Human
Intelligence
01 Data
Pros: Efficient in processing and storing sensory data (visual, auditory, etc.).
Cons: Very limited in processing speed for symbolic data (e.g. 50 bytes/sec for text). Even more limited in processing digital data.
02 Value
Pros: We can estimate the potential value of data through meaning, according to the decision that needs to be made.
Cons: It is very difficult to estimate the value of large volumes of digital data.
03 Prediction
Pros: With sensory data, we are able to make precise predictions based on scarce data and extrapolate them to new situations with great skill.
Cons: Our decisions are subjective and biased; our brain, which produces mental predictive models, is a black box.
04 Decision
Pros: We use our intuition and experience to balance different objectives.
Cons: Our decisions are subject to multiple cognitive biases; we become confused when there are multiple alternatives; our decision-making can be too slow.
05 Action
Pros: A large number of actions to be performed require human intervention.
Cons: If there are many actions to be performed, the process becomes slow.
06 Result
Pros: Able to define the goals of the decision-making process, the corresponding actions, and KPIs.
Cons: It is difficult to measure the result of the action or to determine if it worked, if the impact is implicit in the digital data.
Artificial
Intelligence
01 Data
Pros: Efficient in processing, storing, and integrating any structured or unstructured digital data that represents texts, images, voice, spatio-temporal data etc.
Cons: The data has to be in digital form.
02 Value
Pros: None.
Cons: It cannot infer the meaning or potential value of the data it is using.
03 Prediction
Pros: Can make predictions based on Big, Deep digital data containing a large numbers of predictors. White box models allow for an analysis of the relative importance of each predictor. Is objective if the data is not biased.
Cons: Does not include intuition or experience. In general, needs large quantities of data. Black box models make interpretation of results difficult.
04 Decision
Pros: Can carry out quick and objective decisions.
Cons: Difficult to incorporate ethical elements; has no intuition and experience; cannot balance different objectives.
05 Action
Pros: Many actions can be automated leading to efficiencies and a better cost/benefit.
Cons: Many actions may require human intervention.
06 Result
Pros: Given a KPI, the relationships between the KPI and the prediction, decision-making, and action that the model took as a result can be tracked and updated.
Cons: It is not capable of creating its own KPIs.
Hybrid
Intelligence
01 Data
Pros: Efficient in processing and storing any type of data, both digital and sensory, and in multiple formats.
Cons: None.
02 Value
Pros: We can estimate the potential value of data through its meaning and the decision that needs to be made.
Cons: None.
03 Prediction
Pros: Can include both digital and analog data, considering large numbers of potential predictors and combining both human intuition and experience with digital data-based AI models.
Cons: None.
04 Decision
Pros: AI models can provide objective evidence for decision making while human intuition and experience can be used to balance different objectives and to judge the cost-benefit of automating decisions.
Cons: None.
05 Action
Pros: Actions can be automated if possible and when necessary
Cons: None.
06 Result
Pros: KPIs can be created, and the relationships between a KPI with the prediction, decision-making and action taken as a result can be tracked and updated.
Cons: None.