I Run 6 AI Platforms at Once. Here Are the 8 Rules That Make It Work.
By Mendy Ezagui | Aventary
Most companies are still asking the wrong question about AI.
They want to know which one to use. ChatGPT or Claude? Gemini or Copilot? They're shopping for the one tool that does everything. It doesn't exist. I've looked for 39 months.
I run six AI platforms simultaneously. Claude, ChatGPT, Gemini, Salesforce AI, Creative AI, and Codex. Each one has a job. None of them can do the others' jobs well. And the actual skill — the one that took me 39 months, 4,034 ChatGPT conversations, and more mistakes than I'd like to admit to learn — isn't mastering any single tool. It's the operating system underneath: knowing which one to reach for, when to switch, and how to catch them when they're wrong.
That operating system runs on eight rules. Every major win I've had with AI traces back to one of them. Every expensive mistake traces back to breaking one.
Here's the full timeline. 39 months, six ecosystems, every milestone and hard lesson mapped. Click any dot to see what happened.
Rule 1: There is no single AI.
This is the one that changes everything, and most people resist it the longest.
For the first year, I used AI tools without realizing I was using multiple AI tools. ChatGPT for business operations — API problems, software integrations, back-and-forth strategy work. Claude for precise writing — cleaning emails, rewriting slides at 11pm during consulting projects. Gemini for research and fact-checking. Salesforce AI for enterprise work. MidJourney for images. All running at the same time.
I didn't plan this. I never sat down and said "I need a multi-model strategy." I just kept reaching for whichever tool felt right for the job. ChatGPT logged 100 to 259 conversations a month during a year I thought I'd stopped using AI entirely. I was routing work to different models by instinct — but I wasn't doing it on purpose. And that gap between doing it and doing it intentionally cost me months.
The moment it became deliberate changed everything. I built a Multi-AI Playground — a live web app that sends the same prompt to Claude, GPT-4o, and Gemini 2.5 Pro simultaneously, with a judge model comparing the responses. Not because I was curious. Because I needed to see, in real time, which model was stronger at which task.
That playground was the prototype. Now it's production infrastructure: contextual model routing — the system decides which model handles which task based on the context, not my instinct. Last week I optimized a client's voice AI pipeline and cut per-conversation cost by 50%. A Slack-integrated MCP workflow for another client — 82% cost reduction. Not by switching to a cheaper model. By routing the right parts of each conversation to the right model.
The person who treats ChatGPT as the only AI tool is leaving value on the table. Same as someone who only uses Claude, or only uses Gemini. Each model has a different cognitive fingerprint. Claude thinks carefully. ChatGPT iterates fast. Gemini cross-references. Salesforce AI lives inside the enterprise data layer. Codex audits at scale. The edge isn't in picking the best one. It's in running all of them with intent.
Rule 2: Wrap the system of record. Don't fight it.
This isn't a principle I discovered during consulting projects. It's one I've been operating on for a decade.
In 2016, I was a foundational client during Workato's early build phase — shaping tables, permissions, advanced reporting through real implementation feedback while they were still a young company. Before that: a CRM build for Chabad.org, COVID-era logistics software, financial services integrations at Sallie Mae and Nucleus. Integration has been the spine of my practice since before PwC, before Salesforce certifications, before I ever touched a large language model.
So when clients started asking me to rip out their existing software and replace it with AI, I already knew the answer. Every time, the same problem: the system of record has ten years of data, custom workflows, regulatory compliance baked in, and 200 people trained on it. You're not replacing that. Not in six months. Not ever.
The unlock is wrapping around it. Don't fight Salesforce — extend it. Don't replace the property management platform — build an AI layer that reads from it, writes back to it, and handles the parts it was never designed for.
From October 2024 through October 2025, I led a strategic initiative building out AI capabilities within Salesforce — a real team, a real roadmap, production deliverables. In parallel, I sat on PwC's Agent Factory team and led roughly twenty AI education sessions with partners, teaching them how AI actually works inside the Salesforce ecosystem. Not slides about the future. Implementation patterns they could use that week.
When I launched the first AI SDR inside Salesforce for a client in May 2025, it didn't replace anything. It sat on top of the existing CRM, read the existing data, and handled the conversations the sales team didn't have time for. AI Operations the following month — same pattern. Voice agents in 2026 — same pattern. The system of record stays. AI wraps around it intelligently.
