The Process of AI Adoption in Businesses and Its Challenges
"How can we adopt AI in our company?"
In the previous post, we examined the main challenges businesses face when adopting AI and how these can be addressed.
– What makes adopting AI so challenging for
companies?
In this post, we will continue by discussing businesses' actual process when adopting AI applications or services and the challenges they face along the way.
A sales or marketing team member within the company may have needs such as the following:
When a business attempts to adopt AI apps or AI services based on the person in charge's needs or requirements, the general company's workflow and each group's roles are shown in the image below.
Not only does it take a significant amount of time to begin actual development, but in many cases, the results, delivered months later, often fail to meet the expectations of the team in charge.
Why does AI adoption take so long, and why are the results often disappointing?
Controlling and analyzing unstructured data are essential to developing AI applications or adopting services. This is because more than 90% of today's business data is estimated as unstructured data. However, most groups find data formats such as texts, audio, video, sensors, or other types challenging. According to foundry research, 65% of IT leaders responded that unstructured data management is troublesome to their group, and 24% answered that they do not even update their unstructured data on the analysis list.
Various tools for collecting and preprocessing these unstructured data are included, like the image below.
In traditional methods, companies need to select the necessary tools from these options, install and run them, and then connect the data to build a pipeline. Each tool requires its own specialist, and managing the integration and maintenance of the pipeline is a very complex task. Additionally, many companies already have existing data pipelines built for structured data, and they must also consider how to integrate the newly developed unstructured data pipeline with the existing one.
Many companies have adopted LLM-based services recently. However, collecting and preprocessing unstructured data are causing severe bottleneck phenomena on LLM architectures, shifting from prototype to practice or operating level.
For LLM-based AI services, it is crucial to convert unstructured data into a format suitable for LLMs and to preprocess the data so that the model can efficiently retrieve the required data at the inference stage.
If so, is there any way to overcome these challenges?
The situation becomes quite different if a company has adopted or plans to adopt ThanoSQL. The image below represents the work process flow and the roles of each person in charge.
Compared to the general process for other companies, the procedure is much simpler, and there is no need to collaborate with other organizations, such as procurement or external vendors. Even in one to two days, various analytics of the person in charge can be tried, and their results can be checked instantaneously.
The above image indicates the conceptual targeting of data flow and processing method. Another post (The Easiest Way to Integrate and Utilize Hugging Face's SOTA Models) discusses how to integrate and utilize Hugging Face's SOTA models.
Let us see an example of the questions and queries in the following image.
Like the example of the needs of the person in charge, helpful insight into the relationship between customer emotion and product success can be found by sorting out customer reviews, which are unstructured data with an AI model, and connecting them with the sales data. This type of analysis helps set marketing strategies or product development and customer services. It, ultimately, contributes to customer satisfaction improvements and business growth.
ThanoSQL by SmartMind is an all-in-one platform that supports companies to store structured and unstructured data in database tables and analyze using the latest AI models through an interactive interface. If you adopt ThanoSQL, you do not need to worry about various challenges when adopting AI apps or services in general.
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