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Int'l Business Times
19-05-2025
- Business
- Int'l Business Times
Bridging Fintech and Freight: Insights from Albert Lie on Payment Infrastructure in Global Supply Chains
Albert Lie, the co-founder and CTO of Forward Labs, is making waves in the logistics and fintech industries by applying the very lessons learned in the digital payments sector to the world of freight and supply chain management. With an impressive background in scaling successful startups, including Xendit—a Southeast Asia-based unicorn that has raised billions in funding—Lie is now setting his sights on revolutionizing logistics through the integration of artificial intelligence (AI) and data-driven automation. This feature delves into the innovative strides that Albert Lie is making in transforming how freight companies manage their sales processes and operational efficiency. By applying fintech principles to logistics, Lie's work promises not only to streamline global supply chains but also to contribute significantly to the broader global economy. The Intersection of Fintech and Freight In the world of fintech, transactions are processed at lightning speed. Payments, which were once cumbersome and prone to delays, are now processed in seconds, thanks to the infrastructure that companies like Stripe and PayPal have built. Albert Lie's experience at Xendit, Southeast Asia's version of Stripe, allowed him to witness firsthand the dramatic impact that seamless, automated payment systems can have on a business's growth trajectory. However, when Lie transitioned to the logistics sector, he discovered a critical issue—despite technological advancements in global trade and supply chains, many logistics companies still relied on outdated processes. The friction and inefficiencies that plagued financial transactions were also present in the freight industry. In fact, some logistics sales teams were still manually sifting through static databases, cold-calling prospects, and struggling to find the right leads, often wasting up to 70% of their time on ineffective tasks. Lie realized that by leveraging the lessons learned from fintech, he could offer logistics companies a game-changing solution—one that would automate the time-consuming processes and allow sales teams to focus on what really matters: closing deals. His vision for Forward Labs is to create a platform that mimics the best aspects of digital payments but tailored specifically for logistics sales teams. Building the Future of Logistics Sales Automation At Forward Labs, Albert Lie and his team are developing an AI-powered search engine that indexes and structures fragmented logistics data. By automating the prospecting process, the platform surfaces high-intent shippers based on real-time data, eliminating the need for tedious research and guesswork. Just as fintech platforms like Stripe revolutionized payments by creating reliable, efficient systems, Forward Labs is set to transform the way logistics sales operations function. The technology Lie is building is groundbreaking. The platform's AI engine automatically enriches data from multiple sources—such as warehouse satellite images, carrier networks, and freight activity signals. In fact, the AI doesn't just surface basic information; it intelligently filters and prioritizes leads based on a variety of dynamic data points, such as a company's shipment history, revenue, and operational scale. By turning logistics prospecting into a data-driven, AI-powered activity, Forward Labs is doing for the logistics industry what fintech giants like Stripe and PayPal have done for financial transactions. "Sales reps are not data analysts," Lie explains. "That's why our platform does the heavy lifting—automatically collecting, structuring, and prioritizing leads—so sales teams don't have to spend hours sorting through fragmented, unstructured data." This AI-driven approach is especially crucial as logistics sales teams face increasing pressure to perform in a competitive and fast-paced industry. The integration of fintech-inspired solutions can improve the bottom line of freight companies by making their operations faster and more efficient, thereby reducing friction in their sales process and driving higher conversion rates. A Disruptive Technology with Broad Economic Impact The ripple effect of applying fintech lessons to logistics is profound, not just for sales teams but for global supply chains at large. Logistics plays a critical role in the global economy, with the sector contributing an estimated $8.1 trillion to global GDP in 2021, according to the World Bank. Yet, inefficiencies and delays in freight management still cost companies billions annually. According to McKinsey, logistics costs account for 11-13% of GDP in most developed countries, and the sector loses an estimated $1.5 trillion annually due to inefficiencies. With forward-thinking solutions like the AI-powered prospecting tool from Forward Labs, companies in the logistics space can significantly reduce these costs. As logistics firms become more data-centric and automated, they can scale operations faster, reduce overheads, and ultimately provide better services to their customers. This has the potential to not only drive profits for logistics companies but also boost productivity across the entire global supply chain. Lie is focused on leveraging the data revolution taking place within logistics, offering a glimpse into the future of an industry ripe for transformation. The goal is to make the supply chain as efficient as possible, which, in turn, can improve the overall global economy by reducing delays, optimizing routes, and ensuring goods are delivered on time. The Road Ahead for Forward Labs and Logistics AI Looking forward, Lie and his team are set to expand the capabilities of their platform, pushing forward with innovations such as a smart algorithm that recommends the next best lead, similar to Netflix's recommendation system. They are also working to integrate deeper verticals, adding real-time enrichment signals to improve lead quality and embedding directly into logistics-specific databases and proprietary data sources. As they continue to scale and refine their platform, the potential for AI-driven sales intelligence in logistics is limitless. With the backing of top investors in both AI and logistics, Forward Labs is well-positioned to make a lasting impact on the logistics industry. The company is also already in discussions with major freight brokers, 3PLs, and logistics teams in North America, with early signs of explosive growth. Lie's personal journey from a small-town freight driver family in Borneo to a Silicon Valley tech entrepreneur reflects the same grit and determination that he applies to his professional endeavors. Having helped scale a fintech unicorn in Xendit, he is now channeling his knowledge of payments infrastructure into the logistics industry—a move that promises to change the game for global supply chains. Forward Labs is on a mission to become the "Google for logistics sales," automating prospecting to such an extent that logistics teams can focus entirely on closing deals rather than searching for leads. This shift represents a massive leap forward in the logistics sector, bringing it into the modern, data-driven age that has already transformed other industries. In the near future, Lie believes that the synergy between fintech and freight will only grow stronger. As global supply chains become more interconnected, AI-driven technologies will serve as the backbone, optimizing every step of the logistics process—from sales and customer acquisition to the final mile delivery. The lessons from fintech have clearly found fertile ground in the logistics sector, and as Forward Labs continues to grow, so too will the impact of these innovations on the global economy.
