In light of possible security and accuracy issues with Amazon’s Q, how do you feel about it as an enterprise-level tool? Is it still something you’d consider adding to your stack?

Innovation & Emerging TrendsMachine Learning (ML)
7.8k viewscircle icon2 Comments
Sort by:
Chief Data Officer2 years ago

I would approach Q (and any other GenAI tool) as a potential component in a larger application.  For instance, for internal enterprise search using a RAG architecture, there are multiple tools involved that have to tied together.  If done well, the accuracy issues should be mitigated and security issues more controlled.  

So yes, I would consider it as part of the stack, but I would set expectations that the technology is not intended to be rolled out on it's own as a general purpose tool.

I would also not do so without an AI governance process in place that will evaluate the AI-specific risks during the architecture process etc.

Lightbulb on1 circle icon1 Reply
no title2 years ago

We have successfully implemented RAG in AWS using LLM, langchain, AWS Kendra and streamlit. This is used by internal teams.

Content you might like

What percent of your IT vendor budget is spent on startups?

>20%10%

16-20%35%

11-15%16%

5-10%9%

<5%28%

View Results

How are large professional services organisation deploying GPT4 (or similar LLMs) within their own organisation for internal use, particularly when using sensitive/confidential/PII data?

For example we're a UK based accountancy firm so have created our own internal app based on Microsoft's Open AI GPT4 model deployed to our private Azure hosting. We have built a custom app designed to "preprogramme" personas, i.e. pre-created prompts, for different areas of the profession, so internal users can have GPT4 act in different ways without prior knowledge of prompt engineering.

I would be interested to see how others are building or buying solutions, particularly those based in EU/UK who can't use OpenAI/ChatGPT for with customer/confidential information.

Read More Comments

We are evaluating enterprise AI productivity tools that can be deployed on-premises or in a private cloud and offered to internal users for common daily tasks such as presentation generation, image/graphic creation, content drafting/editing, and summarization. Which vendors offer solutions in this space? Please share vendor names, deployment models, and any practical experience with these tools (strengths, limitations, integration, licensing).

As Artificial Intelligence is moving fast to mimic human-nature also in terms of consciousness, do you believe we should be aware of potential "social engineering" risks related the conversation agent?

Yes, I believe so.12%

Yes, but guardrails will be already present 43%

Yes, and we will not be ready38%

I don't know now3%

No, I don't think so4%

No, because AI will never reach such "status"

View Results

How are you building enterprise AI capabilities? What factors are driving your build-versus-buy-versus-partner decisions?

Read More Comments