Identifying, sorting and recognizing impact of patterns:
Signals and patterns need to be quickly sifted and aggregated into patterns of interests for managers to decide and take action to optimize an organizations opportunities or protect organizations from emerging risks. This sifting, aggregating and learning can be assisted and super charged by cognitive AI. Some of this recognition is likely to involve scout agents at the edge that might be embedded in programmable chips, software bots and senors in and around IoT networks. Other recognition could be done in a more central-like fashion when combined with data, policies, rules and constraints that have been deemed strategic in nature.
Assisting decisions on patterns of interest:
Once patterns are detected, AI can arrange the sequencing of evaluating simulations in parallel environments (twins), test with significant algorithms or run inferencing approaches. AI can additionally select the proper visualization for human interaction and notify the proper managers of a need for decision(s). Over time the AI agents can learn to reduce human interaction leveraging machine intelligence while it learns what patterns are helpful or harmful.
Acting out the proper responses to patterns:
Over time AI will not only assist with better decisions on these patterns, AI will suggest changes in the actions in response to harmful or helpful patterns. AI can help optimize internal actions and test them out. AI can point to proper external actions that are crucial in responding to these strategic patterns of interest. Over time AI can suggest changes to policies, governance and constraints. Ultimately, AI can act alone with notifications after the fact to attain ultimate AI freedom levels.
AI has great potential to help with not only operational behaviors, but strategic responses to opportunities and threats. This may move organizations in the direction of leveraging gaming-like approaches to pattern responses and consequences.
Uses of AI Today
AI for Speed
AI at the Edge