Artificial Intelligence is transforming the way in which companies relate to their customers, how work is managed, the way they work, the way in which [...]
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Software as a Service companies (SaaS) have gained enormous prominence in recent years, mainly because of the novelty of the products they offer and, above all, because of the innovative operating and commercial model they propose.
SaaS companies literally market their services and not their products. The provider is, at all times, responsible for ensuring that its products deliver the expected results.
With this, they solve three types of problems for their customers, which until now caused many headaches, damaging the relationship between the customer and the supplier. We are referring to the implementation operation, the economic model and the project time.
The implementation operations has always been a big source of problems for the CIO: installation, sizing, customization, integration with legacy, data loading, parallel, extension, big bang, regression, testing of all kinds and comparison... are terms that IT professionals "fear" in a generic way as a potential source of problems. Sleepless nights and weekends are witness to this.
SaaS does away with all these problems (or minimizes them quite a lot) because its offer is not to integrate with the existing installation but simply to connect with highly standardized protocols (APIs) and thus avoid the maintenance of a complex integration. It is a highly defined flow of data input and product output between customer and supplier.
Logically, this operational simplicity is reflected in the components of the economic model. Beyond the ROI of each project in each company, SaaS companies bill more clearly because they present a monthly invoice for their services. This avoids the cost of many components that are sometimes not well discussed (customization, OOB extension, cost of license maintenance, integration, etc.) and which are sources of continuous discussion. Experience confirms this.
Derived from the two previous ones, the third advantage factor of SaaS is the reduction of total project time. There is no doubt that if we eliminate - or drastically reduce - the need for integration and extension of the implementation, times are greatly shortened. The same is true if we simplify discussions - previously endless - on a wide variety of contractual aspects.
With these advantages in sight, large SW companies and, above all, customers are looking to SaaS as a very promising source of value.
Thus stated, it seems that the future of both user interaction systems and core systems (Systems of Record vs. System of Engagement), will go through models that prioritize ease and "do not disturb" or interfere with the current architecture (always unstable) and, therefore, towards models managed as a service.
We can consider how AI will affect companies with SaaS models.
Does it benefit them or is it just another service? And most importantly, are they in a better position than their more "traditional" competitors to offer these services to their customers?
If we look at the issue from the perspective of Artificial Intelligence, we see that this is also supported by the basic technologies that have most favored "Software as a Service" companies: the massive use of data (BI), storage and processing capacity (Cloud) and the interactivity of the elements of complex systems through common protocols (APIs).
The current explosion of AI, despite having been conceptually devised in 1956, comes from the current technical ability to extract maximum value from BI, Cloud and APIs (among other technologies), through algorithms capable of learning.
These three components are also at the core of SaaS offerings. These companies, by not claiming to be integrated into legacy, become experts in the use of the main AI levers and therefore make their use absolutely natural.
However, SaaS and AI are not synonymous no matter how much BI, Cloud and APIs are used: it takes hard work to define algorithms, train them and test them. This means that the AI offering is not the same in all companies, nor is it immediately available to all of them, be they SaaS or not.
We know that artificial intelligence technologies are used today in companies for the transformation of current business processes, to boost customer interaction, improve decision making and increase employee productivity.
Companies with Software as a Service models have a much easier time designing an AI offering for their customers based on operational expertise, clear economic model capability and project timing (the three big advantages we saw at the beginning of SaaS).
This leads us to consider that the most important attribute of AI implementation that SaaS companies can benefit from the most is the speed of project start-up.
AI projects need to move fast to receive data, as they need to train and test their algorithms. The operational loop is not cut off (the client continues to run with its entire installation at all times), but the speed of algorithm training is essential: remember that algorithms learn from their successes as well as their mistakes.
We hope that "how artificial intelligence influences software as a service companies" is clear to you. Because the sooner the algorithms start "thinking", the better results the customer will get, and a SaaS model greatly benefits that agility to start working.
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