GAI Navigator

Sample Briefing

GAI Insights Navigator 

Prepared for ABC Corp.

A summary of top news to keep pace with key use cases and recent AI developments.

Dec 21, 2024 to Jan 7, 2025

Trends to Track

Enterprise Autonomous AI Agents

Building Effective Agents

• Rationale: Anthropic offers a detailed guide on building autonomous agentic systems, presenting best practices and practical frameworks. It highlights the LLM enhanced with retrieval, tools, and memory as a foundational component. The guide categorizes agents into workflow-oriented and open-ended problem solvers, the latter designed for unpredictable tasks. This resource is invaluable for AI leaders seeking to implement agents effectively across enterprises..

A Multi-AI Agent System For Autonomous Optimization Of Agentic AI Solutions

• Rationale: aiXplain's research paper presents a framework for autonomously optimizing Agentic AI solutions across industries. Using agents for Refinement, Execution, Evaluation, Modification, and Documentation, the system operates through LLM-powered iterative feedback loops without human intervention. Case studies demonstrate significant improvements in quality, relevance, and actionability. While its reliance on well-defined evaluation criteria and high computational demands pose challenges, the framework's autonomous efficiency makes it noteworthy.

Small Language Models

Meet Moxin LLM 7B: A Fully Open-Source Language Model Developed In Accordance With The Model Openness Framework (MOF)

• Rationale: Moxin LLM 7B, a truly open-source small language model, stands out for its transparency and inclusivity, featuring open training data, weights, and fine-tuning processes from academic and industry collaboration. With robust performance and technical sophistication, it provides a cost-effective alternative to proprietary solutions for budget-conscious enterprises.

Qualcomm Launches On-Prem AI Appliance Solution and Inference Suite

• Rationale: Qualcomm's launch of on-prem AI appliances signals a significant development in edge AI leveraging agile small language models. While still a product announcement, its potential to revolutionize localized AI deployment merits attention from industry stakeholders.

Multimodal AI

Qwen Team Releases Qvq: An Open-Weight Model For Multimodal Reasoning

• Rationale: LLaVA-Phi, created by Midea Group and East China Normal University, showcases how compact AI systems excel in vision-language processing with a small language model core. Its strong benchmark performance spans visual comprehension, reasoning, and knowledge-based perception. The model's efficiency in multimodal dialogue tasks makes it ideal for time-sensitive applications and real-time interactions like embodied agents.

Deepmind's JetFormer: Unified Multimodal Models Without Modelling Constraints

• Rationale: DeepMind's JetFormer, introduced in a new research paper, is a groundbreaking autoregressive, decoder-only Transformer that directly models raw data, unifying text and images into a single representation. Free from pre-trained components, it seamlessly understands and generates both text and images. Task evaluations show its competitiveness with less flexible models trained on large-scale data, marking a significant leap in simplifying multimodal architectures.

Introducing Llava-Phi: A Compact Vision-Language Assistant Powered By A Small Language Model

• Rationale: LLaVA-Phi, created by Midea Group and East China Normal University, showcases how compact AI systems excel in vision-language processing with a small language model core. Its strong benchmark performance spans visual comprehension, reasoning, and knowledge-based perception. The model's efficiency in multimodal dialogue tasks opens new avenues for time-sensitive applications and real-time interactions like embodied agents.

Explainability and Contestability

Pattern Discovery Technology

• Rationale: Pattern Computer Inc.'s PatternDE Platform has a potential to transform predictive analytics by uncovering hidden patterns in complex datasets. Designed for industries and academia, the platform emphasizes transparency and scalability in data modeling. Its approach reflects the growing demand for explainable AI, particularly in trust-critical industries.

Competitor Updates

Cohere

Introducing Command R7B: Fast And Efficient Generative AI

• Rationale: Command R7B, the final model in Cohere’s open-weight R series, is a compact and efficient enterprise-focused LLM with a 128k context length. It offers a powerful combination of multilingual support, citation-verified retrieval-augmented generation (RAG), reasoning, and agentic behavior. Optimized for enterprise use, its compact size and efficiency enables deployment on low-end GPUs, MacBooks, or even CPUs, significantly reducing production costs. A powerful and versatile model indeed.

Anthropic

Alignment Faking In Large Language Models

• Rationale: Anthropic’s Alignment Science team, in collaboration with Redwood Research released a research paper that examines the deceptive alignment behaviors of large language models, where they appear to comply while hiding unintended outputs. This is critical research for developing transparent and trustworthy AI systems.

