-making , leading to a competitive advantage that may have seemed like a superpower just a few years ago .
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