From Leopard Trail to AI Trail: How AI can redefine the Bike Ride experience

 Riding Smarter: How AI Can Change the Way We Bike

This weekend, I went on a ride to Leopard Trail,Gurgaon. If you’ve been there, you know what I mean — winding roads, a mix of gravel and tarmac, greenery on both sides, hillocks and that thrill of a climb followed by the sweet downhill.

But like most riders, I also had the usual concerns:

- Is this the safest route at this time?

- Will I find a group riding at my pace?

- And, of course, will my bike survive the trail without a sudden chain slip?

This got me thinking, what if AI (Artificial Intelligence) and GenAI (Generative AI) could actually make these rides smarter, safer, and more fun?

 Safer Trails with AI

Imagine riding Leopard Trail with a Smart Helmet that warns you about potholes on upcomming route using other biker data or alerts you when a car sneaks up from behind.

Even cooler — AI could study past ride data and tell you, “Hey, the last 3 km of this trail are tricky after rain. Take it easy on the turns.”

 Personalized Ride Assistant

Every rider has their style. On Leopard Trail, some riders go full throttle uphill, while others enjoy a steady scenic ride. AI could personalize the experience by:

Recommending routes and rest points based on your stamina.

Reminding you about Hydration or fatigue signals.

Suggesting a quick bike health check before heading out.

And after the ride? GenAI could whip up a “Ride Story” complete with your stats, a map of Leopard Trail, and even a caption like “Conquered the climbs, breezed the downhills” ready to share on Instagram.

Community App for  Rides Together

One of the best parts about Leopard Trail is bumping into fellow riders at chai stops and Throttle Shottle cafe. A community app ( eg : DRER) powered by AI could make that even better:

-Match you with peers in groups riding at your speed.

-Translate conversations so global riders in Delhi can connect easily.

-Turn weekend rides into mini-challenges like “Leopard Trail Climb King”.

GenAI could add the fun storytelling layer — imagine your app summarizing the ride as “You and 12 riders tackled the trail this morning, burned 1,500 calories.”

 Beyond Just Rides

The beauty of AI is that it doesn’t stop with the rider. Community ride data from places like Leopard Trail could help city planners know where to add safer bike lanes, capture potholes data. Improve adventure sports and rider tourism in city by building local spots that could be smarter about offering brunch, bike maintenance, scenic route and congestion free ride.

 The Road Ahead

For us bikers, rides like Leopard Trail are about the joy of the journey. But with AI and GenAI, the journey could also become safer, smarter, and more shareable.

So next time you head out for a trail ride, picture this:

Your helmet’s got your back, your app knows the best turns, and by the end of it all, GenAI has already drafted a cool story of your ride ready for you to relive and share.

That’s not some future fantasy. That’s biking with AI, and it’s closer than we think.

Agentic AI Paradox : How to beat 40% failure estimated in Gartner report of Agentic AI Hype

Navigating the Agentic AI Paradox: How to Overcome Hype and Mitigate Real Threats

Agentic AI—autonomous systems that can independently plan, decide, and act—is poised to revolutionize industries. Yet, this transformative potential is shadowed by a sobering reality. Tech research firm Gartner predicts that over 40% of agentic AI projects will be scrapped by 2027, citing exorbitant costs, unclear value, and the dangers of hype-driven adoption.

The challenge isn't the technology itself, but how we approach it. To succeed, leaders must learn to see past the hype and proactively address the genuine threats.

The Threat of Hype: "Agent Washing" and Broken Promises

The first major hurdle is deciphering what is real. The market is flooded with "agent washing," a term for vendors rebranding conventional chatbots or automation tools as advanced agentic systems. While thousands of companies claim to offer agentic AI, Gartner found only about 130 genuinely deliver true autonomous capabilities.

This hype creates a dangerous cycle. Businesses, lured by impressive but brittle demos, invest in projects built on over-promises. When these systems fail to deliver true autonomous reasoning, the projects are abandoned, labeled as yet another "AI failure."

How to Overcome the Hype:

To cut through the noise, you must become a discerning adopter. Don't take claims at face value.

Demand Proof of Autonomy: Ask vendors to demonstrate how their system handles unexpected variables, not just pre-scripted tasks. Can it reason, plan multi-step actions, and self-correct when it encounters an error?

Scrutinize the "Agentic" Label: Question what makes the system truly "agentic." Is it merely a workflow automation tool, or does it possess the capacity for independent goal-oriented action?

Focus on Substance, Not Semantics: Look for solutions that solve a concrete business problem, regardless of whether they are labeled the "latest thing in AI."

The Real Threats: Beyond the Marketing Slogans

Once you move past the hype, you face tangible operational and strategic risks that can derail even well-intentioned projects.

Weak Foundations: Agentic AI requires immense support. However, an estimated 65% of companies lack the necessary infrastructure—like clean data, robust APIs, and modern data architecture—to support them. Furthermore, with 78% of firms admitting they aren't data-ready, most are trying to run a race car on a dirt track.

Cascading Errors and Liability: In autonomous systems, small errors compound dramatically. A seemingly minor 1% error rate per step can lead to a 63% project failure rate over 100 steps. This raises critical security concerns like memory poisoning, tool misuse, and cascading hallucinations. It also creates a legal gray area: who is liable when an autonomous agent makes a costly mistake?

Strategic Vacuum: Many projects are launched as tech experiments without clear ROI metrics. Without a defined business case, they are vulnerable to being cut the moment budgets tighten.

How to Mitigate the Threats:

A pragmatic, strategic approach is the best defense.

1.Build Your Foundation First: Before deploying a single agent, invest in data governance, clean up your data repositories, and modernize your API infrastructure. This is non-negotiable groundwork.

2.Start Small, Prove Value Fast: Don't attempt a "big bang" transformation. Pilot a single, high-impact workflow where you can clearly measure ROI—not just in cost savings, but in time saved, user adoption, and trust.

