Since last year, GenAI has been the topic #1 in the industry, everyone is talking about it, and first real implementations of this technology have started to pop up.
But adoption has not been easy, mainly because of two reasons: firstly, teams need to learn how to use GenAI; and secondly, they need to understand how to implement GenAI.
There are lots of moving pieces in an implementation of GenAI, first you have the input, normally in the form of prompt, but most companies will require this step to be fully automated, therefore the use of APIs will be required. Once GenAI does its job and comes back with an answer, that response will need to be processed, and that will need to be automated too.
Having a prompt where you can ask questions on demand is not enough for business purposes, a full automated process is required, where:
- Models can be trained with internal corporate data (and making sure that data does not leave the company)
- APIs are used to interact with the GenAI tool (input and output)
- Output from GenAI is automatically processed by other tools such as Slack/Teams, AIOps solutions or even ServiceNow
It’s not a secret that there are several companies that are delaying GenAI projects, even when they have budget allocated to this initiative, because they don’t have resources that know how to implement this kind of technology and, also, because don’t have enough time due to other priorities. This is being a big problem for those companies, because their competitors are already taking advantage of the productivity gains that GenAI comes with.
Democratize the access to GenAI
One way of fixing this problem is by leveraging no code automation platforms that will help you democratize the access to GenAI, meaning that everyone will be able to implement GenAI in their team.
The main problem that we are facing here is that teams that want to implement GenAI don’t have the expertise to do so, and teams that do know how to use GenAI do not have the SME knowledge on the specific topic where we want to implement GenAI.
For example, let’s review a real use case, IT outages, that are so common nowadays. These outages are having a big (negative) impact in companies, and every single company needs to reduce them or even completely eliminate them. However, finding the root cause of the outages is not an easy task.
Operations teams use a mix of approaches to proactively detect and reduce outages, including checking documentation, tribal knowledge (that person in the company that has seen everything and knows how to fix everything), reviewing what worked and what didn’t work in previous tickets… but this is a very long process, that every single company is trying to speed up.
IBM Concert Workflows
Using no code automation solutions like IBM Concert Workflows can help teams to deploy GenAI faster and effectively. Let’s see an example.
In this scenario, we are using a network performance monitor tool (IBM SevOne) to monitor the network, and SevOne just detected a problem with one of our firewalls, where the CPU used on a specific process (SSL process) is quite high.
Without GenAI, the normal process to investigate these issues would be raising an incident in ServiceNow (or any other ITSM solution), however the information available in the incident is quite limited, because the NPM tool only reports what is detected, that there is a problem on the CPU utilization of a process on a firewall.