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By now weve all heard of tech debtthe costs well have to incur in the future to maintain suboptimal software and technology decisions from the pastbut in three decades as a tech executive, Ive come to observe a far more insidious phenomenon that threatens to undermine business transformation: process and data debt. Unlike tech debt, process debt isnt just ITs problem. The accumulation of manual workarounds, inconsistent data practices, and inefficient workflows that build up over time spreads throughout the organization, affecting every department, from accounting to supply chain. And process debt is the number-one thing that will stand in the way of a companys ability to adopt AI to innovate and reinvent itself. Process debt doesn’t just slow down AI initiatives; it fundamentally stops them from reaching their potential as we move toward more autonomous systems. The Tomato Problem Consider something simple: ordering 1,000 kilograms of tomatoes. Due to natural moisture loss, only 950 kilograms arrive. The supplier invoices for the full amount. Most systems escalate this to human review. But when operational foundations are clean, autonomous AI approaches this differently. It understands tomatoes typically lose 5% in transport, factors in seasonal patterns, then processes autonomously. More importantly, it builds institutional knowledge for future decisions. This is the difference between AI that frustrates and AI that transforms. Your Roof Collapses In insurance, we’ve seen property claims processing transformed from days-long research into minutes of intelligent analysis. AI systems now handle complex items, such as custom artwork, by leveraging deep databases and sophisticated reasoning. The results: an 80% reduction in processing time and a 23% improvement in pricing accuracy, representing millions of dollars in annual value while dramatically improving the customer experience. The lesson wasn’t about efficiency gains. It was about how AI performs when you design processes around its capabilities rather than retrofitting it onto existing workflows. The Learning Gap What we’re seeing validated in research confirms what many suspected: there’s a fundamental difference between organizations that succeed with AI and those that don’t. Recent MIT research shows that 95% of enterprise AI initiatives struggle to deliver value, not because of technology limitations, but because most systems cannot adapt and integrate effectively into existing workflows. Gartner reinforces this trend, predicting that by 2030, more than 50% of AI models will be domain-specific, tailored to industry or function, up from 5% today. The pattern is clear: generic solutions cannot address the unique operational challenges that define real business value. An Agentic Order of Operations The most impactful AI transformations start with addressing process and data debt first. Organizations that clean up their operational foundations unlock AI’s full potential. Those that don’t find themselves constrained by legacy inefficiencies, regardless of their technology investment. This creates an interesting dynamic. As AI becomes more autonomous, competitive advantage increasingly belongs to organizations willing to do the hard work of liquidating process debt before deploying sophisticated systems. What This Means for Leaders We’re entering an era where AI does not just assist. It makes autonomous decisions and manages entire business ecosystems. The organizations that understand this are building the foundations that will define tomorrow’s competitive landscape. In my experience, the answer lies not in the sophistication of your AI models, but in the quality of the operational foundation you build to support them. That foundation work happening today determines who leads in the autonomous economy of tomorrow. I often say, There is no artificial intelligence without process intelligence. The companies that understand this distinction will be the ones that thrive.
Category:
E-Commerce
Imagine that it’s time to file expenses. Instead of logging into a portal, uploading photos of your crumpled receipts, filling out 10 information fields, and then waiting a week for your manager to sign off, you simply answer a text with a photo of a receipt and a short note about what it’s for.Done.That is, to me, the golden experience, says Diego Zaks, VP of design at Ramp, the $22.5 billion fintech company that’s reinventing the business expense landscape.Ramp as a platform is in the business of simplifying. It consolidates corporate cards, expense management, bill payments, and accounting automation into a single system, making it easier for companies to track expenses and keep their finances in order. In the era of AI, Zaks believes the company can do even more to simplify the software for the people who use it. He envisions a world where Ramp’s customers can accomplish any task with the push of a single button. And his ultimate goal? Someday youll forget altogether that youre using Ramp.I don’t want anyone using Rampbecause every minute that you’re on Ramp, it’s a minute that you’re dealing with expenses and not actually doing the job that you’re hired to do, he explains. We actually measure engagement and time spent on Ramp going down as the signal that we are trying to get.A better AI agentIn July, Ramp introduced Ramp Agents, an autonomous, AI-driven system built atop OpenAI’s latest reasoning models. Ramp Agents work behind the scenes to review expenses, enforce company spend policies, and even suggest improvements to those policieswithout users needing to intervene or monitor every transaction manually.Instead of relying on rigid rules or requiring employees to learn new workflows, these agents reason through real business context, handling approvals, flagging anomalies, filling forms, and learning from every bit of feedback. They don’t require a prompt to do things; instead, they do everything on their own, so people interact as little as possible, if at all.