A look at the present and future of AI application in IT asset management.
Benefits and practical applications of generative AI in ITAM.
IT systems that learn from data: we are achieving this to its full power with generative AI, which is based on large language models (LLMs) of a "multi-modal" nature.
The results are there for all to see: AI learns from existing data to create new data with similar characteristics. The possibilities are endless: we can reinvent most customer experiences and applications and achieve new levels of productivity.
The excitement and curiosity about its uses is palpable for anyone working in the IT sector: this is confirmed by the “Snow Software IT Priorities Report”, in which 32% of IT managers said AI integration is their top priority for 2023.
Seventy-two percent of respondents believe their organization will use AI more in the next two to three years, and one of IT's tasks will be to integrate it into business processes to maximize its benefits.
One of its concrete applications will be in the ITAM field: AI will radically transform the way IT managers interact with data related to IT assets.
Let’s imagine that we want to know how many PCs will become obsolete by such a date: to get answers, the data must be analyzed from the information sources that contain them (e.g., by exporting to Excel or PowerBI or by "working" on the processing fields of more advanced ITAM tools). Applying filters and using pivoting is the only way to relate rows and rows of data on technology assets and have useful insights into their management.
Now it is no longer necessary to waste time doing this: one can directly query the system from its user interface. The use of generative AI, in fact, allows us to "activate" new AI assistants that enable users to ask questions in conversation and receive answers in natural language.
An IT manager could then gain insights into forecasting, asset allocation, and identifying trends on data just by querying the system, as if he were chatting to a colleague. He or she can ask, for example, what software and services are running both within IT and across the enterprise, inquire about their status and compliance, and see if there are trends to consider.
It is not just avoiding wasting time exporting and analyzing data: AI can help us in integrating different data sources and normalizing them.
If, in fact, in order to understand the actual status of a device in production we have to query different data sources (Active Directory, Antivirus, the EPM tool, mail on MS Exchange) and these are programmed to record the date of the last client connection, AI can highlight the most recent update and detect any discrepancies (e.g., in one source the data is missing because the provisioning process was not completed correctly). All in a very short time, because the system with generative AI support gives immediate and conversational responses.
These implementations would allow us to reduce 60-70% of the time (as estimated by one of the speakers in the "AI + ITAM”" webinar) spent on processing-often still manual-data, consequently improving the quality of usable data. Generative AI, in fact, helps us address the visibility gap when it comes to understanding and acting on large datasets on IT assets.
Our partner Snow Software recently introduced Snow Copilot, which is an AI assistant that enables the querying of Software Asset Management data within the Snow Atlas platform. It leverages Microsoft Azure's OpenAI service to do this.
This is the first in a series of AI features that Snow is releasing to more effectively solve ITAM and FinOps challenges: through a "window" in the user interface, any person can query the system by asking simple questions (e.g., "How many computers have been installed in the last 90 days?") or exploring "what if" scenarios and receive answers in natural language.
One of the critical issues that often emerges with generative AI is the need for reliable sources on which to train AI. Recommendations, in order to be effective, must be based on quality data.
Snow Copilot does not present critical issues because for its answers it uses proprietary data that comes:
As ITAM expert AJ Witt points out, we may have at our disposal "tthe most comprehensive recognition database in the market”," and this will be an undeniable advantage for Snow's AI assistant training, considering the difficulties in tracing generative AI responses back to individual sources for verification.
In addition, Snow's innovation lab is working to fine-tune new AI features:
We have cited the case of Snow Copilot, but in the coming months-as these AI tools progress-we will increasingly see the shift of AI's role from tactical assistant to strategic advisor. Indeed, a CIO could quickly get strategic answers about resource utilization or upcoming renewals.
Ne parleremo nel nostro webinar “Da Excel all’AITAM: metodi innovativi per gestire i tuoi asset IT” in programma per il 1 febbraio (vai here per iscriverti e guardare il replay!), dove vedremo insieme benefici e applicazioni dell’AI nell’ambito ITAM con dimostrazioni pratiche sui diversi scenari d’uso che CIO e IT Manager potrebbero trovarsi ad affrontare durante il loro lavoro.
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