Product management is a balancing act between vision and execution. Product managers spend their days aligning teams, writing specs, tracking timelines, and navigating feedback from every direction. In this high-context, decision-heavy role, AI can be a game-changer but only when the tool fits the workflow.

Many product managers today use Microsoft Copilot as one of the AI tools for product management because it’s already embedded in the Microsoft ecosystem. And it works well for generating meeting summaries, writing emails, or organizing notes in OneNote or Outlook. But does that make it the best AI tool for product management at scale?

Dot or Copilot? Which of these AI tools for product management actually supports the full scope of product work? Let’s break down how Microsoft Copilot compares to Dot, especially when AI is expected to go beyond surface-level assistance and become part of your product operations stack.

Copilot in Product Workflows: Quick Wins, Narrow Scope

Microsoft Copilot is built directly into tools like Word, Excel, Teams, and Outlook. For product managers, this translates into practical use cases such as:

• Drafting user stories or PRDs in Word
• Summarizing Teams meeting notes
• Creating task lists based on email chains
• Managing project data in Excel

These are helpful features, especially for individuals working inside a Microsoft 365 environment. But there are limits:

• Workflow Fragmentation: Copilot assists inside individual apps, but doesn’t connect actions across tools. You still have to jump between Word, Excel, and Teams manually.
• No Custom Agent Logic: You can’t build your own product-specific agent to follow custom decision paths, prioritize backlogs, or interface with tools like Jira or Notion.
• No Control Over AI Model: Microsoft owns the model, the hosting, and the guardrails. Between AI tools for product management, you can't switch models or fine-tune responses for product domain knowledge.
• Minimal Collaboration Features: There’s no shared workflow or agent memory. Every query starts from scratch, which makes strategic product alignment harder to scale.

In short: As one of the AI tools for product management, Copilot is useful for quick individual tasks, but it doesn’t act as a team-level product assistant or operate across your ecosystem of tools.

What Dot Offers Instead

Dot approaches the AI challenge differently. Instead of embedding inside a single ecosystem, it functions as a flexible orchestration platform for product teams to build and manage their own AI agents, tools, and flows.

In terms of AI tools for product management use cases, Dot enables:

• Product-Specific AI Agents: Create custom agents that understand your backlog structure, product terminology, and decision logic.
• Multi-Step Workflows: Automate research, competitive analysis, user feedback synthesis, and roadmap generation in sequence.
• Cross-Tool Integration: Connect to other AI tools for product management like Notion, ClickUp, and Jira; plus apps like Linear, GitHub, Figma, and internal APIs to automate product lifecycle tasks.
• Team Collaboration: Let agents work collaboratively across product, design, engineering, and leadership workflows, each with tailored roles and memory.

Unlike Copilot, Dot lets you define how the AI behaves, where it runs, and which data it uses. You’re not getting one of those general AI tools for product management. You’re building a smart teammate that’s embedded in your operations.

Comparing the Two: What Product Teams Need in AI Tools

AI Governance and Control: A Critical Need for PM Leaders

Product teams are often the bridge between business strategy and engineering. That means their work touches competitive intelligence, roadmap decisions, and long-term company vision. If AI tools for product management are pulling data from uncontrolled or opaque sources –or storing strategic notes outside your infra– it becomes a compliance and IP risk.

Microsoft Copilot doesn’t let you choose where the AI runs or how it retains product-specific context. Dot, on the other hand, was built to address these challenges:

• Run fully on-premise if you handle sensitive roadmap or customer data
• Choose which AI models are approved for specific product use cases
• Log, audit, and govern all agent activity
• Keep strategic product planning internal, even with AI assistance

Especially for enterprise product teams or companies with regulated environments, Dot provides the infrastructure trust that Copilot lacks.

The Customization Gap

One of the biggest gaps between Dot and AI tools for product management like Copilot is the ability to build custom experiences.

With Dot:

• You can design workflows that reflect how your product org works
• Define trigger points for product tasks based on internal data
• Build UI extensions or internal apps on top of your Dot agents
• Teach the AI your product taxonomy, goals, and quarterly OKRs

Microsoft Copilot does none of this. You get a strong productivity co-pilot but not an adaptable system that learns and grows with your product team.

Seat Management: Built-in vs. Add-on

As product teams grow, so does the need to manage access across tools, departments, and permission levels. For AI to work at scale, seat management is not optional, it is foundational.

Microsoft Copilot does not provide built-in seat management. Admins must rely on third-party tools to define user roles, control agent access, and track team-level usage. This creates complexity, especially when product teams work across multiple platforms outside the Microsoft ecosystem.

Dot includes seat and user management as part of its enterprise-level solutions:

• Assign and manage user roles across teams and functions
• Control which agents or workflows each user can access
• Set department-level permissions and monitor agent usage

This makes Dot easier to scale across fast-growing product organizations without additional tooling or fragmented control.

For companies evaluating AI tools for product management, the difference is clear: Copilot assists individuals. Dot enables teams.

Why Product Teams Are Moving Toward Full Stack AI Tools

As product work becomes more data-driven and collaborative, teams are realizing they need more than just auto-complete or quick summaries. They need:

• Institutional memory across tools
• Agents that handle product ops end-to-end
• Ownership of data and decision logic
• AI that adapts to their organization, not the other way around

This shift is what makes Dot stand out in the growing landscape of AI tools for product management. It’s not just an assistant, it’s an AI-powered product stack.

