#AI@Work: RPA is Everywhere!
The only thing slowing AI down is a lack of understanding of what is possible. AI is following the pattern of many earlier technologies. Tech firms and large companies are the leaders. Startups spring up as new ideas and new markets open up. Mid-sized businesses follow when the technologies become affordable and available. Big companies have the money to invest and the people to make AI happen. Smaller companies are interested in surviving, competing and staying alive. AI is likely to help big companies get bigger and make it harder for small companies to compete. Adopting AI is not easy for large organizations. AI has to be integrated with the current systems and processes.
Business processes evolve over time. Once they are established, they are very hard to change. Simple, isolated use cases are usually successful. Large-scale integrations that require major process modifications or adapting new processes entirely are a different story.
AI still requires humans to ask the questions and define what they want as results. It also requires someone to label data initially, then monitor and audit the results. The titles AI Auditor and Chief Trust Officer are turning up. Usually this requires highly skilled and expensive talent. This talent can be hard to find.
A perfect example of monitoring and auditing results is RPA and customer service. This sounds like it is simple and straightforward, and it can be for smaller scenarios. On a larger scale, it can be very difficult. RPA requires the integration of existing systems and changes in processes and architectures over time. Much of what is out in the marketplace masquerading as AI is not. It is really decision driven, statistical machine learning. It uses labeled data and a set of user defined rules. Rule based systems are pretty easy to develop quickly and inexpensively. But the results are also limited. Just like with natural language recognition, it is the human side of the operation that makes it more challenging. Projects take longer and cost more than expected.
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