Generic AI Models give you Generic Results. There, I said it.
Generic LLMs are a critical part of the overall AI strategy for any solution, but they will not provide the intelligence and insights specific to an industry or company.
Four areas where Industry-AI deliver.
1. Data Integration: understand your data model and organization
2. Relevancy: takes your business nuances into account
3. User Adoption: adds specific value, not noise
4. Infrastructure: confident in security and data visibility
Read about it in our Blog: bit.ly/4d3MmVR
Generic AI Models give you Generic Results. There, I said it.
Generic LLMs are a critical part of the overall AI strategy for any solution, but they will not provide the intelligence and insights specific to an industry or company.
Four areas where Industry-AI deliver.
1. Data Integration: understand your data model and organization
2. Relevancy: takes your business nuances into account
3. User Adoption: adds specific value, not noise
4. Infrastructure: confident in security and data visibility
Read about it in our Blog: bit.ly/4d3MmVR
A recent report found that 92% of businesses use AI-driven personalization to drive growth. However, even the most innovative AI applications will have less impact if your customer data is siloed, inconsistent, stale, or incomplete. A trusted data infrastructure is critical to your AI strategy.
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💡 Transform Your Business Strategy with AI! Discover how AI Business Intelligence Tools can streamline your operations, enhance customer experiences, and drive growth. Start your journey here: https://buff.ly/49KWdOp#AI#DigitalMarketing#BusinessGrowth
What are the potential downsides of using AI agent frameworks in business applications, and how can data pipelines provide a more effective solution?
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AI agent frameworks can be overly complex for straightforward business processes, leading to difficulties in maintenance and scalability. In contrast, data pipelines offer a simpler, more streamlined approach, focusing on linear, efficient processing of tasks. This approach reduces the risk of errors and enhances the overall reliability of AI applications.
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Complete Tutorial On: https://lnkd.in/g2geVsE7
Implementing generative AI successfully?
Most enterprises are focusing on two main use cases for gen AI:
1️, Knowledge Management (KM)
2️, Retrieval Augmented Generation (RAG) models
Both rely heavily on an organization's own data and IT leaders need to focus on 3 key aspects for gen AI projects.
Key Aspect #1: Collect, filter, and categorize data using open-source tools, removing sensitive information, blending sources, and implementing quality controls.
Key Aspect #2: Strengthen data governance by automating quality checks, adhering to AI regulations, and implementing robust security and classification systems.
Key Aspect #3: Safeguard privacy and IP by reviewing access controls, understanding data sharing practices, and protecting valuable information when using public models.
https://lnkd.in/eaFYq_JP#GenAI#DataManagement#DataPrep#DataGovernance#DataPrivacy
High-quality data is essential for building and refining AI models in #Salesforce. 💯 Find out why with our comprehensive guide and learn how a comprehensive backup strategy can help you secure business productivity and innovation. https://bit.ly/4ewjoyr#Kayan_IT#1stchoice#systemintegrator
AI Infra Summit: AI Infrastructure Today and What's Shaping Tomorrow
Last week, I attended the AI Infra Summit—huge thanks to Bill Barry, and Jessica Kobilova for putting on such a great event! The lineup of speakers was outstanding, and overall a nice overview into what’s happening in AI infrastructure and the applications set to drive future demand. One of the highlights for me was Jeremiah Owyang’s talk on AI Agents—a third-stage AI technology that's coming fast, with over 1,000+ companies already working on it.
While we often discuss the future 5th generation of AI, where AI may surpass human intelligence, AI Agents are only months away. These digital co-workers have serious potential to scale our capabilities and I’m excited to see how this technology evolves! If you're curious about how even websites will need to shift from user-friendly (aesthetics) to AI-friendly (efficient) interactions, check out this video on AI Agents and how they’ll transform customer engagement. https://lnkd.in/gu8kwSpk
There were also fantastic presentations at the Summit from Sylvia Acevedo, Vik Malyala, Kumaran Siva, Peter Voss, Google's Mahak Sharma and many others who really helped paint a great picture of where AI is heading.
As AI practitioners, it's important to collaborate with business domain experts for successful adoption of your solutions.
Why? because the understanding the context and subtleties of the data and the problem space is vital for fine tuning the models.
Without domain-specific configuration, practically all out of the box AI solutions require human supervision to work effectively.
This invariably means human insight is indispensable for AI.
However, there’s a strategic advantage in timing this involvement. Early collaboration with domain experts not only streamlines the process but also mitigates the risk of costly revisions and subpar user experiences down the line.
Hope this helps.
#aiml#mlops#businessdomain#data#generativeAI
The idea that one model can solve all enterprise AI tasks—or that traditional model routing can pick the “best” model without trade-offs in accuracy, costs or latency—is outdated.
Enterprise workflows range from simple to complex, and no single model can optimize for every need.
That's why we built 𝗘𝗺𝗮𝗙𝘂𝘀𝗶𝗼𝗻™, 𝘄𝗵𝗶𝗰𝗵 𝘀𝗲𝗰𝘂𝗿𝗲𝗹𝘆 𝗰𝗼𝗺𝗯𝗶𝗻𝗲𝘀 𝗼𝘃𝗲𝗿 𝟭𝟬𝟬 𝗽𝘂𝗯𝗹𝗶𝗰 𝗮𝗻𝗱 𝗽𝗿𝗶𝘃𝗮𝘁𝗲 𝗟𝗟𝗠𝘀 𝘁𝗼 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝘁𝗵𝗲 𝗵𝗶𝗴𝗵𝗲𝘀𝘁 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗮𝘁 𝘁𝗵𝗲 𝗹𝗼𝘄𝗲𝘀𝘁 𝗰𝗼𝘀𝘁𝘀 𝗮𝗻𝗱 𝗹𝗮𝘁𝗲𝗻𝗰𝘆, 𝗳𝗼𝗿 𝗿𝗼𝘂𝘁𝗶𝗻𝗲 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘁𝗮𝘀𝗸𝘀.
Read more in our latest blog on why traditional model selection falls short in enterprise AI—and how EmaFusion™ redefines this
