Digital transformation (DX) has recently become a pressing issue for many companies as the latest digital technologies, such as artificial intelligence and the Internet of Things, can be easily utilized. However, devising new business models is not easy for compa-nies, though they can improve their operations through digital technologies. Thus, business model design support methods are needed by people who lack digital tech-nology expertise. In contrast, large language models (LLMs) represented by ChatGPT and natural language processing utilizing LLMs have been developed revolutionarily. A business model design support system that utilizes these technologies has great potential. However, research on this area is scant. Accordingly, this study proposes an LLM-based method for comparing and analyzing similar companies from different business do-mains as a first step toward business model design support utilizing LLMs. This method can support idea generation in digital business model design.

The power of digital: well understood, but lagging. Next-generation technologies such as artificial intelligence are working well as proofs of concept and small-scale, highly focused applications such as chatbots or predictive analytics. Now, it's time to scale these technologies to the point where they can move the enterprise forward in new directions. That's the challenge, and new research shows they're not there yet. Digital business models employ technologies to deliver not only better products and services, but to also personalized, meaningful experiences to customers.

Today's customers demand more options, more creative solutions, greater flexibility and faster responses from banks and other financial institutions. Survival and success for financial institutions in this new world requires that they operate with intelligence, agility and speed to keep up with evolving customer preferences and technologies. Consequently, more and more customer interactions and financial transactions are going digital as online and mobile payments, customer on-boarding and account opening are on the rise. Yet, while digital interfaces present an opening for innovative business services, they also yield new challenges, such as pressure on back office operations or increased regulatory scrutiny. Largely automated interactions generate more data to analyse, demand higher volumes of sample testing, and expand the compliance burden. To create a flawless customer experience, the back office has to keep up as well.

Capitalizing on new digital business models and the growth opportunities they provide are forcing companies to re-evaluate ERP's role. Made inflexible by years of customization, legacy ERP systems aren't delivering what digital business models need today to scale and grow. Legacy ERP systems were purpose-built to excel at production consistency first at the expense of flexibility and responsiveness to customers' changing requirements. By taking a business case-based approach to integrating Artificial Intelligence (AI) and machine learning into their platforms, Cloud ERP providers can fill the gap legacy ERP systems can't. Companies need to be able to respond quickly to unexpected, unfamiliar and unforeseen dilemmas with smart decisions fast for new digital business models to succeed.

The webinar also discussed how large corporates and innovative and fast-moving startups can work together in this process in order to help customers create new service-based business models. The webinar was held on May 30th 2017.

A Liquid Big Data Platform uses cloud technology and agile ways of working to enable organisations to share and analyse large volumes of data together for their mutual benefit. If this model is scaled to a global level where any organisation (both large and small) anywhere in the world could use it collaboratively, what new business models could potentially emerge? Many organisations are already exploiting Big Data driven Machine Learning to improve their services in real time (such as search engine optimisation, medical diagnosis and fraud detection). In the so-called "arms race", big name tech, automotive and pharmaceutical companies are reportedly spending billions of dollars annually to realise their own IP in this area of Artificial Intelligence. A potential strategic implication is that these first movers will create barriers of entry that prevent other competitors (including small or medium sized enterprises) using AI as a disruptive source of rapid, responsive service design and organisational agility.