A.I. (artificial intelligence) is the buzzword nowadays, especially in sales pitches. Expectations are high.

However, it’s crucial to keep in mind what A.I. means. A simple definition from Wikipedia: Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals. ¹

Now, when researching how A.I. can be leveraged in customer support, it often boils down to improving customer satisfaction or cost efficiency in various manners. In a traditional service desk tool, you’ll quickly notice most of the topics are not as complicated as you’d think.

A.I. is much more generic than the term machine learning. ( Wikipedia: Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. ) ² .

You’ll notice that a lot of ideas are actually still very much rule-based; rather than having some form of self-learning and self-improving neural network.

To put it very simple: With machine learning, the system learns and trains itself from historical data.

So what are the main ideas when it comes to A.I.? What’s currently possible? How can these tools lead to more satisfied customers? How can they assist support agents? Is there a risk (mostly first line) support agents will be replaced? What are the pitfalls? And, in our case, how does it apply to iTop?

Below you’ll find a summary of the most common concepts; their benefits; and their pitfalls.

Note that sometimes the goal is “reduce workload”. This is a very generic phrase. It could indeed mean that a business would need fewer people. It could also mean the staff has more opportunities to invest time in other facets of their job.

Smart suggestions and recommendations

What: Customers and staff could get recommendations on how to solve issues. They can be provided with a limited and accurate answer; to make sure they don’t need to go through an extensive admin manual or knowledge base.

Benefits:

• For customers: Instant accurate answers.
• For staff: Less work. Tickets will be deflected.

Goals:

• Improve customer satisfaction.
• Reduce work load.

Risks:

• Low: Customer frustration.

Challenge:

The suggestions should be “smart”. The system should ideally only show recommendations which actually matter. For example, if a customer is specifically asking about product X, they should not be presented with knowledge base articles relating to product Y.

The list of suggestions should also be short and relevant. The suggestions should not lead to more confusion.

Smart suggestions could be offered in many ways:

• Customers creating a ticket through a support portal, can be shown suggestions right away. This works for the classic creation of a ticket; but also with chatbots.
• Customers creating a ticket through different channels, even e-mail, could receive these suggestions automatically at first.
• Within the back-end of a service desk, recommendations could also be shown to the agent who handles the support case. From the recommendations, the agent can then select which ones truly matter for the customer.

Options in iTop:

• Pro extension: Suggested Articles (FAQs)

Automated ticket triaging

What: A.I. can already do initial processing of a ticket. The urgency or complexity can be determined, categorization can already be performed (e.g. “software” or “hardware” issue), …

The most simple form is to do this rule-based. More advanced forms consist of machine learning, where the system learns for example how urgent a ticket really was.

After categorization, it can also assign the proper teams or agents.

Benefits:

• For customers: Better response times.
• For staff:
  • Less work.
  • Consistency. A system should also be more consistent in its categorization than different people doing the categorization.

Goals:

• Improve customer satisfaction.
• Reduce work load.

Risks:

• Medium: Customer frustration.
• Medium: Staff frustration.

Options in iTop:

• Pro extension: Auto Assigner

Agent routing

What: A.I. can assign teams and agents to tickets. The system can route tickets to the best suited team or person. This could be done based on categorization (see above), but it could also factor in different factors such as work load, availability (holidays, schedules, time zone, …), expertise, spoken languages, other skills, …

The most simple form is to do this rule-based. More advanced forms consist of machine learning, where the system learns for example which agent handled similar cases in the most efficient manner.

Goals:

• Improve customer satisfaction.
• Reduce work load.

Benefits:

• For customers: Better response times.
• For staff: Better work load management.

Risks:

• Low: Customer frustration (Ending up with the wrong team/agent. But those can still manually correctly re-assign it.)
• Low: Staff frustration.

Options in iTop:

• Pro extension: Auto Assigner

Chatbots / virtual assistants

The difference between a chatbot and a virtual assistant, is that a virtual assistant can perform more complex actions and try to offer a more personalized form of assistance. For convenience, I’m only using the term “chatbot” in this section.

What: Customers can chat with a system to get replies to their questions. During this flow, additional information can be automatically obtained already; and if needed, it can be passed on to a human who will take over the chat conversation or who will reach out later in an alternative manner.

Benefits:

• For customers: Instant replies, 24/7. This is something smaller or local business usually can’t offer; as it requires working in shifts.
• For staff:
  • Very basic questions get deflected.
  • Additional info can already be collected, such as for example log files, specific contact info, initial troubleshooting steps, …

Goals:

• Improve customer satisfaction.
• Reduce work load.

Risks:

• High: Customer frustration.
• Medium: Customer alienation. Depending on the services you offer, people appreciate two things: fast responses/solutions; and a personal experience – which becomes even more important when there is no quick fix.

Challenge:
The chatbot must be mature enough to provide accurate replies. The system may face challenges in understanding what the customer means. It may fail to comprehend the context, the actual language, cultural nuances, …

Context is crucial when you want to provide customers a decent level of support. As a support agent, you might have a better grasp of what the customer struggled with in the past, what their environment looks like, what sensitivities there may be

Surely you’ve experienced this form of online chat, where you enter a question and the chatbot asks “did you mean X?” – which is not what you meant at all. An inadequate chatbot can already lead to more customer frustration. Customers may give up already and be very displeased. But most likely, they’ll end up with a (human) support agent. While their original issue might already have caused for some friction, their frustration might have become worse. This would lead to an immediate impact on the first interactions with the support agent who takes over at some point.