Forbes
04-04-2025
- Business
- Forbes
Escaping AI Demo Hell: Why Eval-Driven Development Is Your Path To Production
Albert Lie, Cofounder and CTO at Forward Labs, next-gen AI-driven freight intelligence for sales and operations. getty It happens with alarming frequency: A company unveils an AI product with a dazzling demo that impresses executives. An AI chatbot fields questions with uncanny precision. The AI-powered automation tool executes tasks flawlessly. But when real users interact with it, the system collapses, generating nonsense or failing to handle inputs that deviate from the demo script. This phenomenon is what experts call "Demo Hell"—that peculiar purgatory where AI projects shine in controlled demonstrations but collapse in real-world deployment. Despite billions flowing into AI development, the uncomfortable truth is that most business-critical AI systems never make it beyond impressive prototypes. For executives, Demo Hell isn't just a technical hiccup—it's a balance sheet nightmare. According to a 2024 Gartner report (via VentureBeat), up to 85% of AI projects fail due to challenges like poor data quality and lack of real-world testing. The pattern is distressingly common: Months of development culminate in a showstopping demo that secures funding. But when real users interact with the system, it fails in unpredictable ways. The aftermath is predictable: Engineering teams scramble, stakeholder confidence evaporates and the project often lands in the corporate equivalent of a shallow grave—"on hold for reevaluation." Meanwhile, competitors who successfully operationalize AI pull ahead. Unlike conventional software, AI systems—particularly large language models (LLMs)—are inherently probabilistic beasts. They don't always produce the same output for the same input, making traditional quality assurance approaches inadequate. The standard development cycle often looks like this: 1. Prototype a model with carefully curated examples. 2. Optimize it for an impressive demo. 3. Deploy to production and hope it generalizes. 4. Discover unexpected failures under real-world conditions. 5. Scramble to manually debug issues. This phenomenon is sometimes called the "Demo Trap"—when companies mistake a polished demo for product readiness and scale prematurely. Models functioning under carefully controlled conditions prove little; what matters is AI that delivers consistent value in messy, real-world scenarios. Eval-driven development (EDD) is a structured methodology that makes continuous, automated evaluation the cornerstone of AI development. The framework rests on four pillars: 1. Define concrete success metrics that map directly to business outcomes. 2. Build comprehensive evaluation datasets that mirror real-world usage. 3. Automate testing in continuous integration pipelines to catch regressions. 4. Create systematic feedback loops that transform failures into improvements. By leveraging AI-driven evaluations, companies can enhance efficiency in areas like automated spot quoting and route optimization, leading to measurable improvements in pricing accuracy and operational scalability. Organizations that successfully implement EDD typically follow a systematic approach: Step 1: Map AI behaviors to business requirements: Before writing a single prompt, document exactly what the AI system should and shouldn't do in business terms. Step 2: Build evaluation suites that reflect real-world usage: Create datasets that include common use cases, edge cases, adversarial examples and prohibited outputs. Step 3: Establish quantitative success thresholds: Define clear pass/fail criteria, such as "The system must extract customer intent in 95% of queries," or "Hallucination rate must remain below 2%." Step 4: Integrate evaluations into the development workflow: Automate testing so that every change to prompts, models or retrieval systems triggers a comprehensive evaluation. Treat eval as a first-class citizen, even pre-planning the product. Consider a freight logistics company implementing AI for route optimization. Initial demos showed efficiency gains, but real-world deployment revealed frequent routing errors. By adopting EDD with comprehensive evaluation datasets, the company systematically refined model predictions. Industry research suggests AI-driven logistics optimization can lead to a 15% reduction in logistics costs. Most importantly, the company transitioned from reactive troubleshooting to a scalable, continuously improving AI deployment. In the current AI gold rush, getting to a working demo isn't difficult—but bridging the gap to reliable production systems separates leaders from laggards. Eval-driven development provides the scaffolding necessary to escape Demo Hell and build AI that consistently delivers business value. For executives investing in AI, the question isn't whether teams can create an impressive demo—it's whether they have the evaluation infrastructure to ensure that what wows the boardroom will perform just as admirably in the wild. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
