‘Lazy and Mediocre’ HR Team Fired After Manager’s Own CV Gets Auto-Rejected in Seconds, Exposing System Failure — from ibtimes.co.uk by Vinay Patel
The automated system’s error highlights the potential for bias and inefficiency in technology-driven HR practices

An entire HR team was terminated after their manager discovered and confirmed that their system automatically rejected all candidates — including his own application.

The manager wrote in their comment, “Auto rejection systems from HR make me angry.” They explained that while searching for a new employee, their HR department could not find a single qualified candidate in three months. As expected, the suspicious manager decided to investigate.

“I created myself a new email and sent them a modified version of my CV with a fake name to see what was going on with the process,” they wrote. “And guess what, I got auto-rejected. HR didn’t even look at my CV.”

When the manager reported the issue to upper management, “half of the HR department was fired in the following weeks.” A typographical error with significant consequences caused the entire problem.

The manager works in the tech industry and was trying to hire developers. However, HR had set up the system to search for developers with expertise in the wrong development software and one that no longer exists.

From DSC:
Back in 2017, I had survived several rounds of layoffs at the then Calvin College (now Calvin University) but I didn’t survive the layoff of 12 people in the spring of 2017. I hadn’t needed to interview for a new job in quite a while. So boy, did I get a wake-up call with discovering that Applicant Tracking Systems existed and could be tough to get past. (Also, the old-school job replacement firm that Calvin hired wasn’t much help in dealing with them either.)

I didn’t like these ATSs then, and I still have my concerns about them now. The above article points out that my concerns were/are at least somewhat founded. And if you take the entire day to research and apply for a position — only to get an instant reply back from the ATS — it’s very frustrating and discouraging. 

Plus the ATSs may not pick up on nuances. An experienced human being might be able to see that a candidate’s skills are highly relevant and/or transferable to the position that they’re hiring for. 

Networking is key of course. But not everyone has been taught about networking and not everyone gets past the ATS to get their resume viewed by a pair of human eyes. HR, IT, and any other relevant groups here need to be very careful with programming their ATSs.

 
 

LinkedIn Jobs on the Rise 2025: The 25 fastest-growing jobs in the U.S. — from linkedin.com

Professionals are navigating rapid change, and staying ahead of the curve is no easy feat. Recent LinkedIn research shows that 64% of workers feel overwhelmed by the pace of workplace shifts, from navigating AI to managing multi-generational teams. At the same time, U.S. workers’ confidence in their job securityis the lowest it’s been since the start of the pandemic.

But as the workplace continues to evolve, new opportunities arise. That’s exactly what our annual Jobs on the Rise list uncovers — the fastest-growing jobs over the past three years and the trends defining the future of work. From the rise of AI roles to the resurgence in travel and hospitality positions, the 2025 ranking highlights sectors with sustainable growth in today’s changing workforce. (You can read more about our methodology at the bottom of this article.)

The list is a roadmap that can point you in the right direction at any stage of your career. Under each job title, you can explore the most common skills, top hiring regions, remote and hybrid availability and more. And you can turn those insights into action by exploring open roles, honing your skills through LinkedIn Learning courses (free for all members until Feb. 15) or joining the conversation in the collaborative article for each featured role.

 

How Generative AI Is Shaping the Future of Law: Challenges and Trends in the Legal Profession — from thomsonreuters.com by Raghu Ramanathan

With this mind, Thomson Reuters and Lexpert hosted a panel featuring law firm leaders and industry experts discussing the challenges and trends around the use of generative AI in the legal profession.?Below are insights from an engaging and informative discussion.

Sections included:

  • Lawyers are excited to implement generative AI solutions
  • Unfounded concerns about robot lawyers
  • Changing billing practices and elevating services
  • Managing and mitigating risks

Adopting Legal Technology Responsibly — from lexology.com by Sacha Kirk

Here are fundamental principles to guide the process:

  1. Start with a Needs Assessment…
  2. Engage Stakeholders Early…
  3. Choose Scalable Solutions…
  4. Prioritise Security and Compliance…
  5. Plan for Change Management…

Modernizing Legal Workflows: The Role Of AI, Automation, And Strategic Partnerships — from abovethelaw.com by Scott Angelo, Jared Gullbergh, Nancy Griffing, and Michael Owen Hill
A roadmap for law firms.  

Angelo added, “We really doubled down on AI because it was just so new — not just to the legal industry, but to the world.” Under his leadership, Buchanan’s efforts to embrace AI have garnered significant attention, earning the firm recognition as one of the “Best of the Best for Generative AI” in the 2024 BTI “Leading Edge Law Firms” survey.

This acknowledgment reflects more than ambition; it highlights the firm’s ability to translate innovative ideas into actionable results. By focusing on collaboration and leveraging technology to address client demands, Buchanan has set a benchmark for what is possible in legal technology innovation.