3.Implement Robust Governance and Human Oversight: Never give an AI agent full autonomy without a safety net. Design human-in-the-loop systems where a person can review, approve, or override critical decisions. Continuously monitor for bias, performance drift, and security vulnerabilities.

4.Align Across the Organization: Break down silos. Involve leadership, IT, legal, and the frontline operators who will use the technology from day one. Build evaluation criteria that measure technical performance, human impact, and economic value.

The forecast of a 40% failure rate is not a death sentence for agentic AI; it is a wake-up call. Success won't come from chasing hype. It will be achieved by those who treat agentic AI as a serious strategic discipline—building strong foundations, managing risks proactively, and focusing relentlessly on creating measurable value.

Youtube latest update banned use of AI for content creators?

YouTube’s Not Banning AI — It’s Just Saying “No” to Lazy Content

🚨 Wait… Did YouTube Just Ban AI Content?

Nope. Don’t panic!

There’s a lot of noise out there, but here’s the truth:

> YouTube is NOT banning AI channels.

> It’s just tightening the rules around low-effort, copy-paste content.

If you’re a content creator using AI tools the right way, you’re safe — maybe even ahead of the game.

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 🧠 What’s Actually Changing?

YouTube updated its monetization policy (starting July 15, 2025) to reduce the flood of:

* AI voiceovers reading Reddit posts

* Recycled Top 10 videos with stock clips

* Boring slideshow videos with no personality

This type of content might be demonetized or rejected from YouTube Partner Program (YPP) — not because it's AI, but because it lacks originality.

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 ✅ What YouTube Still Allows

Let’s make this clear. YouTube still welcomes:

-AI tools (scripts, voice, video enhancement)

-Reused content if it’s transformed (e.g., commentary, reactions, edits)

-Voiceovers (human or AI) if they add real value

What matters is: Are YOU bringing something new to the table?

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🛑 What YouTube Doesn’t Want Anymore

-Channels that mass-produce similar videos daily

-Zero personal input or storytelling

-Spammy content meant only to farm views

Basically, AI should assist you, not replace you.

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💡 So… What Should Creators Do?

Here’s how you can adapt (and thrive!):

| ❌ Don't Do This| 

-Use same template for every video instead Add your voice, opinions, or stories.

-Let AI talk over random footage instead Use visuals that match your narrative.

-Upload daily junk for the algorithm instead Upload weekly gems that build your brand

Remember: Original + Helpful = Monetizable

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🎯 Why This Change is Actually Good News

This policy isn’t the end. It’s an opportunity:

Most AI spam channels will fade out.

The creator who puts in thought, will stand out.

YouTube wants real creators to win, not copy-paste bots.

 “AI is a tool. It’s not your channel. YOU are.”

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 💰 Can You Still Make Money with AI?

Yes — if:

-You create educational, entertaining or unique content

-You use AI as an assistant, not a content machine

-You respect YouTube’s rules and your audience’s time

One example shared in the video:

A creator pivoted from Reddit voiceovers to making explainer videos using AI tools + their own voice and scaled to $500,000 revenue.

That’s the power of adapting.

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📝 Final Tips for AI-Enhanced Creators

1. Be present– voice, commentary, even text overlays help.

2. Transform your content – don’t just repackage.

3. Focus on quality, not quantity.

4. Tell a story – even if AI helps build it.

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🚀 TL;DR (Too Long; Didn’t Read)

-Is AI banned on YouTube?**No**               

-Can I still use AI voice/videos?     **Yes, but add value** 

-Is copy-paste content monetizable?   **Not anymore** -Can thoughtful AI creators succeed? | **Big time.**         

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Bottom Line:

YouTube’s update isn’t here to stop creators — it’s here to stop spam.

If you’re creating content with heart, voice, and creativity — you’re exactly what YouTube wants.

Google’s Gemini AI Turns Android Into a Real-Time Digital Assistant: What It Means for Presales and Product Innovation

Android Just Got Smarter: How Google’s Gemini AI Is Redefining the Smartphone Experience

(Refereances from Tech Insights Editorial)

At this year’s Mobile World Congress, Google didn’t just unveil new features—it showcased a vision. One where your Android phone becomes not just a device, but a proactive digital assistant that sees, understands, and guides. This shift, powered by Gemini AI, is poised to reshape mobile UX and offers enormous opportunities for presales teams and tech strategists to reimagine customer engagement.

https://youtu.be/zCMuL7vE9ao?si=Br2CvOoqHTNCQPkz

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From Smart to Insightful: The Gemini Advantage

📱 Screen-Sharing with AI Assistance

Google’s first major announcement is a true game-changer for mobile support and onboarding. Imagine an AI that can view your screen, understand app flows or error messages, and walk you through solutions—live and context-aware.

Presales Impact:
This feature alone can revolutionize how customer onboarding or remote troubleshooting happens in SaaS, telecom, and home automation industries. No more generic help docs—Gemini provides hyper-personalized, real-time guidance, reducing friction and increasing conversion and satisfaction.

Use Case: A field technician trying to configure a smart thermostat can simply share their screen with Gemini and get AI-led guidance without escalating to L2 support.

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📸 AI-Powered Vision Through the Camera

The second feature allows Gemini to act as your AI lens—just point your camera, and the assistant recognizes objects, decodes text, offers reviews, and even fetches contextual data in real time.

Tech Enthusiast Angle:
This blends computer vision + NLP + real-time inference—a major leap in edge AI performance. It marks a shift from reactive input (typing) to proactive perception, which will reshape apps in e-commerce, AR gaming, travel, and accessibility.

Scenario: Spot a poster, product, or QR code—Gemini instantly decodes it, offers purchase options, or links to relevant services, all without needing third-party apps.

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A New AI Business Model: Premium AI as a Service

These innovations are available under the Google One AI Premium tier—Google’s answer to the AI monetization race. By offering ad-free, deeply integrated AI services, Google is setting the standard for subscription-based, value-rich AI ecosystems.