Looking Ahead: Perplexity, Search, and the Next Layer of Product Intelligence

Some product teams rely on tools like Perplexity AI to augment product research or trend analysis. That’s a different kind of AI usage, but one worth exploring next.

In our other article, we compared Dot and Perplexity AI not only as AI tools for product management but also in terms of research capabilities, especially since Perplexity AI is one of the most used products in this field.

Final Thoughts

Microsoft Copilot is convenient, especially if your team is already deep in the Microsoft ecosystem. But for product leaders who want more than isolated smart features –who want AI that can think, remember, and build alongside their teams, Dot offers a more powerful, scalable alternative.

AI is no longer just a productivity tool. It’s becoming the connective tissue in product workflows. Choosing the right platform now will shape how your team scales strategy, execution, and innovation going forward.

Frequently Asked Questions

What is the difference between Dot and Microsoft Copilot for product managers?
Dot is a customizable AI framework built for cross-tool workflows, while Copilot focuses on individual productivity inside Microsoft apps.

Are there better AI tools for product management than Copilot?
Yes. Tools like Dot offer deeper workflow automation, multi-agent orchestration, and infrastructure control that Copilot does not support.

Can Dot manage AI seat permissions across a product team?
Yes. Dot includes built-in seat management, allowing teams to control access, assign roles, and manage permissions without third-party tools.

‍

Artificial intelligence is everywhere, but many people do not realize there are different types working behind the scenes. You may already be using both traditional AI and generative AI in your daily tools without knowing it. Understanding the difference between these two can help you make smarter choices about technology in your business or personal projects.

One key part of this discussion is how multimodal used in generative ai opens up new possibilities. From creating content to interpreting data, multimodal generative AI systems combine text, images, and audio to produce richer, more flexible outputs. This blog explains what sets generative AI apart from traditional AI and shows where you are likely using both right now.

What Is Traditional AI

Traditional AI refers to systems designed to follow rules, classify data, and make predictions based on patterns. It powers many tools you use every day.

• Search engines that rank results based on queries
• Email spam filters that block unwanted messages
• Recommendation systems that suggest movies or products
• Fraud detection tools that monitor transactions

These systems do not create new content. They analyze, sort, and predict based on existing information. They are often focused on accuracy, speed, and efficiency rather than creativity.

What Is Generative AI

Generative AI goes beyond pattern recognition. It produces new content in the form of text, images, audio, or code. Instead of simply predicting an outcome, it generates something that did not exist before.

Examples of where you see generative AI include:

1. Chatbots that write natural-sounding replies
2. Image tools that create pictures from text prompts
3. Music software that composes melodies
4. Code generators that assist programmers

Understanding how multimodal used in generative ai works is key to seeing how these tools handle complex tasks across formats. Generative AI can blend data types to create more complete outputs, such as combining an image with a matching caption or pairing audio with visual elements.

How Multimodal Used In Generative AI Makes A Difference

When you think about how multimodal used in generative ai transforms systems, it helps to look at what multimodal means. A multimodal AI can take in and produce multiple types of data at the same time.

This means generative AI can:

• Interpret an image and describe it in text
• Generate a video with synchronized audio
• Create a chart based on both numerical and text input
• Build content that blends visuals and narrative for marketing

Because of how multimodal used in generative ai works, you get tools that feel more human-like. They understand context better and produce outputs that align across different formats.

Where You Already Use Traditional AI And Generative AI

You might not realize how often you interact with both types of AI.

Traditional AI is at work when:

• Your email filters junk messages
• A map app finds the fastest route
• A credit card company flags a suspicious charge

Generative AI helps when:

• A tool drafts your email response
• An app writes photo captions
• A chatbot answers customer queries in full sentences

In many cases, these systems combine. Understanding how multimodal used in generative ai supports these processes helps explain why these tools feel smoother and more capable than older systems.

Benefits Of Using Both Traditional AI And Generative AI

Both forms of AI offer distinct advantages. When combined, they create powerful tools for businesses and individuals.

Traditional AI delivers:

• Reliable pattern recognition
• Fast, rule-based processing
• Accurate sorting and filtering

Generative AI offers:

• Creative outputs
• Flexible responses
• Custom content creation

One reason to understand how multimodal used in generative ai works is that this blended approach often brings the most value. For example, in marketing, a system might analyze audience data (traditional AI) and generate personalized ads (generative AI). You can explore this further in ''Generative AI in Media and Marketing: Smarter Content, Less Burnout''.

Examples Of How Multimodal Used In Generative AI Works In Practice

Let’s look at real applications that show how multimodal used in generative ai creates richer experiences.

• A social media tool that generates both the image and caption for a post, based on a single prompt
• A customer service assistant that writes replies while pulling in diagrams or product images
• A presentation builder that creates slides, text, and voiceover from a topic outline
• An educational app that generates quizzes with both text and visual elements

Each of these shows how multimodal used in generative ai bridges formats to produce complete, ready-to-use materials.

How To Get Started With Generative AI In Your Work

If you want to try generative AI, especially with multimodal capabilities, here is a simple way to begin.

1. Identify a task where you produce both text and visuals
2. Choose a tool that supports multimodal generation
3. Provide a clear, detailed prompt
4. Review the output and refine as needed
5. Combine with traditional AI tools where useful

The more you understand how multimodal used in generative ai improves results, the more effectively you can guide these systems.

Challenges Of Generative AI And Multimodal Systems

While these tools are powerful, they are not perfect.

