Much of the current buzz in the industry continues to be centered on RPA, a term currently largely synonymous with automation, and this technology clearly has lots of life left in it, for a few years at least. Outside service providers, where its adoption is rapidly becoming mature, RPA is still at the early growth stage in the wider market: while a number of financial services firms have already achieved large-scale roll-outs of RPA, others have yet to put their first bot into operation.

RPA is a great new technology and one that is yet to be widely deployed by most organizations. Nonetheless, RPA fills one very specific niche and remains essentially a band-aid for legacy processes. It is tremendous for executing on processes where each step is clearly defined, and for implementing continuous improvement in relatively static legacy process environments. However, RPA, as TCS highlights, does have the disadvantages that it fails to incorporate learning and can really only effectively be applied to processes that undergo little change over time. TCS also argues that RPA fails to scale and fails to deliver sustainable value.

These latter criticisms seem unfair in that RPA can be applied on a large scale, though frequently scale is achieved via numerous small implementations rather than one major implementation. Similarly, provided processes remain largely unchanged, the value from RPA is sustained. The real distinction is not scalability but the nature of the process environment in which the technology is being applied.

Accordingly, while RPA is great for continuous improvement within a static legacy process environment where processes are largely rule-based, it is less applicable for new business models within dynamic process environments where processes are extensively judgment-based. New technologies with built-in learning and adaptation are more applicable here. And this is where TCS is positioning Ignio.

TCS refers to Ignio as a “neural automation platform” and as a “Services-as-Software” platform, the latter arguably a much more accurate description of the impact of digital on organizations than the much-copied Accenture “as-a-Service” expression.

TCS summarizes Ignio as having the following capabilities:

• “Sense”: ability to assimilate and mine diverse data sources, both internal and external, both structured and unstructured (via text mining techniques)
• “Think”: ability to identify trends & patterns and make predictions and estimate risk
• “Act”: execute context-aware autonomous actions. Here TCS could potentially have used one of the third-party RPA software products, but instead chose to go with their own software instead
• “Learn”: improving its knowledge on a continuous basis and self-learning its context.

TCS Ignio, like IPsoft Amelia, began life as a tool for supporting IT infrastructure management, specifically datacenter operations. TCS Ignio was launched in May 2015 and is currently used by ten organizations, which includes Nationwide Building Society in the U.K. All ten are using Ignio in support of their IT operations, though the scope of its usage remains limited at present, with Ignio being used within Nationwide in support of batch performance and capacity management. Eventually the software is expected to be deployed to learn more widely about the IT environment and predict and resolve IT issues, and Ignio is already being used for patch and upgrade management by one major financial services institution.

Nonetheless, despite its relatively low level of adoption so far within IT operations, TCS is experiencing considerable wider interest in Ignio and feels it should strike while the iron is hot and take Ignio out into the wider business process environment immediately.

The implications are that the Ignio roll-out will be rapid (expect to see the first public example in the next quarter) and will take place domain by domain, as for RPA, with initial targeted areas likely to include purchase-to-pay and order-to-cash within F&A and order management-related processes within supply chain. In order to target each specific domain, TCS is pre-building “skills” which will be downloadable from the “Ignio store”. One of the initial implementations seems likely to be supporting a major retailer in resolving the downstream implications of delivery failures due to causes such as traffic accidents or weather-related incidents. Other potential supply chain-related applications cited for Ignio include:

• Customer journey abandonment
• The profiling, detection, and correction of check-out errors
• Profiling, detecting, and correcting anomalies in supplier behavior
• Detection of customer feedback trends and triggering corrective action
• Profiling and predicting customer behavior.

Machine learning technologies are receiving considerable interest right now and TCS, like other vendors, recognizes that rapid automation is being driven faster than ever before by the desire for competitive survival and differentiation, and in response is adopting a “if it can be automated, it must be automated” stance. And the timescales for implementation of Ignio, cited at 4-6 weeks, are comparable to that for RPA. So Ignio, like RPA, is a relatively quick and inexpensive route to process improvement. And, unlike many cognitive applications, it is targeted strongly at industry-specific and back office processes and not just customer-facing ones.

Accordingly, while RPA will remain a key technology in the short-term for fixing relatively static legacy rule-based processes, next generation machine learning-based “Services-as-Software” platforms such as Ignio will increasingly be used for judgment-based processes and in support of new business models. And TCS, which a year ago was promoting RPA, is now leading with its Ignio neural automation-based “Services-as-Software” platform.