The collective team followed these essential steps for app development:

  • Identify and Prioritize Use Cases…
  • Define App Requirements…
  • Leverage Pre-Built Studio Apps and Templates…
  • Incorporate AI and Automation…
  • Test and Iterate…
  • Deploy and Train…
  • Measure Success…

Navigating Generative AI in Legal Practice — from linkedin.com by Colin Levy

The rise of artificial intelligence (AI), particularly generative AI, has introduced transformative potential to legal practice. For in-house counsel, managing legal risk while driving operational efficiency increasingly involves navigating AI’s opportunities and challenges. While AI offers remarkable tools for automation and data-driven decision-making, it is essential to approach these tools as complementary to human judgment, not replacements. Effective AI adoption requires balancing its efficiencies with a commitment to ethical, nuanced legal practice.

Here a few ways in which this arises:

 

Increasing AI Fluency Among Enterprise Employees, Senior Management & Executives — from learningguild.com by Bill Brandon

In other words, individual learning leaders need to obtain information from surveys and studies that are directly useful in their curriculum planning. This article attempts, in these early days, to provide some specific guidelines for AI curriculum planning in enterprise organizations.

The two reports identified in the first paragraph help to answer an important question. What can enterprise L&D teams do to improve AI fluency in their organizations?


The Future of Workplace Learning: Adaptive Strategies for Navigating Change — from learningguild.com by Rachel Rosenfeldt

The Importance of Building a ‘Change Muscle’
The ability to test and learn, pivot quickly, and embrace change is an increasingly foundational skill that all employees, no matter the level of experience or seniority, need in 2025 and beyond. Adaptable organizations significantly outperform more change-averse peers on nearly every metric, ranging from revenue growth to employee engagement. In other words, having agility and adaptability embedded in your culture pays dividends. Although these terms are often used interchangeably, they represent distinct yet interconnected aspects of organizational success:

  • Agility refers to the ability to swiftly and efficiently respond to immediate challenges or opportunities. It’s about being nimble and proactive, making quick decisions, and adjusting to navigate short-term obstacles.
  • Adaptability is a broader concept that encompasses the capacity to evolve and thrive in the face of long-term shifts in the environment. It’s about being resilient and flexible by modifying strategies and structures to align with fundamental changes in the market or industry.

And a quick comment from DSC:


Addressing Skills Gaps in Enterprise L&D: A High-Level Overview — from learningguild.com by Bill Brandon

Employees’ skills and abilities must match the skills and abilities required for their jobs; when they do, organizational performance and productivity improve.

Skills gaps occur when there are mismatches between employees’ skills and capabilities and the skills and capabilities needed for their work. As technology and work become more complex, identifying and correcting skills gaps become essential to optimizing employee performance.

This article discusses various methods involving skills inference and predictive analytics in addition to traditional methods to pinpoint and prevent skills gaps.


A Practical Framework for Microlearning Success: A Guide for Learning Leaders — from by Robyn A. Defelice, PhD

Another year, another opportunity to bring microlearning into your performance and talent development strategy! This is especially appealing as more and more organizations strive to deliver training in ways that meet the fast-paced needs of their employees.

However, implementing a microlearning strategy that aligns with organizational outcomes and sustains performance is no small feat. Learning and Development (L&D) leaders often grapple with questions like: Where do we start; How do we ensure our efforts are effective; and What factors should we evaluate?

The Microlearning Effectiveness (MLE) Framework offers a practical approach to addressing these challenges. Instead of rigid rules, the framework acts as a guide, encouraging leaders to evaluate their efforts against six key components:

  • Goals or measurable outcomes
  • Purpose
  • Potential
  • Evaluation
  • Implementation
  • Distributed practice
 

NVIDIA’s Apple moment?! — from theneurondaily.com by Noah Edelman and Grant Harvey
PLUS: How to level up your AI workflows for 2025…

NVIDIA wants to put an AI supercomputer on your desk (and it only costs $3,000).

And last night at CES 2025, Jensen Huang announced phase two of this plan: Project DIGITS, a $3K personal AI supercomputer that runs 200B parameter models from your desk. Guess we now know why Apple recently developed an NVIDIA allergy

But NVIDIA doesn’t just want its “Apple PC moment”… it also wants its OpenAI moment. NVIDIA also announced Cosmos, a platform for building physical AI (think: robots and self-driving cars)—which Jensen Huang calls “the ChatGPT moment for robotics.”


Jensen Huang’s latest CES speech: AI Agents are expected to become the next robotics industry, with a scale reaching trillions of dollars — from chaincatcher.com

NVIDIA is bringing AI from the cloud to personal devices and enterprises, covering all computing needs from developers to ordinary users.

At CES 2025, which opened this morning, NVIDIA founder and CEO Jensen Huang delivered a milestone keynote speech, revealing the future of AI and computing. From the core token concept of generative AI to the launch of the new Blackwell architecture GPU, and the AI-driven digital future, this speech will profoundly impact the entire industry from a cross-disciplinary perspective.

Also see:


NVIDIA Project DIGITS: The World’s Smallest AI Supercomputer. — from nvidia.com
A Grace Blackwell AI Supercomputer on your desk.


From DSC:
I’m posting this next item (involving Samsung) as it relates to how TVs continue to change within our living rooms. AI is finding its way into our TVs…the ramifications of this remain to be seen.


OpenAI ‘now knows how to build AGI’ — from therundown.ai by Rowan Cheung
PLUS: AI phishing achieves alarming success rates

The Rundown: Samsung revealed its new “AI for All” tagline at CES 2025, introducing a comprehensive suite of new AI features and products across its entire ecosystem — including new AI-powered TVs, appliances, PCs, and more.