• Outputs can sometimes lack accuracy or subtlety
• Content might not always align with your tone or style
• There can be concerns about data privacy or originality
• Multimodal tools may require more computing resources

By understanding how multimodal used in generative ai operates, you can set better expectations and use human review to refine outputs.

Future Trends For Generative AI And Multimodal Systems

Generative AI will continue to evolve, and multimodal systems will play a central role.

Expect to see:

• Better alignment across text, image, and audio outputs
• Easier ways to control style, tone, and format
• More tools that integrate into daily work without technical setup
• AI that can explain its outputs, increasing trust and adoption

Understanding how multimodal used in generative ai advances will help you stay ahead as these tools become standard in creative and business workflows.

Conclusion: You Are Likely Using Both Types Of AI Already

You may not have realized how often you use both traditional AI and generative AI. From filters that sort data to tools that produce original content, AI is embedded in modern work. The rise of multimodal capabilities makes generative AI even more powerful, helping you create more complete, polished materials with less effort.

Knowing how multimodal used in generative ai works lets you take advantage of these tools while keeping quality high. The future is not about choosing between traditional AI and generative AI. It is about knowing how to use both in the right way.

Frequently Asked Questions

What is the difference between traditional AI and generative AI?
Traditional AI classifies and predicts based on data. Generative AI creates new content like text or images.

How does multimodal used in generative ai improve content creation?
It combines text, image, audio, and more to create richer outputs that align across formats.

Can businesses combine traditional AI and generative AI?
Yes. Many tools use both to analyze data and generate personalized content or responses.

‍

Freelancing has always been about independence, flexibility, and self-management. But now, technology is reshaping what it means to work for yourself. With the rise of ai for freelancers, solo professionals are discovering new ways to scale, streamline, and strengthen their businesses.

From graphic designers and writers to consultants and developers, freelancers are finding that AI tools can help them save time, deliver higher quality, and compete in bigger markets. This shift is not about replacing freelancers. It is about giving them smarter tools that act as assistants, collaborators, and sometimes even business partners.

In this blog, we explore how ai for freelancers is changing the dynamics of solo work  and what it means for the future of the gig economy.

Why AI for Freelancers Is Taking Off Now

Freelancers are natural early adopters of tools that save time or improve outcomes. Unlike large organizations, freelancers can move fast, experiment with new technology, and adopt what works without layers of approval.

There are three big reasons ai for freelancers is gaining momentum:

• Tools have become affordable and accessible to solo workers
• AI no longer requires coding skills to implement
• The gig economy is more competitive, making efficiency a key advantage

As freelancers take on more clients and bigger projects, AI helps them deliver value without working longer hours. It is not about cutting corners. It is about working smarter.

How AI for Freelancers Supports Everyday Workflows

Let’s break down some of the most common ways freelancers are using AI today.

1. Content Creation

Writers, marketers, and content creators are using AI tools to:

• Generate blog drafts
• Suggest headlines and outlines
• Create SEO-friendly content structures
• Repurpose long-form content into social posts

If you are wondering how to keep the human touch while automating, check out Can You Automate Content Creation with AI and Still Sound Human?.

2. Graphic Design and Visual Assets

AI design assistants help freelancers:

1. Create quick mockups
2. Generate variations of a concept
3. Remove backgrounds or clean images
4. Suggest color palettes or layouts
5. Automate resizing for different platforms

Instead of replacing creativity, ai for freelancers accelerates the repetitive parts of visual work.

3. Client Communication

Managing emails, proposals, and follow-ups can eat up a freelancer’s time. AI helps by:

• Drafting client emails and messages
• Organizing tasks based on client requests
• Summarizing meeting notes
• Suggesting next steps

This keeps freelancers focused on delivering work rather than getting lost in admin.

4. Research and Idea Generation

AI for freelancers makes it easier to gather and organize information. Examples include:

• Competitor analysis
• Keyword research
• Market trends
• Inspiration boards for design or writing

The freelancer remains the strategist. AI just speeds up the data-gathering phase.

5. Time Tracking and Invoicing

AI-powered tools help freelancers:

1. Log work automatically
2. Suggest billing summaries
3. Track hours by project
4. Generate invoices
5. Send payment reminders

For a deeper look at how AI fits into gig work structures, see AI and the Gig Economy: Transforming the Future of Work.

Why Freelancers Embrace AI Faster Than Big Companies

Freelancers are often the first to try new tools because:

• They have no bureaucracy slowing them down
• The value is personal and immediate
• Cost savings go straight to their bottom line
• Time savings directly increase income potential

This makes ai for freelancers one of the most dynamic segments of the AI market. Freelancers are not waiting for permission. They are already integrating AI wherever it helps.

The Benefits of AI for Freelancers

Here are some of the biggest advantages solo workers report after adopting AI:

• More output without burnout
• Higher-quality deliverables faster
• More time for creative or strategic work
• Easier client management and reporting
• Better ability to take on larger or more complex projects

Instead of feeling like they are working alone, freelancers using AI often say it feels like having a team in their corner.

What to Look for in AI Tools for Freelancers

Not every AI tool is built with freelancers in mind. The best solutions for solo workers share a few traits:

• Affordable pricing
• No complex setup or integrations
• Simple interfaces that work without training
• Ability to customize outputs for different clients
• Clear privacy and data ownership policies

A good ai for freelancers tool fits into your workflow rather than forcing you to change how you work.

