Why AI Is a Team Sport (For Now)

by Lisa Peyton and her team of AI super heroes

Almost daily, I’m struck by how much my workflows have changed since I started building my AI bench.

At any given time, I have at least three AI tools open — working in tandem — with me as the AI operator coordinating the outputs. I typically kick off every project with Claude. He’s my strategic thinker. Claude builds the prompts for my team of creative experts: ChatGPT for creative copy, Midjourney for jaw-dropping artwork. Then I take those outputs into Canva and assemble whatever visuals my content needs. At last count, I have a bench of at least a dozen AI tools, each one with a different superpower, each one bringing an important skill to the job.

So I’m continually shocked when I survey marketers and many of them tell me they use only one LLM — usually ChatGPT.

I can’t imagine relying on just that one tool. Without Claude, who would tell ChatGPT what to write? Who would help me fact-check and verify the LLM’s output? Not itself, surely.

We are not in a “one and done” AI era. And that makes things harder, yes — but it also makes diversifying your AI tool bench imperative.

The Data Backs This Up: No Single Model Wins Everything

Here’s what I keep discovering every time I run a head-to-head comparison between AI tools: there is no outright winner. Every tool has a superpower. Every tool has a blind spot.

Ethan Mollick, the Wharton professor and author of One Useful Thing, has a term for this: the “jagged frontier.” In his latest piece published this week, he notes that despite exponential improvement across every major benchmark, AI “remains jagged, capable of some tasks at a high level, while messing up others.” That’s the academic framing for what practitioners like me experience every day — no single tool does it all.

The benchmarks tell the same story. According to 2025 AI model rankings, GPT-5 leads on general knowledge and mathematical reasoning, Claude dominates coding and software engineering tasks, Gemini tops overall user preference and multimodal processing, and Perplexity leads on deep research with citations. The Stanford HAI 2025 AI Index Report found that the gap between the top-ranked and tenth-ranked model narrowed to just 5.4% on general tasks — meaning the models are converging broadly but still diverging sharply on specialized capabilities.

As one 2026 AI comparison guide put it plainly: there is no longer a universal “best AI” — only the best AI for your specific use case. Their recommendation? Use different models for different tasks.

That’s exactly what I’ve been finding in my own testing. In my recent Social Media Manager’s AI Tool Guide, I broke down the landscape by task — writing, images, video, audio, research — and every single category had a different winner. ChatGPT for captions. Claude for analysis. Perplexity for research. Midjourney for aesthetic imagery. ElevenLabs for voiceovers. Suno for music. The list goes on.

And it’s not just about LLMs versus LLMs. When I tested Ideogram against Google’s Nano Banana Pro on something as specific as text rendering in images — a bread-and-butter need for social media marketers — Ideogram won on design polish while Nano Banana won on exact text accuracy. Same prompt. Different strengths. The right tool depended entirely on the job.

Even the people building these tools don’t stick with one. Danny Wu, Head of AI Products at Canva, mentioned in a recent interview on The Neuron that he personally switches between Claude and Gemini depending on the task. If the person leading AI product at a 230-million-user platform doesn’t use just one model, why would you?

So What Does This Mean for AI “Taking Over” Our Jobs?

This is the question I get asked most: if AI is so powerful, why can’t it just do everything? And the honest answer is — it can’t. Not yet.

If the best AI tools on the planet each excel at different things and fall short at others, what does that tell us about agents replacing content marketers? It tells us we’re not there.

Mollick himself acknowledges this tension in today’s piece. Even as he documents the exponential improvement in AI capabilities, he writes that “despite these amazing capabilities in tests, companies are still very early in adopting AI, meaning that, as of yet, remarkably little has changed in most organizations.” The benchmarks are impressive. The real-world application is still catching up.

Danny Wu offered a similarly grounded take in his Neuron interview. He described AI agents as essentially LLMs that have been given tools and trained to keep operating in a loop. That’s it. They’re not magic — they’re loops with access to functions. And right now, those loops are only as good as the tools they can access and the human coordinating them.

That human is you. You’re the one deciding which tool handles the strategy, which one writes the copy, which one creates the visual, and which one does the final quality check. Mollick calls this new era one of “managing” AIs rather than working with them. I love that framing. You are the agent — the orchestrator — and that’s a skill that’s becoming more valuable by the week, not less.

