How AI iteration can enhance the customer experience

How AI iteration can enhance the customer experience

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We love stories of dramatic breakthroughs and neat endings: The lone inventor takes on the technical challenge, saves the day, the end. These are the recurring tropes around new technologies.

Unfortunately, these tropes can be misleading when we are actually in the midst of a technological revolution. It’s the prototypes that get too much attention rather than the complex, incremental refinement that really yields a breakthrough solution. Take penicillin. Discovered in 1928, the drug did not actually save lives until it was mass-produced 15 years later.

The story is funny that way. We love our stories and myths about breakthrough moments, but often the reality is different. What really happens – the often long periods of refinement – makes for far less exciting stories.

This is where we currently are in the area of ​​artificial intelligence (AI) and machine learning (ML). Right now we are seeing the excitement of innovation. There have been amazing prototypes and demos of new AI language models, such as GPT-3 and DALL-E 2.


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Regardless of the splash they’ve made, these kinds of big language models haven’t revolutionized industries yet—including ones like customer support, where the impact of AI is particularly promising, never mind general business cases.

AI for customer experience: Why haven’t robots had a bigger impact?

The news about new prototypes and technology demos often focuses on the model’s “best case” performance: What does it look like on the golden path, when everything works perfectly? This is often the first evidence that disruptive technology is coming. But, counterintuitively, for many problems, we should be much more interested in “worst case” performance. Often the lowest expectations of what a model should do are much more important than the upper ones.

Let’s look at this in the context of AI. A customer support robot that sometimes does not give the customers answers, but never misleading them is probably better than a bot that always answers but sometimes is wrong. This is crucial in many business contexts.

That is not to say that the potential is limited. An ideal state for AI customer support bots would be to answer many customer questions – those that don’t require human intervention or nuanced understanding – “free form”, and correctly, 100% of the time. This is rare now, but there are disruptive applications, techniques and built-ins that build against this, even in today’s generation of support robots.

But to get there, we need easy-to-use tools to get a bot up and running, even for less technical implementers. Fortunately, the market has matured over the past 3 to 5 years to get us to this point. We no longer face an immature bot landscape, with the likes of Google DialogFlow, IBM Watson and Amazon Lex – good NLP bots, but very difficult for non-developers to use. It is the ease of use that will make AI and ML a usable and effective product.

The future of robots is not a flashy new application for AI

One of the biggest things I’ve learned from watching companies implement bots is that most people don’t get the deployments right. Most businesses build a bot, have it try to answer customer questions, and watch it fail. That’s because there’s often a big difference between a customer support representative doing their job, and phrasing it correctly enough that something else—an automated system—can do it, too. We typically see companies need to iterate to achieve the accuracy and quality of bot experience they initially expect.

Because of this, it’s critical that businesses don’t rely on scarce developer resources as part of their iteration loop. Such dependence often results in not being able to iterate to the actual standard desired by the business, leaving it with a poor quality robot that loses credibility.

This is the key component of the complex, incremental refinement that doesn’t make exciting stories, but delivers a true breakthrough solution: Bots must be easy to build, iterate, and deploy—independently, even by those not trained in engineering or development.

This is important not only for ease of use. There is another consideration that comes into play. When it comes to bots that answer customer support questions, our internal research shows that we’re facing a Pareto 80/20 dynamic: Good information bots are already about 80% of where they’ll ever go. Instead of trying to squeeze out the last 10 to 15% of the information questions, the industry’s focus must now shift to uncovering how to use the same technology to solve the non-information questions.

Democratizing action with no-code/low-code tools

For example, in some business cases it is not enough to simply provide information; an action must also be taken (ie rebooking an appointment, canceling an order or updating an address or credit card number). Our internal research showed that the percentage of support calls that require an action to be taken has a median of approximately 30% for businesses.

It needs to be easier for businesses to actually set up their bots to perform these actions. This is somewhat related to the no-code/low-code movement: since developers are few and expensive, there is disproportionate value in actually enabling the teams most responsible for owning the bot implementation to iterate without dependencies. This is the next big step for business robots.

AI in customer experience: From prototypes to opportunities

There is a lot of attention around the prototypes of new and upcoming technology, and at the moment there are new and exciting developments that will make technologies like AI, bots and ML, along with the customer experience, even better. However, the clear and present opportunity is for companies to continue to improve and iterate using the technology already established – to use new product features to integrate this technology into their operations, so that they can realize the business already available.

We should spend 80% of our attention on deploying what we already have, and only 20% of our time on the prototypes.

Fergal Reid is head of Machine Learning at Intercom.

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