TL;DR

• Marketers often use multiple AI tools in parallel. That costs time, money, and context.

• An all-in-one AI workspace combines ChatGPT, Gemini, Claude, and other models in one tool.

• That reduces tabs, copy-paste, and duplicate subscriptions.

• iGrow uses this approach to manage research, content, and analysis more efficiently.

• This is especially exciting for teams, agencies, and power users with high AI output.

• The biggest lever is not the next standalone tool, but a better workflow system

AI in marketing has long stopped being an experiment. It is operational day-to-day reality. Anyone producing content, conducting research, preparing strategies, creating reports, or validating ideas today is, in practice, almost automatically working with several models at the same time. But that is now exactly where the real problem lies. Many teams have gotten used to opening a different tool for every task. Perplexity is used for research. ChatGPT for text. Gemini for images. Claude for analysis and structuring. What looks efficient at first glance often creates the exact opposite in daily work: more friction, more costs, more tabs, and above all, more context loss. You described this pain point very clearly in the podcast. Modern platforms are designed to bundle various features and solutions for daily work in one central environment and thus improve collaboration and efficiency.

At iGrow, we see this not only in theory but in real agency operations. As soon as multiple clients, multiple projects, and multiple employees work with AI in parallel, what seems like a smart setup quickly turns into a confusing tool park. The result is not just operational restlessness. It also gets expensive. And that is exactly why the topic of an all-in-one AI workspace is becoming so relevant for marketers, agencies, and companies right now. An all-in-one AI workspace is suitable for everyone – from individuals to large teams – and increases productivity by automating recurring tasks and more functions. Instead of paying for five different AI tools in parallel, jumping between five browser tabs, and manually transferring results from A to B, the more logical development is a central environment where multiple AI models can be used in one tool. This is exactly where izzedo.chat becomes interesting.

Why the classic AI setup does not scale cleanly in practice

In the podcast, Philipp very accurately describes that the question of the “best” AI tool is often actually asked the wrong way. No single model solves everything equally well. Rather, the individual systems have different strengths. Philipp says that he currently uses Gemini heavily for images, Perplexity more for research, and ChatGPT more for content creation. That is exactly the point. Most professional users have long stopped working with just one model. They combine multiple engines because they know different tasks require different strengths.

In the conversation, you then described a very realistic use case from everyday marketing. One model can be great for brainstorming, a second for evaluating the idea, and a third for structuring and concept development. And suddenly marketers are sitting there with multiple open tabs, jumping back and forth between tools and constantly losing the common thread. This is not a fringe issue; it is now a structural disadvantage. AI is supposed to relieve processes. But if the tool landscape itself becomes a burden, the setup is no longer clean. Central management of conversations in an all-in-one AI workspace helps keep track and preserve context across tasks and models.

This becomes especially clear when it comes to context. Philipp sums it up perfectly in the transcript: by constantly switching between tools, you forget what you asked where. Good answers you need again two weeks later disappear across different histories. You no longer know whether that strong strategic answer came from Claude, Gemini, or ChatGPT. And if you did not save it separately, the search starts from scratch. That costs time. But even more importantly: it destroys workflow.

In the podcast, you phrase the frustration even more directly. It is annoying because you can no longer find things. Chats get longer, histories become more confusing, and relevant context gets lost. You add another point: declining quality as soon as results are copied back and forth between systems and then turned into a clean output again. That is an observation many marketers can relate to immediately. One tool provides good research, the next should turn it into analysis, the third into a report. In theory, that sounds efficient. In practice, it is often a chain of friction losses.

The hidden costs behind multiple AI subscriptions

It gets even more serious when it comes to costs. Philipp says very openly that the biggest pain point is, of course, paying separately for each subscription. That will immediately resonate with many listeners. Because that is exactly what reality looks like. Anyone wanting the best tool in each case quickly books several accesses at the same time. Then maybe an additional tool for video, a specialized tool for images, or a stronger version of a single model is added. Suddenly, the total at the end of the month no longer seems small. Philipp says that AI subscriptions alone can quickly add up to 100 to 200 dollars per month. Many platforms now make their costs transparent and, in their pricing models, offer token limits such as 1m per month so companies can better calculate expenses.

In the podcast, you go one step further and make clear what this means in a business context. If not just one person but an entire marketing team works with these tools, these costs multiply immediately. In a mid-sized B2B company, four to ten marketers can quickly be in one department. Then developers are added, some using even more expensive plans. What looks like a manageable amount for individual users becomes a real budget block at team level. That is exactly why the keyword all-in-one AI subscription is not just a nice SEO term but an operational management question. Companies are not simply looking for “another AI tool.” They are looking for a more efficient cost model.

What is an all-in-one AI workspace anyway?

