The Future of AI Integration in Business: Maximizing Opportunities and Overcoming Challenges

AI is widely discussed today, with many exploring the opportunities it presents. Numerous organizations acknowledge AI's potential to integrate seamlessly into various business activities. Generative AI can assist in problem-solving, content creation, innovation, campaigns, efficient work methods, benchmarks, and more; much more. 

Researchers and practitioners are dedicating their skills and time, and all budgets are heading in trying to crack what is, to organizations, both an opportunity as well as a challenge. How can the new technology be maximized to benefit the specific needs, tasks, and projects of some organizations? 

 The Harvard Business Review identified three levels of Generative AI adoption: 

Those who are building their own LLMs, training the algorithms to be based on their specific content. Such an approach sounds ideal. However, a mass of information in each discipline the organization works on must exist, and such projects are costly, very costly. 

Most organizations take a 180 degrees approach. They use Generative tools, out of the box, based on the already trained LLMs. Integration and embedding the insights manually by the people in the organization. Implementing such an approach is easy, and also those choosing other approaches can, until full implementation, use this one. 

And then steps in the hybrid mode, where organizations enrich the out-of-the-box LLM, enriching it by the organization's content. The AI model will combine all sources, offering insights from both external and internal sources, or one may say- a mixed perspective. 

The rationale for such a solution is not as crazy as it may sound. As much as every organization thinks it's unique, it is based on society's shared information and knowledge. We develop the best practices all alone, yet later, when discussing with other experts, we may understand that there is a chance we reinvented the wheel. 

And, of course, with all that has to do with regulation, safety, and theory- all the external content is precisely what we want to have as our foundation. 

In other cases, which are organization sensitive, such as specific proposals or projects, the internal content will be more dominant.   

When implementing such, several additional issues emerge, starting with information security. Depending on the size of the internal content, message segmentation information architecture, and keywords for system utilization, can also be a concern. 

The complete solution will probably involve several AI models of translation, NLP, Generative AI, and personalization, driving a successful user experience journey. But to start, we will need organizational data; a lot.  

This is our best dream as knowledge managers, leveraging external and internal knowledge in the best possible way. I can hardly wait.