Artificial Intelligence in the Enterprise Date:   Tuesday , May 01, 2007 Not too long ago, any mention of the term ‘Artificial Intelligence’ in front of CIOs would most likely evoke in them feelings of disdain, contempt and cynical dismissal. It’s no secret that Artificial Intelligence technology and neural network software suffered from a credibility gap, and the chasm between promise and delivery seemed unbridgeable. All that is history now. Today, no CIO or IT Head worth his salt would risk cocking a snook at AI, for AI technology is firmly embedded in a wide range of applications software, and is gradually weaving its way into enterprise applications as well. In fact, cutting-edge AI techniques are being used to develop enterprise software that can dynamically adapt to rapidly changing business environments, while simultaneously providing high levels of decision support and trends forecasting for enterprise managers to act on. The key difference between traditional, logic-based software and software based on AI is that the latter can be trained, and learns from experience. It is equipped to acquire knowledge from the data generated within the organization, as well as from expert opinion and external data sources. Non-Linear Decision Making The above consideration is extremely important because businesses operate in a non-linear environment, characterised by hard-to-predict (but non-random) cause and effect relationships. Mere logic-based software would only be able to provide a primitive level of decision support in such situations. On the other hand, decision management using AI is a systematic approach to automating and improving decisions across the enterprise. AI-based decision support systems aim to increase the precision, consistency and agility of operational and tactical decisions made in the organization, while reducing the time taken to decide (decision latency), and the cost involved in making each decision. This helps to increase revenues while minimising risk, through greater segmentation, more relevant offers to customers, and better risk management. In addition, business agility is also increased, because with the AI-based system, new competitive and compliance demands can be met without the need for technical personnel to reengineer the system. AI can be applied to virtually any business area that involves high-volume, operational decisions, or the use of analytics and business rules to improve decision strategies. As you can well imagine, this encompasses a wide range of business activities, such as: customer acquisition and retention; matching prospects and customers with product/service offerings; distribution optimisation; fraud detection; debt collection and recovery; product configuration and design and regulatory compliance. Many of these activities fall under the purview of Supply Chain Management, Customer Relationship Management, Data Mining and Business Intelligence. Supply Chain Management, The objective of supply chain management (SCM) is the integration and optimization of all the components and processes involved. AI-based supply chain management is generally agent-oriented. It is composed of a set of intelligent software agents, each responsible for one or more activities in the supply chain and each interacting with other agents in the planning and execution of their responsibilities. For example, one could have a ‘logistics agent’ responsible for coordinating the factories, suppliers, and distribution centres to achieve on-time delivery, cost minimization and inventory optimisation’. This agent would provide inputs to the ‘transportation agent’, responsible for the assignment and scheduling of transportation resources to optimize movements of goods. In reality, activities may not go according to plan or schedule for various reasons. The problem-solving abilities of the intelligent software agents take these constraints into account and dynamically cooperate among themselves to optimize the chain based on the set goals. Agents can develop plans that satisfy internal constraints, as well as those of other agents, in a coordinated manner. AI plays a key role in the distribution logistics not only by making the best use of capacities in the system (asset utilization), but also by ensuring that all forecast demands are met. Customer Relationship Management In simple terms, customer relationship management is all about giving customers what they want, while minimizing hassle and maximizing satisfaction. AI can play a significant role in the exercise of customer profiling and devising the perfect product-customer fit since it takes into consideration actual transaction data to detect hidden patterns in consumer behaviour. Add to this expert opinion and relevant factors from the external environment and you have a system that provides for razor-sharp precision marketing. While cost of marketing automatically reduces, the impact on customer retention and yield is significantly positive. Once cross-selling, up-selling and general promotional offers are no longer based on clumsy brute-force marketing, customer indifference and even irritation soon give way to satisfaction. What’s more, intelligent profiling can help reduce defaulting and fraud—this is especially significant in the BFSI sector for matters such as credit card misuse and loans approval. Data Mining Data Mining can be looked at as one stage in the development of an AI-based system. In essence, data mining applies sophisticated mathematical techniques to search for useful patterns in large databases. The use of AI techniques such as neural networks and Bayesian inferencing augments the process significantly. Using AI techniques, it is possible to formalize data relationships and predict future behaviour consistently. Business Intelligence The considerable worth of AI technology is most evident in the Business Intelligence (BI) arena. Artificial Intelligence can take BI beyond mere decision support and rule-based profiling, to offer true decision automation. Historical information on company operations and customer activity can be used to channel decisions in the right direction. The AI software sets or helps set the criteria to make predictions from existing sets of data and generates scoring algorithms for weighting different data characteristics. It can also segment populations into sub-groups, enabling variable treatment for each business decision. It is only in AI-based software that predictive analytics—the ability to predict likely future results and scenarios from historical data—can be accurate, meaningful and beneficial. Conclusion In summary, Artificial Intelligence capabilities have added a new dimension to enterprise applications, helping organizations quickly adapt and respond to changing business scenarios, as well as direction of market movement and actions of competitors. AI-based enterprise solutions are particularly useful in helping organizations devise a unified and integrated approach to creating, modifying and deploying decisions relating to customer profiling and precision marketing. This enables addition and retention of high-value and high-potential-value customers, reduced cost of service per customer, and greater agility in response to changing business environments. Organizations that wish to maintain a sustainable competitive edge would do well to evaluate the positive impact that Artificial Intelligence can have on their bottom line—and the time to do that evaluation is right now!