The details:

  • Vision AI brings features like real-time translation, the ability to adapt to user preferences, AI upscaling, and instant content summaries to Samsung TVs.
  • Several of Samsung’s new Smart TVs will also have Microsoft Copilot built in, while also teasing a potential AI partnership with Google.
  • Samsung also announced the new line of Galaxy Book5 AI PCs, with new capabilities like AI-powered search and photo editing.
  • AI is also being infused into Samsung’s laundry appliances, art frames, home security equipment, and other devices within its SmartThings ecosystem.

Why it matters: Samsung’s web of products are getting the AI treatment — and we’re about to be surrounded by AI-infused appliances in every aspect of our lives. The edge will be the ability to sync it all together under one central hub, which could position Samsung as the go-to for the inevitable transition from smart to AI-powered homes.

***

“Samsung sees TVs not as one-directional devices for passive consumption but as interactive, intelligent partners that adapt to your needs,” said SW Yong, President and Head of Visual Display Business at Samsung Electronics. “With Samsung Vision AI, we’re reimagining what screens can do, connecting entertainment, personalization, and lifestyle solutions into one seamless experience to simplify your life.”from Samsung


Understanding And Preparing For The 7 Levels Of AI Agents — from forbes.com by Douglas B. Laney

The following framework I offer for defining, understanding, and preparing for agentic AI blends foundational work in computer science with insights from cognitive psychology and speculative philosophy. Each of the seven levels represents a step-change in technology, capability, and autonomy. The framework expresses increasing opportunities to innovate, thrive, and transform in a data-fueled and AI-driven digital economy.


The Rise of AI Agents and Data-Driven Decisions — from devprojournal.com by Mike Monocello
Fueled by generative AI and machine learning advancements, we’re witnessing a paradigm shift in how businesses operate and make decisions.

AI Agents Enhance Generative AI’s Impact
Burley Kawasaki, Global VP of Product Marketing and Strategy at Creatio, predicts a significant leap forward in generative AI. “In 2025, AI agents will take generative AI to the next level by moving beyond content creation to active participation in daily business operations,” he says. “These agents, capable of partial or full autonomy, will handle tasks like scheduling, lead qualification, and customer follow-ups, seamlessly integrating into workflows. Rather than replacing generative AI, they will enhance its utility by transforming insights into immediate, actionable outcomes.”


Here’s what nobody is telling you about AI agents in 2025 — from aidisruptor.ai by Alex McFarland
What’s really coming (and how to prepare). 

Everyone’s talking about the potential of AI agents in 2025 (and don’t get me wrong, it’s really significant), but there’s a crucial detail that keeps getting overlooked: the gap between current capabilities and practical reliability.

Here’s the reality check that most predictions miss: AI agents currently operate at about 80% accuracy (according to Microsoft’s AI CEO). Sounds impressive, right? But here’s the thing – for businesses and users to actually trust these systems with meaningful tasks, we need 99% reliability. That’s not just a 19% gap – it’s the difference between an interesting tech demo and a business-critical tool.

This matters because it completely changes how we should think about AI agents in 2025. While major players like Microsoft, Google, and Amazon are pouring billions into development, they’re all facing the same fundamental challenge – making them work reliably enough that you can actually trust them with your business processes.

Think about it this way: Would you trust an assistant who gets things wrong 20% of the time? Probably not. But would you trust one who makes a mistake only 1% of the time, especially if they could handle repetitive tasks across your entire workflow? That’s a completely different conversation.


Why 2025 will be the year of AI orchestration — from venturebeat.com by Emilia David|

In the tech world, we like to label periods as the year of (insert milestone here). This past year (2024) was a year of broader experimentation in AI and, of course, agentic use cases.

As 2025 opens, VentureBeat spoke to industry analysts and IT decision-makers to see what the year might bring. For many, 2025 will be the year of agents, when all the pilot programs, experiments and new AI use cases converge into something resembling a return on investment.

In addition, the experts VentureBeat spoke to see 2025 as the year AI orchestration will play a bigger role in the enterprise. Organizations plan to make management of AI applications and agents much more straightforward.

Here are some themes we expect to see more in 2025.


Predictions For AI In 2025: Entrepreneurs Look Ahead — from forbes.com by Jodie Cook

AI agents take charge
Jérémy Grandillon, CEO of TC9 – AI Allbound Agency, said “Today, AI can do a lot, but we don’t trust it to take actions on our behalf. This will change in 2025. Be ready to ask your AI assistant to book a Uber ride for you.” Start small with one agent handling one task. Build up to an army.

“If 2024 was agents everywhere, then 2025 will be about bringing those agents together in networks and systems,” said Nicholas Holland, vice president of AI at Hubspot. “Micro agents working together to accomplish larger bodies of work, and marketplaces where humans can ‘hire’ agents to work alongside them in hybrid teams. Before long, we’ll be saying, ‘there’s an agent for that.'”

