Artificial Intelligence is all the rage in technology right now.  Basically, if you aren’t using it to streamline your business, you may still be stuck in 2023.  We used to think in terms of being stuck in the 90s or the early 2000s with our technology.  Technology seemed to advance noticeably each decade.  Now, it is advancing so quickly that if you aren’t using the most current level of technology, you may struggle to stay ahead of the competition in today’s fast-paced business environment.  AI technology offers a wide range of capabilities that can automate tasks, analyze data, and improve decision-making, ultimately optimizing business processes for greater efficiency and effectiveness.

You don’t want to trust your business to unreliable AI models from Microsoft, Google, and Amazon, among others.   Luckily, we’ve been developing models that are entirely internal to your organization, where your data is imported to a custom AI model, and you can run your queries that will search, analyze, and run comparisons on your data in a matter of seconds. All without compromising even sensitive data because it’s internal and not published externally.

How can this help make your business more efficient?  I was recently watching an episode of one of my regular TV shows, set in the early 90s, where agents were investigating a shooting.  The ME posted a picture of the bullet pulled from the victim, in which he pointed out unique markings.  He stated that he had seen that bullet before in a previous case.  Then he patted an 18-24” stack of case files and told them he just had to read through those files to find it.  I immediately thought of AI.  If we had this agency’s data imported to a custom AI model, he could just pose the questions to retrieve the information to pass on to the agents, who could then immediately proceed with their investigation in hopes of more quickly finding the perpetrator.  It would cut hours off the investigation.  What could you do with those extra hours?

Large Language Models are advanced artificial intelligence systems that excel at processing and generating human-like text, in which models employ deep learning techniques and use extensive datasets to comprehend and generate natural language.  So LLMs develop an understanding of patterns, grammar, and context necessary for generating coherent and contextually relevant text by training them on massive amounts of data.

They can:

• Engage in conversations
• Analyze text, video, and audio conversations
• Comprehend prompts
• Summarize information

These LLM AI models can streamline and improve efficiency in businesses in these areas:

• Automation of routine tasks
• Resource allocation and management
• Workflow optimization
• Scalability and Flexibility
• Predictive maintenance
• Employee productivity and engagement
• Customer service and support
• Data analysis and insights sites
• Supply chain management
• Risk management and compliance

Based on the provided search results, AI Large Language Model (LLM) models can streamline search company data in the following ways:

1. Improved question-answering capabilities: LLMs can analyze user queries and provide more precise and relevant answers by understanding the context and semantics of the question. This enables more accurate search results and reduces the need for manual filtering or refinement.
2. Semantic search: LLMs can perform semantic search, taking into account the meaning and intention behind a user’s query, rather than just matching keywords. This leads to more relevant and granular search results.
3. Data enrichment: LLMs can analyze company descriptions, identify emerging categories, and define keywords, enriching technographic data and providing a better understanding of company profiles.
4. Automated data extraction: Fine-tuned LLMs can auto-fill ESG questionnaire data using information obtained from company documents, reducing manual effort and increasing data accuracy.
5. Streamlined data analysis: LLMs can help data engineers by simplifying tasks such as data processing, enrichment, and analytics, freeing up resources for more complex and strategic activities.
6. Integration with graph databases: LLMs can be used in conjunction with graph databases to organize complex semantic relationships in data, enabling more relevant and granular search results.
7. Reducing data complexity: LLMs can help reduce data complexity by identifying patterns, relationships, and entities in unstructured data, making it easier to analyze and search.
8. Enhanced customer sentiment analysis: LLMs can detect nuances in textual data and interpret the semantics of written content at a massive scale, providing more accurate customer sentiment analysis.
9. Optimized data processing: LLMs can be used to optimize data processing by identifying the most relevant information and filtering out noise, reducing the need for manual data curation.
10. Scalability: LLMs can process large volumes of data and scale to meet the needs of growing companies, making them an attractive solution for businesses with increasing data demands.

To achieve these benefits, companies can fine-tune LLMs on their specific datasets, leveraging their proprietary knowledge and expertise. This enables the models to understand the company’s unique context and provide more accurate and relevant insights.

From the days when Alan Turing introduced us to AI to where we’ve come today has been a long process of discovery, development, and growth.  I remember the days when we struggled to keep certain threatening elements of technology out of the business environment.  It doesn’t seem that long ago that we in IT put rules in place to keep people from using social media in the workplace.    Then we had to start allowing individuals to access it because it had the potential to be a powerful tool.  There are still a lot of dangers when it’s used carelessly or incorrectly, but it has found its place.  AI encountered a similar beginning.  AI models were pushed onto us by our browsers and OS providers when it was simply not ready, and we in IT saw the dangers of it being implemented and used carelessly and unethically.  So, what did we do?  We blocked it!

However, as we have had time to learn about it, how to use it securely, and how to train it correctly, we see the impact it can have on tasks we perform on a daily basis.  As Rodney Brooks said, “Artificial Intelligence is a tool, not a threat.”  Likewise, Ginni Rometty said, “AS artificial intelligence evolves, we must remember that its power lies not in replacing human intelligence, but in augmenting it.  The true potential of AI lies in its ability to amplify human creativity and ingenuity.”  And Craig Mundie added, “Data are becoming the new raw material of business.”   So, what and where are the pain points that are holding you back from realizing the success you are striving to achieve?

Still wondering how AI can help with that?  Let us show you how we can give you an AI solution for the vast amount of data you have to make you more efficient.  Oren Etzioni said, “AI is a tool.  The choice about how it gets deployed is ours.”  Remember those case files the ME had to read through?  The data was already in their system, they just didn’t have an effective way to find it.  The info you need is most likely already in your mountain of data; you just need an effective way to find it.  At the end of the day, AI is about empowering people by streamlining business processes and making them more productive.

So, what can AI do for you?  Let’s find out!