In 2023, the financial sector is predicted to experience massive changes as traditional financial institutions (FIs) compete with Fintechs and digital services for supremacy. The launch of new digital banks like Maribank, Boost Holdings, and Sea Ltd has utilized technology and data to deliver innovative and personalized financial services to draw new customers in Singapore and Malaysia. In Singapore, Deputy Prime Minister and Minister for Finance Lawrence Wong emphasized the potential for digital technologies to create streamlined and efficient financial operations. Amplifying this point, the Monetary Authority of Singapore (MAS) and the Ministry of Finance (MOF) collaborated with FIs to provide digital solutions that reduce processing time for government guarantees and insurance bonds. Digital transformation will be key to altering the way financial institutions deliver positive customer engagement in 2023.

A total of 34% of AI research and development projects fails or are abandoned, according to a recent survey by Rackspace Technology of 1,870 companies. We propose a new strategic framework, aiSTROM, that empowers managers to create a successful AI strategy based on a thorough literature review. This provides a unique and integrated approach that guides managers and lead developers through the various challenges in the implementation process. In the aiSTROM framework, we start by identifying the top n potential projects (typically 3-5). For each of those, seven areas of focus are thoroughly analysed. These areas include creating a data strategy that takes into account unique cross-departmental machine learning data requirements, security, and legal requirements. aiSTROM then guides managers to think about how to put together an interdisciplinary artificial intelligence (AI) implementation team given the scarcity of AI talent. Once an AI team strategy has been established, it needs to be positioned within the organization, either cross-departmental or as a separate division. Other considerations include AI as a service (AIaas), or outsourcing development. Looking at new technologies, we have to consider challenges such as bias, legality of black-box-models, and keeping humans in the loop. Next, like any project, we need value-based key performance indicators (KPIs) to track and validate the progress. Depending on the company's risk-strategy, a SWOT analysis (strengths, weaknesses, opportunities, and threats) can help further classify the shortlisted projects. Finally, we should make sure that our strategy includes continuous education of employees to enable a culture of adoption. This unique and comprehensive framework offers a valuable, literature supported, tool for managers and lead developers.

Most companies are struggling to develop working artificial intelligence strategies, according to a new survey by cloud services provider Rackspace Technology. The survey, which includes 1,870 organizations in a variety of industries, including manufacturing, finance, retail, government, and healthcare, shows that only 20 percent of companies have mature AI/machine learning initiatives. The rest are still trying to figure out how to make it work. Lower costs, improved precision, better customer experience, and new features are some of the benefits of applying machine learning models to real-world applications. But machine learning is not a magic wand.

The UK appears to be losing ground in the artificial intelligence (AI) race, a new study has concluded. The results in Rackspace Technology's How are organizations succeeding at AI and machine learning? The survey found that just one in 10 organisations can boast mature capabilities, compared with one in six (17%) worldwide. The vast majority (90%) of IT decision makers say they are either at the early stages of exploring the technology's potential (54%) or still requiring significant organisational work to implement an AI/ML (36%). More than one-third (35%) of UK respondents report AI research and development initiatives have been tested and abandoned or failed.