Voice becomes default
Stop typing and start talking. Adam Biddlecombe, head of brand at Mindstream, predicts a shift in how we interact with AI. “2025 will be the year that people start talking with AI,” he said. “The majority of people interact with ChatGPT and other tools in the text format, and a lot of emphasis is put on prompting skills.

Biddlecombe believes, “With Apple’s ChatGPT integration for Siri, millions of people will start talking to ChatGPT. This will make AI so much more accessible and people will start to use it for very simple queries.”

Get ready for the next wave of advancements in AI. AGI arrives early, AI agents take charge, and voice becomes the norm. Video creation gets easy, AI embeds everywhere, and one-person billion-dollar companies emerge.



These 4 graphs show where AI is already impacting jobs — from fastcompany.com by Brandon Tucker
With a 200% increase in two years, the data paints a vivid picture of how AI technology is reshaping the workforce. 

To better understand the types of roles that AI is impacting, ZoomInfo’s research team looked to its proprietary database of professional contacts for answers. The platform, which detects more than 1.5 million personnel changes per day, revealed a dramatic increase in AI-related job titles since 2022. With a 200% increase in two years, the data paints a vivid picture of how AI technology is reshaping the workforce.

Why does this shift in AI titles matter for every industry?

 

1-800-CHAT-GPT—12 Days of OpenAI: Day 10

Per The Rundown: OpenAI just launched a surprising new way to access ChatGPT — through an old-school 1-800 number & also rolled out a new WhatsApp integration for global users during Day 10 of the company’s livestream event.


How Agentic AI is Revolutionizing Customer Service — from customerthink.com by Devashish Mamgain

Agentic AI represents a significant evolution in artificial intelligence, offering enhanced autonomy and decision-making capabilities beyond traditional AI systems. Unlike conventional AI, which requires human instructions, agentic AI can independently perform complex tasks, adapt to changing environments, and pursue goals with minimal human intervention.

This makes it a powerful tool across various industries, especially in the customer service function. To understand it better, let’s compare AI Agents with non-AI agents.

Characteristics of Agentic AI

    • Autonomy: Achieves complex objectives without requiring human collaboration.
    • Language Comprehension: Understands nuanced human speech and text effectively.
    • Rationality: Makes informed, contextual decisions using advanced reasoning engines.
    • Adaptation: Adjusts plans and goals in dynamic situations.
    • Workflow Optimization: Streamlines and organizes business workflows with minimal oversight.

Clio: A system for privacy-preserving insights into real-world AI use — from anthropic.com

How, then, can we research and observe how our systems are used while rigorously maintaining user privacy?

Claude insights and observations, or “Clio,” is our attempt to answer this question. Clio is an automated analysis tool that enables privacy-preserving analysis of real-world language model use. It gives us insights into the day-to-day uses of claude.ai in a way that’s analogous to tools like Google Trends. It’s also already helping us improve our safety measures. In this post—which accompanies a full research paper—we describe Clio and some of its initial results.


Evolving tools redefine AI video — from heatherbcooper.substack.com by Heather Cooper
Google’s Veo 2, Kling 1.6, Pika 2.0 & more

AI video continues to surpass expectations
The AI video generation space has evolved dramatically in recent weeks, with several major players introducing groundbreaking tools.

Here’s a comprehensive look at the current landscape:

  • Veo 2…
  • Pika 2.0…
  • Runway’s Gen-3…
  • Luma AI Dream Machine…
  • Hailuo’s MiniMax…
  • OpenAI’s Sora…
  • Hunyuan Video by Tencent…

There are several other video models and platforms, including …

 

Best of 2024 — from wondertools.substack.com by Jeremy Caplan
12 of my favorites this year

I tested hundreds of new tools this year. Many were duplicative. A few stuck with me because they’re so useful. The dozen noted below are helping me mine insights from notes, summarize meetings, design visuals— even code a little, without being a developer. You can start using any of these in minutes — no big budget or prompt engineering PhD required.

 

The State of Flexible Work: Statistics from The Flex Index — from flexindex.com

Flex Report Q4 2024
Hybrid and Remote Work by the Numbers

And you thought return to office policy was settled! For a while, it looked like 2-3 days per week in the office would be the future of work in America.

Yet this quarter has brought significant changes to the landscape. Major companies like Amazon, Dell, and The Washington Post announced their plans for a full return to office. Then came a shift in the political atmosphere, with Trump’s victory and potential incoming changes requiring full-time office work for government employees.

These developments raise important questions about where workplace flexibility is headed. Are we witnessing the beginning of a broader shift back to Full Time In Office? Is the era of fully flexible work coming to an end? Or is this simply another evolution in how companies structure their workplace policies?

In this report, we dig into US-wide trends to see if the high-profile shifts toward Full Time In Office reflect broader market movement or just isolated cases. We examine how different industries are approaching flexibility, from Technology’s continued embrace to the challenges faced by sectors dependent on physical presence. Plus, we explore the divide in how companies of different sizes approach workplace flexibility. Are we truly heading back to the office full time, or is the future of work more nuanced than the headlines suggest?

 

Introducing Gemini 2.0: our new AI model for the agentic era — from blog.google by Sundar Pichai, Demis Hassabis, and Koray Kavukcuoglu

Today we’re excited to launch our next era of models built for this new agentic era: introducing Gemini 2.0, our most capable model yet. With new advances in multimodality — like native image and audio output — and native tool use, it will enable us to build new AI agents that bring us closer to our vision of a universal assistant.

