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Artificial Intelligence enters the mainstream news
30 April 2007

AI has added a new dimension to enterprise applications, helping organisations quickly adapt to changing business requirements as well as direction of market movement and actions of competitors. By Dr Kaustubh Chokshi*, CEO of UK-based Intelligent Business Systems.

Dr Kaustubh Chokshi2006, marked 50 years since the term "Artificial Intelligence" was coined. Over this period of half a century, AI has captured our imagination of as we fantasised about the day when machines would routinely carry out normal conversations with humans and do all our work. However, it''s no secret that on this count AI has so far only flattered to deceive.

Indeed, the AI story, in actuality, has turned out to be quite different from what was originally envisaged by the pioneers of the technology. While the quest to replicate general human intelligence continues, AI researchers have meanwhile concentrated on producing software with specific expertise that in many cases far outstrips human intelligence in the niche functionality it is designed for. As a result there are myriad AI applications all around us that we hardly even notice, and the technology has become rather pervasive in today''s world.

In previous articles on domain-b, I attempted to explain some of the philosophy and concepts that go into AI-based software, and briefly described artificial neural networks-the basic building blocks forming the architecture for software that learns from experience and gets better the more it is used.

One has to understand that the massive amounts of data being generated today cannot possibly be analysed effectively enough either by humans or traditional software, if one wishes to derive all the knowledge inherent in that data or ascertain the intrinsic hidden patterns. It is here that AI is playing a big role, whether in biotechnology or in banking.

AI technologies are diverse, and find application in a very wide range of fields. In this series, I restricted discussion to business applications, but AI has proved useful in other areas as well. To take just a couple of examples, today, vision-recognition systems alert security guards to potential threat or danger situations in buildings, car parks, supermarkets and even beaches.

AI software can actually remotely pilot airplanes, and in the near future, most cars would be equipped with AI-based "auto drivers". Of course, even today most modern cars are equipped with significant levels of AI to optimise fuel efficiency and engine longevity.

While I discussed specific business applications in my previous articles-such as forecasting tourism demand, anti money laundering, customer profiling, and loyalty systems-I would like to wrap up this brief series by giving you a broad perspective on the inroads made by AI technology into enterprise-level applications. 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.

Non-linear decision making
AI software garners knowledge from the data generated within the organisation, as well as from expert opinion and external data sources, and learns as it goes along. This has great relevance, because businesses operate in a non-linear environment, characterised by hard-to-predict (but non-random) cause and effect relationships. Tiny changes in conditions could lead to widely varying outcomes, and mere logic-based software would be hard-pressed to provide meaningful decision support in such scenarios.

On the other hand, decision management using AI enables automated improvement of operational and tactical decisions across the enterprise, with higher levels of precision, consistency and agility. All this while reducing the time taken to decide (decision latency), and the cost involved in making each decision.

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. Many of these activities fall under the purview of supply chain management, customer relationship management, data mining and business intelligence. In all of these enterprise level applications, AI can play a significant role in providing the enterprise with that elusive competitive edge.

Supply Chain Management
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. So, for example, one could have a "logistics agent" responsible for coordinating the factories, suppliers, and distribution centres to achieve on-time delivery, cost minimisation, inventory optimisation, etc. This agent would provide inputs to the "transportation agent", responsible for the assignment and scheduling of transportation resources to optimise movements of goods.

The problem-solving abilities of the intelligent software agents take real world uncertainties and constraints into account and dynamically cooperate among themselves to optimise 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. Constraints that cannot be satisfied are modified by the subset of agents directly involved, in a process of negotiation that has optimisation of the overall supply chain as the ultimate goal. AI plays a key role not only by making the best use of capacities in the system (asset utilisation), but also by ensuring that all forecast demands are met.

Customer Relationship Management
The aim of AI-based customer relationship management is to accurately map consumer wants and desires to product offers and promotions, in order to maximise satisfaction and increase yields. AI can play a significant role in this exercise of customer profiling and devising the perfect product-customer fit. This is because AI-based software 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. Once cross-selling, up-selling and general promotional offers are no longer based on clumsy or brute-force marketing, customer indifference and even irritation soon give way to satisfaction.

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. AI techniques can be used to take data mining a step further and utilise the established data patterns to make forward-looking predictions or dynamically generated multiple categorisations of customers by evaluating multiple data patterns.

Business Intelligence
The true worth of AI technology is most evident in Business Intelligence applications. With AI, BI goes beyond decision support and fixed rule-based analysis 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 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.

Artificial Intelligence capabilities have added a new dimension to enterprise applications, helping organisations quickly adapt and respond to changing business scenarios, as well as direction of market movement and actions of competitors. This represents a new and emerging paradigm in software architecture and offers enterprises that take advantage of it greater agility in everything they do. AI has well and truly arrived in the enterprise. The big question: Has it arrived in your enterprise? Or only at your competitor''s?

* Dr Kaustubh Chokshi is CEO of Intelligent Business Systems (IBS), a UK-based AI Enterprise Solutions company, which has recently expanded into India. Dr Chokshi has a PhD in Artificial Intelligence from the University of Sunderland, UK.


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Artificial Intelligence enters the mainstream