Don’t Buy More AI Tools Before You Fix Your Data Mess
You bought the AI tool. Your team watched the demo, nodded along, and the salesperson promised it would “just work” with your current setup. A month later, you’re manually copying data out of the tool into the same spreadsheets you’ve always used, wondering what went wrong.
Here’s the uncomfortable truth: the AI is probably fine. The mess underneath it — your systems, your data, your workflows — is the real problem. And it’s not just a small-business problem. Multinational companies hit the same wall, and an entire new profession has appeared just to help them climb over it.
TL;DR: A new AI startup called June, backed by Marc Benioff’s Time Ventures, argues that the hardest part of AI isn’t the AI — it’s the messy, fragmented systems it has to plug into. For Malaysian SMEs, the takeaway is simple: before you buy another AI tool or automation platform, fix the data and workflow mess underneath. The company that cleans up first wins.
What This Means
June emerged from stealth in August 2026 with backing from Marc Benioff’s Time Ventures, Michael Dell, Aaron Levie, and George Kurtz. Their founding argument comes from cofounder Efrat Rapoport, a former Salesforce executive: “Before AI can create value, someone has to deal with legacy systems.” Source
Here’s the context. Big companies are struggling so much to get AI agents working that forward-deployed engineers (FDEs) — specialists who parachute into a company and manually wrestle AI into working with existing systems — have become a booming profession. That approach is slow, fragile, and creates what one customer calls a “black box” only certain people can figure out.
June’s alternative: their platform scans a company’s existing systems, maps out business processes, finds bottlenecks, and produces a step-by-step implementation roadmap — “remove these duplicates. Connect to this data source.” — then builds each task for you. Rapoport says the company gives you “the full roadmap automatically of what needs to happen step by step for you to actually implement this agent successfully in an enterprise environment.” Source
How This Applies to Malaysian SMEs
You might think that’s a big-company problem. “I don’t have legacy systems,” you say. But you have something messier: your customer data lives in WhatsApp chats, a Google Sheet, and a dusty CRM nobody updates. Your inventory sits in one system, your accounting in another, and orders arrive through Shopee, Facebook, and walk-ins. When you connect an AI assistant to that mess, it pulls from all of these at once and produces confidently wrong answers.
The article tells the story of CMG, a major U.S. mortgage lender, whose team moved to Claude Code quickly but then hit a wall integrating it with Salesforce. They’d promised to have 100 AI agents running and weren’t going to make it. Weeks of meetings with architects and consultants got them nowhere. Source Your version of that story is smaller, but the damage is the same: a month-end closing that takes three extra days, a customer who receives two conflicting messages, a stock count that never matches the system.
Here’s the detail that should hit home. Rapoport describes enterprise databases with “10 duplicate [database] fields that say the same thing, and different teams are using them.” Source At a Malaysian SME, that’s the three spellings of the same customer — “Ahmad,” “Ahmad bin Ali,” “ahmad ali” — scattered across WhatsApp and your CRM. Your AI agent tries to reconcile sales and quietly double-counts. That’s not an AI failure. That’s a data hygiene problem that AI simply exposes in broad daylight.
“If your product requires FDEs, I don’t want your product. I’ve already done that and I’m getting annoyed by it. I don’t want a black box. I don’t want something only certain people can figure out. I want an easy-to-use tool.” — Paul Akinmade, chief strategy officer at CMG Source
That quote should resonate with every Malaysian business owner. You don’t have the bandwidth or patience for a black box. You need tools you can understand, and you need to see what’s happening in plain language. The June approach — audit first, roadmap second, build third — is a process you can copy even without their software. Before you let any automation tool near your business, know exactly what data you have, where it lives, and which parts contain duplicates. Then you’re ready.
There’s another angle for SMEs. The article notes that “the industry’s answer to AI implementation is, ‘let’s hire more and more and more people.’” Source That’s exactly what a small business cannot do. You don’t have a bench of integration specialists. Your only realistic move is simplification: fewer tools, cleaner data, documented processes. That gives you something big companies desperately want — an environment where AI works on the first attempt.
Practical Takeaways
- Audit before you buy. List every place customer, inventory, and financial data lives. If it’s in five places, no AI tool will fix that.
- Find your duplicates. Search for repeated customer records, conflicting product names, mismatched prices. Clean them before connecting any AI agent.
- Pick a source of truth. Decide which system wins when data disagrees — and tell your team in writing.
- Demand the roadmap. Any vendor should show you, step by step, how their tool integrates with your actual systems before you commit.
- Refuse black boxes. If nobody can explain how the AI makes decisions in plain language, walk away.
- Start with one workflow. One process, real data, measurable outcome. Resist the urge to run 100 agents.
Here’s what June’s story reveals in numbers — and what you should look for in your own operation:
| Detail from June’s story | What it tells you |
|---|---|
| “10 duplicate [database] fields” (source) | Even enterprise systems contradict themselves. Find your own conflicts before automating. |
| 100 promised agents (source) | The gap between an ambitious target and actual implementation. Build one workflow at a time. |
| 2017 (source) | The founders launched their first AI company that year — the deployment problem is years old, not a passing phase. |
| 2 years (source) | Time from that company’s launch to its Salesforce acquisition. Even deep AI expertise doesn’t make integration trivial. |
The Bigger Picture
Here’s the long-term view. AI models keep getting smarter, but “smarter” doesn’t mean “easier to install.” For the next few years, the winners won’t be the businesses with the most advanced algorithms. They’ll be the ones with the cleanest data and clearest processes. That’s good news for Malaysian SMEs: while large enterprises send in armies of specialists to untangle years of technical debt, you can build a simple, clean operation right now. When AI tools eventually become self-deploying, you’ll be ready in weeks — not years.
June’s underlying bet is that AI can eventually solve its own deployment problem. Until that arrives, the practical reality stands: you cannot automate chaos. The SME owners who treat data hygiene as a competitive advantage, who keep their tool stack small, and who refuse to accept black-box systems will get value from every AI tool they adopt.
So before you sign up for the next AI assistant or automation platform, do the unglamorous work. Map your systems. Clean your data. Write down your workflows. That’s the only deployment plan that matters — and no vendor can sell it to you.
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