Examples of Freelancers Winning with AI

Let’s take a look at realistic examples of freelancers using AI to level up:

A content strategist

Uses AI to draft blog posts, generate outlines, and create social snippets. This allows them to take on twice as many clients without sacrificing quality.

A graphic designer

Uses AI for quick concept mockups and resizing. They focus on high-value creative work while AI handles variations and technical adjustments.

A virtual assistant

Uses AI to manage inboxes, draft client communications, and produce simple reports. This frees up time for more billable tasks.

In each case, ai for freelancers amplifies talent instead of replacing it.

The Future of AI for Freelancers

Looking ahead, ai for freelancers will become even more integrated into daily work. Here is what we can expect:

1. AI agents that handle multi-step projects end to end
2. Voice-activated AI assistants for managing tasks on the go
3. AI marketplaces where freelancers license their own AI-generated templates or models
4. Smarter AI that adapts to each freelancer’s unique style and clients
5. Better integrations with platforms freelancers already use

The goal is not to replace the freelancer’s unique value. It is to give them more leverage in a competitive world.

Conclusion: AI Is Freelancers’ New Business Partner

The rise of ai for freelancers is not about turning creative people into machine operators. It is about giving solo workers the power to do more, better, and faster without losing what makes their work special.

Freelancers who adopt AI thoughtfully gain an advantage. They work more efficiently, serve more clients, and build businesses that scale on their own terms. The tools are here. The opportunity is here. And those who move first will see the biggest benefits.

Frequently Asked Questions

Is ai for freelancers expensive to get started with?
No. Many tools offer free tiers or affordable plans designed for solo users. You can try features without committing to high costs.

Will ai for freelancers replace human creativity?
No. AI supports freelancers by handling repetitive tasks and generating ideas. The human touch is still essential for quality and originality.

What’s the best first step to use ai for freelancers?
Start by automating one part of your workflow, such as drafting emails or generating outlines. This gives you a feel for what AI can do without overwhelming your process.

‍

The age of simple search is behind us. For modern teams, research means more than asking questions and reading answers. It means conducting deep, ongoing investigations across multiple sources, tools, and domains. That’s why today’s most valuable AI research tools do more than summarize, they orchestrate, synthesize, and integrate.

In this space, Perplexity AI has gained popularity as a fast and factual AI answer engine. Dot, on the other hand, positions itself as a fully customizable research framework for teams. Both tools address key research needs, but take very different approaches.

In this post, we explore how they compare, especially when it comes to their “deep research” capabilities, and what to consider when choosing between individual assistants and team-ready AI research tools.

What Perplexity AI Offers

As one of the most popular AI research tools, Perplexity AI is often described as an AI-powered search engine. At its core, it delivers answers sourced from live web content, paired with citations. It is designed for individual users who want quick summaries, factual overviews, and verified information.

Where it excels:

• Fast, citation-backed answers
• User-friendly interface with no setup required
• Public web access for real-time information
• Search-optimized outputs for general knowledge and trending topics

As one of the fastest-growing AI research tools, Perplexity gives users a sense of reliability through its transparent source linking. It is especially useful for surface-level research and personal knowledge building.

Perplexity’s Deep Research Feature

Perplexity’s new Deep Research feature is its first major step beyond quick answers. Instead of responding to one question with a static answer, it:

• Performs iterative searches, adjusting queries to find the most relevant data
• Reads and analyzes multiple sources before answering
• Summarizes results into a comprehensive, longer-form report
• Provides source citations and structured output

Available in the Pro plan, Deep Research is designed to mimic how a human researcher might follow a thread across the web. It's a significant improvement but still web-only, non-customizable, and built solely for one-off tasks. There’s no long-term memory, no API access, no workflow integration.

For individuals or students, it’s one of the most accessible AI research tools. For teams and enterprises, it’s a limited starting point.

Dot’s Deep Research Agent: Designed for Systems, Not Just Sessions

While Perplexity’s Deep Research improves personal insights, Dot’s Deep Research Agent is built for operational depth. It is not a feature, it’s a workflow that integrates into your systems.

Powered by Gemini 1.5 Pro, Dot’s Deep Research Agent:

• Breaks research into multi-step tasks handled by separate agents
• Combines external and internal data (documents, tools, APIs, storage)
• Delivers structured outputs to Notion, Slack, Google Docs, or dashboards
• Maintains session memory for continuous research threads
• Provides full citation and traceability, including internal sources
• Can be deployed on-premise for secure environments

This makes Dot one of the few AI research tools that operates like an internal team that learns, evolves, and delivers insights that adapt to your business context.

You can create a Deep Research Agent using CrewAI and Deepseek but that is not as easy as creating an agent at Dot. In addition to that Dot already offers a Deep Research Agent that you can build an AI workflow around.

If you want to learn more about Dot and CrewAI comparison as multi agent AI systems, you can check our ''Dot vs. CrewAI: Multi Agent AI Systems for Business'' blog post.

Feature-by-Feature: Dot vs. Perplexity AI

Use Case Comparison: Researching a Competitive Landscape

Let’s say your team needs to gather intelligence on five competitors for a strategy brief.

Using Perplexity AI Deep Research:
You can run a Deep Research query on each competitor. You’ll get summaries with sources, quickly. But you’ll need to copy, organize, and combine them manually and there’s no way to pull in your own historical notes or compare results across queries.

