Digital intelligence is a term that has emerged in recent years and has become a popular concept after digitalization.
So what exactly is digital intelligence?
Is it just an intelligence tax?
Is it a misleading concept?
Is it something companies invented to harvest users?
To answer these questions, we will introduce several topics related to digital intelligence across a series of articles, including what digital intelligence is, why the life sciences industry needs it, how to implement and land it, what stages and steps it requires, which companies it suits, and real-world case studies that everyone surely cares about.
In digital intelligence, digital means data, and intelligence represents algorithms.
Digital intelligence is the combination of data and algorithms.
Compared with digitalization, the distinction becomes clear: digitalization moves offline activities and processes online.
If arranged properly, the process of digitalization generates large amounts of data. But digitalization itself does not rely on that data to operate, and the data contributes little to the continuous improvement of the digital system. Digital systems solve the problem of going from nothing to something: they move business processes online, create full traceability, and produce large amounts of data. Yet their design and improvement are still based on human-designed processes, business needs, and business logic.
In other words, digitalization does not solve how to continuously use accumulated data to improve efficiency. That is why people often feel that a digital system clearly "knows" a lot of information, but still feels rigid and unintelligent when used. The missing key factor is algorithms. Algorithms can be understood as the processing, understanding, and application of data.
With algorithms, large volumes of data become valuable.
The best-known algorithms today are of course widely discussed artificial intelligence tools such as ChatGPT.
But algorithms are far more than ChatGPT. Some are more complex, and some are simpler.
Digital intelligence means data + algorithms = intelligent applications.
The efficiency gains and experience improvements brought by digital intelligence are a qualitative leap compared with the digitalization stage. Based on the massive data formed during digitalization, algorithms create an entirely new experience.
Consider several typical digital systems: CRM, content approval systems familiar to medical department colleagues, and various internal OA systems, including enterprise messaging tools. The large amounts of data generated by these systems during use may mainly serve to create usage reports for management reference. They do not truly improve user experience or work efficiency.
Typical digital intelligence tools, by contrast, are familiar platforms with "personalized for everyone" capabilities, such as Toutiao, Xiaohongshu, and Taobao. Even browsing behavior data can be enhanced by algorithms to deliver personalized content or product recommendations. This not only gives users a new experience, but also greatly improves key metrics such as usage rate and time spent on digital products.
Here, I used GPTs to automatically generate a comparison chart between digitalization and digital intelligence. It is quite interesting, so I am sharing it with you:
A GPTs-generated comparison of digitalization vs. digital intelligence
Finally, to summarize the difference in one sentence:
Digitalization moves past management processes online, emphasizing functions over experience; digital intelligence is intelligent and future-facing.
Now that we have clarified the difference between digital intelligence and digitalization, next time we will look at why the life sciences industry needs digital intelligence. Before that, you can read a successful case of digital intelligence marketing: in 2023, MeDomino used digital intelligence capabilities to help a multinational pharmaceutical company increase the content open rate of digital channels by more than tenfold. Read here: MeDomino Helps a European Pharmaceutical Company Achieve Integrated Strategy Execution