At its core, an all-in-one AI workspace is a central work environment in which multiple AI models are bundled in one tool. Instead of opening, paying for, and managing ChatGPT, Perplexity, Gemini, Claude, and other systems separately, these models come together in a shared interface. The interface serves as a central control hub through which access to various models, agents, and agenten takes place. This is exactly how you describe izzedo.chat in the podcast: as a workspace where the tools mentioned are centrally connected and run directly via the APIs of their respective providers. Users can work in different languages, use integration with Google Drive, and efficiently manage files such as PDFs, code, or transcripts. So you can work there just like in ChatGPT, while also using Gemini, Claude, and other premium models at the same time. In addition, features such as image generation, images, audio, web search, and web are integrated. Privacy and data protection for all users, including management of email addresses, play a central role. Advanced models are supported, such as GPT 5, Opus 4.6 from the Anthropic line, as well as a mix of different models and deep research functions. In addition, cowork features, a points system for controlling content, the ability to generate questions, and the option to review content are part of the offering.

This is exciting for marketers because the platform does not just mean “more models,” but a different workflow. It is no longer about one tool doing everything perfectly. It is about one tool organizing the use of multiple specialized models within a single work interface. That is exactly why the keyword multiple AI models in one tool is so relevant. It does not merely describe a technical feature, but the actual business value behind it.

ChatGPT, Perplexity, Gemini in one tool: why this really changes everyday work

In the podcast, Philipp states the biggest USP of izzedo.chat very clearly: you can use multiple AIs in one tool. He says this is the central use case. Instead of constantly switching back and forth between ChatGPT, Gemini, Grok, or Perplexity, everything is in one interface, and you can choose directly below the text input field which model you want to ask. It gets even more exciting because the same prompt can also be sent to multiple models at once. This allows a topic to be answered in parallel by GPT, Gemini, and Perplexity, so you can compare the different answers directly and choose the best one.

That is enormously valuable for agencies. In marketing, there is rarely one perfect answer. It is often about comparing perspectives, sharpening formulations, validating ideas, and filtering out the best version from multiple directions. Anyone who can use ChatGPT, Perplexity, and Gemini in one tool saves more than clicks. They create a faster evaluation process. This is strategically relevant because better results are created not only after multiple manual loops, but within one bundled workflow.

In the podcast, you also show very concretely what this looks like in practice. A content plan for a SaaS HR product is first created with one model. Then you have another model work on the same prompt or task. Afterwards, the response of one model is evaluated by a third. This exact chain happens in the same workspace without switching tabs. That is operationally clean. And that is exactly where the real efficiency gain arises. Meetings can also be planned and documented more efficiently through the integration of different models in the workspace, for example through automatic logging and centralized traceability.

Why memory is more than just a nice feature for marketers and teams

A particularly relevant point in the podcast is the memory function. Philipp explicitly highlights it as one of the strongest functions. The idea behind it is highly interesting for agencies: anyone who builds a separate work environment per client or project can not only generate relevant information in chats but also permanently assign it to a project context. The AI “remembers” key information and can use it again in future tasks. Philipp describes this very fittingly as a kind of agent that you continuously feed with information and that keeps developing thematically. 

For an agency like iGrow, this is strategically strong. In practice, it means: fewer repeated briefings, fewer redundant prompts, less manual context reconstruction. Instead of explaining to each model again with every new task who the client is, what industry is involved, which tone is desired, and which goals are being pursued, this context can be stored by project and reused. That is exactly what makes an all-in-one AI workspace not just convenient, but scalable.

Integration with Google Workspace: How all-in-one AI workspaces fit seamlessly into existing work environments

All-in-one AI workspaces with Google Workspace: direct integration instead of tool hopping. ChatGPT, Claude, and Gemini run natively in the familiar Google environment. Result: emails, documents, calendars, and tasks remain in the standard workspace. AI models: available where they are operationally needed. No app switching. No interruptions.

Context window: central function for direct data use in the workspace. Marketers access AI-supported suggestions, analyses, and text modules while creating presentations or composing emails. Application switching: eliminated. Time required: reduced. Copy-paste errors: minimized. Workflow: uninterrupted.

Own models and AI tools: fully integrable. Industry-specific analyses and individual workflows: can be mapped directly. Central management of tasks, data, and models: ensured. Team efficiency: maximized. Information: always available in the correct context. For Google Workspace-intensive companies: Claude, Gemini, and ChatGPT integration means a measurable productivity boost.

Security and data protection: What companies and teams must pay attention to in all-in-one AI workspaces

Security and data protection are non-negotiable when companies rely on all-in-one AI workspaces. Sensitive data, internal documents, personal information — everything must be absolutely protected. Modern AI tools must guarantee encrypted data transmission. Data storage takes place only where necessary. No compromise.

Access control is mandatory. Only authorized persons get access to data and functions. Period. Companies must protect confidential information from unauthorized access. Crucial: AI models work transparently. Answers and analyses must be traceable. Errors and biases are identified and eliminated immediately. No exceptions.

Clear rule: companies use modern AI tools without security compromises. Technical safeguards plus defined processes ensure optimal protection. Anyone using all-in-one AI workspaces selects only providers with the highest data protection and security standards. Full AI potential without risk — that is the only acceptable option.