Using Dot’s Deep Research Agent:
You assign an agent to:

1. Gather public data from the web
2. Pull past competitor notes from Notion and Drive
3. Summarize market changes over the last 6 months
4. Create a side-by-side competitor table
5. Export it to your team’s workspace and alert the product lead

It’s not just a search. It’s a self-contained AI research tool workflow that integrates with your work.

Team Needs Are Different from Individual Needs

Most AI research tools are still built with individuals in mind. They answer questions well but fall short when research becomes a process that is shared, recurring, and embedded in team outputs.

Dot shifts the mindset:

• From answer generation to workflow orchestration
• From isolated users to collaborative research systems
• From web scraping to data-rich integration

For teams working on go-to-market research, product strategy, content planning, or knowledge management, Dot turns AI from a helper into infrastructure.

The Open-Source Advantage

Dot also supports open-source models like Mistral and DeepSeek, which makes it one of the more flexible AI research tools available today.

This means your team can:

• Run research workflows with different models depending on the task
• Keep data private and compliant by avoiding vendor lock-in
• Experiment and evolve your stack as the ecosystem changes

Most tools, including Perplexity, only offer fixed models. With Dot, you can choose and switch without breaking workflows.

Final Thoughts: Choosing the Right AI Research Tool

Both Perplexity and Dot offer value but for very different audiences.

• Choose Perplexity AI if you need fast, factual, citation-backed insights for individual use.
• Choose Dot if you want scalable, secure, customizable AI research tools that operate across your data, systems, and teams.

As AI becomes part of how companies think, the tools we choose will either support deeper insight or slow us down with surface-level answers.

Dot is built for the next generation of AI-enabled teams.

Frequently Asked Questions

What are AI research tools used for?
AI research tools help users gather, analyze, and summarize information from multiple sources to support faster, smarter decision-making.

Is Dot better than Perplexity AI for deep research?
Yes, Dot offers deeper workflows, model flexibility, and team collaboration, while Perplexity focuses on fast individual search results.

Can Dot be used with internal company data?
Yes, Dot integrates with internal tools, files, and APIs, making it ideal for teams needing private and secure research infrastructure.

‍

We have all been in meetings where no one really knows who owns what. Tasks fall through the cracks. Updates are scattered across tools. And worst of all, productivity slows down while people chase clarity.

But this is no longer just a communication problem. It is a coordination problem. And it is exactly the kind that AI for teams problems is built to solve.

When powered by smart systems, teams can finally stop wondering what is happening and start focusing on doing the work.

The Team Chaos Problem

Even high-performing teams face daily friction. Here are some reasons why:

1. Disjointed Tools
   Teams use separate systems for tasks, docs, and communication. Things get lost between the gaps.
2. Unclear Ownership
   Without clear roles, work often gets duplicated or ignored.
3. Outdated Information
   Manual updates mean project dashboards are rarely accurate.
4. Constant Check-Ins
   When visibility is low, people default to status meetings and Slack follow-ups.

This is where systems built with AI for teams become a game changer. It does not just track tasks. It becomes the invisible layer that connects your people, tools, and timelines.

What Does AI Actually Do for a Team?

Let’s get specific. Here is what modern AI systems handle behind the scenes:

• Monitor conversations and extract tasks automatically
• Assign owners based on team roles or workload
• Surface the right information to the right person at the right time
• Send reminders before things slip
• Summarize progress in clean, digestible updates

It is not just about automation. It is about coordination without micromanagement. The assistant works in the background so your team does not have to chase updates.

Dot: A Powerful Example of AI for Teams

Dot is one of the few platforms designed with full-team workflows in mind. Rather than just being a chatbot or task list, it acts as a central brain that understands your tools, priorities, and workflows.

With Dot, teams get:

• Smart agent orchestration for managing complex processes
• Clear role-based task assignments
• Project updates built from real-time data, not manual entries
• Integration across Slack, Notion, CRM, and more

Dot also offers powerful seat management features. Admins can easily organize users, assign permissions, and scale access as the team grows.

And for teams tired of juggling apps, Dot simplifies everything with unified access. You can read more about that in our post on Tired of Switching Tabs? Dot’s Integrations Bring It All Together.

If you are serious about team efficiency, this is not just nice to have. It is foundational.

AI for Teams in Action: Real Use Cases

Wondering what this looks like in real life? Here are examples of how different departments use AI for their teams.

1. Product Management

• Auto-assigns features to the right sprint
• Detects blockers and alerts the right person
• Generates summaries for weekly standups

2. Customer Support

• Routes tickets based on issue type
• Suggests next steps using knowledge base content
• Escalates unresolved issues automatically

3. Marketing Teams

• Tracks campaign deadlines
• Syncs briefings with content calendars
• Shares progress updates with leadership

4. Finance Teams

• Tracks invoice and payment workflows
• Flags discrepancies and delays automatically
• Generates end-of-month summaries with minimal manual input

These are not futuristic workflows. Teams are already doing this now with the help of multi-model agentic AI platform Dot.

Benefits Beyond Productivity

While faster workflows are great, the real win with AI is the culture it helps create. Here is what changes:

• Fewer Silos
  Everyone sees the same context. No more guessing what another team is doing.
• More Accountability
  Tasks are visible, owners are clear, and nothing gets forgotten.
• Less Burnout
  When AI takes care of the boring updates, teams can focus on meaningful work.
• Better Trust
  Transparency builds confidence. No one feels like they are in the dark.

This is not just about getting things done. It is about making work feel better.