For Product Leaders and Presales Pros:
This move unlocks new monetization strategies. Imagine upselling AI-powered diagnostics or concierge services inside your own app, mirroring this model.

Insight: AI is no longer a backend feature—it’s becoming the core differentiator in consumer and enterprise apps alike.

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Competitive Edge: Why This Matters Now

With OpenAI, Apple, and Anthropic doubling down on AI-powered agents, Google’s move to bake Gemini into Android’s native UX (rather than just cloud services) provides a massive moat.

Why This Should Excite the Tech Community:

• No third-party dependencies

• Real-time, multimodal interaction (text, voice, vision, screen)

• Hardware + AI synergy at scale

It’s a perfect showcase of vertical AI integration, and for solution architects and developers, it’s a signal to start rethinking UI/UX design paradigms—from buttons to conversations, from flows to intent detection.

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Bottom Line: Smartphones Are Becoming AI-first Devices

Google’s new features don’t just enhance Android—they transform it. This is a key moment where mobile UX takes a quantum leap from smart to contextually intelligent.

For presales and product teams, this opens up a new frontier of AI-powered engagement models.
For tech enthusiasts, it’s a glimpse of an exciting, frictionless future where your device doesn’t just respond—it understands.

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🚀 Next Steps for Presales & Tech Leaders:

• Build POCs using Gemini APIs for camera-based product recognition.

• Explore AI-guided screen-sharing for enterprise onboarding or customer support.

• Identify opportunities to add AI Premium tiers to your product roadmap.

From Clicks to Conversational UI: Why GenAI Chatbots Are the Future of UX

Chatbots Are the New UI: Guided Interactions Are the Future

In the evolving landscape of digital experiences, one thing is clear: chatbots are no longer just support tools—they are becoming the front-end interface for user engagement. Traditional user interfaces (UIs) are being reimagined as guided, conversational journeys that enhance user satisfaction, simplify workflows, and reduce operational costs. At the heart of this transformation lies the convergence of conversational AI, NLP, and now, Generative AI (GenAI).

🔍 Why UI Is Shifting to Chatbot-Based Interactions

Users no longer want to search for features through dropdowns, tabs, and menus. They expect natural, fast, and personalized responses—like chatting with an assistant who knows their needs.

Whether it’s a customer booking a flight, a doctor checking patient data, or a CXO exploring sales KPIs, chatbots offer a frictionless way to access functionality without complex UI navigation.

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🌐 Industry-Wise Applications

1. Banking & Fintech

• Use Case: Customers can ask for account summaries, block cards, initiate transactions, or resolve disputes via conversational flows.

• Impact: Reduces dependency on app menus; 24x7 intelligent customer service.

2. E-commerce & Retail

• Use Case: Product search, order tracking, and returns handled via chat.

• Impact: Increases conversions and cart value with personalized product suggestions.

3. OTT & Media

• Use Case: Content recommendations, subscription upgrades, contextual help.

• Impact: Improves content discoverability and user engagement with dynamic dialogue.

4. Healthcare

• Use Case: Appointment scheduling, symptom triage, insurance queries.

• Impact: Improves patient access while reducing staff workload.

5. Enterprise SaaS / B2B Tools

• Use Case: Guided onboarding, analytics insights (“Show me churn rate last quarter”), workflow automation.

• Impact: Speeds up adoption, enhances productivity with AI copilots.

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🚀 Benefits of Guided Interaction via Chatbots

Benefit	Description
🧠 Simplified UX	Users can express needs in natural language rather than navigating menus.
⏱ Time Efficiency	Reduces the number of clicks to task completion.
🎯 Context-Awareness	Learns user preferences and tailors responses dynamically.
💰 Operational Savings	Reduces the need for human support staff and redundant UI features.
📊 Data Insights	Captures structured & unstructured feedback directly from user chats.
🌍 Omnichannel Presence	Can be embedded across web, mobile, WhatsApp, voice, and smart devices.

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🛠️ How to Build a Modern Chatbot (Tech Stack & Strategy)

✅ 1. Define the Use Cases

• Focus on high-frequency, high-impact journeys.

• Prioritize tasks with repetitive logic or contextual value.

✅ 2. Choose the Architecture

• Rule-based for deterministic flows (e.g., FAQ).

• NLP-based for intent recognition.

• GenAI-based for dynamic, open-ended conversation.

✅ 3. Use Core Technologies

Layer	Technology Options
NLU/NLP Engine	Dialogflow, Rasa, Microsoft LUIS, OpenAI GPT
Backend Logic	Node.js, Python Flask/FastAPI, Azure/AWS Lambda
Frontend Integration	React, Vue, Flutter, WhatsApp API, WebChat
Knowledge Base	Vector DBs (e.g., Pinecone, FAISS) + GenAI embeddings
Analytics & Monitoring	Google Analytics, Power BI, Botpress Analytics

✅ 4. Train with Contextual Data

• Use domain-specific dialogues.

• Add retrieval-augmented generation (RAG) for knowledge-centric bots.

✅ 5. Continuously Optimize

• Monitor drop-off points, user feedback, and task success rate.

• Use A/B testing on flows and tone personalization.

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🤖 Why GenAI-Based Chatbots Are Game-Changers

Traditional chatbots struggled with flexibility and context. GenAI flips that limitation—it can dynamically understand context, retrieve relevant information, and generate human-like responses.

GenAI Adds Value By:

• Answering open-ended queries: “Summarize my last 3 meetings.”

• Synthesizing knowledge: “Compare two loan offers based on T&Cs.”

• Understanding natural context: “I need something to watch tonight with kids.”

• Generating creative responses: For marketing, content, support personalization.

⚙️ GenAI Stack Highlights:

• Foundation Models: GPT-4, Claude, Gemini, Mistral

• RAG Systems: Combine chatbot with internal company docs.

• Prompt Engineering: Design “personas” for bots (advisor, concierge, coach).

