Let’s begin here: Yes, the opportunities for Generative AI (GenAI) are immense. Yes, it’s transforming the world as we understand it (and faster than most of us predicted). And yes, technology is getting smarter. Nonetheless, the implications for GenAI, with its ability to generate text, imagery, and narratives, on enterprises and businesses are very different from the impact on most of the people — in spite of everything, most businesses don’t write poems or stories (which is popular with ChatGPT users), they serve their customers.

Many firms have experience with natural language processing (NLP) and low-level chatbots, but GenAI is accelerating how data will be integrated, interpreted, and converted into business outcomes. Subsequently, they should quickly determine which GenAI use cases will solve their most pressing business challenges and drive growth. To know how enterprises could make GenAI enterprise-ready with their data, it’s essential to review how we arrived at this point.

The Journey from NLP to Large Language Model (LLM)

Technology has been attempting to make sense of natural languages for many years now. While human language itself is an evolved type of human expression, the proven fact that humans have evolved into so many dialects worldwide — from symbols and sounds into syllables, phonetics and languages — has left technology counting on more easy digital communication methods with bits and bytes, etc., until relatively recently.

I began working on NLP programs almost a decade ago. Back then, it was all about language taxonomy and ontology, entity extraction, and a primitive type of a graph database (largely in XML’s) to try to maintain complex relationships and context between various entities, make sense of search queries, generate a word cloud, and deliver results. There was nothing mathematical about it. There was a number of Human within the Loop to construct out taxonomy databases, plenty of XML parsing, and most significantly, plenty of compute and memory at play. Pointless to say, some programs were successful, and most weren’t. Machine learning got here next with multiple approaches to deep learning and neural nets, etc., accelerating natural language understanding (NLU) and natural language inference (NLI). Nonetheless, there have been three limiting aspects— compute power to process complex models, access to volumes of information that may teach machines, and primarily, a model that may self-learn and self-correct by forming temporal relationships between phrases.

Fast forward twenty years later, and GPUs deliver massive compute power, self-teaching and evolving neural networks are the norm, supervised/unsupervised/semi-supervised learning models all exist, and above all, there is larger access to massive amounts of information in several languages, including various social media platforms, that these models can train on. The result’s AI engines that may connect with you in your natural language, understand the emotion and meaning behind your queries, sound like a human being, and respond like one.

All of us, through our social media presence, have been unknowingly a ‘Human’ within the ‘Loop’ to coach these engines. We now have engines claiming to be trained on trillions of parameters, in a position to take a whole bunch and hundreds of input parameters, that are multi-modal and reply to us in our language. Whether it’s GPT4/5, PaLM2, Llama or another LLMs which were published to this point, they’re emerging as more contextual verticalized problem solvers.

Systems of Engagement and Systems of Record

While the journey from NLPs to LLMs has been great due to the Silicon Evolution, data models and the provision of massive amounts of coaching data that all of us have generated, Enterprises — retail providers, manufacturers, banking, etc. — each need very different applications of this technology. Firstly enterprises can’t afford AI hallucination — they need 0% hallucination and 100% accuracy for users who interact with AI.  There are a number of queries that demand absolute accuracy with a purpose to be of any business use — e.g.

To counter AI hallucination, enter the age-old concept of Systems of Engagement and Systems of Records. Systems of Engagement, be it along with your customers, suppliers, or employees can leverage a GenAI-based conversational platform out of the box, after being trained for business-specific prompts — that’s the “easier” part. The challenge is embedding Systems of Records into the worth chain.  Many businesses are still in a static table- and entity-based world and can remain that way because most enterprises are static at an organizational or corporate level, while events and workflows make them dynamic at a transactional level.

That is where we discuss next generation conversational platforms that not only address conversations, interfaces, and queries, but in addition take customer journeys all of the strategy to fulfilment. There are different architectural approaches to such conversational platforms. One immediate option is to make use of hybrid middleware that acts as a consolidator of sorts between vectorized and labelled enterprise data and LLM-driven conversational prompts and delivers a 0% hallucination consequence to consumers.

There’s a large amount of information prep work required by enterprises to make it intelligible for an LLM engine. We call it flattening of the standard table and entity-driven data models. Graph databases, which represent and store data in a way that relational databases cannot, are finding a latest purpose on this journey. The goal is to convert enterprise databases to more intelligible graph databases with relationships that outline context and meaning, making it easier for LLM engines to learn and due to this fact reply to prompts from end customers through a mixture of conversational and real-time queries. This task of enabling enterprise data to be LLM-ready is the important thing to providing an end-to-end Systems of Engagement to Systems of Record experience and taking user experiences all of the strategy to fulfilment.

What Comes Next

At this point, with these advancements in data and AI, probably the most immediate impact is available in the world of software code generation — as evidenced by the rise of Microsoft Copilot, Amazone CodeWhisperer and other tools amongst developers. These tools are jumpstarting legacy modernization programs, lots of which are sometimes stalled as a result of time and value concerns. With code generation tools powered by GenAI, we’re seeing modernization projects speed up their timetables by 20-40%. In greenfield code development projects, these tools will allow developers to shift time and productivity savings toward design pondering and more progressive projects.

Beyond software code development, GenAI tools are resulting in the creation of latest vertical use cases and scenarios which might be geared toward solving enterprises’ most pressing challenges, and we are only beginning to scratch the surface of what must be done to take full advantage of this trend. Nonetheless, we’re already solving several problems and questions within the retail and logistics sector by leveraging GenAI:

How much inventory do I even have within the warehouse, and when should I trigger replenishment?  Is it profitable to stock upfront? Is my landed price right or is it going to escalate? What items can I bundle or what form of personalization can I provide to raise my profit?

Answering these sorts of questions takes a mixture of conversational front ends, high accuracy data-driven queries within the back end, and a domain-heavy machine learning model delivering predictions and future guidance. Thus, my advice for enterprises can be, whether you might be an AI explorer or a Generative AI disruptor, partner with service providers which have proven AI expertise and robust data and analytics capabilities which might arm you to capitalize on GenAI models suited to your corporation needs and enable you to stay ahead of the curve.