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WHAT IT TAKES TO TRAIN A CONVERSATIONAL CHATBOT
Vasudevan Swaminathan
Forbes Councils Member
Forbes Technology Council
COUNCIL POST
Expertise from Forbes Councils members, operated under license. Opinions
expressed are those of the author.
| Membership (fee-based)
Oct 19, 2022,08:15am EDT|
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Vasudevan Swaminathan is the Founder and CEO of Zuci Systems — a software firm
specializing in Digital Transformation solutions.
getty
According to a recent Gartner, Inc. projection, "conversational artificial
intelligence (AI) deployments within contact centers will reduce agent labor
costs by $80 billion" by 2026.
Being a contact center agent is no easy job—handling several calls, resolving
customer issues, meeting targets and more while ensuring high customer
satisfaction levels. This can create stressful situations that can negatively
impact agent productivity, leading to poor customer experience (CX).
Bots helping contact center agents improve their productivity is great news, and
many companies have already started investing in that. However, despite
organizations purchasing expensive AI software, systems and technologies to
build conversational bots and help lessen the job pressures on contact center
agents, we see that agents are still struggling. How come many of these AI
systems aren't truly serving their purpose when it comes to helping contact
center agents?
About 12 months back, a bank in the Asian region asked Zuci Systems to present a
proposal for building an AI-based conversational chatbot that would help improve
the bank's contact center agents' productivity. As part of the first phase of
the chatbot building exercise, we aimed at addressing the most common queries
that contact center agents handled every day, the handling of complaints and the
servicing of queries using natural language processing (NLP), a form of AI,
thereby limiting the manual intervention of agents and improving the CX.
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During the initial due diligence, our team discussed the following information
with the bank's C-suite and teams.
1. The most common queries and servicing queries that the bank's contact center
agents handle.
2. Details of common complaints that contact center agents came across from
customers.
3. How is the contact center set up today? How do agents pick up relevant
content and answer queries?
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4. A process workflow that shows how queries are routed.
5. The average contact center time spent by clients, along with service level
agreements (if any).
6. The path to data creation models to answer questions.
7. Collaborate on building a knowledge base with about 800 to 900 queries to
train the conversational chatbot.
As we started discussing more, we realized that the bank did not have enough
data. The bank personnel felt we were not moving at the pace it wanted us to
move. After a while, we parted ways amicably after explaining why we felt the
conversational chatbot wouldn't solve the purpose for which it was being
built—improving agent productivity. The reason was that there was not enough
data to train the model.
Many organizations think they are ready for AI when, in fact, they are not. You
don't need an Archimedes to find this and shout, "Eureka!" If the organization
lacks a sound data collection system that leads to a sizeable data store (which
is needed for training any kind of AI system), you know that the model you plan
to build is not going to give you the accuracy you are looking for.
As Monica Rogati points out in "The AI Hierarchy of Needs":
"At the bottom of the pyramid, we have data collection. What data do you need,
and what's available? If it's a user-facing product, are you logging all
relevant user interactions? If it's a sensor, what data is coming through and
how? How easy is it to log an interaction that is not instrumented yet? After
all, the right dataset is what made recent advances in machine learning
possible."
Not having enough data can result in a poorly built AI system—and that is just
the starting point of the problem. There is more to tackle for organizations as
they build AI systems, but the fundamental premise or foundation for any AI
system is data.
As AI companies keep making claims about "conversational chatbots" that can be
purchased and deployed readily in contact center environments, organizations
need to really look at their context and see how much of these ready-made
chatbots can directly fit into their contact centers. Maybe they can answer the
first few generic questions, but they are not your real agents who have exposure
and experience handling your customer issues. You need to train these chatbots
with a significant amount of data based on what your agents deal with every day
in the contact center.
--------------------------------------------------------------------------------
Forbes Technology Council is an invitation-only community for world-class CIOs,
CTOs and technology executives. Do I qualify?
--------------------------------------------------------------------------------
Follow me on LinkedIn. Check out my website.
Vasudevan Swaminathan
Vasudevan Swaminathan is the Founder and CEO of Zuci Systems — a software firm
specializing in Digital Transformation solutions. Read Vasudevan Swaminathan's
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