• Multi-modal Support: Input/output across text, image, voice.

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🎯 Strategic Value for CXOs & Product Leaders

Role	Value Chatbots Deliver
CXO	Lower CAC, improved digital NPS, faster go-to-market for digital tools.
Presales	Live product demos via chatbot, faster PoC iterations.
Product Managers	Replaces multiple UI workflows with a unified conversation layer.

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🧩 Final Thoughts

Chatbots aren’t replacing humans—they’re replacing clunky UIs. With GenAI, they become intelligent digital front doors to services, support, and sales. The future of product interfaces is not static screens, but smart conversations that guide users seamlessly.

The interface is now the conversation.

Code at Lightning Speed: AI Agents Are Changing the Dev Game Forever

AI Coders Take the Lead: How Big Tech’s Newest Teammates Are Rewriting the Rules of Software Development

What if your next teammate never sleeps, debugs instantly, and can generate hundreds of lines of code before your first coffee? Welcome to the age of AI-powered coding agents—the latest revolution sweeping through the software world.

This week, industry giants Microsoft, Google DeepMind, and OpenAI launched major upgrades to their AI development agents, signaling a bold new phase in software engineering: AI is no longer just assisting developers—it’s building alongside them.

For IT students and presales professionals, this is more than a tech update—it’s a glimpse into the future of how software will be created, sold, and delivered.

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Meet Your New Coding Colleague

These aren’t just smarter autocomplete tools. The new generation of AI coding agents are built to:

• Fix bugs proactively

• Implement new features based on user intent

• Multitask across languages and environments

• Self-validate code to reduce logic errors

Big Announcements This Week:

• Microsoft GitHub Copilot: Now acts as an autonomous agent—identifying bugs, writing new features, and aligning with project goals.

• OpenAI Codex: Upgraded with multitasking capabilities; can now handle multiple programming tasks simultaneously.

• Google DeepMind AlphaEvolve: Specializes in complex computational problems, using internal evaluators to minimize errors and hallucinations.

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Why It Matters: The Coding Workflow Reimagined

Unlike writing or design tasks, software offers immediate feedback—either it compiles and runs or it doesn’t. That makes it the perfect arena for agentic AI: autonomous systems that plan, act, and self-correct.

The result? Dramatic efficiency gains.

Development Time Cut Nearly in Half

Project Phase	Without AI	With AI
Requirement to Code	5 days	2 days
Testing & Debugging	4 days	2 days
Deployment	2 days	1 day
Total	11 days	5 days

“We're seeing 2x faster deployments in early-stage companies using AI agents,” says a senior developer at a Bengaluru startup.

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By the Numbers: AI’s Growing Role in Code

AI Contribution to Codebases (2025)

Company/Startup	AI Code Contribution
Microsoft	~35%
Google	~30%
Indian Startups	40–80%
Global Startups	25–60%

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What AI Agents Can Do – At a Glance

Capability	GitHub Copilot	OpenAI Codex	AlphaEvolve
Code Generation	High	High	Medium
Bug Fixing	High	Medium	Medium
Multitasking	Medium	High	Medium
Intent Understanding	Medium	High	Medium
Self-validation	Low	Medium	High

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AI Use Cases Driving Developer Adoption

Use Case	Adoption Rate
Code Suggestions	85%
Bug Detection	72%
Unit Test Generation	65%
Feature Development	54%
Code Documentation	58%
Legacy Code Refactoring	49%

These use cases align closely with presales pitches: faster delivery, fewer bugs, and leaner teams.

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India in the AI Coding Fast Lane

It’s not just Silicon Valley. Indian startups have quickly embraced AI agents—often contributing up to 80% of code in early-stage ventures using tools like ChatGPT, Claude, and Gemini.

Global Adoption Heatmap (2025)

Countries leading AI coding adoption:

• India

• USA

• UK

• Germany

• Israel

• Singapore

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What This Means for Students and Presales Professionals

• Students: Learning how to collaborate with AI will be as critical as learning a programming language.

• Presales Teams: AI-powered dev cycles offer a new value narrative—faster GTM, fewer resources, and smarter delivery pipelines.

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The Bottom Line

From prototypes to production systems, AI agents are reshaping how software gets built. This isn’t just the next step in software development—it’s a leap.

The future of coding isn’t just human. It’s hybrid. And it’s here.

Meta Launches Powerful New AI App with Llama 4: Experience Smarter, More Human Conversations

Meta’s New AI App: A Simple Guide to What’s New and Why It Matters

What,s New?

Meta—the company behind Facebook, WhatsApp, and Instagram—has launched a brand-new AI app powered by their latest AI model called Llama 4. This app is now available as a standalone application, which means it works separately from other Meta apps.

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What Can This New AI App Do?

• You can talk to Meta’s AI using voice or text—whatever you’re comfortable with.

• It’s designed to help you chat naturally, like speaking with a real person.

• You don’t have to take turns waiting for it to stop talking—it listens and responds in real time.

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Cool Technology Behind It

Meta’s app uses a feature called full-duplex speech.
This means:

• The AI can listen and respond at the same time (just like in real-life conversations).

• No more waiting for long pauses or needing to say "Hey AI" again and again.

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Where Is It Available Right Now?

• Currently, it's being tested in the United States, Canada, Australia, and New Zealand.

• Because it’s still in testing, you might see a few glitches or limited features for now.

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Why Is Personalization a Big Deal Here?

If you connect your Facebook or Instagram profile to the app:

• The AI can use your past interactions, posts, or likes to make replies more relevant.

• You can also tell the AI what you like or what to remember—like hobbies or preferences.

Note: This feature is only available in the US and Canada for now.

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Use Across Devices

• The new app will replace the Meta View app for users of Ray-Ban Meta smart glasses.

• Conversations that start on the smart glasses can be continued on the AI app or website.

• You can’t start chats on the app and send them to the glasses yet, but it’s coming.

