Enterprise AI Monetization: The Intelligent Strategy for Artificial Intelligence

The concept and practice of generating and creating revenue from technical goods or services that would not in itself create cash is referred to as “Technology Monetization”. Depending on the nature of the technology and its application, this may be accomplished using a variety of tactics and methods. For example, in its simplest form, selling “Digital Products or Services” is the typical option. This might be an online course, software, or any other digital product or service. However, the concept and practice usually cover more complex details and usually need a deep understanding of the economic dynamics, not only operational costs, and simple revenue calculation. Being able to manage the “Total cost of ownership” for these technologies to achieve a significant “Return on Investment-ROI” can be a clear example of these complex economic dynamics.

In recent years, a similar and yet so important concept and practice gained momentum, which is “Data Monetization”. Data monetization may be defined as the process of deriving demonstrable economic advantages from existing data sources using various levels of sophisticated “Business Analytics” methods. These advantages often manifest in the form of “Income Generation” and/or “Expenditure Savings”. Although data monetization can take an external form, which occurs when the organization makes its data available to external consumers in order to produce a profit, this newsletter edition focuses on “Internal Data Monetization”.

Internal data monetization is now the most important and popular method of monetization. This method usually requires significantly fewer security considerations, easier intellectual property constraints, and simpler legal safeguards than external data monetization. The potential economic rewards from internal data monetization, in the form of income generation and/or expenditure savings, are largely constrained by the organization's internal "Core Competencies" in the domains of “Data Management” and “Data Analytics”.

Recently, the concept of data monetization expanded beyond the simple form of income generation and/or expenditure savings to cover "Managing Critical Business Risks", such as protecting the brand against negative attacks and/or law enforcement such as in the case of personal data protection laws breaches. Hence, the enterprise needs to manage its data not as operational assets, but as a “Strategic Asset” that can provide a “Competitive Strategic Advantage”. In brief, data monetization aims to have a positive impact on both the enterprise’s “Tangible Assets” such as the revenue and operational costs, and “Intangible Assets” such as the business brand equity.?

In the age of artificial intelligence, the data is usually used to train several different “AI Models”, and this makes it logical to migrate from the concept of “Data Sets Monetization” towards the concept of “AI Models Monetization”. This requires a shift in the perception of the decision-makers about the artificial intelligence models from being technology-driven products and/or services to strategic assets that provide competitive strategic advantages and produce a positive impact on the enterprise’s tangible and intangible assets.

Hence, enterprises shouldn’t design their “AI Strategy” from a technical perspective solely or solely due to its foundational benefits and shouldn't favor a certain AI model over another based on its technical function. However, the “Enterprise AI Strategy” should be designed taking into consideration the deep understanding of the enterprise’s environment and its economic dynamics and be extended into an “Enterprise AI Monetization Strategy”. This specific intelligent strategy is going to be a crucial part of the “Enterprise DNA” of Strategy.