In early 2018, Carrefour Taiwan decided on an AI Business Strategy and to develop a system to forecast demand and reduce perishable waste across select stores. The model looked promising in development. But store-level data was inconsistent, floor staff didn’t trust the alerts, and the system wasn’t integrated into day-to-day operations. Within months, the pilot was shelved not loudly, just quietly, like so many others.
This wasn’t a one-off. It was normal.
Back then, AI primarily resided within IT teams or innovation labs. Business leaders were interested but not involved. Most pilots lacked a clear goal, a strong business sponsor, or a plan to scale. And because no one owned it, no one missed it when it failed.
AI in business wasn’t expected to deliver results. It was there to check a box. That mindset kept it on the sidelines for years.
The COVID Effect: Why 2020 Changed the Game
In 2020, businesses stopped experimenting and started firefighting. Offices shut down. Supply chains cracked. Manual processes became liabilities. Automation was no longer a future priority. It became the only way to keep things running.
AI Business Strategy found its opening. Not because it was new or flashy, but because it could move faster than most teams. Chatbots handled the spike in customer queries when call centers went offline. Inventory models helped retailers manage shortages and shifting demand. HR teams used AI to track employee sentiment across remote teams. What had once lived at the edges of operations moved to the center, almost overnight.
And this time, it wasn’t just IT pushing the tools. Business heads needed fast solutions. They cleared pilots, skipped multi-layer approvals, and focused on what could be deployed within weeks. The usual blockers, such as long planning cycles, limited budgets, and internal hesitation, gave way to something else entirely. The Urgency!
That urgency didn’t just accelerate AI. It made it real.
Where AI Business Strategy started delivering real value
Urgency got AI through the door. But what kept it there was performance. Once companies saw it working in live environments, it stopped being just another untested backup plan and started becoming part of their core operations.
H&M used AI to fine-tune merchandising decisions across regions. Instead of pushing the same products everywhere, they adjusted inventory to match local demand and moved stock faster with less waste. American Express upgraded its fraud detection with machine learning and cut false positives. Fewer blocked cards. Fewer angry customers. Bosch used AI in its factories to predict equipment failures before they happened. Downtime dropped. Planning got easier. Costs went down.
These weren’t side projects. They were operational wins. And they changed the way business leaders looked at AI in Business. The question was no longer, “Should we try this?” It became, “Where else can we apply it?”
That’s when AI stopped being interesting and started being useful.
From Tech silo to strategic investment
Once AI Business Strategy started delivering real results, it stopped living quietly inside IT. Business heads stopped asking, “Can we try this?” and started asking, “Where else can we use it?”
Budgets followed. What used to be tucked into innovation funds has become full-line items within annual plans. AI was no longer a sandbox project. It was part of how the business expected to run, which is visible on dashboards, tied to KPIs, and built into timelines.
The shift wasn’t just in funding. It was in structure. Companies began looking beyond isolated tools and started thinking in terms of platforms. AI wasn’t something one team played with on the side. It became the engine behind CRM upgrades, customer journeys, pricing logic, and even hiring processes.
Leadership took notice. Some firms created new roles such as a Chief AI Officer. Others didn’t change titles, but made AI the quiet thread running through strategy reviews, vendor choices, and quarterly ops plans. Whatever the model, one thing became clear: AI Business Strategy had moved out of the lab and into the room where business decisions were being made.
What decision-makers need to act on now
AI is no longer a wait-and-watch space. The companies that are ahead didn’t get there by decoding how it works. They got there by figuring out where it fits. They picked a problem that was slowing them down or costing too much, and acted on it.
This isn’t about building complex models from scratch. It’s about asking simple but pointed questions. Where are we relying on guesswork? Which processes still feel manual? Where are we losing time that we can’t afford to waste?
The real risk now isn’t missing the next big innovation. The risk is doing nothing while others move ahead with AI in business.
Start with what you already have – data, processes, people. Choose one use case that’s visible and valuable. Run a controlled pilot. Track the outcome. If it works, expand it. If it doesn’t, adjust and try again.
You don’t need a perfect strategy to move forward. You need a clear one and a willingness to start.
Conclusion
AI didn’t go mainstream because of hype. It happened because real businesses found real value. What was once experimental is now embedded. The companies that moved early didn’t wait to understand everything; they picked a problem, tested fast, and scaled what worked.
That’s the shift. AI is no longer an edge initiative or a tech showcase. It’s a business tool. It saves time, reduces risk, improves outcomes, and creates room for people to focus on what matters most.
If you’re still watching from the sidelines, now is the time to step in. Start with one use case that makes sense. Learn quickly. Build from there.
Because AI Business Strategy isn’t coming. It’s already in the room.