Problems that may arise when companies try to adopt generative AI, and solutions

The Era of Generative AI

Do you remember the Google DeepMind Challenge Match in 2016 when Google DeepMind's AlphaGo defeated Lee Sedol, a 9-dan professional? It was a big event of the era that simultaneously evoked amazement and awareness about artificial intelligence (AI) when it won 4 games to 1 against Lee Sedol, the world's best Go player, in the game of Go, which was considered an impossible domain for computers at the time.

After AlphaGo, the AI market was relatively quiet until the end of 2022 when an AI appeared like a comet - it was OpenAI's ChatGPT, which is now known to everyone. At the time of its release, ChatGPT made a huge impact, able to converse in human-like language and creating the illusion that it could think like a human. With the emergence of generative AI like ChatGPT, there coexists an anxiety that it will replace humans in the near future and an expectation that it can upgrade human productivity to the next level.

Is generative AI being widely adopted in various industries?

In the first quarter of this year, 32% of companies that reported earnings (out of more than 85,000 companies worldwide) mentioned generative AI at least once, which is a 150% increase compared to the same period last year. (IoT Analytics Research 2024) This shows that generative AI is still positioning itself as a hot issue in the market.

Then, is generative AI, which has received so much attention and expectation, actually being widely adopted by businesses?

The adoption of generative AI is becoming more active in the IT sector, which directly develops and operates AI. According to Capgemini research, up to 85% of respondents in the IT sector stated they would adopt generative AI within the next two years. However, non-IT sectors, such as marketing, sales, and finance, show different results. A survey conducted by Coatue of 600 CEOs revealed that only 9% have fully adopted AI, and even among these, 60-75% responded that they are still in the early verification stage.

Problems when adopting generative AI

1) External leakage of internal source data

What is the reason for this poor performance in non-IT sectors, which are directly linked to a company's revenue growth? The biggest cause is security issues. For most companies, especially large corporations, the biggest obstacle to adoption is the need to export internal company data to external clouds in order to use generative AI models. In fact, in July, there was a shocking incident in the United States where a hacker attack on Snowflake cloud resulted in the leak of call and message records of 110 million AT&T customers. For these reasons, many companies are blocking the use of external LLMs and are attempting to build their own LLMs internally.

2) High implementation costs and time

In cases where companies try to build their own LLM based applications internally, they can solve the problem of external leakage of internal data. However, another problem arises: the issue of cost and time. These problems occur because it needs to be built directly in an SI (System Integration). Especially if they try to build a platform for data analysis that can utilize LLM as well, the cost and time entry barriers will become even greater. Realistically, this becomes the reason why it's difficult for small and medium-sized enterprises to adopt generative AI, except for large corporations that have the capacity to invest. If they try to solve this by using public cloud services, the problem of data leakage occurs again.

Implementing generative AI intelligently

Generative AI is a powerful tool that can greatly help improve a company's productivity. How can companies easily and quickly adopt such generative AI? While it depends on the company's situation, the easiest way would be to eliminate the two major entry barriers we looked at earlier. In other words, a solution is needed that can reduce implementation costs and time while preventing the external leakage of internal source data.

SmartMind's ThanoSQL solves the problem of external leakage of source data by utilizing only the company's metadata (table schema) for analysis. Additionally, it significantly reduces implementation costs and time by providing LLM and data analysis in a single platform.

Are you curious about SmartMind's Secure Enterprise Gen AI, ThanoSQL, which is solving the market's pain points with its proprietary technology? Contact us right now.