Develop AI Strategy: Lessons from Four Companies That Got It Right (and One That Didn’t)
When companies set out to build their AI strategy, the temptation is to think big—go all in, transform everything, and lead with the latest tools. Here’s how different businesses approached it, rightly or sometimes wrongly—and what they learned. Isn’t it better to learn from others, than to make your own mistakes?
1. The Retail Chain That Focused on Returns
Faced with spiralling return rates and bloated inventory, a national retailer decided to develop AI strategy—but with a twist. Instead of starting broad, they focused on a narrow use case: predicting product returns by customer segment. The internal data science team partnered with operations and customer service to co-create the model logic.
Within six weeks, the AI model flagged six high-risk SKUs, leading to changes in product descriptions, sizing charts, and packaging. The result? A 19% drop in return-related losses over the next quarter. They didn’t invest in sweeping transformation. They simply picked the right problem to solve first.
2. The Financial Services Firm That Got Lost in the Tools
This one started with fanfare: a new AI task force, a high-profile consulting partner, and a roadmap that promised everything from fraud detection to robo-advisory services. But no one asked the hard questions upfront. When they tried to develop AI strategy, they skipped stakeholder alignment and rushed to deployment.
Three months in, the fraud detection model produced too many false positives. Customer service agents ignored the alerts. The robo-advisor never made it past beta due to compliance gaps. By the time leadership stepped in, $1.2 million had been spent with no meaningful impact. AI tools don’t replace strategy. They execute it. And strategy must come first.
3. The Manufacturer That Started in HR
An industrial manufacturer might not seem like the place you’d expect AI innovation—but that’s exactly what made their approach so effective. Instead of looking to optimise machines, they focused first on people. The company’s Head of HR wanted to understand patterns behind attrition, absenteeism, and performance plateaus.
Their goal to develop AI strategy began with workforce analytics, not machinery. The pilot revealed that shifts in middle management had a disproportionate impact on shop-floor engagement. They created an intervention programme for new managers—and saw a 14% improvement in staff retention within 90 days. AI doesn’t have to begin in IT. Sometimes it starts in culture.
4. The Telecom Provider That Built a Data Playbook First This telco giant had a sprawling tech stack, siloed data sources, and multiple vendor relationships. Instead of rushing into AI, they created a centralised “Data Integrity Taskforce.” For the first 60 days, their goal wasn’t to build anything—it was to clean, align, and document. Only once they’d built trust in the data did they begin to develop AI strategy, starting with churn prediction for prepaid customers.
Their model had a 92% confidence threshold—so reliable that it became part of weekly sales planning. Not because the AI was advanced, but because the foundations were. They learned the hard way: trust is the first layer of any AI stack.
5. The Startup That Believed AI Would Save It
This SaaS startup was struggling with customer acquisition and product adoption. Pressured by investors, the CTO insisted on an AI-driven onboarding engine. They hired two engineers and rushed the build. What they didn’t do? Talk to their customer success team, map the current onboarding journey, or evaluate readiness.
The AI engine launched—on time, but out of sync. Drop-off rates spiked. NPS scores fell. What followed was a costly rollback and even costlier reputational damage. The lesson? You can’t develop AI strategy from pressure. You develop it from precision.