What to Look For in an AI for Teams Platform

Not all tools claiming to use AI actually support team workflows. Look for systems that offer:

Core Capabilities

• Multi-user coordination features
• Real-time task and timeline syncing
• Natural language understanding
• Role-based access and updates

Bonus Features

• Integration with your existing tools
• Meeting-to-task conversion
• Support for AI agents to automate workflows
• No-code customization options

These features make the difference between another notification machine and a true AI for teams solution and Dot has all these features, including bonus ones.

Start Small, Then Scale

You do not have to transform your whole workflow overnight. Here is how most teams successfully roll out AI for teams:

1. Pick One Use Case
   Start with something simple like daily task tracking or meeting summaries.
2. Assign a Pilot Team
   Let one team test it out and report back.
3. Set Measurable Goals
   Track improvements in update frequency, task completion, or fewer meetings.
4. Expand to Other Teams
   Once you prove value, roll out across departments.

The good news is that AI gets better the more you use it. So every small step builds momentum.

The Future Is Clear and Coordinated

Imagine a team where:

• Everyone knows what they need to do
• Deadlines are visible and updated in real time
• Meetings are shorter because everyone is aligned
• Progress reports are instant and accurate

That is the future AI for teams is creating. It removes the friction that slows people down and replaces it with a calm, steady rhythm of focused work.

When teams are aligned, they are unstoppable. And with the right AI support, that alignment becomes automatic.

Conclusion: Teams Deserve Better Tools

Your team should not be held back by scattered tools and vague updates. With AI for teams systems, clarity is no longer a luxury. It is built in.

This is not just about technology. It is about respect for your team’s time, energy, and talent. When you give your team the right tools, you get better results and a happier work culture.

Let AI do the heavy lifting, so your people can do the work that actually matters.

Frequently Asked Questions

Does AI for teams systems work with remote or hybrid teams?
Yes. In fact, Dot is especially helpful when teams are distributed. It provides consistent updates and visibility across time zones.

Is it hard to train my team on these tools?
No. Most platforms offer simple interfaces with natural prompts. Many also come with onboarding support and templates. Dot also offers no-code use for non-technical teams.

Can I use AI for teams if I already have project management software?
Absolutely. Agentic AI platform Dot integrates with tools you already use. It enhances, rather than replaces, your current stack.

‍

We all know that feeling. Your calendar is packed, messages flood in faster than you can read them, and your task list keeps growing. What if the solution was not working harder, but working smarter? With an AI assistant?

This is not just another productivity hack. It is about using smart tools to reclaim your time, reduce context switching, and let AI help with the invisible work that usually eats up your day.

What Is AI Assist, Really?

An AI assist system is not a single chatbot or automation. It is a collaborative and adaptive assistant that helps you:

• Track tasks, deadlines, and dependencies
• Summarize emails, documents, and meeting notes
• Route work between teammates and tools
• Handle repetitive tasks for you
• Keep the bigger picture visible to everyone involved

It is a system that not only responds but learns how your team works and adjusts itself to your patterns. It is proactive rather than reactive.

Why Daily Chaos Is the Norm (And Why It Should Not Be)

Let us break down what creates the chaos in most modern workdays:

1. Fragmented Tools
   You are juggling a dozen platforms like email, Slack, CRM, Notion, and multiple spreadsheets. Constantly switching tabs is mentally draining.
2. Too Many Meetings
   You had five meetings today, yet you still do not know what the main decision was or who is responsible for what.
3. Information Overload
   You have read ten emails, three internal threads, and a long slide deck before lunch. And you have remembered almost nothing.
4. Hidden Work
   Following up, documenting, assigning, and organizing are all necessary but often unrecognized tasks.

This is where an AI assist system becomes valuable because it makes your existing tools and apps work better together.

How AI Assist Clears the Noise

You do not need another dashboard. What you need is clarity with:

• Smart Summaries
  Get concise recaps of meetings, chats, and long documents automatically.
• Task Extraction
  The assistant identifies action items in your Slack threads or Zoom calls and assigns them directly.
• Deadline Management
  AI flags delays, follows up on pending tasks, and gently reminds responsible team members.
• Unified View
  All tasks, files, and updates appear in one place based on what matters to you today.

All these are things you can achieve with an AI support system like Dot. The real benefit lies in its understanding of context. It knows what you are working on and what you will need next.

Use Cases of Dot: Solving Real Life Problems

1. Product Teams

• Automatically create sprint plans from brainstorming notes
• Share daily summaries across Slack and Jira
• Highlight delays without needing a status meeting

2. Marketing Teams

• Summarize briefs and turn them into Asana tasks
• Draft outlines from meeting transcripts and analytics
• Generate one-click campaign reports from multiple tools

3. Operations Teams

• Direct customer queries to the right team with full context
• Build weekly reports from CRM and dashboard data
• Manage inventory and resources with smart workflows

All teams experience chaos. Only some have an AI assist solution like Dot to handle it.

Letting AI Handle the Invisible Work

Invisible work is the stuff that slows you down. It includes:

• Finding the right document
• Tracking replies across threads
• Checking whether tasks are moving forward
• Writing the same follow-up again and again

Multi-model AI platforms like Dot solve this by automating those micro-tasks. It does not just help you do your work. It helps you avoid wasting time on tasks that should not need your manual effort.

If you want to see how AI can reduce friction across a team, check out our blog post on No More “Who’s Doing What?”: AI for Teams Keeps Everyone on Track. It explores how AI can transform visibility and accountability in team workflows.

