AI is an integral part of Digital Process Automation, and the potential of AI optimizations for on-chain (Blockchain transactions) and off-chain data (IoT, customer, etc.) are tremendous.
Sources of “Intelligence”
- Regulations, Knowledge, Policies, & Procedures: There is an enormous amount of knowledge in written documents. In any application domain or industry, there are policy and procedure manuals with inherent and embedded knowledge. This spans operations manuals, organizational procedures, as well as regulatory compliance documents. Rules-based systems, as well as language processing, can be leveraged to extract the knowledge and then operationalize it in Blockchain applications. Blockchain Smart Contracts can be leveraged to digitize the business rules in the Policies & Procedures (e.g., by-laws or operational regulations) and execute them on the Blockchain. For more complex policies, declarative business rules can digitize the policies and work in conjunction with Smart Contracts.
- Human Intelligence: Cognitive workers – people who use data or information to do their jobs – have a lot of deep knowledge in their heads that needs to be harvested. As noted above, AI-assisted human work often digitizes the knowledge of cognitive workers. Their expertise also needs to be understood, digitized, and operationalized, sometimes in intelligent virtual assistants or bots. Blockchain Decentralized Applications can leverage the cognitive workers’ knowledge either through the digitization of business rules or through fully or semi-automated intelligent assistants. Cognitive workers participate in end-to-end digitized value streams – operationalized through Digital Processes Automation (DPA).
- Legacy Code: Another important source of business intelligence is embedded in legacy code that contains business logic. The embedded policies can become ossified, with little or no business visibility. They are difficult to change or extend. The challenge is to leverage the intelligence in legacy systems while allowing the organization to modernize and be agile. Blockchain Architectures involve orchestration of Smart Contracts with API invocations of AI decisions through secure exchanges. Here again, the DPA layer can be an intelligent modernization bridge between legacy applications and Blockchain messaging – much the same way it is now with business-to-business protocols.
- Data: Blockchain stores all the business transactions: the addresses of the senders/receivers of exchanges, if applicable, the amounts of exchanges, the meta-data of transactions, and the execution of code in the Blockchain, etc. The chain in Blockchain only grows and is never deleted. Information cannot be modified. Only validated blocks can be added to the Blockchain. So, each Blockchain grows. Blockchain Data Mining and Machine Learning techniques with Blockchain transaction data sets are a tremendous source of intelligence. There are patterns of knowledge and intelligence hidden in the Blockchain data. AI can learn from the Blockchain data and potentially predict outcomes (positive or adverse) before they happen. Decisions based on AI predictions can be fed back to the data source to improve the precision of the predictions continuously.
From all of the aforementioned sources of intelligence, the discovery of intelligence needs to be followed with action in Value ChainsThis action is the execution of decisions in the context of end-to-end value streams orchestrating people, enterprise applications, devices, robots, and of course, the Blockchain! Each is carrying specific tasks assigned to them through the underlying AI-enabled DPA.
Example: Warranty Value Chain
- Warranty Business Rules and Policies: these are decision tables, decision trees, constraints, calculations, and expressions, etc. that are authored by Warranty experts. Business rules can be leveraged in all the milestones of the Warranty Value Chain.
- Predictive Analytics for Repairs: The connected device IoT Data can be mined for predictive analytic models that can be digitized and automated in especially the repair milestone of the Warranty Value Chain.
- Machine Learning and Adaptive Analytics: In addition, the combination of business rules, predictive models, and continuous learning feedback loops can be leveraged to prevent repairs ahead of time and avoid costly fixes for devices or vehicles under Warranty.
- Customer Experience Next-Best-Actions: The customer context and customer interactions are another source of data that could be analyzed for both predictive and machine learning prioritization for actions that optimize the customer experience.
- Blockchain Ledger Analytics: The transactions that are recorded on the Blockchain for the entire value chain are another rich source of potential predictive and machine learning models that could be periodically analyzed to optimize the entire value chain.