The enterprise of the future will increasingly depend on a wide range of rigorous artificial intelligence systems, algorithms and techniques to facilitate its operation at all levels of management.
As described in The Adaptable Enterprise blog, major decisions incorporating sophisticated levels of intelligent problem-solving will increasingly be applied autonomously and within real time constraints to achieve the level of adaptability required to survive in an ever changing and uncertain global environment. This trendline describes these techniques and their application.
A number of artificial techniques and algorithms are rapidly reaching maturity and will be an essential component of Intelligent Enterprise Architecture of the future including:
Genetic algorithms- solution discovery and optimisation modelled on the genetic operators of cross over, replication and mutation to explore generations of parameterised options.
Bayesian networks- graphical models representing multivariate probability networks; providing inference and learning based on cumulative evidence.
Fuzzy Logic- non-binary methods of decision-making -allowing information inputs to be weighted and an activation threshold established.
Swarm Intelligence- combining multiple components to achieve group intelligent behaviour.
Neural networks- pattern discrimination techniques modelled on neuron connection.
Expert Systems- rule based inference techniques targeted at specific problem areas.
Intelligent Agents- this form of AI is particularly relevant to the future enterprise architecture, because it is designed to be adaptive to the web's dynamic environment; that is, an agent is designed to learn by experience. They can also act collaboratively in societies, groups or swarms. Through swarming behaviour agents can achieve higher levels of intelligence capable of making increasingly complex decisions autonomously
The above techniques will continue to be enhanced and packaged in different combinations to provide immensely powerful problem solving capability over time. The technology is slowly being applied discretely within business intelligence, data mining and planning functions of enterprise systems.
However AI is yet to realize its full potential within the enterprise model by being applied to decision-making in a targeted autonomous fashion. When this happens over the next decade, the quality of decision-making and concommitant reduction in operational and amanagement risk is likely to be significantly improved.
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