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strategies , with 88 % citing working across current cloud environments as a barrier , while 32 % struggle to align on a clear vision at the leadership level .
Complex decisions
This complexity is impacting the business too , as diversification , changing consumer demand , multiple product , brand and service lines all must be managed and analysed . While this has always been a challenge , omnichannel engagement and the emergence of new consumption trends , has accelerated the situation , further burdening the business . Research has found that nearly two thirds ( 65 %) of business leaders report that decisions they make are more complex now than just two years ago , with more than half ( 53 %) admitting to facing more pressure to explain or justify their decisions .
Tech and consumer brands can find themselves managing multiple products , distribution channels , promotion campaigns and marketing channels at any one time , each more data rich and diverse than ever before .
AI can deal with large amounts of data and reduce complex patterns into manageable loads . This strength of AI means infrastructure and data complexity can be reduced and minimised through optimised design and AI-monitored operation .
When decision-makers have trustworthy AI to cut through this complexity and the deluge of data , they can focus their time on identifying the best option from the recommendations , to develop a competitive advantage in the market .
Trust in AI
As seen with the recent experiences of generative AI tools such as ChatGPT , Artificial Intelligence can serve as a powerful tool to extend human insight and judgment . The growing opinion is that AI has enormous potential to support more and more decision areas in business . However , it must be seen to be working effectively , to develop trust and support from decision-makers and users .
When AI models start guiding strategic decisions , there is a shift in requirements . Users must be able to deeply trust the applications , say researchers . They have to find them indispensable when making major choices . If not , they can end up abandoning them .
The same principles apply to business . Decision support must be applied in a transparent way , allowing the user to keep a key level of control at first , while the system proves itself to be consistently effective and helpful .
A follow on from this is the need for strategists to have clear evidence from the system to back up any actions advised . Transparency of process allows people to follow the logical steps made by AI , should the need arise . Socalled ‘ black box ’ AI does not give the reassurance needed if a query arises .
Cultural shift
Going a little further , for AI decision support to be adopted across the enterprise , a cultural shift is 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 .
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 ( DCIM ) and environmental management systems ( EMS ) can not only manage and optimise , but can also derive insights for wholesale
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