Stay ahead with the most recent breakthroughs—here’s what’s new and making waves in AI-powered productivity:

• Leena AI’s Voice-Enabled, Agentic AI Colleagues: Unveiled a new class of AI “colleagues” that use voice and autonomy to actively boost employee-centric tasks and enterprise productivity.

• Microsoft 365 Copilot Expansion: Microsoft shared over 1,000 stories of businesses successfully boosting productivity with Copilot, now reaching new verticals and broadening features including content creation, automating research, and workflow integration.

• New Google Workspace Video AI (Google Vids): Google rolled out AI avatars for professional video creation—record videos without a camera, harnessing AI avatars, new high-quality video generation tools, automated audio cleanup, and more. Gemini 2.5 AI updates for deep search and advanced video capabilities were also launched.

Leena AI’s Voice-Enabled, Agentic AI Colleagues

Transforming the Modern Enterprise Workplace

Leena AI’s latest announcement introduces a paradigm shift in AI for the workplace: voice-enabled, agentic AI colleagues. Unlike traditional chatbots or workflow automation, these AI agents act proactively, speaking and making decisions to streamline tasks, support employees, and enhance enterprise productivity.

What Makes Leena AI Colleagues Stand Out?

• Natural Voice Interaction: Interact with your AI teammate via natural conversations, simplifying complex workflow instructions and task delegation.

• Autonomous Action: These agents can independently initiate and complete tasks—think of an AI that follows up on requests, schedules meetings, fetches information, or compiles reports without even being prompted.

• Scalable Employee Support: Whether handling onboarding questions, HR processes, or project management check-ins, Leena AI’s solution turbocharges employee support while freeing humans to focus on strategic work.

• Enterprise-Grade Security: Built to operate within enterprise environments, ensuring data privacy and compliance.

Why it matters: Leena AI’s new approach to “agentic” AI is more than just streamlining repetitive work—it’s about building a supportive, always-on digital teammate that makes businesses more responsive and agile. As organizations implement these agents, expect to see higher employee satisfaction, reduced manual workload, and acceleration in operational efficiency.

AI’s Most Useful Tool

AI is an awesome new tech, doing things that would have been unimaginable just a couple of years ago. It can read documents, write text, and solve problems with astonishing speed. Still, the question is: What is it actually useful for?

Pretty much the first thing people use AI for is as a sort of Google replacement – to directly get an answer to your question instead of a Wikipedia article you have to skim through. That’s the first-level use of AI. It’s nice – you get more direct answers than a web search. Not completely revolutionary, but it is certainly convenient.

But what if you need more than just a quick answer to a simple question? What if you need a large amount of data analyzed – or you’re not even sure where to find the data you need? What if you have a complex question with lots of parts that would each require separate research? In cases like these, a new approach emerges as the most useful tool: Deep Research.

The Research Agent

Normally, when you’re using a chatbot, it’s doing a quick processing of all the data it was trained on – basically like texting a well-read friend who answers instantly from memory. Using Deep Research, on the other hand, is like sending that friend to the library (and scouring the internet) with a notepad in hand to gather all the information on a subject, then having that friend sit down and think about all that data before writing you a detailed report. In other words, Deep Research mode turns the AI into a research agent that actively goes out to find new information, rather than just regurgitating what it already “knows.”

Most chatbots perform amazing feats in a small amount of time – for example, writing a whole essay in seconds that might take a human hours. With Deep Research, that same extremely fast AI now spends ten minutes or more processing the task – effectively doing hours of reading and analysis on your behalf in one go. It achieves this by breaking down your query and then finding, analyzing, and synthesizing information from hundreds of online sources to produce a comprehensive answer. The end result is like a well-researched and sourced report specialized for your needs.

How does it actually work under the hood? In practice, an AI doing Deep Research will go through iterative steps much like a human researcher would:

1. Question Analysis: First, the AI analyzes your query and may break it into sub-questions or topics it needs to investigate. It plans a research strategy instead of answering immediately off the top of its head. Often, it will ask follow-up questions to clarify your needs and avoid ambiguity (as ambiguity often leads to problems with AI, such as hallucinations).
   

2. Searching: The AI then performs web searches or searches your provided documents for each of those sub-questions. It might run multiple searches, often using different keywords, to cover various angles of the topic. (This is like it systematically checking the library catalogue or Google for relevant material.)
   

3. Reading and Extraction: Next, the AI “reads” the content it finds — scanning articles, documents, maybe even PDFs or images — and extracts the key information. It will usually comb through a large number of sources in parallel. As it reads, it takes notes or “learnings” from each source.
   

4. Iterative Refinement: Based on what it learns, the AI may generate follow-up questions or do additional searches. It can identify gaps or new leads (perhaps discovering that one source mentioned a related issue that needs more digging). It then repeats the search-and-read cycle for a few rounds, refining its understanding with each iteration.
   

5. Synthesis into a Report: Finally, the AI consolidates all these findings and reasoning into a structured answer or report. It cites the sources of each major claim or piece of data, so you can see where the information came from. The result isn’t just a paragraph of text, but often a well-organized summary of the topic, sometimes even with sections, bullet points, or data tables if appropriate.