Features to Look For in a Great AI Assist System

Must-Have Features

• Natural language understanding
• Integrations with your core tools like Slack, Notion, Jira, or CRM
• Workflow and task automation
• Visibility into timelines and dependencies
• Views tailored to different team roles

Bonus Features

• Auto-generated updates for projects
• Turning meetings into action items in real time
• Custom workflows without any code
• Support for collaborative AI agents

One standout platform in this space is Dot, Novus’ multi-model agentic AI platform built to handle real operational complexity with clarity. Dot brings together advanced orchestration, multi-agent capabilities, and seamless integration with tools like CRM, ERP, and project management systems.

Whether you are a technical user or someone who prefers simplicity, Dot helps you create no-code AI agents or automate daily flows. It scales with your business and adapts to how you work.

The real goal is not to collect more tools. It is to remove friction, reduce confusion, and improve coordination.

The Human Side of AI Assistance

Some people worry that AI tools might replace human workers. In reality, these tools are built to support us. People using them report benefits like:

• Feeling more confident about decisions
• Experiencing less anxiety about missing details
• Gaining time for deeper thinking
• Reducing burnout from constant notifications

Leveraging AI is not just about time saved. It is about creating headspace and peace of mind.

Where to Begin with AI Assistance Tools?

1. Pick One Use Case
   Begin with a single workflow, such as meeting summaries or task routing.
2. Assign a Pilot Owner
   Let one team member test it out and collect feedback.
3. Track Value
   Measure things like saved time, shorter meetings, or fewer delays.
4. Roll Out in Phases
   Once the value is clear, expand to more departments and processes.

The best part is that every small step makes an immediate impact.

Conclusion: Assist, Not Overwhelm

The future of work is not about doing more. It is about doing what matters with clarity and confidence.

Instead of stacking on more apps or increasing pressure, AI assistance systems quietly improve how your day flows. They eliminate the noise, bring focus to your priorities, and handle the behind-the-scenes tasks that usually weigh you down.

Whether you are in product, marketing, operations, or leadership, having an intelligent assistant by your side can be the difference between constant catching up and actually getting ahead.

If your workday feels out of sync, maybe it is time to let AI Assist bring the rhythm back.

Frequently Asked Questions

Are AI assist systems secure for companies handling sensitive data?
Yes. Leading platforms offer enterprise-grade security, especially Dot, including SOC 2 and GDPR compliance. Dot also offers on-premise deployment options too.

Do I need to know how to code to use AI assist?
No, not for Dot. This AI platform works through plain language prompts. It also offer advanced options for users who want to customize deeply.

Can AI assist more than one department?
Definitely. Teams in sales, HR, support, marketing, and operations all benefit from AI systems designed for flexibility.

‍

We’re excited to launch our new event series: Novus Meetups.

At Novus, we believe that building a strong community is just as essential as building great technology. As one of Turkey’s first AI-native startups, we see community not just as an audience, but as collaborators in shaping the future of AI.

That’s why we’re kicking off a series of gatherings that bring together people from different teams, sectors, and roles. Novus Meetups are designed to create space for real conversations around AI, knowledge-sharing, professional exchange, and meaningful connections.

Last Friday, we officially hosted our first-ever community gathering: “Novus Meetups: AI Teams & Developers Day.”

And what a beginning it was.

We would like to thank the generous support of QNBEYOND for helping us realize this dream and providing us with a great atmosphere.

The day opened with a deep-dive session by our Head of AI, Dr. Halit Örenbaş, where he explored the power of AI agents, orchestration, and the future of intelligent systems. His insights sparked thoughtful dialogue and set the stage for the collaborative spirit that defined the day.

‍

We ended with a casual networking session, where participants, from developers to AI leads, connected over coffee, shared ideas, and asked honest questions. That final part? It really made everything feel alive.

To everyone who came: thank you.

The energy, the conversations, the shared excitement around AI and startups, it all came together in the best way possible. You made the day what it was.

‍

We’re also incredibly grateful to our team:

• To our community team, for leading every part of the event with such care and heart,
• To our design team, for shaping the unique visual identity of the Meetups,
• To our sales and product teams, for making sure every guest felt genuinely welcomed during the networking session.

This is just the beginning. And we’re already excited for what’s next. Stay tuned!

Also, to stay updated on our upcoming meetups, feel free to follow us on social media and subscribe to our newsletter:

LinkedIn
Instagram
X
Subscribe to our Newsletter

‍

‍

It was an honor to take part in what has been called the largest AI summit in Cyprus’ history!

Our Head of AI, Dr. Halit Orenbaş, was invited as a guest speaker at AI Summit 2025, hosted by Eastern Mediterranean University (EMU). In his talk, he explored how multi-agent orchestration is transforming the AI landscape sharing how AI agents collaborate in workflows and support real-world decision-making across industries.

The event brought together a wide range of attendees, from technical experts and researchers to students and AI enthusiasts. The sessions were packed with insights, new perspectives, and genuine curiosity, showing just how rapidly AI is evolving and inspiring people from all walks of life.

Missed the talk or want to revisit it?

You can watch the full broadcast here: Watch AI Summit 2025

A heartfelt thank you to the EMU Software & AI Development Club for the kind invitation and warm hospitality. It was a privilege to be part of such a meaningful and forward-looking event.

Hey there!

Duru here from Novus, and May has been anything but slow in the AI world.