We’re getting 2.0 into the hands of developers and trusted testers today. And we’re working quickly to get it into our products, leading with Gemini and Search. Starting today our Gemini 2.0 Flash experimental model will be available to all Gemini users. We’re also launching a new feature called Deep Research, which uses advanced reasoning and long context capabilities to act as a research assistant, exploring complex topics and compiling reports on your behalf. It’s available in Gemini Advanced today.

Over the last year, we have been investing in developing more agentic models, meaning they can understand more about the world around you, think multiple steps ahead, and take action on your behalf, with your supervision.

.

Try Deep Research and our new experimental model in Gemini, your AI assistant — from blog.google by Dave Citron
Deep Research rolls out to Gemini Advanced subscribers today, saving you hours of time. Plus, you can now try out a chat optimized version of 2.0 Flash Experimental in Gemini on the web.

Today, we’re sharing the latest updates to Gemini, your AI assistant, including Deep Research — our new agentic feature in Gemini Advanced — and access to try Gemini 2.0 Flash, our latest experimental model.

Deep Research uses AI to explore complex topics on your behalf and provide you with findings in a comprehensive, easy-to-read report, and is a first look at how Gemini is getting even better at tackling complex tasks to save you time.1


Google Unveils A.I. Agent That Can Use Websites on Its Own — from nytimes.com by Cade Metz and Nico Grant (NOTE: This is a GIFTED article for/to you.)
The experimental tool can browse spreadsheets, shopping sites and other services, before taking action on behalf of the computer user.

Google on Wednesday unveiled a prototype of this technology, which artificial intelligence researchers call an A.I. agent.

Google’s new prototype, called Mariner, is based on Gemini 2.0, which the company also unveiled on Wednesday. Gemini is the core technology that underpins many of the company’s A.I. products and research experiments. Versions of the system will power the company’s chatbot of the same name and A.I. Overviews, a Google search tool that directly answers user questions.


Gemini 2.0 is the next chapter for Google AI — from axios.com by Ina Fried

Google Gemini 2.0 — a major upgrade to the core workings of Google’s AI that the company launched Wednesday — is designed to help generative AI move from answering users’ questions to taking action on its own…

The big picture: Hassabis said building AI systems that can take action on their own has been DeepMind’s focus since its early days teaching computers to play games such as chess and Go.

  • “We were always working towards agent-based systems,” Hassabis said. “From the beginning, they were able to plan and then carry out actions and achieve objectives.”
  • Hassabis said AI systems that can act as semi-autonomous agents also represent an important intermediate step on the path toward artificial general intelligence (AGI) — AI that can match or surpass human capabilities.
  • “If we think about the path to AGI, then obviously you need a system that can reason, break down problems and carry out actions in the world,” he said.

AI Agents vs. AI Assistants: Know the Key Differences — from aithority.com by Rishika Patel

The same paradigm applies to AI systems. AI assistants function as reactive tools, completing tasks like answering queries or managing workflows upon request. Think of chatbots or scheduling tools. AI agents, however, work autonomously to achieve set objectives, making decisions and executing tasks dynamically, adapting as new information becomes available.

Together, AI assistants and agents can enhance productivity and innovation in business environments. While assistants handle routine tasks, agents can drive strategic initiatives and problem-solving. This powerful combination has the potential to elevate organizations, making processes more efficient and professionals more effective.


Discover how to accelerate AI transformation with NVIDIA and Microsoft — from ignite.microsoft.com

Meet NVIDIA – The Engine of AI. From gaming to data science, self-driving cars to climate change, we’re tackling the world’s greatest challenges and transforming everyday life. The Microsoft and NVIDIA partnership enables Startups, ISVs, and Partners global access to the latest NVIDIA GPUs on-demand and comprehensive developer solutions to build, deploy and scale AI-enabled products and services.


Google + Meta + Apple New AI — from theneurondaily.com by Grant Harve

What else Google announced:

  • Deep Research: New feature that can explore topics and compile reports.
  • Project Astra: AI agent that can use Google Search, Lens, and Maps, understands multiple languages, and has 10-minute conversation memory.
  • Project Mariner: A browser control agent that can complete web tasks (83.5% success rate on WebVoyager benchmark). Read more about Mariner here.
  • Agents to help you play (or test) video games.

AI Agents: Easier To Build, Harder To Get Right — from forbes.com by Andres Zunino

The swift progress of artificial intelligence (AI) has simplified the creation and deployment of AI agents with the help of new tools and platforms. However, deploying these systems beneath the surface comes with hidden challenges, particularly concerning ethics, fairness and the potential for bias.

The history of AI agents highlights the growing need for expertise to fully realize their benefits while effectively minimizing risks.

 

Where to start with AI agents: An introduction for COOs — from fortune.com by Ganesh Ayyar

Picture your enterprise as a living ecosystem, where surging market demand instantly informs staffing decisions, where a new vendor’s onboarding optimizes your emissions metrics, where rising customer engagement reveals product opportunities. Now imagine if your systems could see these connections too! This is the promise of AI agents — an intelligent network that thinks, learns, and works across your entire enterprise.