Examples of successful applications: How marketers and teams benefit from all-in-one AI workspaces

Operational rule: all-in-one AI workspaces deliver measurable added value for marketing teams and companies. Mandatory use: automation of customer inquiries with ChatGPT. Inquiries must be handled directly in the workspace — systematically, personalized, continuously available. Result: team relief and consistent, quality-assured responses.

Central guideline for content creation and campaign optimization: AI tools such as Claude must be used. Marketers should generate texts, emails, or social media posts directly in the workspace and have the AI check them for tone, target audience appeal, and relevance. Integration with Google Workspace and Gemini: mandatory for central data management, analysis, and provision — from research to reporting.

Implementation rule for workflow optimization: teams must automate tasks and standardize workflows. Productivity increases are therefore systematically achievable. Through central management of all data and tools in the workspace: full overview and rapid responsiveness to changes are ensured. Cost reduction follows automatically — automation and task bundling make resource use systematically more efficient.

Application areas: fast message response, content creation, data analysis, central project management. All-in-one AI workspaces like izzedo.chat define this: with the right AI tool selection, suitable models, and well-designed workspace integration, companies and teams achieve their goals systematically faster and more efficiently.

Why we take this topic seriously at iGrow

In the podcast, it becomes clear that you are not discussing izzedo.chat theoretically, but testing and actively using it in practice. You show your own usage area there, mention that you have already become a power user, and make it transparent that multiple tools such as ChatGPT, Grok, Claude, and Gemini are used via the same workspace. This operational usage is important for a blog article because it creates credibility. It is not about praising some AI tool from a distance. It is about us as an agency evaluating tools based on whether they really save time in everyday work, reduce costs, and map contexts more cleanly.

And that is exactly why izzedo.chat fits into the discussion about modern marketing workflows. Anyone who uses multiple models in parallel does not need another standalone solution. They need a system that reduces complexity. In our view, that is the real lever. Not even more tools. But less friction between the tools that are already good.

Edin, CEO of iGrow, confirms: “As an all-in-one AI workspace, izzedo.chat has made our daily processes significantly more efficient and has become indispensable for us as an agency.”

The most important statements from the podcast summed up

One of Philipp’s strongest statements is this:

“The biggest USP that izzedo.chat provides is simply that you can use multiple AIs in one tool.” 

This is so strong because the benefit is not artificially overcomplicated here. No hype, no buzzword fireworks. Just a real everyday advantage: multiple models in one work environment.

Equally strong is Philipp’s assessment of the problem with today’s tool usage:

“With all the switching back and forth, you simply forget where you asked what.” 

This sentence captures one of the biggest operational weak points of many AI setups.

From you, in turn, comes a statement that is particularly relevant for marketers and agencies. You describe that what especially frustrated you was not finding content again while quality also declined when transferring contexts back and forth between different tools. That is a very realistic insight into agency day-to-day work. What looks clean in demos often fails in practice due to retrievability, context loss, and friction between multiple tools. 

Another key statement from you is the positioning of izzedo.chat itself. You describe the tool as an all-in-one workspace with all AI models, where the mentioned tools are centrally connected. This exact wording can also be used cleanly from an SEO perspective because it aligns search intent and product value one-to-one. 

Why this topic is especially relevant for marketers right now

Marketers rarely work linearly today. They brainstorm, research, cluster, test perspectives, build outlines, formulate texts, create images, condense reports, and develop campaigns from them. The problem is not AI itself. The problem is orchestrating AI. Anyone working with multiple tools today needs a workflow that makes this variety productive rather than chaotic.

That is exactly where the strength of an all-in-one AI workspace lies. It does not simplify the tasks themselves. It simplifies the environment in which those tasks are completed. And in the end, that is often the bigger lever. Operational excellence rarely comes from a single “magical” tool. It comes from better systems.

For us as an agency, this is crucial. We need solutions that work in real operations. Not just in a single-user test. If a tool helps use multiple AI models in one tool, store project knowledge, compare different answers directly, and bundle costs better, then that is not a gimmick. It is a real productivity factor.

Conclusion: The future is not in the next standalone tool, but in a better AI work environment

The discussion about which model is currently the best will stay with us for a while. But for teams, agencies, and professional users, the actually more important question has long been a different one: how do we work efficiently with multiple strong models at the same time without ending up in tool chaos, context loss, and exploding costs?

That is exactly why searches for terms like all-in-one AI workspace, all-in-one AI subscription, or ChatGPT, Perplexity, Gemini in one tool will become more relevant in the future. Not because these terms sound fancy, but because they address a real market problem.

Anyone already using multiple models today should look at whether a central environment is not the more logical next step. From our perspective, that is exactly the exciting approach behind izzedo.chat. So if you are tired of jumping between different AI tools, paying multiple times, and rebuilding context over and over, then take a look at izzedo.chat. The podcast made very clear why exactly this approach is currently so interesting for marketers, agencies, and power users. 

FAQ: All-in-One AI Workspace, AI Subscription, and multiple AI models in one tool