Most AI implementations fail because they try to be the new system instead of the new layer. The companies that get this right move fast. The ones that don't spend a year in "digital transformation" and end up exactly where they started.
Rule 3: AI will lie to you with confidence. Build verification loops.
This is the rule that separates people who use AI from people who use AI well.
I caught an AI-generated strategy deck with the wrong unit count — 9,500 units instead of 95,000. Made-up assumptions about API capabilities that didn't exist. A fundamental misunderstanding of how the client's software actually worked. The deck looked polished. The numbers were wrong. If I hadn't caught it, it would have gone to a real client with real money on the line.
That was the easy version. Here's the harder one.
I was reviewing an AI's analysis of medical call center data — real calls, real patients, real operational metrics. The analysis looked thorough. Complete with percentages, trends, and recommendations. Then I ran the same data through Claude as an audit. Claude caught inflated percentages, hallucinated citations, and conclusions that didn't follow from the data. AI auditing AI. That's the verification loop.
This is where the "slop factory" lesson comes in. I got excited about AI at work early on and encouraged my team to use it. They did. Then I had to read what they produced. AI-generated garbage, submitted as finished work. I stopped recommending AI to anyone who couldn't look at the output and ask: "Is this actually right?"
The tool is only as good as the person checking its work. And the best way to check AI's work is often to use a different AI to challenge it — then apply your own judgment to the disagreement.
Rule 4: The interface is disappearing. Don't build on top of it.
October 2025. I was deep in property management consulting, and I realized something that changed how I evaluate every AI project: the interface itself is temporary.
Every company was investing in dashboards. Polished UIs. Beautiful front-ends for AI features. And every one of those investments was at risk — because the trend is unmistakable. AI is moving from "tool you interact with through a screen" to "layer that runs underneath your existing workflow." The chat window, the dashboard, the drag-and-drop builder — they're all transitional.
A leader at the time told me to quadruple my output because "we have AI now." Not better work. More work. That's the same mistake as the dashboard problem — optimizing the visible layer instead of rethinking the workflow underneath.
Don't polish the dashboard. Rethink what the dashboard was doing in the first place. If a human is looking at a screen to make a decision, ask whether that decision can be made automatically by an AI agent with the right data, the right rules, and the right system of record underneath it.
The companies that win aren't building better interfaces for AI. They're eliminating the need for the interface entirely.
Rule 5: Just because you can build it doesn't mean you should.
This one's fresh. Spring 2026.
AI makes building so cheap that it's tempting to build everything. I know because I did it. I built a custom AI email pipeline — classifier, draft agent, routing logic, the full hand-rolled stack. Three days of work. Clean code. Working system.
Day four, I killed it. Found an off-the-shelf tool that did 120% of what I needed, maintained by a team, updated automatically, for less than the cost of the infrastructure I would have paid to run my custom version.
Three days of work, straight into the trash. And it was the right call.
The real cost of building isn't the code — AI writes the code now. The real cost is maintenance, edge cases, security updates, and the attention tax of running something yourself when someone else will do it better. Your time has a price. Spend it on the things only you can build.
I've spent the last two months killing projects, not starting new ones. Every "should I build this?" now gets filtered through: "Can I buy something that does 80% of this? If yes, buy it. If no, build it. If I'm not sure, wait."
That filter has saved me more than any tool I've ever used.
Rule 6: Your AI has no memory. Build it one.
This is the one nobody talks about yet.
You can run six AI platforms. You can orchestrate them perfectly. You can verify every output. And none of it matters if every conversation starts from zero.
No single AI remembers enough on its own. Claude doesn't know what you told ChatGPT yesterday. ChatGPT doesn't know the decision you made in Salesforce last week. Gemini doesn't know your voice, your clients, your history. Every platform is an island. Your context — the accumulated knowledge of what you've built, what you've tried, what worked and what didn't — lives in your head and nowhere else.
In March 2026, I built a Second Brain — a personal knowledge base with its own MCP server, about 300 lines of Python — so Claude could access accumulated context across sessions. Not because it was a fun project. Because I was re-explaining the same context for the fifteenth time and realized: if I don't build the memory layer, nobody will.
The companies that figure this out early will move faster than the ones that don't. Right now, most teams treat each AI interaction as a one-off. Ask a question, get an answer, close the tab. The compounding value of AI doesn't come from individual prompts. It comes from continuity — and continuity is your job to build.