Today, organizations operate in artificial silos. Tomorrow, they could be fluid and responsive. The transformation has already begun. The question is: will your company lead it?

The journey to agent-enabled operations starts with clarity on business objectives. Leaders should begin by mapping their business’s critical processes. The most pressing opportunities often lie where cross-functional handoffs create friction or where high-value activities are slowed by system fragmentation. These pain points become the natural starting points for your agent deployment strategy.


Create podcasts in minutes — from elevenlabs.io by Eleven Labs
Now anyone can be a podcast producer


Top AI tools for business — from theneuron.ai


This week in AI: 3D from images, video tools, and more — from heatherbcooper.substack.com by Heather Cooper
From 3D worlds to consistent characters, explore this week’s AI trends

Another busy AI news week, so I organized it into categories:

  • Image to 3D
  • AI Video
  • AI Image Models & Tools
  • AI Assistants / LLMs
  • AI Creative Workflow: Luma AI Boards

Want to speak Italian? Microsoft AI can make it sound like you do. — this is a gifted article from The Washington Post;
A new AI-powered interpreter is expected to simulate speakers’ voices in different languages during Microsoft Teams meetings.

Artificial intelligence has already proved that it can sound like a human, impersonate individuals and even produce recordings of someone speaking different languages. Now, a new feature from Microsoft will allow video meeting attendees to hear speakers “talk” in a different language with help from AI.


What Is Agentic AI?  — from blogs.nvidia.com by Erik Pounds
Agentic AI uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems.

The next frontier of artificial intelligence is agentic AI, which uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. And it’s set to enhance productivity and operations across industries.

Agentic AI systems ingest vast amounts of data from multiple sources to independently analyze challenges, develop strategies and execute tasks like supply chain optimization, cybersecurity vulnerability analysis and helping doctors with time-consuming tasks.


 

What Students Are Saying About Teachers Using A.I. to Grade — from nytimes.com by The Learning Network; via Claire Zau
Teenagers and educators weigh in on a recent question from The Ethicist.

Is it unethical for teachers to use artificial intelligence to grade papers if they have forbidden their students from using it for their assignments?

That was the question a teacher asked Kwame Anthony Appiah in a recent edition of The Ethicist. We posed it to students to get their take on the debate, and asked them their thoughts on teachers using A.I. in general.

While our Student Opinion questions are usually reserved for teenagers, we also heard from a few educators about how they are — or aren’t — using A.I. in the classroom. We’ve included some of their answers, as well.


OpenAI wants to pair online courses with chatbots — from techcrunch.com by Kyle Wiggers; via James DeVaney on LinkedIn

If OpenAI has its way, the next online course you take might have a chatbot component.

Speaking at a fireside on Monday hosted by Coeus Collective, Siya Raj Purohit, a member of OpenAI’s go-to-market team for education, said that OpenAI might explore ways to let e-learning instructors create custom “GPTs” that tie into online curriculums.

“What I’m hoping is going to happen is that professors are going to create custom GPTs for the public and let people engage with content in a lifelong manner,” Purohit said. “It’s not part of the current work that we’re doing, but it’s definitely on the roadmap.”


15 Times to use AI, and 5 Not to — from oneusefulthing.org by Ethan Mollick
Notes on the Practical Wisdom of AI Use

There are several types of work where AI can be particularly useful, given the current capabilities and limitations of LLMs. Though this list is based in science, it draws even more from experience. Like any form of wisdom, using AI well requires holding opposing ideas in mind: it can be transformative yet must be approached with skepticism, powerful yet prone to subtle failures, essential for some tasks yet actively harmful for others. I also want to caveat that you shouldn’t take this list too seriously except as inspiration – you know your own situation best, and local knowledge matters more than any general principles. With all that out of the way, below are several types of tasks where AI can be especially useful, given current capabilities—and some scenarios where you should remain wary.


Learning About Google Learn About: What Educators Need To Know — from techlearning.com by Ray Bendici
Google’s experimental Learn About platform is designed to create an AI-guided learning experience

Google Learn About is a new experimental AI-driven platform available that provides digestible and in-depth knowledge about various topics, but showcases it all in an educational context. Described by Google as a “conversational learning companion,” it is essentially a Wikipedia-style chatbot/search engine, and then some.

In addition to having a variety of already-created topics and leading questions (in areas such as history, arts, culture, biology, and physics) the tool allows you to enter prompts using either text or an image. It then provides a general overview/answer, and then suggests additional questions, topics, and more to explore in regard to the initial subject.

The idea is for student use is that the AI can help guide a deeper learning process rather than just provide static answers.


What OpenAI’s PD for Teachers Does—and Doesn’t—Do — from edweek.org by Olina Banerji
What’s the first thing that teachers dipping their toes into generative artificial intelligence should do?

They should start with the basics, according to OpenAI, the creator of ChatGPT and one of the world’s most prominent artificial intelligence research companies. Last month, the company launched an hour-long, self-paced online course for K-12 teachers about the definition, use, and harms of generative AI in the classroom. It was launched in collaboration with Common Sense Media, a national nonprofit that rates and reviews a wide range of digital content for its age appropriateness.

