Bots Should Be in Your Contact Center’s Future

Businesses do not want to start with too simple of a business process because the results will be insignificant, he says. Nor do they do want to take on too complex of a task because the project might fail. Furthermore, if the current business process is weak, the bot will not necessarily strengthen it; often firms will have to revamp their operations before they can begin to automate them.

Next, companies will need to build up their systems’ reasoning. Bots typically rely on knowledge that the companies deploying them have made available to the systems. When customers ask questions or provide information, bots select the closest-matching responses from preprogrammed repositories and then use that knowledge to craft the appropriate responses. But mistakes are possible, especially if the data on the back end is inaccurate or out of date.

And then companies should never lose sight of the fact that bots are still an evolutionary technology, one involving a lot of trial and error. “When we started, we tried too hard to be perfect right away,” Autodesk’s Spratto admits. “Instead, I would recommend that companies get something up fairly quickly and then focus on improving it.”

Initially, Spratto worried that a bad experience would sour customers so much that they would never use the bots. Such thinking now seems erroneous.

Nevertheless, both the front end and back end of today’s systems have become even more complex. Customers today can interact with companies in so many ways, from phone and email to social media. Bots need to support all of these possible user options.

The software also has to be tailored to the different interfaces. “We found developing with Watson was easy, but tying the software to the back systems was difficult,” Spratto adds.

Autodesk relies on inContact, SAP, Salesforce.com, and Tibco for its back-end processing, and each required its own integration. As a result, deploying these systems costs time and money. Autodesk started with a team of about half a dozen IT pros, and that number has grown fourfold since then.

For many businesses, justifying such expenditures could be a real challenge. “Right now, few metrics or benchmarks are available to help businesses understand bots’ impact,” COPC’s Aitchison says. But, he points out, such a lag is typical when any new technology first emerges.

Going forward, many vendors expect to add sentiment to their current bot technologies. Conversational bots, they hope, will be able to act like empathetic humans, understanding not only the literal translation of the words but also the consumer’s emotional state. In this case, the bots’ expressions and responses could change dynamically during one interaction or from one interaction to the next.

But in the meantime, bots continue to be a new, emerging technology creating significant buzz because of the potential they have to dramatically alter the customer experience. The software infrastructure needed for companies to realize that potential still needs to be developed. Time, money, and manpower will be needed to build it up if machines will ever be able to interact as effectively as (if not better than) humans now do with consumers.

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Paul Korzeniowski is a freelance writer who specializes in technology issues. He has been covering CRM issues for more than two decades, is based in Sudbury, Mass., and can be reached at paulkorzen@aol.com or on Twitter at #PaulKorzeniowski.

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How AI Differs from Machine Learning

What are the differences in how humans and machines think? Historically, the distinctions were easy to discern. But when IBM’s Watson is able to get the better of humans on the TV game show Jeopardy!, the dividing lines have become murkier. Those lines will blur even further as artificial intelligence and machine learning continue to evolve.

AI is a generic term for the “smarts” exhibited by machines. The field operates under the assumption that human intelligence can be described so precisely that a machine could be programmed to simulate it. Typically, a machine mimics the cognitive functions that humans associate with reasoning, such as problem-solving and learning. Programmers outfit systems with intelligent agents that can make assumptions and take actions that maximize the chances of reaching predefined goals.

AI’s recent resurgence follows advances in computing power that have enabled developers to use larger data pools and build more complicated programs.

Machine learning, a follow-up to AI work, is a method of data analysis that further improves system reasoning. Relying on algorithms that monitor interactions and iteratively learn from them, this approach enables computers to perform functions without explicit programming. Machine learning differs from AI in that as the system is exposed to new data, it adapts independently. AI systems, by contrast, have to be told what to do.

At the moment, AI and machine learning are relatively unsophisticated, automating only the most routine interactions. Eventually, the technology is expected to mature and perform more intelligent, human tasks, such as recognizing that a caller is upset and taking steps to defuse his anger. —P.K.