AI has collapsed the time it takes to build a company that relies on software. For established businesses, once-protective systems are now vulnerabilities new competitors can exploit.
The Competitor You Have Not Met Yet
Every CEO carries a mental list of competitors. It includes the rivals you meet in deals, the names analysts compare you to, and perhaps one well-funded upstart. That list rarely includes the company that doesn’t exist yet.
With the rise of AI, the company that doesn’t exist yet may be the most dangerous one on the board. An AI-native company builds its products and operations on AI from day one. New companies will launch with no legacy platform, no decade of workarounds, and no multi-year modernization roadmap. They will build on AI from their first line of code. They will also reach your customers faster than any competitor ever has.
For most of the last 30 years, a mature software estate worked like a moat. It held your data, your workflows, your integrations, and your customers’ habits. Replicating it took years and serious capital. That equation is changing quickly, and the change favors whoever carries the least weight.
Three realities explain why this matters now:
New Competitors Can Now Build Fast
Building software used to be slow and expensive. A new company needed large teams, lots of money, and years of work to catch up to an established player. That gap protected you.
AI has shrunk that gap. Tools that write, test, and check code are now cheap and easy to get. A small, focused team can do work that once needed a large department.
Related reading: Coding Assistants Made Developers Faster. They Did Not Compress the Pipeline.
You can already see the shift in how fast new companies grow. Stripe studied the top 100 AI companies on its platform in 2025. The typical company reached $1 million in yearly revenue in just under a year (11.5 months). That is about four months faster than the fastest-growing subscription software companies at the height of their boom [1].
Financial services are already feeling this pressure. McKinsey projects that fintech revenues will grow about 15% a year through 2028, compared with 6% for traditional banking. That is almost three times faster [9]. If you lead a bank, lender, or financial services firm, the next fast-moving competitor may already be in your market.
Manufacturing is facing the same shift. In Deloitte’s 2025 Smart Manufacturing and Operations Survey, 92% of manufacturing executives said smart manufacturing is now the main driver of competitiveness [10]. Smart manufacturing runs on connected data and modern software. Manufacturers still running on decades-old systems will find it harder to keep pace with competitors built for that world.
Building with AI also costs far less than it did a few years ago. Stanford’s 2025 AI Index found that running a model at GPT-3.5-level performance became more than 280 times cheaper between November 2022 and October 2024 [2]. That drop lowers the bar for anyone who wants to enter your market.
Nearly everyone now has access to these tools. McKinsey reports that nearly nine in ten organizations use AI in at least one business function [3]. Gartner expects 90% of enterprise software engineers to use AI code assistants by 2028, up from less than 14% in early 2024 [4]. When everyone has the same tools, the winner is whoever turns them into products customers can use first.
Old Systems Give New Competitors a Way In
Your current systems built your business. They also limit what you can do next. Every feature you cannot add, every connection your platform cannot make, and every change that takes too long is a gap. A new competitor will look for those gaps first.
Think about the requests your customers make that your team keeps pushing to next year. A startup with no old systems can build those features first. It can then make them the reason your customers switch.
Old systems also eat up time and money meant for growth. McKinsey surveyed CIOs at large financial services and technology companies about this burden. They said technical debt, the build-up of old and patched-together systems, equals 20% to 40% of the value of their technology. They also said 10% to 20% of the budget for new products goes to fixing old problems instead [5]. Sixty percent said the problem had grown over the prior three years [5].
Related reading: Legacy System Modernization Cost in 2026: How AI Changes the Business CaseÂ
In plain terms, part of what you spend on growth goes to keeping the past running. A new competitor does not carry that weight. It can put all of its effort into what your customers want.
The same problem holds back AI plans. Gartner found that 63% of organizations either lack or are unsure they have the right data practices for AI. It predicts that through 2026, organizations will abandon 60% of AI projects that lack AI-ready data [6]. Much of that data sits in older systems. While your AI plans wait on those systems, a competitor with a clean start keeps delivering.
Waiting to modernize is the biggest risk
Speed is now one of the few advantages that is hard to copy. Anyone can buy AI tools. Few companies can turn ideas into working products quickly and keep doing it. McKinsey names development speed as one of the advantages that lasts [3]. Its research found that companies in the top quarter for software speed grew revenue four to five times faster than those in the bottom quarter. They also delivered 60% higher returns to shareholders [7].
The gap between leaders and everyone else is growing. McKinsey reports that the gap between digital leaders and laggards has widened by roughly 60% in recent years [3]. Each quarter spent waiting makes it harder to catch up.
Long update projects add to the risk. A plan that takes 18 to 24 months locks in today’s ideas about the market. By the time it is done, customers and competitors have moved on. McKinsey and the University of Oxford studied more than 5,400 IT projects. Large ones delivered 56% less value than expected, and about 17% went so badly they threatened the company itself [8].
