Professional Services, Cloud Platform Services, AI
Companies that actually use AI make more money. Most businesses are just testing small ideas, but the ones using AI for their daily work are already seeing higher profits, according to research done by MIT.
We see that many companies that approach Revolgy are stuck with AI. They run small tests and build simple pilots on their own. But when it comes to actually growing the business, these AI tools are left unused.
There is a big difference between being ready for AI and being mature with it.
- AI-ready is just having the right data and tools, but not yet using them. You might have a database and a ChatGPT subscription. You use it to write emails. You save 10 minutes a day.
- AI-mature means using those tools every day to get results. You’ve connected your CRM data to an AI that predicts which customers are about to leave. Your sales team calls those customers first, resulting in a saved $100k in revenue in one month.
If you want AI to increase your profits, you should stop experimenting and start using it at a large scale. Here is what the latest data says about “winning” with AI, and how Revolgy helps you get there.
Why AI maturity matters
If you want to know why some companies succeed with AI while others fail, look at their maturity level. It isn’t just about having the latest technology; it is about how that technology changes the business.
June 2025 research from Gartner and recent data from MIT Sloan show a clear divide between “mature” companies and “low-maturity” companies:
| Benefits | Mature AI companies | Low-maturity companies |
| Money | They make more profit than their competitors | They struggle to see any financial gain |
| Success rate | 45% of their projects last for 3+ years | Only 20% of their projects survive |
| Trust | 57% of employees trust the AI tools | Only 14% of employees trust the tools |
| Leadership | 91% have a dedicated AI leader | AI is treated as a side task for IT |
Where do you stand? The 4 stages of AI maturity
You cannot build a roadmap if you do not know where you are starting. Research from MIT Sloan shows that most companies fit into one of these four categories.
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(Source: MIT CISR 2024)
How to move to the next stage
Knowing your stage is only useful so you know how to leave it. Each stage requires a different strategy to move forward.
Stage 1 to 2: Moving from curiosity to proof
If you are in stage 1, your main task is to show that AI can work for your specific business. So, stop reading general news and start testing.
Pick a few simple, daily tasks (like writing reports or summarizing long documents) and track how much time they save. Your goal here is to help your team feel comfortable using the tools and to prove that the technology is worth the investment.
Stage 2 to 3: Moving from pilots to scaling
This is where most businesses see the biggest obstacle. To reach stage 3, you have to stop approaching AI as a series of separate tests. The main problem is usually disconnected data.
If your sales information and customer information are stored in different systems, your AI just won’t be accurate. But a bit more on that in the section below.
Stage 3 to 4: Becoming future-ready
At the highest level, you stop using the same generic tools as your competitors. You move to Stage 4 by using your own company data to build unique solutions.
At this point, AI is not just an extra tool; it is built into the way you make decisions. You are no longer just following what’s trending but instead using your own technology to create a competitive advantage that others cannot easily copy.
Why bad data kills good AI
It does not matter if you are in stage 1 or stage 4. Every company faces the same problem of data hygiene. If you spend $50,000 on an AI model but your data is still in 10 different spreadsheets, you just wasted $50,000.
In other words, if your information is disorganized, hard to find, or stored in old systems, even the most expensive AI will fail. You cannot get a good return on your investment if your foundation is weak. This is the hidden work that many leaders ignore.
You need a modern cloud where your tools can actually run, and a clean, organized information that is ready to use, and a secure environment to protect your company.
If your departments do not share their data or your network is outdated, you are losing before you even start. You must fix your data and your systems before you can be successful with AI. And the best way to do that is by working with a partner.
How Revolgy makes the cloud (and AI) work for you
At Revolgy, we believe technology only works if people know how to use it. We are experts in Google Cloud and AWS, and we help you move past the testing phase so you can actually see results.
1. Building a strategy that makes money
The biggest mistake is adopting AI just because it is popular. We sit down with you to find one or two real business problems. We then show you how to use tools like Gemini to solve those specific issues, making sure your AI investment actually pays for itself.
2. Connecting your data
We clean your data, connect your different departments, and move your systems to a modern cloud. This ensures your AI has a strong, safe foundation to run on.
3. Scaling while staying safe
AI can get expensive fast. We manage your cloud spending so you stay profitable. We also handle your security and daily operations.
Read next: How to make the most of AI in Google Workspace (free ebook)