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Discover and Share Prompts

• Inside the app, there’s a "Discover" section where you can:

  • See prompts created by other users.

  • Try them out, modify them, or create your own.

  • You control what gets shared—nothing is public unless you choose to post it.

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Privacy Controls Matter

Meta includes safety and privacy options:

• You’ll see a visual indicator (like a light) when the mic is on.

• You can decide if voice input is always active or only when you want it.

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In Summary

Meta is trying to make AI smarter, more personal, and easy to talk to. With this app, you can enjoy a new kind of digital assistant—one that learns from you (with your permission), speaks naturally, and works across devices.

5 Game-Changing Ways to Use Gemini Live for Creativity, Productivity & Real-Time Help

Unlocking the Power of Gemini Live: 5 Smart Ways to Use It for Creativity, Productivity & More

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The future of productivity and creativity is here—and it fits right in your pocket. Meet Gemini Live, now with camera and screen sharing capabilities on Android. This powerful update makes Gemini more than just a voice assistant—it becomes a visual co-pilot ready to support your day-to-day tasks, spark inspiration, and simplify life.

Gemini Live is now rolling out to Gemini Advanced subscribers, starting with Pixel 9 and Samsung Galaxy S25 users. Whether you’re a student juggling assignments, a presales pro crafting winning pitches, or a marketing wizard designing your next campaign, here’s how Gemini Live can elevate your workflow in real-time.

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1. Turn Chaos into Calm: Organize Your Space

Let’s face it—clutter kills focus. But spring cleaning doesn’t have to be a solo battle. Open Gemini Live, point your camera at your cluttered closet or messy desk, and ask for help. Gemini can suggest smart organization tips, identify donation-worthy items, and guide you on how to maximize space. It’s like having a personal organizer on standby.

Use Case for Students: Tame your dorm room chaos and create a productivity-boosting study space.  

Use Case for Marketers: Keep your creative area clean to think clearly.  

Use Case for Presales: Stay tidy to ace those client demos and calls.

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 2. Brainstorm Without Limits: Unlock Your Creative Flow

Stuck in a creative rut? Gemini Live lets you brainstorm visually and vocally. Share inspiration images or textures from your screen, or point your camera at your sketches or surroundings. Gemini can help spark design ideas, campaign concepts, or even storylines for content.

Students: Ideate art or design projects in real time.  

Presales Teams: Visualize client needs with mood boards and get instant feedback.  

Marketers: Craft campaign themes or social media content with ease.

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 3. Fix It Fast: Real-Time Troubleshooting

Got a wobbly desk chair or a blinking projector before a client pitch? Just show Gemini the problem using your phone’s camera. Gemini will walk you through the fix—or help you find quick solutions—so you don’t lose precious time.

Students: Solve tech or dorm issues instantly.  

Presales: Be the hero when equipment acts up before a pitch.  

Marketers: Troubleshoot gear during events or shoots.

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4. Shop Smarter: Personalized Shopping Advice

Say goodbye to decision fatigue. While browsing products, share your screen with Gemini to get product comparisons, style tips, or fashion advice. Want to know what matches your current outfit? Point the camera at your closet and Gemini will help you style it out.

Students: Shop on a budget with informed choices.  

Presales & Marketers: Look sharp for meetings and events with AI-assisted fashion insights.

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5. Level Up Your Skills: Get Instant Feedback

Share your screen with Gemini to get real-time feedback on your content—be it a social media post, a presentation deck, or a visual portfolio. Gemini analyzes and provides suggestions to help improve layout, tone, design, and engagement.

Students: Improve your essays, projects, or digital portfolios.  

Presales: Polish proposals and demos to win deals.

  

Marketers: Refine visuals, taglines, and messaging before launching a campaign.

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Gemini Live isn’t just a cool tech update—it’s a game-changer for anyone looking to work smarter, create faster, and solve problems intuitively. As Gemini continues to evolve, the future of hands-on, intelligent assistance is only getting brighter.

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Google Launches Gemini 2.5 Pro: Smarter and Cheaper Alternative to GPT-4 and Claude

Google Unleashes Gemini 2.5 Pro: Smarter, Cheaper, and Open to All Developers

April 4, 2025 — Google is shaking up the AI world again!

 The tech giant has officially opened up its powerful new AI model Gemini 2.5 Pro, to the public — and it's turning heads for two big reasons: it's incredibly smart and surprisingly affordable.

After quietly launching Gemini 2.5 Pro to developers, Google received overwhelming feedback and interest. In response, they've now increased usage limits and made the model publicly available in Google AI Studio and soon on Vertex AI.

What makes Gemini 2.5 Pro special?

Google says it’s their most intelligent model ever— built to handle complex reasoning tasks with speed and precision. It’s now available at a much lower cost than major rivals like OpenAI and Anthropic.

Here’s a quick look at the pricing:

- $1.25 per million input tokens

- $10 per million output tokens

That’s way cheaper than Claude 3.7 Sonnet from Anthropic ($3 input / $15 output) or OpenAI’s top model ($15 input / $60 output)!

Even better? The experimental version is free for now, though with limited access.

Why it matters:  

AI developers and companies are looking for high-performance models that won’t break the bank. Google’s move makes advanced AI more accessible, especially for startups and small teams. 

Early users are calling Gemini 2.5 Pro the most useful reasoning model yet, and social media is buzzing with excitement, calling the price point a “game-changer.”

The AI pricing battle is heating up, with companies like DeepSeek already offering low-cost models. Google’s aggressive pricing strategy signals a new phase of competition — one that could benefit developers and businesses worldwide.

Stay tuned — it’s about to get wild in the world of AI.

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Rise of AI Agents: How Businesses Are Unlocking the Future of Automation

The Rise of AI Agents: How Businesses Are Embracing the Next Wave of Automation  

AI agents are rapidly transforming workplace operations, with major software companies integrating them to enhance efficiency and streamline processes. These AI-driven systems, designed to perform tasks autonomously while collaborating with human workers, are becoming a focal point of business innovation.  