The better path is to take on legacy modernization in smaller steps. Pick one real piece of work, prove the new approach on it, and measure the results. Expand based on what you learn. Each step lowers risk and builds momentum.
Related reading: AI-Enabled Application Modernization: Modernize Core Systems in Months Not YearsÂ
The question for leaders has changed.
Five Questions for Your Next Leadership Meeting
If you have read this far, you may be feeling the weight of it. Old systems, new competitors, and a market that will not wait make it hard to know where to begin. That feeling is common, and it is often what keeps good companies stuck.
The first step is smaller than it seems. It starts with an honest conversation among the people who lead the business. These five questions give the CEO, CIO, and CFO a simple place to start.
1. What would an AI-native competitor build against us?
Ask a small team to imagine they are launching a startup to win your best customers. Give them two weeks and a simple brief: what would they build, who would they target first, and how fast could they launch? Have them list everything their startup would offer that your current platform cannot deliver.
Then bring that list to the leadership team. Rank each item by how much it matters to your customers and how easy it would be for a competitor to build. The items near the top of both rankings are your biggest risks, and they should shape your modernization priorities.
2. Which parts of our strategy are our systems blocking?
Ask the CIO to list every roadmap item that has been delayed, scaled back, or shelved because of platform limits. Include the reason for each one, such as data that cannot be reached, systems that can’t connect, or changes that take too long to test.
Compare this list with the one from the first question. Where the two overlap, your systems are blocking the exact features a competitor would use against you. Those overlaps are where modernization should start.
3. How long does an approved idea take to reach a customer?
Pick three recent features and trace each one from the day it was approved to the day customers could use it. Note where the work waited, such as for requirements, approvals, testing, or release windows. The waiting time usually tells you more than the building time.
Share the results with the leadership team as one number: your idea-to-customer time. Set a target to shorten it, and track it every quarter. If the honest answer today is several quarters, assume a well-funded entrant’s answer is far shorter.
4. Are we funding modernization as one large bet or in proven stages?
Long programs put all the risk on one result that is years away. Ask the CFO to review how current and planned modernization work is funded. Look for programs where money is committed for years before anyone sees working results.
Then move toward staged funding. Start with one small, real project, agree on how success will be measured, and release the next round of funding only when the results are in. This gives the CFO evidence at every step and the freedom to shift money as the market moves.
5. Who stays accountable when AI helps us build faster?
Speed without control simply trades one risk for another. Before scaling AI in software delivery, agree on who approves each change, who reviews the work, and how every decision is recorded.
Write these rules down and share them with your audit, risk, and compliance teams early. When they help set the guardrails, they can support faster delivery with confidence. Every change should have a named person who owns it, a clear approval, and a record you can trace.
Related reading: AI Agent Security: How to Scale Agentic Software Delivery Without Losing Control
Putting the Answers Together
Taken together, the five answers show where you’re exposed, what is slowing you down, and where to begin. The best first project usually sits where the answers point in the same direction. Look for a real customer need, blocked by your current systems, that you can prove out in weeks.
The companies that answer these questions early keep the choice of how and when to move. The companies that wait often find a competitor has made that choice for them.
How QAT Global Speeds up Legacy Modernization
We have spent more than 30 years helping companies modernize the systems that run their business and build new features that keep them competitive. The pattern we see most often is simple. Leadership knows the platform must change, and the timeline is what keeps the decision on hold.
Nelnet Payment Services faced that exact pressure. The team needed a childcare billing and payments platform in market within a fixed window. Missing it would have pushed the opportunity back a full year. Working with QAT Global, the platform was built, tested, and live in five months. The work was originally estimated at 18 to 24 months with traditional delivery. Four engineers delivered it instead of the roughly 10 a conventional team would need. Quality assurance (QA) cycles dropped from three weeks to two days, and all seven delivery phases finished on time.
That speed came from Diamond AI, QAT Global’s AI-accelerated software delivery model. Diamond AI applies AI agents across the entire delivery lifecycle, from requirements through release. It is built around two of the questions this article asks leaders to raise.
On speed, Diamond AI shortens the path from approved idea to customer, which is exactly what question three measures. It delivers 5 to 10 times the delivery throughput of traditional methods, as the Nelnet program showed against a real deadline.
On accountability, Diamond AI keeps humans in the loop at every step, just as question five requires. Every piece of work starts from an approved specification. AI agents do the building, while a named person approves every change, reviews the work, and owns every release. Each step is recorded, so leaders get the speed of an AI-native company with the traceability a regulated business requires.
Decide on your timeline, before a competitor decides it for you
If your platform is holding back the strategy your board expects, a faster path exists today. Talk with a QAT Global Diamond AI expert and discuss how to build what an AI-native competitor would build, before they do. Explore Diamond AI Solutions to start the conversation.