Crucially, throughout this process, the AI uses advanced reasoning abilities (far beyond the old copy-paste approach of simple assistants). The newest generation models have a “chain-of-thought” reasoning mode, meaning they can plan steps and adjust on the fly. For Deep Research, the AI is not just spitting out the next likely sentence; it is actively figuring out what it needs to look up and where to find the answers, step by step. The design behind ChatGPT’s Deep Research, for instance, was trained specifically on real-world research tasks requiring browsing and even using tools like Python for data analysis. All this makes the output far more reliable and detailed for complex questions.

What Is Deep Research Good For?

If your question is “How many centimeters are in an inch?”, a regular AI chatbot will answer that just fine, but when you need lots of information plus analysis, that’s where Deep Research shines. Here are some scenarios where it’s especially useful:

• Multi-faceted Questions: If you have a complex question with many parts (for example, “Analyze the market trends, key players, and recent innovations in renewable energy storage”), Deep Research will handle the multiple subtopics and provide a structured, comprehensive answer. It will pull in data and facts from numerous sources and give you a coherent overview. This is far beyond what a standard AI reply or a quick Google search result could offer.
  

• Large-Scale Data Gathering: Need to sift through a large amount of information? A deep research AI can read dozens of articles or reports across the web and summarize the findings for you. It’s like having an army of interns reading everything and distilling it down. For instance, professionals in finance or science can use it to survey news, papers, or financial filings and get a report on what’s relevant. It’s also great for academic literature reviews or getting up to speed on an unfamiliar field.
  

• Niche or Hard-to-Find Information: Deep Research is particularly good at digging up those nuggets of information that are buried in obscure corners of the internet. Because it doesn’t stop at just the first answer, it can find non-obvious facts or insights (for example, a statistic from an old conference paper, or a solution discussed on a niche forum) that you might miss on your own. If the answer requires browsing numerous websites and piecing things together, an AI agent can do that grunt work for you.
  

• Comparative Analysis and Decision Support: The tool can be a huge help for big decisions or comparisons – say you’re trying to decide on the best car to buy for your needs, or you want a breakdown of competing software products. A Deep Research agent can gather all the specs, reviews, and expert opinions and then give you a personalized report (with citations) on the pros and cons of each option. It essentially does all the comparison shopping research and hands you the findings, saving you countless hours.

Another big advantage is improved confidence and transparency in the answers. Because the AI provides citations and even a summary of its thought process, you can see where each piece of information came from. Traditional chatbots sometimes just make up answers (those aforementioned hallucinations), leaving you unsure what’s true. Deep Research greatly reduces that problem by grounding its output in actual sources. Every claim it makes can be traced back to something it found, which is a game-changer for trustworthiness.

That said, it’s not infallible. You still have to use some judgment. The AI might occasionally misinterpret information or still get things wrong – just less often than before. It’s also limited to the data it can access (for example, it might not retrieve paywalled academic papers or very recent news, depending on the tool). And if the information out on the web is misleading or incorrect, the AI could potentially incorporate that unless it’s clearly flagged as dubious. Many of them sometimes have trouble getting the most up-to-date information on very recent things (like the current state of AI tools, ironically). Basically, this tool can save you hours of time in research, but some human intelligence is still required to evaluate the results.

Tools Offering Deep Research Capabilities

Deep Research as a feature has only become available earlier this year, but already, there are a few different AI tools or platforms offering this kind of capability. Here are some of the options:

• ChatGPT (OpenAI) – The first to market with Deep Research and still one of the best options. OpenAI’s Deep Research tool uses their latest cutting-edge model (nicknamed o3) and conducts an extensive web crawl and analysis for each query. Paid users of ChatGPT get a number of Deep Research queries included per month (there’s even a lightweight version powered by a smaller model for quicker, less costly runs, available to free users).
  

• Perplexity AI – Perplexity specializes in AI combined with search to have AI give up-to-date information, and they’ve now also made their own Deep Research for more in-depth web searches. It’s even available to free users with a more advanced version and unlimited use to paid users. Simply click the “Research” button before entering your query.
  

• Claude (Anthropic) – For paid tiers, Claude now has a Research option using its most advanced Claude 4 models. You can even link it to such things as your Google Drive to make your own data available in the research. Just enable the “Research” button before entering your query.
  

• Gemini (Google) – Available to paid and free users and using Gemini’s 2.5 Pro model. It can also hook into your Google account and use your data as part of the research. Just hit “Deep Research” before entering your query.
  

• Grok (xAI) – Uses Grok 3 and the new Grok 4 and is available to paid users (including paid users of X). Its distinguishing feature is including posts on X as part of its research (great for up-to-date information, but not always for reliability). Just hit the “DeepSearch” button before entering your query.

Final Thoughts

“Deep Research” could very well be AI’s most useful tool so far. It takes what is essentially the grunt work of research – searching, reading, cross-checking, summarizing – and automates a huge chunk of it. We’re talking hours of research time saved – a real game changer for a lot of jobs.

Give it a try if you haven’t already. Everyone could use a research assistant.

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Stay productive, stay curious—see you next week with more AI breakthroughs!