The survey data confirms this is real, working behavior. According to the Social Media Examiner’s 2025 report, 60% of marketers now use AI tools daily, up from 37% in 2024. And the usage patterns show heavy overlap: ChatGPT at 90%, Google Gemini at 51%, Claude at 33%. These marketers aren’t picking one — they’re running multiple tools simultaneously.

Zapier’s 2025 enterprise survey found that 28% of enterprises are already using more than 10 different AI applications. And 66% plan to increase their tool count over the next year.

The Burden Is Real — And It’s OK to Feel Overwhelmed

I want to pause here and be honest: this is exhausting sometimes.

The pace of new model releases is relentless. In November and December of 2025 alone, four frontier AI models launched in just 25 days — Grok 4.1, Gemini 3, Claude Opus 4.5, and GPT-5.2 — each one immediately challenging the last one’s benchmark leadership. Keeping up with what’s best at what feels like a part-time job on top of your actual job.

Then there’s the cost. If you’re juggling subscriptions to ChatGPT, Claude, Midjourney, Canva, and a handful of specialized tools, you’re looking at $60 to $200 per month in AI subscriptions. That adds up.

And HubSpot’s 2025 research found that 35% of marketers say there are simply too many AI tools that all seem to do the same thing but don’t connect to one another. The frustration is real and valid.

But here’s the reframe I keep coming back to: the marketers who are building multi-tool workflows right now are developing a skill — AI orchestration — that becomes more valuable as the landscape evolves, not less. You’re not just learning tools. You’re learning how to think about tools, how to match capabilities to tasks, and how to coordinate outputs across systems.

As Mollick puts it: “uncertainty is not the same as helplessness.” The window to figure out how AI gets used in your work is open right now. The content marketers who are actively experimenting — building their bench, testing new tools, developing their orchestration instincts — are the ones setting the precedent for everyone else.

The Brighter Future: This Won’t Always Be This Hard

Here’s the good news. We’re probably two to three years away from a world where tool-hopping is dramatically less painful — and the evidence is already building.

The biggest signal is MCP — the Model Context Protocol — which has quickly become the standard for connecting AI systems to external tools and data. Originally open-sourced by Anthropic in late 2024, MCP has since been adopted by OpenAI, Google DeepMind, Microsoft, and AWS. By the end of 2025, it had been donated to the Linux Foundation’s Agentic AI Foundation, with 97 million monthly SDK downloads and over 10,000 active servers.

What MCP means in practical terms: your AI tools are learning to talk to each other. Danny Wu pointed to this directly in his interview — Canva uses MCP to expose its design tools across ChatGPT, Claude, and Microsoft Copilot with a shared codebase. He described this kind of cross-platform interoperability as a genuinely new shift in technology.

Meanwhile, the biggest AI companies know that tool fragmentation is a burden on their users and they’re investing heavily to solve it. Google is embedding Gemini across its entire Workspace suite — Docs, Sheets, Slides, Drive — and just launched Google Workspace Studio, a no-code automation tool that lets teams build AI-powered workflows across Gmail, Docs, Sheets, and more. Microsoft is doing the same with Copilot across its ecosystem. OpenAI is expanding ChatGPT’s app and plugin infrastructure.

The race isn’t just to build the best model anymore. It’s to build the platform where marketers can do everything — write, design, research, analyze, publish — without ever leaving. Eventually, a company like Google will most likely have tools that excel at the most important tasks, making tool-hopping far less necessary.

Gartner predicts that 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025. That’s a massive shift toward embedded, connected AI.

The marketer who deeply understands the landscape now — who knows which tools excel at what, who has built the orchestration muscle — will be best positioned to take full advantage of consolidated platforms when they arrive.

What to Do Right Now

You don’t need to master every tool on the market. But you absolutely need more than one.

My advice: build your bench by task, not by hype. Identify the core jobs in your workflow — writing, research, visuals, video, audio — and find the tool that performs best for each one. Start with two or three. Get good at coordinating them. Add more as your needs evolve.

The “one and done” era isn’t here yet. But the orchestration skill you’re building today? That’s your moat tomorrow.

Ready to start building your AI bench? I’ve assembled a full collection of AI marketing resources, tool guides, and tutorials to help you find the right tools for your workflow: AI Marketing Resources Hub

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