Artificial Intelligence For Business Apps Is Back With A Bang!

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: Artificial Intelligence (AI) has been used in business applications since the early eighties. As with all technologies, AI initially generated much interest, but failed to live up to the hype. However, with the advent of web-enabled infrastructure and rapid strides made by the AI development community, the application of AI techniques in real-time business applications has picked up substantially in the recent past.

AI is a broad discipline that promises to simulate numerous innate human skills such as automatic programming, case-based reasoning, neural networks, decision-making, expert systems, fuzzy logic, natural language processing, pattern recognition and speech recognition etc. AI technologies bring more complex data-analysis features to existing applications.

Business applications utilise the specific technologies mentioned earlier to try and make better sense of potentially enormous variability (for example, unknown patterns/relationships in sales data, customer buying habits, and so on). However, within the corporate world, AI is widely used for complex problem-solving and decision-support techniques (neural networks and expert systems) in real-time business applications. The business applicability of AI techniques is spread across functions ranging from finance management to forecasting and production.

The proven success of Artificial Neural Networks (ANN) and expert systems has helped AI gain widespread adoption in enterprise business applications. In some instances, such as fraud detection, the use of AI has already become the most preferred method. In addition, neural networks have become a well-established technique for pattern recognition, particularly of images, data streams and complex data sources and, in turn, have emerged as a modeling backbone for a majority of data-mining tools available in the market. Some of the key business applications of AI/ANN include fraud detection, cross-selling, customer relationship management analytics, demand prediction, failure prediction, and non-linear control.

Numerous software vendors in the market, such as Ward Systems Group and Neural ware, provide off-the-shelf tools for ANN. However, a majority of the enterprises adopt horizontal or vertical solutions that embed neural networks such as insurance risk assessment or fraud-detection tools from HNC, or data-mining tools that include neural networks (for instance, from SAS, IBM and SPSS) as one of the modeling options.

Additionally, autonomic computing concepts derived from AI technologies, which facilitate self-healing systems, have generated a lot of hype in the recent past. Autonomic systems auto-configure for changing conditions, continuously monitor the constituent system parts and fine- tune workflow to achieve pre-determined system goals.

Of late, AI has found a home in financial services and is recognised as a valuable addition to numerous business applications. Sophisticated technologies encompassing neural networks and business rules along with AI-based techniques are yielding positive results in transaction-oriented scenarios for financial services. AI has been widely adopted in such areas of risk management, compliance, and securities trading and monitoring, with an extension into customer relationship management (CRM). Tangible benefits of AI adoption include reduced risk of fraud, increased revenues from existing customers due to newer opportunities, avoidance of fines stemming from non-compliance and averted securities trade excetions that could result in delayed settlement, if not detected.

It is also being widely adopted in diagnostics and testing. Diagnostic systems are used to examine networks, aircraft engines, manufacturng machinery and other types of equipment, energy pipelines, hazardous materials, and so on. Similarly, in the transportation industry, which is also fast catching up with the trend, AI is being used for traffic management systems, aircraft maintenance operations, airport gate scheduling, railroad planning and forecasting assignments.

In the fiercely competitive and dynamic market scenario, decision-making has become fairly complex and latency is inherent in many processes. In addition, the amount of data to be analysed has increased substantially. AI technologies help enterprises reduce latency in making business decisions, minimise fraud and enhance revenue opportunities.

Enterprises that utilise AI-enhanced applications are expected to become more diverse, as the needs for the ability to analyse data across multiple variables, fraud detection and customer relationship management emerge as key business drivers to gain competitive advantage. Though the financial services sector is an evangelist in the implementation of AI technologies, other sectors such as manufacturing, transportation, logistics, and healthcare have fast caught on.

According to a report from a business research company, the total worldwide AI market was $11.9 billion in 2002 and is expected to grow at an average annual growth rate of 12.2 per cent to reach $21.2 billion by 2007. Isn’t this testimony to the fact that AI for business applications is truly back with a bang!