Rule 7: Build the feedback loop — or waste the work.
This one cost me real time before I learned it.
Without AI checking AI, you risk overwriting your own work and burning hours on output nobody verified. You make a change, the model makes a change on top of your change, and somewhere in the stack the original intent gets lost. Three hours later you're debugging something that was working before the AI touched it.
The feedback loop isn't optional. It's the difference between shipping and spiraling.
This is different from Rule 3. Rule 3 is about catching hallucinations — the AI lying to you. This rule is about building systems where verification happens automatically, before the output reaches you. AI auditing AI. One model writes, another model reviews. The disagreements surface before they become production bugs.
Right now I'm building self-feedback systems — infrastructure where the AI surfaces its own issues, logs them, and queues them for my review. Not me hunting for problems. The system telling me where they are before they reach the client.
Most people run AI in open-loop mode: prompt in, output out, trust the result. The moment you close that loop — where the output gets checked, challenged, and validated before it ships — everything changes. Your error rate drops. Your confidence goes up. And you stop wasting time fixing things that should have been caught upstream.
If you're using AI without a feedback loop, you're driving without mirrors.
Rule 8: The learning compounds — but only if you're intentional.
Here's the proof.
In April 2026, I started using OpenAI Codex — my sixth AI platform. In 36 days, my usage went through the same stages that took me 39 months across the other five tools. Day one: search assistant. Week two: implementation partner. Week five: running system audits and architecture decisions.
36 days to replay 39 months. That's not because Codex is better. It's because the pattern was already installed. Once you've done the long version of the learning curve — once you've built the instincts for how AI thinks, where it breaks, what it's good at and bad at — you can run that pattern inside any new tool at 20x speed.
But here's the part most people miss: this only works if the learning was intentional. If you're using AI casually — a prompt here, a summary there, never stopping to understand why one approach worked and another didn't — the learning doesn't compound. You just accumulate hours without accumulating skill.
This isn't even the first time I've done this. Integration platforms in 2013. Strategy consulting in 2019. AI in 2023. Each time the same pattern: deep immersion, public building, teaching what I'm learning, then moving to the next layer. Four cycles. The platforms change. The process doesn't.
And here's what most people get wrong about timing: the thesis arrives before you recognize it. I was writing "pay attention to the downstream impacts of your software choices" in 2022. "Your knowledge base is the foundation — garbage in, garbage out" in 2023. "AI won't fix a broken process" in early 2024. The core operating principle — that AI exposes your systems more than it fixes them — was substantively complete by mid-2024. What changed in 2026 wasn't the thinking. It was the packaging: turning a recurring observation into a practice.
That means the next thesis is probably already in my 2026 writing in raw form. I just haven't named it yet. If I had to guess: the conversation layer is the missing middle in every RevOps stack. I've written that three times in three different ways in the last six weeks. That's usually the signal.
In November 2023, I asked Claude what "vectirization" was. I misspelled vectorization. Thirty months later, I'm shipping production voice agents, maintaining my own AI skill stack, and running six platforms in parallel. That trajectory isn't normal. But it is repeatable — if you invest in the learning until the learning starts investing in you.
Where are you?
Most people I talk to are somewhere in the first two stages. That's not a knock — it's how the curve works.
Just using one tool. You have a ChatGPT account. You ask it questions. It feels like magic. You haven't tried anything else because why would you?
Using it as an assistant. You use AI daily for writing, editing, research. It saves you time on things you already know how to do. You measure its value in hours saved.
Starting to orchestrate. You've got accounts on several platforms. You're developing preferences — this one for writing, that one for research. You might not realize you're doing it yet.
Building systems. You're not just using AI — you're creating things with it. Automations, agents, tools that run without you typing a prompt. You measure its value in systems built.
Running operations. AI isn't a tool you open. It's infrastructure you rely on. Multiple platforms, intentional choices, production-grade work. You'd notice within an hour if it went down.
The biggest gap is between "using it as an assistant" and "building systems." That's where most people and most companies stall. The eight rules above are how you cross it. Not by finding a better tool — by building better judgment about the tools you already have.
Mendy Ezagui is the founder of Aventary. A decade of integration work (Workato, Salesforce, enterprise CRM) before AI, five years of strategy consulting at PwC, 14 Salesforce certifications, and 39 months of building AI systems across six platforms. If your team is stuck between "we have AI tools" and "AI is actually working for us" — that's the gap he closes.