Scale High-Volume Marketing Variants with Gemini, Not Headcount

The payments industry faced a surge in online fraud, particularly enumeration attacks where threat actors use automated scripts and botnets to test stolen card details at scale. These attacks exploit vulnerabilities in card-not-present transactions, causing $1.1 billion in annual fraud losses globally and significant operational expenses for issuers. Visa needed real-time detection to combat this without generating high false positives that block legitimate customers, especially amid rising e-commerce volumes like Cyber Monday spikes. Traditional fraud systems struggled with the speed and sophistication of these attacks, amplified by AI-driven bots. Visa's challenge was to analyze vast transaction data in milliseconds, identifying anomalous patterns while maintaining seamless user experiences. This required advanced AI and machine learning to predict and score risks accurately.

Unilever, a consumer goods giant handling 1.8 million job applications annually, struggled with a manual recruitment process that was extremely time-consuming and inefficient . Traditional methods took up to four months to fill positions, overburdening recruiters and delaying talent acquisition across its global operations . The process also risked unconscious biases in CV screening and interviews, limiting workforce diversity and potentially overlooking qualified candidates from underrepresented groups . High volumes made it impossible to assess every applicant thoroughly, leading to high costs estimated at millions annually and inconsistent hiring quality . Unilever needed a scalable, fair system to streamline early-stage screening while maintaining psychometric rigor.

Jet engines are highly complex, operating under extreme conditions with millions of components subject to wear. Airlines faced unexpected failures leading to costly groundings, with unplanned maintenance causing millions in daily losses per aircraft. Traditional scheduled maintenance was inefficient, often resulting in over-maintenance or missed issues, exacerbating downtime and fuel inefficiency. Rolls-Royce needed to predict failures proactively amid vast data from thousands of engines in flight. Challenges included integrating real-time IoT sensor data (hundreds per engine), handling terabytes of telemetry, and ensuring accuracy in predictions to avoid false alarms that could disrupt operations. The aerospace industry's stringent safety regulations added pressure to deliver reliable AI without compromising performance.

In the demanding world of maritime logistics, Maersk, the world's largest container shipping company, faced significant challenges from unexpected ship engine failures. These failures, often due to wear on critical components like two-stroke diesel engines under constant high-load operations, led to costly delays, emergency repairs, and multimillion-dollar losses in downtime. With a fleet of over 700 vessels traversing global routes, even a single failure could disrupt supply chains, increase fuel inefficiency, and elevate emissions . Suboptimal ship operations compounded the issue. Traditional fixed-speed routing ignored real-time factors like weather, currents, and engine health, resulting in excessive fuel consumption—which accounts for up to 50% of operating costs—and higher CO2 emissions. Delays from breakdowns averaged days per incident, amplifying logistical bottlenecks in an industry where reliability is paramount .

DBS Bank, Southeast Asia's leading financial institution, grappled with scaling AI from experiments to production amid surging fraud threats, demands for hyper-personalized customer experiences, and operational inefficiencies in service support. Traditional fraud detection systems struggled to process up to 15,000 data points per customer in real-time, leading to missed threats and suboptimal risk scoring. Personalization efforts were hampered by siloed data and lack of scalable algorithms for millions of users across diverse markets. Additionally, customer service teams faced overwhelming query volumes, with manual processes slowing response times and increasing costs. Regulatory pressures in banking demanded responsible AI governance, while talent shortages and integration challenges hindered enterprise-wide adoption. DBS needed a robust framework to overcome data quality issues, model drift, and ethical concerns in generative AI deployment, ensuring trust and compliance in a competitive Southeast Asian landscape.