A few years ago, the Software as a Service logic was "struggling" to gain market share and had to answer basic questions such as data security, access to reliable internet, technological competence of vendors and their products. Today, these questions have been answered. No one hesitates to adopt such a solution if it suits them functionally and cost-wise, while the underlying technology is not a matter of discussion, as long as the service level conditions are met.
On the other hand, just recently we started to see a boom in AI capabilities and applications like chatGPT, Google Gemini etc.
But what if AI tools had access to databases of well-organized SaaS services.
And one can't help but wonder what could happen if AI tools had access to databases of well-organized SaaS services.
In the first place, the two "worlds" (SaaS & A.I.) meet in the big data perspective: we can consider a SaaS service to have big data both in terms of volume and velocity. On the other hand, the "food" of A.I. is just that: big data. It cannot work so well with small, local and isolated Databases.
A.I. applications based on big data can be imagined in many ways:
Automation of processes, e.g. chatbots that could answer users' routine questions.
Personalization of offers to customers of SaaS users. Based on the buyer profile what cross-sales campaigns could be proposed to each individual customer? Imagine an A.I. that has access to e.g. sales information of an e-marketplace.
Risk management: by examining evidence of (good and bad) behaviour, to classify a customer into levels of risk for the company. Here many might be banking applications, with which E-ON has already experimented.
Basic questions of the everyday life of middle and high management: e.g. Which product sells me the most? Which product has the highest profit margin for me? Which of my products has a higher margin? These questions, until now, are answered through ERP databases with specific queries (queries, reports maybe even dashboards for top management). But how are they answered quickly and reliably when they arise ad-hoc? In this fast evolving and very competitive environment, can we say to the CEO "wait until we tell I.T. to build the report you asked for..." ;!
Of course, one cannot ignore intellectual property issues that exist on a SaaS, multitenant basis. Similarly, there is governance issues in these processes: How is personal data protected, who has access to it, where does profiling of individuals start and stop, is there a regulatory framework for what we are attempting to do, etc. We simply don't open this topic in this article, as we only look at technical feasibility and practicability.
With care and respect for the governance that must accompany any A.I. initiative, we look at how to leverage AI in our existing SaaS platforms for the benefit of the end customer.
At the same time, predicting the long-term future is a challenge for us, both in the field of Climate Change Risk Management and in the more general field of Business Analysis performed by the Board of Directors. And this is difficult to achieve through classical reporting or modelling techniques.