So the question could be: How many wrong guesses (if any) are acceptable?

Nowadays, depending on the type of customer service, customers may also be looking for a much more personalized experience.

Options in iTop: None natively available.

Language translation (machine translation)

What: We all know Google Translate or Deepl nowadays. This is only important for organizations who offer services to customers who speak different languages.

But A.I. could assist with language translation in both directions: the agent gets a translation from the customer’s inquiry; and the customer receives a translated version of the agent’s response.

Goals:

• Improve customer satisfaction.

Benefits:

• For customers and staff: They understand each other.
• For customers: A more personalized experience.

Risks:

• Medium: Customer frustration.
• Medium: Staff frustration.

Challenge:

Translations may be incorrect. The more context there is, the better modern translation systems are. Imagine an organization which uses one service desk tool for all their departments. A ticket saying nothing more than “The window is stuck”: is this a computer issue; or is there actually a window in a building which can’t be opened?

Also, some parts should perhaps not be translated at all; or could be translated incorrectly. Imagine you are a vendor of software, and have localized versions of it for your end users. Will the translation algorithm used by the service desk provide the same output? If for example the agent sends a reply to a customer: Go to Settings > Federation; will it translate this as ‘Instellingen > Federatie’ or ‘Instellingen > Vereniging’ in Dutch (which may be different from what your localized software looks like).

Options in iTop: None natively available. If you’re interested in having this developed, please reach out.

Language suggestions

What: There are also systems available now which can adjust the tone of a message. The tone may depend a lot on the customer you’re writing to, or on the organization’s policy. Common techniques are rephrasing, elaborating, shortening, translating / synonyms, and grammar correction.

Do you want to send short bullet point responses to customers, or lengthy paragraphs? Are you going for an informal or formal style?

It will also help your agents (or systems) to send responses with less spelling or grammar mistakes.

Goals:

• Improve customer experience.
• Improve brand experience.

Benefits:

• Offer a personalized experience to the customer (for instance: a lengthy formal reply vs. short informal reply).
• Offer a unified brand experience to the customer, where all communication sent by staff is in a unified tone.

Risks:

• Low. Customer frustration. (Approached in the wrong personalized or brand style, but most people are quite easy-going here.).

Options in iTop: None natively available. If you’re interested in having this developed, please reach out.

Quick summary

What: The A.I. system summarizes the entire conversation and actions which were taken.

Benefits:

• For customers: Better response times.
• For staff: Better overview.

Goals:

• Improve customer satisfaction.
• Reduce work load.

Risks:

• Medium: Customer frustration.
• Medium: Staff frustration.

Challenge:

The summary should be accurate. It should contain all the really relevant information. Agents will be frustrated if crucial info is missing in this summary (as the goal of a summary is to avoid needing to go through an entire history). Customers will become frustrated if agents ask for information again which they already provided, but wasn’t part of the summary.

Options in iTop: None natively available. If you’re interested in having this developed, please reach out.

Predictive analysis

What: A.I. can identify common issues. We already discussed how it could also offer suggestions. This reduces the need for manual intervention. It can also help customers solve potential issues before there’s any form of escalation.

In some situations, it may also be possible to use historical data to predict when there will be more new cases. As a human, we may have some experience (new software releases, holiday periods, knowing which regions your customers are mostly situated in, …). A.I. can be a tool to support this; or to make predictions based on some event or pattern that you didn’t even consider yet.

Benefits:

• For customers: Less resources lost with issues.
• For staff: Better planning.

Goals:

• Improve customer satisfaction.
• Reduce work load.

Risks:

• Low: Customer frustration.
• Low: Staff frustration.

Options in iTop: None natively available. If you’re interested in having this developed, please reach out.

What is available though, is the Report Generator . Reports can be created from historical data, for example to see during which hours or in which months or on which days most cases were raised.

Sentiment analysis

What: The sentiment or emotional tone used by a customer, can be derived from their request. Based on indicators such as the use of certain words or phrases or emojis, it’s possible to determine whether a customer had a positive or negative feeling while writing certain responses.

Like the above, it could be a very simple mechanism; or it could be powered by a machine learning algorithm.

Goals:

• Improve customer satisfaction.

Benefits:

• For customers and staff: The customer experience can be influenced by this in many ways (see the challenge description below).

Risks:

• High: Customer frustration.
• High: Staff frustration.

Challenge:

Just like humans, also more advanced A.I. systems may struggle to correctly identify a sentiment. Especially when sarcasm is involved. For example, what if a simple system analyzed these sentences: “The customer support is really great ”, “Thank you very much for wasting my time”. Or what if it does not consider the context, such as in “Your competitor had really terrible support compared to you guys”.

Assuming the agent A.I. system correctly identifies the sentiment, what actions will be taken?

Will a more personalized support be offered? And what does it mean? Will a human agent reach out, or will the A.I. system or employee adjust their tone?

Will the case be handled with priority? If so, you could stimulate customers to act upset to jump to the front of the line. This could lead to a lot of frustration within a support team, as the customer may keep up this act and be difficult to co-operate with for the agent. What if a really nice customer actually has an urgent issue going on but isn’t as pressing, while this one gets priority instead?

In my personal experience, this could be helpful information; but a real person should verify how relevant and accurate this analysis is. It’s best value is probably in following up relationships with customers outside of support cases.

Options in iTop: None natively available. If you’re interested in having this developed, please reach out.