AI Agents: A New Phase in Automation

Initially, generative AI was primarily used for chatbots and content generation. However, businesses are now moving toward AI agents—advanced systems capable of reasoning, planning, and orchestrating workflows with minimal human oversight. While human workers remain integral to decision-making, AI agents are increasingly handling repetitive tasks such as customer support inquiries, invoice processing, and email drafting.  

Companies like ServiceNow, Salesforce, and SAP have launched AI agents to optimize their operations. ServiceNow, for instance, has implemented AI-driven customer support that resolves 80% of cases without human intervention. Similarly, Salesforce's AI platform has automated thousands of customer inquiries, leading to significant efficiency gains.  

The Shift Toward Multi-Agent Systems

Many organizations anticipate a future where AI agents will collaborate within multi-agent systems, where multiple AI entities work together to complete complex tasks. While full autonomy is still in development, businesses are actively training their employees to interact with AI tools effectively. Transparent communication about AI capabilities and clear guidelines on human oversight remain crucial for successful adoption.  

Enhancing Productivity and ROI with AI Agents

 

Early adopters of AI agents have reported substantial productivity boosts. ServiceNow estimates that AI-driven workflows have saved over half million labor hours annually, contributing to millions in efficiency gains. AI-powered financial assistants, like Intuit Assist, are also proving valuable by accelerating invoice processing and improving cash flow for businesses.  

Despite these advancements, AI agents are not without challenges. Organizations must continuously refine their AI models to prevent errors, such as AI-generated misinformation (hallucinations). To mitigate risks, companies are implementing rigorous testing frameworks to monitor AI performance and ensure accurate outputs.  

Future Outlook: AI at the Core of Business Strategy

 

For AI agents to deliver sustained value, businesses must integrate them strategically within their workflows. Companies are investing in specialized training programs, ensuring employees understand how to leverage AI effectively while maintaining control over automated processes.  

Ultimately, the organizations that successfully align AI agent capabilities with business needs will gain a competitive edge. By fostering collaboration between human workers and AI, businesses can unlock new

Google Unveils Gemini 2.0 Pro AI: Smarter Thinking and Advanced Reasoning

Google Unveils New AI Models, Including Gemini 2.0 Pro

Google has launched its latest AI models, including Gemini 2.0 Pro Experimental Flash Thinking. These models are designed for better reasoning and problem-solving and are now available in the Gemini app and Google’s AI development platforms like Vertex AI and Google AI Studio.  

Key Highlights:

- Gemini 2.0 Pro is Google’s most advanced AI yet. It excels at coding, handling complex tasks, and even running searches or executing code.  

- It has a massive context window of 2 million tokens (about 1.5 million words), meaning it can process huge amounts of information in one go.  

- Gemini 2.0 Flash Thinking is now available in the Gemini app, offering powerful reasoning capabilities.  

- Gemini 2.0 Flash-Lite is a more affordable model with improved performance at the same speed and cost as the older Gemini 1.5 Flash.  

Why This Matters: 

Google is responding to competition from DeepSeek, a Chinese AI company offering cheaper and highly capable models. By making Gemini models widely available, Google aims to stay ahead in the AI race.  

HowFoundation Models vs LLM ( VLM) differ and which IT Giant to select for...

Understanding Foundation Models (FM) and Large Language Models (LLM) Across Cloud Providers

With the rapid advancements in AI, cloud providers like AWS, Azure, Google, NVIDIA, and Meta offer powerful Foundation Models (FM) and Large Language Models (LLM) to cater to various business needs. This article aims to provide a clear understanding of these offerings, their pricing models, and specific LLMs for video tagging generation—essential knowledge for a pre-sales team.  

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AI Offerings by Major Cloud Providers

A. Google Cloud (Vertex AI & Gemini)

Foundation Models (FM):

Gemini → A multimodal AI model capable of handling text, images, audio, and video.  

Vertex AI → A platform offering access to foundation models, including Gemini.  

Large Language Models (LLM):

 Gemini Pro → Optimized for text-based applications like chatbots, summarization, and code generation.  

Pricing Model:

 Vertex AI Pricing: Based on API usage and model type (on-demand or provisioned throughput).  

  Gemini Pricing: Pay-per-token usage.  

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B. Amazon Web Services (AWS)

Foundation Models (FM):

 Amazon Bedrock → Provides access to multiple FMs from partners like Anthropic, AI21, and Meta.  

  Titan Models → Amazon’s proprietary foundation models.  

Large Language Models (LLM):

Titan Text → Amazon’s text generation model.  

 Claude (Anthropic), Llama (Meta), Jurassic (AI21) → LLMs available via Bedrock.  

Pricing Model:

Bedrock Pricing: 

    - On-Demand & Batch: Pay-per-use.  

    - Provisioned Throughput: Subscription-based pricing for enterprise needs.  

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C. Microsoft Azure  

Foundation Models (FM):

 Azure OpenAI Service → Hosts OpenAI's foundation models.  

 Phi-2 → Microsoft’s smaller-scale foundation model.  

Large Language Models (LLM):

  - GPT-4, GPT-3.5→ OpenAI’s advanced LLMs hosted by Azure.  

  -Turing-NLG → Microsoft’s in-house NLP model.  

Pricing Model:

  - Azure OpenAI Pricing: Pay-per-token for API usage.  

  - Custom Model Training: Additional costs apply.  

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D. NVIDIA AI

Foundation Models (FM):  

  - NVIDIA AI Foundation Models→ Includes models for text, vision, and scientific applications.  

  - Nemotron-3 → A general-purpose foundation model.  

Large Language Models (LLM):

  -Nemotron-3 8B → NVIDIA’s LLM for text generation.  

  -BioNeMo → LLM focused on biomedical applications.  

Pricing Model:

  - Pricing depends on GPU usage and API call volume (available upon request).  