From Google dropping 100 AI announcements at I/O to Baidu working on a system to talk to animals (yes, really), this month’s updates have been equal parts ambitious and bizarre.

Meanwhile at Novus, we’ve been out in the world — from panels to summits — sharing what we’re building with Dot, and connecting with the growing AI community both locally and globally.

If you’re new here, welcome! I round up the biggest AI news, Novus updates, and personal insights in this monthly digest. You can also subscribe to our bi-weekly newsletter if you want the latest updates delivered straight to your inbox.

Let’s get into it.

May 2025 AI News Highlights

Google Goes Full Gemini at I/O 2025

Google I/O 2025 was an AI extravaganza. The company unveiled 100 new features, with Gemini models powering everything from search to smart glasses.

The most ambitious reveal? Gemini 2.5 Pro and Flash, which support 1 million-token context windows and a new “Deep Think” mode for tougher reasoning tasks.

Key Point: Google launched 100 AI features at I/O, showcasing Gemini 2.5 Pro, generative video tools, and a new $250/month AI Ultra plan.

🔗 Further Reading

Baidu Wants to Make Animals Talk

Baidu has filed a patent for a system that would use AI to interpret animal sounds and convert them into readable human language.

It’s early days, but the goal is to decode vocal patterns in pets and potentially understand what they’re trying to communicate.

Key Point: Baidu’s new AI patent aims to translate animal sounds into human language, opening up new frontiers in cross-species communication.

🔗 Further Reading

Duolingo’s AI-First Pivot Sparks Backlash

Duolingo cut 10% of its contractor workforce and announced an AI-first strategy for course content, leading to user complaints about quality.

Longtime learners, especially of Japanese, say cultural nuance is disappearing—and that AI can’t yet match the human touch in language learning.

Key Point: Duolingo’s AI-first shift led to layoffs and user backlash, raising concerns about quality and cultural accuracy in AI-generated education.

🔗 Further Reading

FutureHouse’s AI Tool Tackles Scientific Discovery

FutureHouse, a Google X spinout, launched a new AI platform that helps scientists explore biological research data without writing code.

Its first application in microbiome research allows researchers to model bacteria-human interactions more intuitively.

Key Point: FutureHouse launched an AI tool that turns biological data into interactive knowledge graphs, simplifying scientific exploration.

🔗 Further Reading

Novus Updates

The Novus team has been everywhere this month — from Istanbul to İzmir to Denver — sharing our vision for purposeful AI and showcasing how Dot is making it real.

• At Zorlu Holding’s “Geleceğini Yaz” event, our co-founders Egehan and Vorga spoke about solving real-world challenges with Dot, while Halit, Rehşan, and Doğa gave hands-on demos that sparked great conversations.
• At Webrazzi XYZ 2025, Egehan joined a panel on how AI agents are reshaping post-ChatGPT brand strategy, highlighting what sets Dot apart in this shift.
• During MEXT’s gathering for Denver’s startup ecosystem, Vorga introduced Dot to a global audience, while Duru and Sercan connected with leaders and innovators on the ground.
• At the AI Summit in İzmir, our CRO Vorga Can shared insights on building AI systems with real-world impact, in a session moderated by Ali Rıza Ersoy and hosted by ESİAD and EGİAD.
• Meanwhile, our CEO Rıza Egehan Asad attended the TRAI Mayıs Çalıştayı, exchanging ideas with local AI pioneers in a refreshing and forward-thinking environment.

Each event reminded us why we do what we do: to build technology that doesn’t just impress — but actually empowers.

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

Educational Insights from Duru’s AI Learning Journey

Why Telling Chatbots to Keep It Brief Can Backfire

We all want quick answers. But a new study from Paris-based Giskard reveals that asking AI for short replies might actually lead to more hallucinations. The research, tested on GPT-4o, Claude 3.7 Sonnet, and Mistral Large, found that when brevity is prioritized, accuracy suffers.

Why? Short answers leave no room for nuance. The AI might sense something’s off in the question, but without space to explain or clarify, it just blurts out an answer — confidently wrong.

This matches findings from another recent paper, Calibrating Verbal Uncertainty, where researchers reduced hallucinations by 32% simply by adding phrases like “it’s likely” or “this may be.”

So next time you prompt an AI, consider this: giving it space to elaborate might be the smartest move.

🔗 Further Reading

Is the Internet Still Human?

Have you ever read something online and thought, “This feels a little… machine-made”?

You’re not imagining things. According to Amazon Web Services, 57% of all online text is now generated or translated by AI. A separate arXiv study puts it at 30% minimum, and that number is growing fast.

Some analysts believe that by 2026, 90% of web content will be AI-generated (The Living Library).

That’s wild. It raises serious questions: How do we tell what's real? Can we still trust what we read? Is creativity getting flattened into algorithmic sameness?

I use AI in my own work, and I love what it can do — but I also try to stay aware. I still think it matters when something is written with intention, by an actual person. So for now, I’ll keep writing my own words — slowly, but meaningfully.

And I hope we all keep asking: who’s really behind what we’re reading?

Until Next Time, Stay Curious

That wraps up this month’s AI learning journey from my corner of Novus. If you’ve made it this far, I hope it gave you a fresh perspective or at least a few things to think about the next time you prompt your favorite model.

And if you want something a little more fun than a newsletter (yes, I said it), head over to our YouTube channel and check out Açık Kaynak — our podcast where we talk about AI, startups, and the messier side of building in public. It’s honest, unfiltered, and way more casual.

See you next month!