In the 2019 MIT Sloan Management Review and the Boston Consulting Group (BCG), Artificial Intelligence Global Executive Analysis and Research Survey, 9 out of 10 survey participants believed that AI was a market opportunity for their firm. In 2019, 45 percent portrayed some threat from AI, up from a significant 37 percent in 2017. More and more leaders consider AI as a challenge if they are behind others their implementation.
Financial companies have been at the frontline of the implementation of AI. Yet we’re seeing different patterns inside financial institutions. Deloitte polled 1,100 executives from US-based businesses spanning sectors who are prototyping or introducing AI. Of the 206 participants, 206 worked with financial services. Based on the feedback received, the organizations were divided into three segments: Frontrunners Followers, and Starters.
Integrate AI in Corporate Plans: Incorporating AI into the strategic goals of the company has allowed many frontrunners to build an enterprise-wide AI approach that can be adopted by various market segments. The increased strategic value accorded to AI often contributes to a higher degree of spending by these officials.
Apply AI to prospects for revenue and user engagement: Most frontrunners have begun to investigate the application of AI for different corporate improvements and consumer interface programs.
Using multiple AI acquisition options: Frontrunners tend to be open to the use of multiple AI acquisition and growth approaches. This approach helps us increase the implementation of AI programs by access to a broader pool of expertise and technological solutions.
The Frontrunners across the globe are leaving no stone unturned in embracing and leveraging AI in their business operations. More than Millions of Fintechs licensed last year to re-imagine financial systems using AI Financial Institutions are at high risk of losing to competitors. Banking in the world today is somewhat distinctive from the banking of the last century.
The implementation of AI in financial institutions was motivated by the use of predictive models for risk and fraud detection, but today AI / Ml models have become the center of each method, ranging from trading to operations to marketing.
Big Data is expected to hit more than $273 billion by 2023, which is a strong indicator of the confidence that companies are investing in technology and analytics. Analytics has grown from low key data analysis dashboards to full-blown machine learning immersive applications over the past two decades.
We also moved from analytical to predictive to prescriptive analytics. Organizations do not only want to avoid suggesting that they are looking forward to strategies that recommend what action needs to be taken. While we cannot fully do away from manual processes, adding more and more information allows us to make smarter choices.
When we move on, companies not only want ways to anticipate when maintenance will be needed but also to recommend and fix maintenance schedules based on data from various plant installations. Solutions also provide the built-in feature to auto contact repair engineers for a patch appointment.
While we are embarking on new heights and building new heights with the support of AI, but to benefit from these strategies, companies need to re-imagine their Organisation Approach in the light of AI and welcome the transformation that is en route in this path.
