TALKING

What if there was a way to make those decisions in a better-informed , more structured way , and faster ? And could such an approach be developed for the whole enterprise ?

Technical and organisational challenges

As the pace of technological change becomes more rapid , complexity too , has crept further into enterprise data architectures . A 2023 industry survey found that 98 % of senior IT leaders have been impacted by increasing cloud complexity in some capacity , potentially leading to poor IT performance , loss in revenue and barriers to business growth .

Added to this , data complexity has grown as a major pain point for companies globally , with tech executives feeling the pressure to contain its impact on the business . Technical and organisational challenges may further stunt their enterprise 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 decisionmakers 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 . So-called ‘ 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

Natalya Makarochkina , Senior Vice President , Secure Power Division , International Region at Schneider Electric

www . intelligentcio . com INTELLIGENTCIO APAC 37