IT IS ADVANTAGE AI WITH YOUR OWN DATA CORPUS
How companies use Artificial Intelligence (AI) or how they approach using it for potential benefits to the business is the burning topic right now. AI is arguably the most talked about technology development in recent times. Particularly, in the context of the sophisticated way in which AI solutions uses data algorithms to create new and original content.
AI, as we have all seen and heard, is gaining traction both in the business world and in our personal lives and in the business world, particularly for its potential to improve and transform business decision making, business execution, and, of course, business results with a new dimension.
There’s definitely been an AI Revolution taking place. Of late, nearly 700 AI startups have been created in the last five years. The buzzword right now is definitely generative AI, but it’s important to remember there are many different forms of AI out there. There’s machine learning (ML).
Generative AI and companies will need to pull together all different types of technology to activate their entire workflow using AI as there are several applications stacked and built on the AI layer — ones that integrate into a company’s main business model and these are really applications that have emerged to be point solutions that fit certain problems or needs in a certain area.
Marketing and content creation, is one of the main use cases for generative AI where we are seeing quite a bit of market movement, and the other area is legal — applications that are point-specific functions with specific solutions. Even imagery falls into this category where the stacks are large language models, which many of these apps are built on. A lot depends on how these large language models are trained, to give different answers.
Over the last decade, companies have been collecting tonnes of data, but very few have really viable business models. Generative AI is really the ability to be able to leverage an already trained model and be able to accelerate certain business outcomes.
The question that companies should really be asking themselves is how does this allow my organization to optimize my business and how do I use this to create new businesses for my company?
We are seeing big players emerge, startups emerge and companies making their own solutions using their own differentiated data and I think whatever partnership a company goes into depends on which way they go.
Companies should really think about taking a two-pronged approach here. The first is really around digital transformation and re-imagining ways of working for themselves through with the Microsoft’s and the Googles and we are seeing companies working closely with startups in the space with the above Point Solutions.
The second prong of this is how a company uses its own data corpus and its own differentiated data to create new forms of revenue for themselves and I think it’s important to focus on both areas equally.
When companies deploy generative AI, they need to put certain guardrails on it and must ensure that people would really optimize its use in their day-to-day job. But it’s also important to give people the right training and critical thinking skills to ask the right prompts and questions from this technology.
That brings us to the question — how do we get humans and AI to work together? And what does that model look like to amplify value? Not just for the employer but also for the employee, the partnerships that companies make, whether it is with the big provider or with a start-up. It’s going to be critical to make sure that the person component is there too.
So far, we haven’t fully been able to capitalize on how with this new AI, we can do that through large language models and automation. Given the velocity of change, one of the things that cyber security professionals are starting to see is how we secure something that has such a rapid response to it.
More and more developers are thinking about software development life cycle that incorporates AI that is correct as there is a greater scope for misuse of AI given the pace at which it is evolving. So having a good cybersecurity strategy in place at the get-go helps organizations.
A company must have their data in order to understand the pipeline of data that they have. What is the data that they have and how can they leverage it to create differentiated outcomes in the market? It must also have to think about the people side of it, like having the right change management in place.
Companies must train people to optimize the interactions and draw the best answers from this model because some it’s very conversational. So it’s important that people are asking the right questions, asking them the right way, being trained in how to do things to draw the best knowledge out of these models. It’s really an investigative technique and many times that is not incorporated into training for many companies. And that’s one of the main things they interact with the models.
All of this technology is really transforming the way that we work in one of things — a new form of intellectual property. IP is really how people think and apply thinking in the context of their organizational process and culture and that’s where differentiated knowledge-based intellectual property lives. So companies need to be thinking about how they capture and protect that knowledge?
It’s easy to just rely on the answer that comes out of a large language model, but it’s important to remember generative AI is a prediction engine. It’s not thinking, it’s not reasoning like humans. So let’s do that piece of it, too.
We have to train humans in the loop to ensure that they are actually doing that piece and being critical about the information that’s coming out. So, it’s a very holistic digital transformation project and change management is really at the core of it ., much to unpack there.
Let’s limit people’s ability to use chat GPT because they’re asking questions around intellectual property and critical engineering schematics are being uploaded into chat.
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