ContentMap was forged under pressure, not guesswork. According to founder Tomas Hultgren, during the COVID-19 pandemic, organizations were forced to interpret vast amounts of information at speed, often with lives and security on the line. "Innovation was not optional; it was critical," he explains. "As existing data structures buckled under crisis-level demands, the limits of traditional tools became unmistakable." It was in this environment of absolute necessity that ContentMap's approach to visualizing complex, fragmented information emerged, shaped by the reality that a clearer understanding had become a prerequisite for action.
Research cited in industry studies shows that approximately 80% of enterprise data remains unstructured, making it difficult to analyze, validate, or reuse at scale. The repercussions of this can be seen clearly at an operational level; in fact, 25% of all documents created are lost, never to be found again. At the same time, organizations continue to store information across an expanding number of repositories, formats, and platforms. Hultgren explains that this fragmentation places increasing cognitive strain on professionals who are expected to interpret massive datasets using tools that were never designed for that level of complexity. Long before generative AI entered the mainstream, organizations were already approaching a breaking point.
According to Hultgren, in pharmaceutical development, this challenge becomes especially visible. Clinical trials routinely generate millions of data points across multiple phases of research. From Hultgren's perspective, handing these datasets to clinicians or researchers in spreadsheet form assumes that humans can interpret patterns buried in rows and columns. "These are not just numbers," he explains. "They represent patient outcomes, safety signals, and decisions that determine whether a therapy ever reaches the people who need it."
Research shows that approximately 22% of respondents now allocate about half a working day per week to information searches, while just over 10% spend as much as one and a half workdays. In regulated environments such as pharmaceuticals, these inefficiencies can slow analysis, delay validation, and complicate regulatory submissions. "What COVID revealed was that existing data structures were never designed for crisis-level decision-making," Hultgren says.
From Hultgren's perspective, ContentMap was developed as a response to this structural mismatch. He explains that the company focuses on transforming large volumes of unstructured and fragmented data into visual, navigable information environments that reflect how people naturally understand complexity. Rather than relying on linear lists, folders, or dashboards, the platform applies AI to detect relationships within data and present them as spatial maps, allowing users to explore context, patterns, and connections across entire information ecosystems. According to him, this approach is designed to support faster comprehension and more informed decision-making in environments where time, accuracy, and clarity are critical.
"The brain processes visuals faster than text," says Hultgren. "Maps, charts, and spatial relationships are not simply aesthetic choices; they align with fundamental neurological processes. When information is structured visually, patterns emerge more naturally, and context becomes easier to retain."
This disconnect between human cognition and traditional data interfaces, Hultgren explains, is not limited to healthcare. In defense and security contexts, the stakes are similarly high. Modern defense systems integrate data streams from drones, radar, satellites, and sensor networks, each producing continuous flows of metadata. According to Hultgren, much like clinical trials, these systems generate overwhelming volumes of data that must be interpreted in real time.
He notes that defense operations increasingly rely on rapid interpretation rather than retrospective analysis. Situational awareness depends on understanding how disparate signals relate to one another in real time. When information remains siloed or flattened into lists, the ability to see relationships between events is reduced. From his perspective, visual structures allow teams to identify connections, anomalies, and priorities without requiring exhaustive manual review.
Compounding this challenge is the pace at which data volumes continue to grow. Forecasts suggest that the amount of data produced and consumed globally will continue to rise sharply. "The pre-AI world was already overwhelmed by the sheer amount of data, but this problem is growing more severe as AI intertwines into the very foundations of the modern world," Hultgren adds.
While generative AI has accelerated content creation, Hultgren argues that it has also intensified the underlying information crisis by flooding organizations with more information than humans can realistically process.
"AI is extraordinary at generating information, but the real breakthrough lies in something else: recognizing patterns inside chaos. That is what the human brain needs, and that is what modern AI, when used correctly, is finally capable of supporting," Hultgren explains.
Hultgren suggests that future progress depends less on producing new insights and more on navigating existing ones. From his standpoint, technologies that help organize, contextualize, and visualize information can act as cognitive infrastructure, supporting professionals rather than overwhelming them. This shift reframes information management as a human-centered challenge, not merely a technical one.
Importantly, he emphasizes that such evolution is not about replacing expertise. In pharmaceuticals, clinicians remain responsible for judgment and ethics. In defense, human oversight remains essential. Instead, better information structures aim to reduce friction between data and decision-making, allowing experts to focus on interpretation rather than retrieval.
As organizations across high-stakes industries confront increasing complexity, Hultgren views this moment as a turning point. "The question is no longer how much data we can store," he explains. "It's whether we can still see what matters inside it, and in a world shaped by crisis, speed, and AI, that ability has never been more important."
