Conversational AI Playbook

Roadmap for Conversational AI

Owing to the technological development and changes in the customer

behaviour, e-commerce has developed over different levels of maturity in

recent years. The challenge for companies is to recognise relevant technological and market trends and assess them accordingly.

Companies are currently facing the challenge of achieving the next level

of maturity—so-called Conversational Commerce. This level of maturity

seems desirable at present because current trends could revolutionise the

sales sector. This means that those who proceed slowly with the implementation of Conversational Commerce could lose customers to competitors. On

the other hand, companies could, for example, benefit from public attention

by incorporating bots at an early stage (Fig. 4.8).

Thereby, the leap to Conversational Commerce does not represent a gradual, but a fundamental advancement of e-commerce. This is not only about

another voice-controlled touch point. It is much rather about a new ecosystem which automatically initiates and coordinates ordering processes driven

by customers and situations. Intelligent assistants either follow the instructions of consumers or recognise the need to take action by themselves, e.g.

reordering of detergents or travel booking according to the appointments

diary.

However, it is also decisive that the transition to Conversational

Commerce is well thought out and planned. One possibility to do this sys-

tematically is the DM3 model presented in Part II AI Business: Framework

and Maturity Model

Comments

Possible Limitations of AI-Based Bots

The examples above already show the present-day potential of AI-based bots. At present, these systems are still in an early stage and still have certain limitations and potentials for optimisation. Twitter Bot Tay by Microsoft Most bots at present are reactive service bots. Engagement bots that actively interact with the users as market and brand ambassadors go one step further. The most famous example here is the chatbot Tay by Microsoft. Microsoft removed Tay from the web apologetically within one day. The example shows that the uncontrolled training of bots by the community can lead to fatal consequences. AI systems still have to learn ethical standards. It thus becomes apparent that even bots require a kind of guideline. Like a journalist has to observe editorial guidelines, bots have to observe certain standards. The next generation of AI-based bots must control and create the possible room for communication. IBM Watson has been able to celebrate quite a few respectable resul...

Robot Journalism Is Becoming Creative

Algorithms are able to automatically search the Web for information, pool it and create a readable piece of writing. In addition, data-based reports in the area of sport, the weather or finances are already frequently created automat- ically today. Recently, for example, merely a few minutes after Apple had announced their latest quarterly figures, there was a report by the news agency Associated Press (AP): “Apple tops Street 1Q forecasts”. The financial report deals solely with the mere financial figures, without any human assistance whatsoever. Yet, AP was able to publish their report entirely via AI in line with the AP guidelines. For this purpose, AP launched their corresponding platform Wordsmith at the beginning of 2016, which automatically creates more than 3000 of such financial reports every quarter, and which are pub- lished fast and accurately. It is no longer that easy to distinguish between whether an algorithm or a human has written a text. Another exception of rece...

Why Use Machine Learning?

Consider how you would write a spam filter using traditional programming techniques (Figure 1-1): 1. First you would look at what spam typically looks like. You might notice that some words or phrases (such as “4U,” “credit card,” “free,” and “amazing”) tend to come up a lot in the subject. Perhaps you would also notice a few other patterns in the sender’s name, the email’s body, and so on. 2. You would write a detection algorithm for each of the patterns that you noticed, and your program would flag emails as spam if a number of these patterns are detected. 3. You would test your program, and repeat steps 1 and 2 until it is good enough. Since the problem is not trivial, your program will likely become a long list of com‐ plex rules—pretty hard to maintain. In contrast, a spam filter based on Machine Learning techniques automatically learns which words and phrases are good predictors of spam by detecting unusually fre‐ quent patterns of words in the spam examples compared to the ham e...

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