Artificial intelligence, such as the one used in Google’s search function, is proliferating rapidly. Such systems can help evaluate customer feedback or answer customer questions. The first thing to be mentioned in this context is Natural Language Processing (NLP), a sub-domain of AI. NLP-type software can understand and process human language.
A growing number of companies are recognizing the importance of consumer insights. The more you know about your customers, markets, and product usage, the better you can make decisions. However, there are many cases in this field where incorrect data is collected, no data is collected at all, or the data is not evaluated sufficiently.
Qualitative data is needed to accurately understand your target group and what they want. Because only qualitative data can put quantitative data in the right context.
What is needed is feedback in the form of User Generated Content (UGC). Businesses can generate this through social media, email surveys, or customer reviews. But without AI support, systematic analysis of this type and amount of content is almost impossible. In addition, the subjective perception of the person in charge may distort the overall result.
At first glance, machine language processing may not be a very interesting field. One thing to keep in mind is that human language is complex and unstructured. For humans, the result can only be seen as a misunderstanding of communication. But in the case of machines, basic processing is also a big task.
Some of the current rules (grammar, punctuation, spelling) are only observed sporadically, especially in user-generated content. A suitable program is required to process unstructured data from chat or product reviews.
Thanks to machine learning (ML), or more precisely, deep learning (DL), modern AI can learn languages independently (without human supervision). However, this requires a large amount of data to learn a language. The more data you have, the more accurate the AI can be.
Factors to consider when deciding how to implement
To make this decision, companies need to look at three themes:
Purpose Of Using
AI Does the problem that AI has to solve relate to a company’s core functions or contribute to sales development? If so, you should bear the cost and consider developing your own program. By developing AI internally, it can significantly contribute to revealing its presence in the market by leading differentiation from competitors.
Of course, even when purchasing a finished AI solution, businesses can differentiate themselves by choosing the right vendor. This is because the quality and functionality of AI solutions vary.
AI Operational Costs
Building AI on your own has a development cost, but the cost is offset by the cost of purchasing a license or product when using the finished solution. Self-build requires a large development team and many development teams, and especially large and complex projects such as NLP have high development risk. On the other hand, by purchasing a solution, the project period can be shortened by several months.
In some cases, long lead times (ramp-up times) must also be expected. SaaS or purchase options are usually cheaper and are available almost immediately. However, this also usually requires specific in-house expertise, as the software must be integrated into existing systems and the results must be interpreted accurately. For example, if you are trying to evaluate UGC, a system that can identify different topics and related moods based on context would be helpful. To do this, AI requires several competencies:
- speech recognition
- Tagging part of the voice
- Dependency Parsing
- Named object recognition
- Topic extraction
- Sentiment Analysis
Selecting the appropriate model architecture, appropriate algorithms, and other AI modules for the application is also a complex area.
Risk Of Choice
Delays in software development projects are not uncommon. This can lead to sharp cost increases. Recruiting suitable talent is also difficult, and not only development but also a continuous operation of software requires specialized personnel. There is also the risk of not achieving the desired quality.
As for the purchase option, it can be argued that “it is impossible to change immediately because most of the control over the source code lies with the solution provider. For example, if there is a bug in the system, you should first contact the solution provider to fix the error. This external support can take a long time.
There is also the question of whether AI will do everything precisely in practice. Did you find the subject and emotion correctly? It is a question of how much quality, accuracy, precision, and hit rate of the system are. After all, artificial intelligence solutions are basically probabilistic systems. So there is no such thing as 100% reliability. However, human reliability also cannot reach 100%, and there is always a difference in interpretation.
Another risk lies in the data itself and its location. The speed of your computing and the security of your data will be impacted by whether the AI and necessary data are on-premises or in the cloud.