The spotlight study GenAI in Business Intelligence and Analytics was conducted by BARC on behalf of HICO and Qlik – and as the title suggests, it sheds light on the adaptation and use of GenAI in current events. For the survey, 238 decision-makers in the field of business intelligence in various industries were asked about their assessment of the adoption status, benefits, risks and best practices around GenAI in BIA.
Readers in a hurry can download it directly here:
We had already wondered about this in the previous article: The picture is one of a more insecure guild.
Despite all the hype, the introduction of GenAI in BIA is still in the early stages, with only a small percentage of companies actually implementing it. Around 29% of those surveyed are discussing GenAI, 9% are still evaluating it and 22% are already experimenting with GenAI in controlling, finance and business intelligence.
Only 9% are in the implementation phase, with 6% partially and 3% fully in use.
But where do the fault lines actually run here?
Between the Leads and the Laggards
As the previous, more detailed study shows, the BARC researchers make a distinction between leads and laggards, or pioneers and rearguards, if you like it to sound less like a heavy metal song. And these, in turn, apparently correlate closely with the career types and, ergo, the types of people who are on the move here. That’s what the study says:
Advanced AI/ML users are more likely to adopt GenAI and integrate the technology into existing data science programs. More than a third of respondents expect GenAI to moderately improve their BIA usage in the next 12-18 months, with data engineers and business users being the most optimistic.
But what does that mean? What are the hurdles and roadblocks for all the others involved?
Partial and full closures on the road(map) to data integration
The concerns expressed by decision-makers are almost classic, especially in Western Europe. Aspects of data security and compliance are particularly frequently identified as obstacles. But the popular process aphorism “Shit In – Shit Out” can also be found here:
Data privacy and security concerns
At 61% of respondents, an obvious majority expressed concerns about security.
Skill gaps and training needs
Almost half of the respondents claimed a lack of knowledge or expertise.
Compliance + regulatory challenges
Similarly, over 40% of responses related to challenges posed by legislators to companies regarding the collection and processing of data.
Insufficient data quality and availability
Where there is no data, it cannot be analyzed. And if it is collected, it must also appear in a form that allows experts to deal with it in a meaningful way. More than a third of respondents also recognize this as a problem.
Integration complexities and inaccurate or biased outcomes
Another challenge for 40% of respondents is the sometimes high complexity of data and/or system integration.
Nice problems you’ve got there!
How about a few solutions for once?
The fact that all these challenges can now be seen as different perspectives on the same problem should be quite obvious: Data security, the secure and functional integration of various upstream systems as well as meaningful data and its visualization ultimately go hand in hand.
For those responsible for finance, planning and business intelligence, the question still arises: where to start with the implementation of AI in finance, planning and BIA?
Development in this area is still in its infancy – so the chances of catching up are good. In the upcoming third part of this series, AI and BI experts from BARC and HICO provide tips: What are the right steps to take to get started with GenAI in business intelligence, controlling and finance.
Small spoiler: It’s a truism that interdisciplinary problems are best tackled by interdisciplinary teams, … right?
For rushes, other grasses (and their respective allergies) and pickled dates, it is best to speak to our experts.