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E. Meta (Facebook AI)

Foundation Models (FM): 

  - Llama Models (1, 2, 3) → Meta’s open-source foundation models.  

Large Language Models (LLM):

  - Llama 2, Llama 3→ Designed for text-based AI applications.  

Pricing Model:

  - Available via Azure, AWS, and Google Cloud with platform-dependent pricing.  

Conclusion

For pre-sales teams, understanding which AI model best fits a business need is crucial:  

- If you need a multimodal AI solution (text, images, video, code), opt for a Foundation Model like Gemini, Titan, or Nemotron.  

- For text-only applications (chatbots, summarization, translation), consider LLMs like GPT-4, Claude, or Llama 2.  

- For video tagging applications,Gemini Pro Visio , TagGPT and Azure AI Video Indexer are leading solutions.  

Each cloud provider offers different pricing models, so choosing the right cost-effective solution depends on usage, scalability, and enterprise needs.  

Steps for Pre-Sales Teams:

✅ Understand client requirements (multimodal vs. text-based AI).

✅ Compare pricing models for cost-efficiency.

✅ Recommend the right cloud provider based on model availability and integration needs.

By leveraging these insights,  teams can effectively identify AI solutions tailored to their needs.

AI Agents vs. Traditional RPA: How Google, Salesforce, and Microsoft Are Redefining Business Automation

AI Agents vs. Traditional RPA: What's the Difference?

Automation is transforming businesses by speeding up processes, making them more efficient, and reducing errors. Two key players in this space are Traditional Robotic Process Automation (RPA) and AI Agents. While both help with automation, they do so in different ways and for different types of tasks. Let’s break down the differences in simple terms, along with examples of AI tools from Google, Salesforce, Adobe, and Microsoft that surpass RPA in various use cases.

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What Is Traditional RPA?

Traditional RPA uses software bots to perform repetitive, rule-based tasks that humans usually do. These tasks are predictable and follow a specific pattern, such as:

• Data Entry: Entering customer information into a database.
• Invoice Processing: Moving invoices from email to a finance system.
• Report Generation: Automatically creating daily sales reports.

RPA bots mimic human actions like copying and pasting data or filling out forms. However, RPA struggles with tasks involving unstructured data (like free text) or situations that change often.

Examples of Traditional RPA Use Cases:

• Finance: Automating account reconciliations.
• Healthcare: Scheduling patient appointments.
• Retail: Managing inventory updates.

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What Are AI Agents?

AI Agents are more advanced. They use Artificial Intelligence (AI) to learn, reason, and adapt to new situations. Unlike RPA, they don’t need detailed instructions for every task. They can handle complex tasks by understanding data and making decisions.

Examples of AI Agent Use Cases:

• Customer Service: Chatbots that understand and respond to customer inquiries.
• Fraud Detection: Identifying unusual patterns in financial transactions.
• Predictive Maintenance: Analyzing machine data to predict when maintenance is needed.

AI agents use technologies like Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision to perform tasks. They can work with unstructured data like images, audio, and free text.

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AI Tools from Tech Giants That Surpass RPA

1. Google's AI Tools:

   • Google Cloud AI: Offers tools like AutoML and Dialogflow, Vertex AI powered Gemini Models which help in building intelligent chatbots and processing large datasets. These tools can learn and improve, unlike RPA, which only follows predefined rules.
   • Google Vision AI: Can analyze images and extract insights, something traditional RPA cannot handle.

2. Salesforce’s AI:

   • Salesforce Einstein: This AI platform helps with predictive analytics, customer sentiment analysis, and automated recommendations, making it far more versatile than traditional RPA bots.

3. Adobe's AI:

   • Adobe Firefly,Sensei: Powers intelligent services in Adobe’s creative and marketing tools. It helps in automating complex creative tasks, like image recognition and customer experience personalization, which are beyond the capabilities of RPA.

4. Microsoft’s AI:

   • Azure AI: Offers tools like Cognitive Services and Azure Machine Learning, which help in building intelligent applications. For example, Azure Bot Service can create chatbots that understand natural language and provide real-time customer support, surpassing RPA’s basic automation.

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Key Differences Between RPA and AI Agents

1. Task Complexity:

   • RPA: Best for simple, repetitive tasks.
   • AI Agents: Ideal for complex, evolving tasks that require decision-making. For instance, Google Dialogflow can handle dynamic conversations, unlike RPA bots that follow fixed scripts.

2. Learning Abilities:

   • RPA: Cannot learn or adapt; needs reprogramming for new tasks.
   • AI Agents: Can learn from data and improve over time. Salesforce Einstein, for example, improves customer service by learning from past interactions.

3. Data Handling:

   • RPA: Works with structured data (like spreadsheets).
   • AI Agents: Can handle both structured and unstructured data. Adobe Sensei can analyze and optimize digital content, which RPA cannot do.

4. Decision-Making:

   • RPA: Follows set rules; needs human intervention for exceptions.
   • AI Agents: Can make decisions and handle exceptions autonomously. Microsoft Azure AI services can interpret complex scenarios and act accordingly.

5. Scalability:

   • RPA: Scales by adding more bots.
   • AI Agents: Scales through advanced algorithms and cloud computing. Google Cloud AI can process vast amounts of data and scale seamlessly, unlike traditional RPA.

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When to Use RPA

Use RPA for tasks that are repetitive and rule-based, where the process doesn’t change much. It’s perfect for automating manual tasks in industries like:

• Finance: Data migration.
• Healthcare: Claims processing.
• Retail: Inventory management.

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When to Use AI Agents

AI Agents are better for tasks that require adaptability and decision-making. They’re useful in scenarios like:

• Customer Service: Personalized chatbot responses with tools like Google Dialogflow.
• Marketing: Personalized product recommendations using Adobe Sensei.
• Supply Chain: Real-time optimization using Salesforce Einstein.

