In boardrooms across the industry, a comforting narrative is taking hold:
"Our competitors are also holding back. The economy is bad. We don't need to lead; we just need to be one step ahead of the firm next door when the time is right."
Is that a strategy? Or does it mistake a relative race against known competitors for an absolute transformation of the entire playing field.
Is this a footrace on a track where you can see the other runners or is this a race to build a boat while the tide is going out. The firm that builds a functional vessel first will sail away; the ones waiting for a better price on lumber will be left stranded on the mudflats, arguing about who is "one step ahead" of whom.
The Downturn is the Perfect Camouflage for a Revolution
A slow economy is precisely the time when the most significant competitive separations occur. It is the ultimate cover for a strategic land grab.
- Talent
- In a booming market, the talent you need to drive this change is expensive and already employed. In a downturn, you have a unique window to recruit top-tier, forward-thinking talent who are disillusioned with stagnant firms.
- Resistance
- When the alternative is layoffs or stagnation, a strategic investment in future-proofing the business can be a powerful morale booster. It gives your best people a mission and a reason to stay, signaling that the firm has a plan beyond mere survival.
- Lifeline
- The goal of AI investment in a downturn isn't to win a glamorous new award; it's to radically lower your cost base and de-risk delivery. It's about doing the same (or better) work with fewer people and less risk of costly errors. This isn't a discretionary spend; it's a strategic imperative for survival and future profitability.
Error 404
The fundamental error is believing that AI adoption is a switch you can flip later. It is not. It is a deeply embedded organizational competency that takes years to cultivate.
The firm that invests now is not just buying software; it is:
- Building its Proprietary Data Asset: They are accumulating a structured dataset of their projects, decisions, and outcomes that will become the training ground for their own proprietary AI.
- Training its "AI Muscle Memory": Their teams are learning to think and work in partnership with AI. They are failing, learning, and adapting in a low-stakes environment.
When (if) the economy recovers (i.e. 2028 when the property market in China bounceback in a conservative manner), the "waiting" firm will have to buy the same software as the "prepared" firm. But they will be years behind in data, experience, and culture. They will be forced to pay a premium to poach talent from their more advanced competitors, all while trying to retrofit a new technology onto old, broken processes.
The gap will not be a single step; it will be a chasm. The "prepared" firm will be competing on an entirely different plane—bidding on projects the "waiting" firm is technically incapable of delivering, with a speed and efficiency they cannot match.
The Flawed Analogy: Asset vs. Capability
‘Why should I use this version of AI (whatever it is) now when the newer version is coming out soon’
The "Upgrade Cycle" is a Learning Curve, Not a Tax. Viewing new versions as a burden misses the point. Each iteration represents a leap in capability. A team that has mastered Version 1.0 is perfectly positioned to harness the power of Version 2.0. The team starting with Version 2.0 from scratch is already years behind in practical experience. The learning curve is the real investment, and it cannot be fast-tracked.
Ride
Focusing on being "one step ahead" of your known rivals is like meticulously polishing a horse-drawn carriage while Henry Ford perfects the assembly line. You are optimizing for a race that is about to be rendered obsolete.
The Data Chasm Grows
AI thrives on data. A company that implements AI today isn't just automating a task; it is beginning to accumulate a structured, actionable dataset that will become its most valuable asset. The company that waits has a data void. In three years, the gap between the data-rich and the data-poor will be unbridgeable, creating a permanent competitive moat.
Two cents. Or pence. Thoughts? All welcome.
Responses