Oct 114 min read

Generative AI in the corporate world

Chances are, many of you have tried out one of the many Generative AI solutions available today (there are said to be over 10,000 at our fingertips), and some of you might even be advanced users.

But what about in your companies? How is the adoption of Generative AI going?

-Are you using it? In which departments/areas?

-Have you developed any usage protocols or ethical guidelines for its implementation?

-Do your teams understand how it works and what its limitations are?

-How satisfied are you with the results so far?

-...

Recently, writer.com, published a study that provides an in-depth analysis of the use of Generative AI in the corporate world. Here are some of the key takeaways:

1- The "hype" around this technology is undeniable. A majority of companies (96%) expect significant benefits, and 82% are planning to roll it out across multiple departments. Right now, it's mainly being used in IT, customer experience, and security.

However, despite these high hopes, 69% of companies believe it will take some time to see clear, tangible benefits.

2- That said, there are naturally some concerns with its implementation (as is the case with any new technology). In the context of Generative AI, these concerns focus mainly on two areas: the security of the AI solutions themselves (95%) and the protection of the data used to train them (94%).

Breaking these fears down, we see specific worries:

-77% are concerned about the types of data these solutions can access — how they are trained and how we can control the inclusion of sensitive information in the training sets.

-71% are worried about who will be authorized to access and modify the training data.

-66% wonder who will be able to access the AI solution and change its operational parameters.

-65% are concerned about what other applications the AI solution can connect to - sending and receiving data.

-53% are worried about access control and query types- "log reporting"

-45% focus on which users are authorized and how their usage is regulated.

3- Once a company decides to implement Generative AI, there’s a big question: do you go for an off-the-shelf commercial solution, or build your in-house? The latter option, fueled by security concerns, is gaining ground, with 78% of companies opting for it.

However, in-house development can take many forms:

-52% are going for a hybrid model (leveraging third-party solutions as a base).

-29% are starting from scratch, building a custom model.

-23% are adopting open-source LLMs (like Meta’s LLaMA).

-19% are choosing to develop a fully in-house model.

But how successful is this approach? The results show that 61% of companies are unhappy with the outcomes. Developing these systems is more complex and resource-intensive than many expect.

Most companies attribute their struggles to "internal talent," with 90% citing it as the primary reason their projects haven’t succeeded. That’s a pretty radical stance, but perhaps a convenient excuse.

Those choosing commercial solutions are typically prioritizing three key factors:

-Performance (making sure it works as expected)

-Security (ensuring it doesn’t put the company at risk)

-Ease of integration (so that implementation isn’t overly complex)

4-Speaking of resources and timelines, companies often underestimate what it takes. They expect their internal AI projects to be fully operational within 6-12 months (43%), with moderate increases in investment (45%).

In reality, many fail to account for the full scale of developing, training, and optimizing an LLM model. Unsurprisingly, this often leads to dissatisfaction with the results.

5- When it comes to potential value, there are two clear levels of impact. On the tactical side, companies hope to:

-Improve efficiency (72%)

-Save time (68%)

-Reduce costs (58%)

On the strategic side, they’re looking to:

-Improve quality (44%)

-Enhance customer satisfaction (42%)

-Drive innovation (41%)

-Speed up "time to market" (40%)

You can find the full report here- Report

At The Brain Mixers, we’re here to help you implement Generative AI in your company in two key ways:

Level 1- How should we introduce this technology into the organization? What applications should we consider? What are the best use cases and best practices? How should we structure the adoption process? How will we measure success? What governance tools and frameworks do we need to establish?

Level 2- Turnkey AI-powered virtual assistant solutions: That’s why we created KNOWSME, a service that allows us to develop virtual assistants trained on your company’s own data sets in an easy, agile, and efficient way.

Our assistant uses OpenAI’s ChatGPT4o, model, but the training data is entirely defined by each client, ensuring that:

-The training is fully aligned with your business needs.

-Responses won’t be influenced by external, unknown sources.

-The system operates based on your business rules.

-You have full access to query and access logs.

-Data is stored securely within the EU (GDPR compliant).

-The system’s behavior is customized to your specifications.

-The data and outputs are 100% owned by you.

All this comes with rapid deployment: assistants without document engineering can be up and running in 48-72 hours, and with document engineering in 15 days. Plus, the business model is completely scalable and adaptable to your usage needs.

On our web site, you’ll find demo assistants available to try. If you’re interested in seeing a demo using your own data, just let us know — you’ll be amazed!