Category: Platforms

IBL News | New York

More than half of colleges and universities are focused on staying ahead in the generative AI race by harnessing their data.

A white paper from Google Cloud, released by the Chronicle of Higher Education, highlights that campuses are demonstrating remarkable resilience and creativity by using GenAI and multimodal models for personalized learning and other uses.

“With the right approach and a trusted platform, GenAI can help your institution personalize content across every medium, gain valuable insights from student data, and achieve measurable ROI —all with enterprise-grade security and scalability,” says the report.

Roy Daiany, Industry Director for Education and Careers at Google, explained, “A university’s first-party data is its competitive differentiator. By effectively harnessing that data and combining it with AI, institutions are able to lower costs and increase efficiency.”

• Download the Report (PDF)

[Disclosure: ibl.ai, the parent company of iblnews.org, is a partner of Google Cloud]

IBL News | New York

OpenAI released a free online course titled “ChatGPT Foundations for K-12 Educators,” which encourages teachers to use its tool to create lesson plans, interactive tutorials for students, and other pedagogical practices.

The course was created in collaboration with the nonprofit organization Common Sense Media. It’s one hour long and has a nine-module program covering the basics of AI and its pedagogical applications.

OpenAI says the course has already been deployed in “dozens” of schools, including the Agua Fria School District in Arizona, the San Bernardino School District in California, and the charter school system Challenger Schools.

OpenAI is aggressively going after the education market, which it sees as a critical growth area.

In September, OpenAI hired former Coursera chief revenue officer Leah Belsky as its first GM of education and charged her with bringing OpenAI’s products to more schools. In the spring, the company launched ChatGPT Edu, a version of ChatGPT that was built for universities.

According to Allied Market Research, AI in education could be worth $88.2 billion within the next decade.

However, a poll by the Rand Corporation and the Center on Reinventing Public Education found that just 18% of K-12 educators use AI in their classrooms, reflecting many skeptical pedagogues.

Late last year, the United Nations Educational, Scientific and Cultural Organization (UNESCO) pushed for governments to regulate the use of AI in education, including implementing age limits for users and guardrails on data protection and user privacy. However, little progress has been made on those fronts, especially on AI policy in general.

IBL News | New York

Udacity, now an Accenture company, released its 2025 State of AI at Work Report this month. The report details how this technology is reshaping workplaces across industries and where there are the most significant opportunities for upskilling.

These are the main outcomes:

• Nearly 90% of workers are eager to build their AI skills through additional training and certifications, but only one in three say their organization provides the resources to do so. Over half of workers report that their employers lack clear AI policies or guidelines.

• More than half (54%) of Millennials believed that AI could increase revenue or income, while only 24% of Generation Z and 16% of Generation X felt this.

• AI Writing Assistants are a favorite tool for end users at work.
AI writing assistants: ChatGPT, Claude, Grammarly, and Jasper AI
AI image generation: Canva AI, MidJourney, Stable Diffusion, and DALL E
Machine translation: DeepL Translator, Google Translate, and Microsoft
Translator Data analysis and visualization: Tableau, Power BI, and DataRobot
Notetaking and transcription: Zoom AI Assistant, Fathom.video, and Otter.ai

• Most Commonly Used Categories of AI Technology
AI frameworks and libraries (e.g., PyTorch, TensorFlow)
AI models and techniques (e.g., Supervised Learning, Transfer Learning)
AI tools and platforms (e.g., OpenAI API, Google AI Studio)
AI applications and use cases (e.g., Image Generation, Chatbots)
AI Infrastructure and operations (e.g., Vector Databases, MLOps tools)

IBL News | New York

AI spending surged to $13.8 billion in 2024 from $2.3 billion in 2023 as enterprises embed AI at the core of their business strategies and daily work, according to a study conducted by Menlo Ventures.

This research, titled “2024 State of Generative AI in the Enterprise Report,” done after surveying 600 U.S. enterprise IT decision-makers, points out that we are still in the early stages of a large-scale transformation.

This spending will continue: 72% of decision-makers anticipate broader adoption of generative AI tools soon.

Investments in the LLM foundation model still dominate spending, but the application layer segment to optimize workflows is now growing faster.

These app layer companies—mostly in highly verticalized sectors—leverage LLM’s capabilities across domains to unlock new efficiencies. Enterprise buyers will invest $4.6 billion in generative AI applications in 2024, an 8x increase from the $600 million invested in 2023.

The use cases that deliver the most ROI through enhanced productivity or operational efficiency are:

• Code copilots, such as GitHub Copilot, Cursor, Codeium, Harness, and All Hands.

• Support knowledge-based chatbots for employees, customers, and contact centers. Aisera, Decagon, Sierra, and Observe AI are some of the examples.

• Enterprise search, retrieval, data extraction, and transformation to unlock the knowledge hidden within data silos. Solutions like Glean and Sana connect to emails, messengers, and document stores, enabling unified semantic search across systems.

• Meeting summarization to automate note-taking and takeaways. Examples are Fireflies.ai, Otter.ai, Fathom, and Eleos Health.

AI-powered autonomous agents capable of managing complex, end-to-end workflow processes are emerging and can transform human-led industries. Forge, Sema4, and Clay are some tools.

When deciding to build or buy, 47% of solutions are developed in-house, while 53% are sourced from vendors. Often, organizations discover too late that they have underestimated the difficulty of technical integration, scalability, and ongoing support.

Most customers (64%) prefer buying from established vendors, citing trust.

The leading vertical AI applications are:

• Healthcare, with examples like Abridge, Ambience, Heidi, Eleos Health, Notable, SmarterDx, Codametrix, Adonis, and Rivet.

• Legal, with examples like Everlaw, Harvey, Spellbook, EvenUp, Garden, Manifest, and Eve.

• Financial Services, with examples like Numeric, Klarity, Arkifi, Rogo, Arch, Orby, Sema4, Greenlite, and Norm AI.

• Media and entertainment, with examples like Runway, Captions, Descript, Black Forest Labs, Higgsfield, Ideogram, Midjourney, and Pika.

Rather than relying on a single provider, enterprises have adopted a multi-model approach, typically deploying three or more LLM in their AI stacks, routing to different models depending on the use case or results.

To date, close-source solutions underpin the vast majority of usage, with Meta’s Llama 3 holding at 19%, according to the Menlo Ventures research.

Regarding architectures for building efficient and scalable AI systems, RAG (retrieval-augmented generation) dominates with 51% adoption, while fine-tuning of production molded is only 9%. Agentic architectures, which debuted this year, power 12% of implementations.

Databases and data pipelines are needed to power RAG. Traditional databases like Postgres and MongoDB remain common, while AI-native vector databases like Pinecone gain ground.

Menlo Ventures made three predictions for what lies ahead:

1. Agentic automation will drive the next wave of transformation, tackling complex, multi-step tasks beyond the current systems of content generation and knowledge retrieval. Examples are platforms like Clay and Forge

2. More incumbents will fall. Chegg saw 85% of its market cap vanish, while Stack Overflow’s web traffic halved. IT outsourcing firms like Cognizant, legacy automation players like UiPath, and even software giants like Salesforce and Autodesk will face AI-native challengers.

3. The AI talent drought will intensify. AI-skilled enterprise architects will notably increase their salaries. 

Squint, Typeface…