[Image: Ramp]People didnt come to Ramp to engage with AIthey come to do a specific job, and we have AI in the background doing 90% of that job or as much of that job as we possibly can and just displaying the outcome, Zaks says. We skip 17 steps and we just got [managers] to the last moment of Does this look good? And they can just say yes, and it’s done.The agents are part of Ramps larger ecosystem. When companies sign up for Ramp, they receive corporate charge cards with preconfigured spending policies and limits. Employee transactions trigger receipt capture through email integrations, mobile apps, or merchant partnerships with Amazon Business, Lyft, and Uber. The system categorizes expenses, enforces policies in real time, and flags violations automatically.Zaks believes that when deployed smartly, agents will be able to eliminate nearly all of the grunt work that once fell to humans. The agents currently approve 85% of expenses without human review, and early customers report 99% accuracy in expense approvals. The AI doesn’t just process receiptsit cross-references calendar data, checks policy nuances, and delivers outcomes that make sense to human managers.Zaks says this vision is most clearly exemplified by the SMS example. After using his Ramp card, Zaks receives a text asking what the expense was for. The system checks his calendar, sees a scheduled one-on-one meeting, and when he confirms it was for that meeting, the entire expense process completes automatically. No apps to open, no forms to fill, no categories to select.Managers no longer see dashboards filled with every transaction. Instead, they find two categories: expenses that look good to go with a single approval button, and transactions that need attention with focused context about why. Each flagged item includes a summary in 15 to 20 words, the full policy context highlighted on demand, and a feedback mechanism that improves the system’s accuracy.It’s not an AI view. It’s like a transaction view. You have the receipt, all the information you need, Zaks says. And there’s a module at the top that just says a recommended outcome for the manager to take and the reasoning why.From left: Ramp founders Karim Atiyeh, Eric Glyman, and Gene Lee [Photo: Ramp]Why Ramp existsRamp’s move toward the near-total elimination of UX is logical, given the companys mission. Ramp’s founders, Eric Glyman, Karim Atiyeh, and Gene Lee, all worked in Capital One’s credit card division in the late 2010s. They joined after Capital One acquired their company Paribus, a price-tracking app that secured refunds for 10 million users when online purchase prices dropped.At Capital One, they realized there was a fundamental tension in the business model: Credit card companies incentivized spending, while businesses desperately wanted to save money. In early 2019, they left the bank to start Ramp.Ramps core premise flipped the script. Instead of rewards programs that encourage more spending, the company built a financial operations platform that actively helps companies spend less while automating the tedious work that consumes finance teams.For accounts payable, invoices are processed through customizable approval workflows and payments are scheduled automatically. All transactions sync to business management software in real time. Ramp generates revenue primarily through interchange fees earned on every card transaction (typically 1.5% to 2.5% of the purchase value shared among Visa, the issuing bank, and Ramp). Ramp is now one of the hottest tech unicorns, with a valuation of $22.5 billion after capturing les than 2% of the U.S. corporate card market. The company claims that its customers save an average of 5% on spending, close their books 75% faster, and have collectively saved more than $10 billion and 27.5 million hours of work. [Image: Ramp]Building an invisible interfaceSo much of Ramps success hinges on the idea that the best AI is the AI you cant see. Zaks calls this background AI, and its what will power the next generation of Ramps zero-interface ambitions.Right now, every time a human makes a decision on Ramp’s platform that information is used to create a better experience. When a manager disagrees with an agent’s decision, for example, their explanation is fodder for better policies. The agents can alert teams to suspicious receipts and invoices, uncover trends that signal fraud or careless spending, answer employee questions about spend policy, and suggest edits to company expense policies based on usage patterns.The experience data aggregates into policy suggestions for finance teams. Zaks explains that the system basically says, Hey, your policy could be a little clearer about these five things on the work-from-home stipend, for example. If the finance team agrees, they can approve policy updates with one click.Eventually, Ramp will be so optimized that its UI will disappear in the future. Thats Zakss objective: He wants most users to forget Ramp even exists. My approach has been to just pretend like we’re already in 2030 and that AI is just standard, nobody cares, it’s just always in the background, he says. Kind of like how software now is in the cloud. We don’t make a big deal out of that. It just is.
Category:
E-Commerce
Lyft is hoping to get smartphone users out of their routines. The ride-sharing app is rolling out a new “Check Lyft” campaign in New York City and San Francisco, and it’s designed to nudge people into considering a second travel option for a change. “Our customer obsession led us to discover that most people were on rideshare autopilothabitually opening the same app without thinking they had optionseven though our data shows riders are happier and drivers strongly prefer us,” Lyft CMO Brian Irving tells Fast Company. He says they found their most loyal customers kept saying the same thing of their friends: “They just need to wake up and check Lyft.” Hence the new campaign, which Lyft is rolling out with out-of-home advertising and influencer collaborations, like with Subway Takes host Kareem Rahma. (Hey, guys, don’t forget about us!) [Photo: Lyft] Switching costs Getting smartphone users to change their habits can be hard. Researchers at the University of Cardiff found smartphone users tend to use a few popular apps every time they open their phonefollowing that, there’s a steep drop-off. An individual’s second most popular app is about 73% less popular than their first, and their third most popular app is about 73% less popular than their second, a pattern that continues until reaching increasingly unpopular apps, according to the study, published in 2019. That’s great news for popular apps like Uber, the leading rideshare app by marketshare, but it leaves competitors like Lyft fighting for screen time. “When talking to our audiences about why they choose Lyft, it’s a combination of emotional and rational decisions,” Irving says. “You have to be there on-time and be competitive on prices. That’s the baseline rational work that we excel at.” [Image: Lyft] Signalling a new Lyft By leaning into other differentiators, though, like allowing women to match with female drivers, an updated app for older riders called Lyft Silver, Lyft is hoping to set itself apart further. It seems to be working. On its most recent earnings call, CEO David Risher said the company had a record number of active riders in the second quarter. “A new Lyft is emerging,” Risher said. “Not only are we consistently delivering for riders and drivers, but that customer obsession is producing record results quarter after quarter, and our momentum is building.” If riders prefer Lyft rides but Lyft isn’t their preferred ride-sharing app, the company faces an uphill battle in getting users to switch more often. Muscle memory with app habits is real, but a simple “Check Lyft” campaign could help.
Category:
E-Commerce
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