Fast-Track Explainable AI with Multimodal Generative AI: The Future of AI Monetization & Information Economics

In the 12th edition of my first newsletter, “Digital CXO Advisor,” entitled?“Enterprise AI Monetization: The Intelligent Strategy for Artificial Intelligence,”?it was concluded that enterprises shouldn’t design their?“AI Strategy”?from a technical perspective solely or solely due to its foundational benefits and shouldn’t favor a particular 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”?that should positively impact the?“Tangible”?or?“Intangible”?assets.?

Furthermore, in the 68th edition of the same newsletter, “Digital CXO Advisor,” entitled?“AI Monetization and Information Economics Strategies to Maximize Enterprises ROI,”?it was concluded that the?“Enterprise AI Monetization Strategy”?is by nature deeply rooted in the dynamics of?“Information Economics,”?and is by definition highly impacted by?“Internal Data Monetization Strategies,”?which aims to positively impact both these enterprise’s?“Tangible Assets,”?and?“Intangible Assets.”?The success of this enterprise AI monetization strategy is primarily constrained and determined by the organization’s internal?“Core Competencies”?in the domains of?“Data Management”?and?“Data Analytics”?and can happen only when the enterprise manages its data, not as operational assets but as a?“Strategic Asset”?through an explicit?“Data Management Strategy” that can provide a?“Competitive Strategic Advantage.”

However, to positively impact both these enterprises’ tangible and intangible assets in the real world, the decision-makers will need to not only understand the most?“Critical Factors”?that are directly able to affect these assets but also need to deeply understand which of these factors are controllable at the same time (i.e., the?“Controllable Critical factors.”). The reason is so simple: if these most critical factors are uncontrollable, taking real actions toward monetization may seem almost impossible. For example,?suppose the enterprise’s internal data monetization strategy aims to positively impact the enterprise’s operational costs (i.e., tangible assets) and business brand sentiment analysis (i.e., intangible assets), and the decision makers found that the operational costs are highly affected by outside business environment drivers and that the business brand sentiment analysis is highly affected by the negative collective consumers’ deeply rooted cultures and perceptions, the decision maker may find that taking real actions toward monetization may seem almost impossible.?

“Artificial intelligence Systems”?themselves are a clear example of this. The total cost of ownership of complex AI systems is dependent on the high operational costs, and these high costs are, by definition, environment drivers. Furthermore, the?sentiment analysis for artificial intelligence as a general technology is highly affected by the negative collective consumers’ deeply rooted cultures and perceptions about AI as a job replacement.?

Here comes that real added value of what is called the?“Explainable AI”?as it has the ability to identify the most critical factors that directly affect the outcome under interest and investigation, even if the utilized AI model to study this case was inexplicable at the first place by nature, such as?“Deep Learning Neural Networks,”?which is irreplaceable by simpler highly explainable AI models. Hence, explainable AI may theoretically appear to be a significant solution in this situation. However, there is still a practical, down-to-earth barrier in front of this solution. The total cost of ownership of these deep learning neural network models is significantly high due to the massive processing power required for training this type of AI models. This makes the search for a practical solution seem necessary and unavoidable at the same time.?

Here comes the value of what is called the?“Fast-Track Explainable AI.”?This fact-track explainable AI can save massive amounts of effort, time, resources, and money by being able to perform an accurate determination of the most critical factors that directly affect the outcome under interest and investigation with fractional effort, time, resources, and cost. Thanks to the unprecedented abilities of the?“Multimodel Generative AI Models,”?which can be prompted to act as an experienced?“Data Sceintst”?and?“Machine Learning Engineer”?who have the abilities to dig deep into the massive amounts of data, finding hidden patterns, identifying the critical factors, visualize their contribution towards the outcome under interest and investigation, and finally logically provide legitimate reasoning about the controllability of these factors. Please let me know in the comments if you are interested in learning more about this advanced algorithm to elaborate on this topic in further editions of this newsletter.

Hence, and to conclude, 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”?that should positively impact the?“Tangible”?or?“Intangible”?assets. However, this?“Enterprise AI Monetization Strategy”?is by nature deeply rooted in the dynamics of?“Information Economics”?and is, by definition, highly impacted by?“Internal Data Monetization Strategies,” which are?primarily constrained and determined by the organization’s internal?“Core Competencies”?in the domains of?“Data Management”?and?“Data Analytics”?and can happen only when the enterprise manages its data as a?“Strategic Asset”?through an explicit?“Data Management Strategy”?that can provide a?“Competitive Strategic Advantage.”?This makes decision-makers need to understand the?“Controllable Critical factors”?deeply. The?“Explainable AI”?helps identify these most critical factors even if the utilized AI model was inexplicable in the first place. The?“Fast-Track Explainable AI”?can remove the significant barriers toward this goal with just fractional effort, time, resources, and cost by capitalizing on the unprecedented abilities of the?“Multimodel Generative AI Models.”