…the above article links to:

ChatGPT Foundations for K–12 Educators — from commonsense.org

This course introduces you to the basics of artificial intelligence, generative AI, ChatGPT, and how to use ChatGPT safely and effectively. From decoding the jargon to responsible use, this course will help you level up your understanding of AI and ChatGPT so that you can use tools like this safely and with a clear purpose.

Learning outcomes:

  • Understand what ChatGPT is and how it works.
  • Demonstrate ways to use ChatGPT to support your teaching practices.
  • Implement best practices for applying responsible AI principles in a school setting.

Takeaways From Google’s Learning in the AI Era Event — from edtechinsiders.substack.com by Sarah Morin, Alex Sarlin, and Ben Kornell
Highlights from Our Day at Google + Behind-the-Scenes Interviews Coming Soon!

  1. NotebookLM: The Start of an AI Operating System
  2. Google is Serious About AI and Learning
  3. Google’s LearnLM Now Available in AI Studio
  4. Collaboration is King
  5. If You Give a Teacher a Ferrari

Rapid Responses to AI — from the-job.beehiiv.com by Paul Fain
Top experts call for better data and more short-term training as tech transforms jobs.

AI could displace middle-skill workers and widen the wealth gap, says landmark study, which calls for better data and more investment in continuing education to help workers make career pivots.

Ensuring That AI Helps Workers
Artificial intelligence has emerged as a general purpose technology with sweeping implications for the workforce and education. While it’s impossible to precisely predict the scope and timing of looming changes to the labor market, the U.S. should build its capacity to rapidly detect and respond to AI developments.
That’s the big-ticket framing of a broad new report from the National Academies of Sciences, Engineering, and Medicine. Congress requested the study, tapping an all-star committee of experts to assess the current and future impact of AI on the workforce.

“In contemplating what the future holds, one must approach predictions with humility,” the study says…

“AI could accelerate occupational polarization,” the committee said, “by automating more nonroutine tasks and increasing the demand for elite expertise while displacing middle-skill workers.”

The Kicker: “The education and workforce ecosystem has a responsibility to be intentional with how we value humans in an AI-powered world and design jobs and systems around that,” says Hsieh.


AI Predators: What Schools Should Know and Do — from techlearning.com by Erik Ofgang
AI is increasingly be used by predators to connect with underage students online. Yasmin London, global online safety expert at Qoria and a former member of the New South Wales Police Force in Australia, shares steps educators can take to protect students.

The threat from AI for students goes well beyond cheating, says Yasmin London, global online safety expert at Qoria and a former member of the New South Wales Police Force in Australia.

Increasingly at U.S. schools and beyond, AI is being used by predators to manipulate children. Students are also using AI generate inappropriate images of other classmates or staff members. For a recent report, Qoria, a company that specializes in child digital safety and wellbeing products, surveyed 600 schools across North America, UK, Australia, and New Zealand.


Why We Undervalue Ideas and Overvalue Writing — from aiczar.blogspot.com by Alexander “Sasha” Sidorkin

A student submits a paper that fails to impress stylistically yet approaches a worn topic from an angle no one has tried before. The grade lands at B minus, and the student learns to be less original next time. This pattern reveals a deep bias in higher education: ideas lose to writing every time.

This bias carries serious equity implications. Students from disadvantaged backgrounds, including first-generation college students, English language learners, and those from under-resourced schools, often arrive with rich intellectual perspectives but struggle with academic writing conventions. Their ideas – shaped by unique life experiences and cultural viewpoints – get buried under red ink marking grammatical errors and awkward transitions. We systematically undervalue their intellectual contributions simply because they do not arrive in standard academic packaging.


Google Scholar’s New AI Outline Tool Explained By Its Founder — from techlearning.com by Erik Ofgang
Google Scholar PDF reader uses Gemini AI to read research papers. The AI model creates direct links to the paper’s citations and a digital outline that summarizes the different sections of the paper.

Google Scholar has entered the AI revolution. Google Scholar PDF reader now utilizes generative AI powered by Google’s Gemini AI tool to create interactive outlines of research papers and provide direct links to sources within the paper. This is designed to make reading the relevant parts of the research paper more efficient, says Anurag Acharya, who co-founded Google Scholar on November 18, 2004, twenty years ago last month.


The Four Most Powerful AI Use Cases in Instructional Design Right Now — from drphilippahardman.substack.com by Dr. Philippa Hardman
Insights from ~300 instructional designers who have taken my AI & Learning Design bootcamp this year

  1. AI-Powered Analysis: Creating Detailed Learner Personas…
  2. AI-Powered Design: Optimising Instructional Strategies…
  3. AI-Powered Development & Implementation: Quality Assurance…
  4. AI-Powered Evaluation: Predictive Impact Assessment…

How Are New AI Tools Changing ‘Learning Analytics’? — from edsurge.com by Jeffrey R. Young
For a field that has been working to learn from the data trails students leave in online systems, generative AI brings new promises — and new challenges.

In other words, with just a few simple instructions to ChatGPT, the chatbot can classify vast amounts of student work and turn it into numbers that educators can quickly analyze.

Findings from learning analytics research is also being used to help train new generative AI-powered tutoring systems.

