All the publicity around structurely conversational ai has certainly put it in the spotlight and show people its fundamental importance. Companies understand the benefits that an artificial mind can offer, but at the same time, they need to know more about how to put it into practice.
Recommendations On AI Adoption
This article presents recommendations on AI adoption. Check out:
1) Ensure The Quality And Volume Of Data
Data has to go a long way from entering the storage system to creating value. Data discovery, tagging, and organization are tedious activities, mainly since today’s expectations center on rapid innovation.
However, the quantity of information alone is not enough, and it is also necessary to guarantee the quality. A kilobyte of structured data can bring much more value to a business than terabytes of confusing data.
Furthermore, modern companies are likely to lack the information they need to navigate a changing environment. For example, a supermarket may not know what customers are researching online, but this data potentially represents an opportunity for the retailer to increase sales.
Therefore, companies need to take responsibility for putting their data in order and evaluating the missing information to get a complete picture. Taking a systematic approach to data collection and cataloging is crucial in the global enterprise data initiative.
2) Finding The Balance Between Predictive Accuracy And The Necessary Computational Power
The company needs to analyze to understand its needs and apply the most appropriate solution. Artificial intelligence is a series of mathematical problems, but the number of problems grows at an accelerating rate as the model’s accuracy increases.
There are several types of artificial intelligence. You can choose from more complex models that can consume an enormous amount of computational power in each of them, resulting in assembly costs.
At the same time, increasing model complexity means that incremental improvements in algorithm efficiency become less necessary over time. An example of this is that implementing a model with 50% accuracy can, in some cases, offset more than a more robust model with 90% accuracy, as hardware costs will be lower.
Factors That Discourage Decision Makers
In addition, there are some factors, in addition to the cost of hardware, that can discourage decision-makers regarding the application of any of the types of artificial intelligence in the company, such as:
- Specialist salaries to keep the AI running smoothly;
- Energy consumption cost.
Artificial intelligence from structurely conversational ai is a real thing and has many advantages for companies, but it also requires consideration before application. Therefore, it is essential to assess its real need to solve its main problems. The question that should be asked is, “Does my company’s needs require more accurate artificial intelligence models, or should we prioritize lower costs and invest in a less accurate model?”