During its AI Reality Tour this week, Qlik offered to transform participants into princes/princesses, rock stars or even Vincent van Gogh. Despite the wonders of generative AI, I was acutely aware, in front of the photo booth, that it would take a fairy godmother to achieve this kind of transformation.

As the hype about AI continues to inflate press releases and announcements, the pressure to achieve a return on value looms like a very sharp pin. Executives are eager to see a return on the value of their investments. According to Qlik’s latest study, 91% of senior leaders say AI is essential to their future, but 41% don’t trust it. This contradiction highlights the primary challenge facing enterprise technology buyers today: AI is no longer a “nice to have,” but a necessity. Yet skepticism about its reliability and security remains high, both within organizations and among customers.

Qlik’s study finds that 58% of executives are trying to secure investments in AI technologies, but the rush to AI can be fraught with mistakes. As some organizations have discovered, much to public embarrassment, launching AI projects without a clear roadmap or understanding of their long-term impact can lead to costly failures. More than 30% of AI early adopters have seen more than 50 AI projects fail after the development phase.

Dispel fairy tales

During the keynote, James Fisher, Chief Strategy Officer of Qlik, emphasized that AI should not be seen as a silver bullet for all business problems:

We tend to think that AI is the answer to everything, without asking ourselves if it is the right tool to solve the problem.

For business technology buyers, this advice is particularly relevant. The power of AI to transform data analysis is well established, but the key lies in asking the right questions from the start. Fisher asked the fundamental question every business should ask before diving into AI: “Which use case will actually benefit from it, and what data do I need to support that use case?” » This focus on practical use cases, supported by robust data, is essential to extracting value from AI investments.

Although the Qlik survey reveals that the majority of senior executives recognize the importance of AI, only 17% believe their customers trust it. This lack of trust poses a significant barrier for companies looking to integrate AI into customer-facing applications. Fisher urged businesses to approach AI with transparency and a commitment to ethical practices, ensuring customers feel safe when engaging with AI-based solutions.

So how can BI buyers ensure they’re making the right AI decisions? Fisher’s advice was clear: focus on the fundamentals. Rather than getting caught up in the hype around generative AI or chasing competitors’ initiatives, businesses should start by identifying real, actionable use cases where AI can make a difference. From there, they need to ensure they have the right data infrastructure to support AI applications. Without high-quality data, even the most advanced AI models will struggle to provide meaningful insights.

The phrase “Garbage in, garbage out” is regularly used at Qlik events – and for good reason. During demos, customer panels, and conversations with Fisher, the need to work hard with data before trying to transform it was emphasized time and time again. Even though Qlik specializes in data integration and analysis, it doesn’t pretend to be a fairy godmother with a magic wand.

Be realistic

Coming back to the Qlik study results, there are multiple factors that hinder or block AI projects: data governance challenges (28%), regulatory issues (22%), and no one within the company assumes responsibility for carrying out AI projects. completion (20%). AI dominates discussions in business technology, but documentation and ownership are critical to success and compliance. Qlik has extensive experience in this area.

The first time I spoke with Fisher, he emphasized that AI was not new to Qlik; This week, we discussed the company’s vision for AI and how the vendor’s deep expertise in analytics and governance got it to this point. He then described how Qlik’s core technologies, like its associative analytics engine, were designed with the goal of allowing users to explore data intuitively:

When we created this, it wasn’t just about data: it was about combining human intuition with technology to help people make better decisions. The same principle applies to AI today.

This deep expertise is essential in an industry where many organizations are rushing to integrate AI without fully understanding its complexities. Fisher noted that many companies embark on AI projects simply because they feel obligated to do so, often without a clear strategy. He noticed:

Too often we hear, “Here’s AI: find a problem to solve with it.” This approach is counterproductive. This leads to bad investments and projects that do not yield significant results.

One of the reasons Qlik’s AI strategy is so robust is the company’s focus on providing practical, usable tools that can generate immediate value. Fisher stressed that Qlik is not in the business of offering overly promising futuristic capabilities. Instead, they focus on integrating proven AI, with real-world use cases in mind:

Our job is to ground AI in reality. What we want to create is an environment where customers can leverage AI on trusted data, in a structured way that allows them to quickly realize value.

This pragmatic focus extends beyond AI to areas such as environmental, social and governance (ESG) reporting. As sustainability becomes a priority for many organizations, Qlik has developed tools to help businesses effectively meet new compliance requirements. In response to the European Sustainability Reporting Directive (CSRD), Qlik has introduced a reusable ESG reporting framework. The new reporting model allows organizations to track, measure and report on sustainability indicators, providing a data-driven approach to compliance. Fisher said:

We have published an ESG reporting template directly in Qlik to help companies adapt to new EU regulatory standards. It’s not just about fancy dashboards: it’s about solving real problems and ensuring that organizations can meet their reporting obligations without relying on manual processes or disjointed Excel workflows.

This aspect of Qlik’s values ​​is not a showcase for ESG visibility. Last month, the supplier also announced its participation in the United Nations Global Compact, marking a new chapter in its partnership with the United Nations (UN) to promote global sustainability initiatives. With participants in 167 countries, the UN Global Compact is the world’s largest corporate sustainability initiative. The goal is for Qlik to expand its work with the UN to enable agencies and stakeholders to use data and AI for more transparent and effective decision-making.

My opinion

The importance of data governance and quality is something that Qlik has always taken seriously. During the product presentations, Director of Technical Product Marketing Adam Mayer demonstrated in detail how the platform actively facilitates and encourages documentation, data tracing, and clear ownership – a topic that deserves its own article.

This event follows Qlik’s annual conference, and the publisher shows no signs of slowing down. While recognizing the rapid pace of AI technological development, it remains grounded in its history of AI in analytics. And even if an AI photo booth brings a little levity to the proceedings of an event, there is a strong emphasis on transparency on the part of managers and customers. Key themes revolve around making small but scalable changes, recognizing that transformation can only be achieved by doing something differently. Qlik’s approach is rooted in reality, not just theory.

As Fisher summarizes:

We are not here to sell pipe dreams. We’re here to deliver real, practical solutions that enable businesses to use AI effectively, on their terms and at their own pace.

During the event, I was on breadcrumbs for customers with use cases. My next article will focus on dashboard results, the power of integrated analytics, and whether data’s “unsexy” reputation is justified.