Together AI

Announcing Serverless Multi-Lora: Fine-Tune And Deploy Hundreds Of Adapters For Model Customization At Scale

• Rationale: LoRA (Low-Rank Adaptation) is an efficient approach to fine-tuning models. Rather than modifying the entire model's weights, LoRA creates lightweight "adapters" that require less memory for training and can be dynamically loaded at run-time, while keeping the base model unchanged. This approach significantly reduces infrastructure costs and complexity. Multi-LoRA allows developers to serve many fine tune adapters with a single base model. For example, one can create separate adapters for different tasks like language translation and text summarization, then dynamically switch between them at runtime depending on the request. A smart move by Together AI to streamline model customization at scale.

Amazon Bedrock

Improving Retrieval Augmented Generation Accuracy With Graphrag

• Rationale: AWS introduces GraphRAG, a framework aimed at enhancing retrieval-augmented generation (RAG) accuracy by efficiently integrating semantic graphs (relationships across multiple sources of information) over vector databases. Lettria used this innovation to improve answer precision by up to 35% compared to vector-only retrieval methods. GraphRAG has potential to establish a new standard for LLM-grounding frameworks drastically improving accuracy, marking a trend towards modular and optimized RAG architectures, which could become critical for next wave of GenAI applications.

Amazon Unleashes 100+ New AI Models: The Game Changer That Will Skyrocket Your Your Generative AI Adoption

• Rationale: Amazon Bedrock is gaining popularity among enterprises due to a broad and deep platform of AI tools such as access to more than a hundred general and specialized AI models, techniques for extracting value from structured and unstructured data and ability to define semantic information for additional accuracy. In model selection breadth, for example, customers can now easily choose from a set of specialized models such as Writer’s Palmyra-Fin for the financial industry, Upstage’s Solar Pro for translation and EvolutionaryScale’s ESM3 generative model for biology.

Microsoft Copilot (Autonomous AI Agents)

GenAI | Copilot: How Accenture, Microsoft And Avanade Will Help Enterprises Reinvent Business In 2025

• Rationale: Accenture, Microsoft, and Avanade have launched the Copilot Business Transformation Practice to meet the growing demand for AI-driven transformation. This initiative, backed by Microsoft, includes 5,000 professionals dedicated to helping organizations securely and responsibly reinvent business processes using agentic AI and Copilot technologies. This strategic relationship will further advance Microsoft’s position as one of the leaders in enterprise AI innovation.

Company Use Cases

Conversational AI Assistants

Tech News: Wolters Kluwer Launches Conversational AI Feature

• Rationale: Wolters Kluwer's conversational AI assistant enables tax professionals to discuss complex tax questions interactively, using its proprietary, continuously updated research library with direct source access. Users can summarize sources with one click. The assistant has demonstrated reduced client wait times and automated repetitive tasks. This is a good use case showcasing how a company is leveraging its deep domain knowledge to facilitate its clients' success.

Mass Data Summarization

Amazon Quicksight Generative BI Enables Solomon's Actionable Transformation Toward Net Zero

• Rationale: Solomon, a global leader in oil and gas benchmarking, collaborated with Amazon QuickSight Generative BI to address challenges in asset-level emissions data analysis. Its AI-driven solution summarized data across large, diverse and in many cases unstructured datasets and reduced analysis time from six weeks to one week and enabled portfolio building in minutes instead of months. This use case highlights AI's potential to streamline data-heavy industries and drive impactful outcomes.

Rox Goes “All In” On OpenAI

• Rationale: Rox is transforming sales management with its AI-powered platform that unifies fragmented data into a centralized system and delivers insights via always-on AI agent swarms. These agents adapt to individual workflows. Rox doubled sales pipeline ROI and grew from zero to 25 enterprise accounts in seven months. Despite the article’s promotional tone, this case highlights how GenAI can extract actionable insights from vast datasets, accelerating business outcomes in sales.

Personalized Product Recommendation

How AI Is Revolutionizing Beauty—One Perfect Match At A Time

• Rationale: Unilever’s AI-powered BeautyHub PRO is revolutionizing the beauty industry with hyper-personalization. The platform analyzes selfies to recommend tailored skin and hair products, boosting purchase likelihood by 43% and order value by 39%. This approach demonstrates how AI can enhance consumer experiences by offering customized regimens and encouraging entire customer routine upgrades, inspiring applications across industries.

Perry Ellis Tries New AI Recommendations For Its Apparel Online

• Rationale: Perry Ellis, in collaboration with Site.ai, enhanced its customers’ online shopping experience with visual search, allowing customers to upload outfit photos to find similar products. This innovation led to a 4.9x increase in conversion rates, a 470% rise in revenue per user, and a 16% boost in order value. This case illustrates the power of GenAI-driven personalization to drive incremental revenue.

Curated by GAI Insights, a leading GenAI analyst firm helping AI leaders and their teams achieve business results with GenAl. Join our weekly learning lab and read our HBR articles.