Another big application is in assessment, says Pardos, the Berkeley professor. Specifically, new AI tools can be used to improve how educators measure and grade a student’s progress through course materials. The hope is that new AI tools will allow for replacing many multiple-choice exercises in online textbooks with fill-in-the-blank or essay questions.


Increasing AI Fluency Among Enterprise Employees, Senior Management & Executives — from learningguild.com by Bill Brandon

This article attempts, in these early days, to provide some specific guidelines for AI curriculum planning in enterprise organizations.

The two reports identified in the first paragraph help to answer an important question. What can enterprise L&D teams do to improve AI fluency in their organizations?

You could be surprised how many software products have added AI features. Examples (to name a few) are productivity software (Microsoft 365 and Google Workspace); customer relationship management (Salesforce and Hubspot); human resources (Workday and Talentsoft); marketing and advertising (Adobe Marketing Cloud and Hootsuite); and communication and collaboration (Slack and Zoom). Look for more under those categories in software review sites.

 

What We Talk about When We Talk about Networking — from michelleweise.substack.com by Dr. Michelle Weise, Julia Freeland Fisher, and Nitzan Pelman
Networking, Social Capital & the Goldilocks Ask

I recently had a chance to sit down with Julia Freeland Fisher, Director of Education at the Christensen Institute, and Nitzan Pelman, CEO of Climb Together and founder of Climb Hire, for a live CGN webinar on tapping into our networks (some of you may recall, I wrote about these two phenomenal women in my post, “Who You Know … A Little Bit: The Power of Weak Ties”).

I love getting to learn from their constantly evolving thinking on cultivating and mobilizing social capital. And in this episode, we get super tactical on the how-to’s of networking for young people.

From DSC:
Tell your kids or grandkids to watch this. I didn’t have a CLUE about networking when I graduated from high school — and even from college. It took me years to get an accurate understanding of the place and power of networking. And that it’s not all about looking out for #1 and taking from/manipulating/exploiting others. But it’s about sharing resources, learning and connecting with others, helping others connect with relevant others, and more.

I hope that we can produce more items like this to help the next generation get started and navigate their careers.

 

2024: The State of Generative AI in the Enterprise — from menlovc.com (Menlo Ventures)
The enterprise AI landscape is being rewritten in real time. As pilots give way to production, we surveyed 600 U.S. enterprise IT decision-makers to reveal the emerging winners and losers.

This spike in spending reflects a wave of organizational optimism; 72% of decision-makers anticipate broader adoption of generative AI tools in the near future. This confidence isn’t just speculative—generative AI tools are already deeply embedded in the daily work of professionals, from programmers to healthcare providers.

Despite this positive outlook and increasing investment, many decision-makers are still figuring out what will and won’t work for their businesses. More than a third of our survey respondents do not have a clear vision for how generative AI will be implemented across their organizations. This doesn’t mean they’re investing without direction; it simply underscores that we’re still in the early stages of a large-scale transformation. Enterprise leaders are just beginning to grasp the profound impact generative AI will have on their organizations.


Business spending on AI surged 500% this year to $13.8 billion, says Menlo Ventures — from cnbc.com by Hayden Field

Key Points

  • Business spending on generative AI surged 500% this year, hitting $13.8 billion — up from just $2.3 billion in 2023, according to data from Menlo Ventures released Wednesday.
  • OpenAI ceded market share in enterprise AI, declining from 50% to 34%, per the report.
  • Amazon-backed Anthropic doubled its market share from 12% to 24%.

Microsoft quietly assembles the largest AI agent ecosystem—and no one else is close — from venturebeat.com by Matt Marshall

Microsoft has quietly built the largest enterprise AI agent ecosystem, with over 100,000 organizations creating or editing AI agents through its Copilot Studio since launch – a milestone that positions the company ahead in one of enterprise tech’s most closely watched and exciting  segments.

The rapid adoption comes as Microsoft significantly expands its agent capabilities. At its Ignite conference [that started on 11/19/24], the company announced it will allow enterprises to use any of the 1,800 large language models (LLMs) in the Azure catalog within these agents – a significant move beyond its exclusive reliance on OpenAI’s models. The company also unveiled autonomous agents that can work independently, detecting events and orchestrating complex workflows with minimal human oversight.


Now Hear This: World’s Most Flexible Sound Machine Debuts — from
Using text and audio as inputs, a new generative AI model from NVIDIA can create any combination of music, voices and sounds.

Along these lines, also see:


AI Agents Versus Human Agency: 4 Ways To Navigate Our AI-Driven World — from forbes.com by Cornelia C. Walther

To understand the implications of AI agents, it’s useful to clarify the distinctions between AI, generative AI, and AI agents and explore the opportunities and risks they present to our autonomy, relationships, and decision-making.

AI Agents: These are specialized applications of AI designed to perform tasks or simulate interactions. AI agents can be categorized into:

    • Tool Agents…
    • Simulation Agents..

While generative AI creates outputs from prompts, AI agents use AI to act with intention, whether to assist (tool agents) or emulate (simulation agents). The latter’s ability to mirror human thought and action offers fascinating possibilities — and raises significant risks.

 
© 2025 | Daniel Christian