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Conclusion

Both RPA and AI Agents are useful for automation but are suited for different types of tasks. RPA is great for simple, repetitive processes, while AI Agents shine in complex, dynamic environments. Often, the best approach is to use both, combining the strengths of RPA's reliability with AI's intelligence, leveraging tools from Google, Salesforce, Adobe, and Microsoft for a comprehensive automation strategy.

Salesforce's AI Revolution: How Agentforce is Transforming Business Efficiency and Reducing Costs

Salesforce: Targeting to lead AI Revolution

Salesforce, traditionally known for customer relationship management (CRM) software, is making significant strides in artificial intelligence (AI). They’ve introduced a powerful new AI tool called "Agentforce," which could transform how businesses operate and deliver customer service.

The Shift to AI Agents:

Unlike basic chatbots that only provide information, Agentforce’s AI agents can perform tasks like filing complaints, booking appointments, or updating customer details. This advancement reduces errors because the AI operates only within the data it’s trained on, making it more reliable. Salesforce claims that their AI agents will significantly reduce "hallucinations"—instances where AI generates incorrect or irrelevant information—by limiting responses to pre-approved, accurate data sources.

Controlling Hallucinations:

A key feature of Agentforce is its ability to control hallucinations effectively. Since it generates content solely from the data and sources businesses have trained it on, the risk of misinformation is minimized unlike training from openweb. This makes Agentforce more reliable than some other AI tools that pull information from the vast, and sometimes inaccurate, public internet.

Impacts on Jobs and Efficiency:

Salesforce openly acknowledges that AI tools like Agentforce can replace certain jobs, but they highlight the efficiency gains. For instance, John Wiley & Sons, an educational publisher, used Agentforce to significantly reduce customer service response times, avoiding the need to hire extra staff during busy periods. This shift allows companies to handle higher volumes of customer interactions without increasing their workforce.

Cost Advantages:

Agentforce’s new pricing model charges per conversation instead of per user, creating significant cost advantages for businesses. This means companies can scale their customer service or sales operations without corresponding increases in staff costs. By reducing the need for hiring additional employees during peak periods, businesses can lower their operational expenses while maintaining or even improving service levels. Additionally, the automation of routine tasks can free up human employees to focus on more complex and value-adding activities, further enhancing productivity.

Use Cases for eCommerce, Media, and Telecom Companies:

1. eCommerce : Agentforce can automate customer support by handling order inquiries, returns, and tracking issues, providing instant solutions that improve customer satisfaction. It can also assist in personalized product recommendations and streamline the purchasing process.

   

2. Media: Media companies can leverage Agentforce to manage subscription services, content recommendations, and user inquiries. AI agents can help in automating the distribution of content and enhancing user engagement through personalized suggestions.

3. Telecom: In the telecom sector, Agentforce can be used to handle customer service tasks such as troubleshooting, account management, and billing inquiries. This reduces the workload on human agents and allows telecom companies to offer faster, more efficient service.

 Bold Business Strategy:

Salesforce’s new pricing model for Agentforce charges per conversation instead of per user. This approach benefits companies by saving on hiring costs while still generating revenue for Salesforce, even if fewer people are employed. The cost savings from reduced staffing needs can be substantial, allowing businesses to allocate resources more efficiently.

Positioning in the AI Market:

Salesforce’s widespread use in businesses gives it a competitive edge. Their deep integration with existing business processes means companies can adopt these AI tools without overhauling their systems, making Salesforce a tough competitor for newer AI firms like OpenAI and Anthropic.

Conclusion:

Salesforce’s proactive approach to AI adoption and its potential to displace jobs show their commitment to leading in the AI space. Their strategy not only enhances efficiency for clients but also offers significant cost savings and sets a strong foundation for future growth in the evolving AI market. With applications in eCommerce, media, and telecom, Salesforce’s AI solutions are poised to drive innovation across various industries.

Insights in this article are basis some of the recent news and actual offering might differ.

AI Agents in 2025: How Businesses Can Leverage Advanced AI Workflows for Success

AI Agents in 2025: What Business Leaders Need to Know

Introduction to AI Agents for Business

In 2025, AI agents will play a crucial role in businesses. While the buzz around AI started a few years ago, many early AI projects didn't succeed because they weren’t fully integrated into business systems or lacked necessary controls. This year, that will change, although challenges remain.

A Simple Example: Email-Answering Tool

Imagine a tool that automatically drafts email replies. This simple example shows the potential of AI agents but also highlights the difficulties companies face when using AI.

Why Basic AI Tools Aren’t Enough

Many businesses used basic AI tools called GPT wrappers, which connect AI to simple interfaces. While easy to set up, these tools have major shortcomings:

- They don’t integrate with other systems (like checking your calendar).

- They lack context (like knowing your relationship with the sender).

- They have no security, guardrails, or user control.

- They sometimes provide incorrect or made-up information.

Building Better AI Solutions

Instead of using basic tools, businesses need AI agents that work within a more complex system, or workflow. This involves multiple AI models working together, much like tools in automation platforms like Gemini, Chatgpt, Zapier powered by AI.

An Improved Email Tool Workflow

A more advanced AI agent could:

1. Check your calendar to see if you're free.

2. Review past emails with the sender.

3. Predict whether you'd want to attend based on your past behavior.

4. Create several reply options for you to choose from.

This workflow addresses many of the issues with simple AI tools, by integrating with systems, providing context, and offering better control and accuracy.

Key Components of AI Workflows

For AI agents to work effectively, they need:

- System Integration: To connect with other tools like calendars.

- Context Search: To find relevant past information.

- Traditional AI: To analyze data and make predictions.

- User Design: To present choices that the user can control.

The Future of AI in Business

In 2025, AI agents will streamline and improve business processes across various industries, but there won’t be a single dominant AI tool. Instead, there will be many specialized workflows for tasks like customer service, legal support, and sales.

Conclusion

To succeed with AI, business leaders and product managers must focus on creating solutions that integrate well, provide value, and are easy to use. This approach will help businesses fully benefit from AI agents in 2025 and beyond.