Differentiating Your Business in the Age of Generative AI: 2 Questions to Ask to Stand Out from the Crowd

1. Have you ever thought that if everyone is using the same LLM model, how can that be a competitive edge for your business? What’s your differentiation?
2. When embracing GenAI and AI for your business, it’s inevitable that “you don’t know what you don’t know”. The only way out is to try, fast and low-cost experimentation. But how?

1_What’s your differentiation? Have you ever thought that if everyone is using the same LLM model, how can that be a competitive edge for your business?

Leaders thought if they can come up with the most ground breaking GenAI use case then they get themselves a winner. You won’t. Just like when mobile apps came out around 15 years ago, the most successful companies that leverage mobile apps for their digital transformation aren’t those that came up with a game app or AR navigation app, but those that focus on apps that improve internal processes, automate and scale customer services, those use cases won’t get them a PR but they are essential. Fast track to today, GenAI and AI as a whole will bring your business to the next level, only when you grasp that the differentiation is your proprietary data, your mastery in prompt engineering and understanding features that work to the max advantage to your own business setting, e.g using price sensitivity towards the dynamic demand of your unique customer base, will GenAI and AI be truly useful to your business.

Business leaders do not lack use cases. But when it comes to production, they lack the business justification (a real differentiator) that help them prioritize and calculate the cost-benefit analysis. That’s the real blocker.

That brings me to my second point.

2_When embracing GenAI and AI for your business, it’s inevitable that “you don’t know what you don’t know”. The only way out is to try, fast and low-cost experimentation. But how?

First, you have to understand the key steps when deploying AI in an enterprise environment. It can never be as easy as downloading a public app, login with your personal email and ask away. You want to replicate that onboarding experience to your internal users and external customers but your value as a business is what comes before that.

Let’s walk you through a most common use case that enterprises request - searching internal documents to create a generative AI conversational bot that makes internal information more useful. As a business decision maker, the high-level GenAI implementation is:

• Data source: Identify the proprietary data to be used in the project
• Data security and governance: Check and redact any PII / Sensitive info
• Where user input their questions: Context Stuffing & Prompt Engineering
• Select the right LLM and Inference with LLM, to come up with the “answers” for the questions input
• Delivering the answer in the desirable visualization and presentation layer

Going back to my point of experimentation, only through testing one full cycle of the above process can decision makers plan out the resources and cost implications of an AI initiative. Also, it is very likely that you are thinking of bringing in an external implementation partner. Which is a good strategy to accelerate the process and on top of which, how about we use this as an opportunity to upskill our colleagues?

To facilitate your communicating of your business requests to your technical team, you can share this article with them, How to Simplify access to internal information with GenAI, you may find the cost breakdown in the article useful: https://aws.amazon.com/blogs/machine-learning/simplify-access-to-internal-information-using-retrieval-augmented-generation-and-langchain-agents/

Reference:

What Will Generative AI Mean for Your Business? https://aws.amazon.com/blogs/enterprise-strategy/what-will-generative-ai-mean-for-your-business/