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The debate around AI ROI has gotten loudand, frankly, a little cyclical. One moment, were hearing that AI is the key to exponential growth; the next, that 95% of AI pilots fail. At Addi, weve been able to leverage AI to grow 4x faster while operating at ~2x the profitability of BNPL peers. This year alone, weve saved more than $500,000 from our AI initiatives. But how have we accomplished such strong AI ROI? The difference between performative AI and AI with returns isnt in which model or tool youre using; its how your team is using them. Heres how weve driven genuine AI-native team adoption and built a workflow/data pipeline that actually makes sense. 1) Hire and grow for fluency We run nationwide, admissions-style assessments to find talent in unexpected places (from the Amazon to the Ecuador border), then teach AI-native workflows from day one. From our intern program through our senior leadership, we design our interview process for the AI age. We assign a relevant projectsomething candidates could use AI to help withbut then have a panel interview where they present their project, ensuring that candidates actually know the ins and outs of their work without an AI aid. Our interviews additionally probe into potential candidates own familiarity with AI tools, while our intern cohorts get hands-on with agents and graduate into teams already expecting that fluency. The pipeline is designed to recruit for an AI era from the get-go, versus being an afterthought once already employed. 2) Codify AI-native rituals into culture When it comes to cultivating an AI-native work culture, AI-native is a learned behavior. We invested in extensive AI onboarding and habit-building, pairing every knowledge worker with the right agent or copilot, and encouraging AI usage as the company default. Today, more than 90% of our engineers are weekly active copilot users and ~80% of AI-generated code is accepted. This translates into efficiency gains of up to 60% without increasing headcount. Weve kept our core product engineering team flat for three years while shipping more products. The story here isnt in the savings; its in the deep level of AI adoption weve witnessed among our employees by securing their buy-in, setting expectations for an AI-friendly environment, and offering targeted training. Rollouts fail when AI is treated as a here only if you need it tool. They work when companies rewire rituals around ite.g., code reviews with AI diffs, CX stand-ups that inspect agent transcripts, legal postmortems that include our AIs outputsto normalize the behavior. You might even consider baking AI proficiency into employee reviews. In other words, dont over-index on tools; over-index on culture. That cultural shift is why AI usage at Addi is voluntary yet ubiquitous. 3) Design AI as a colleague Theres a reason our in-house agents have regular names like Addri and Aegis. Every agent at Addi is treated like an employeeone with a clear scope, service-level agreements (SLAs), and metrics. Addris job is first-contact resolution with target customer satisfaction (CSAT); the merchant agent owns KYP throughput and reactivation; Aegis owns escalation latency and evidentiary completeness. Human owners review outputs and tune prompts like they would a new hires playbook, and we always welcome teamwide feedback on how our fellow agentic employees can improve before their next review cycle. Moreover, our AI employees have the same depth of contextual knowledge and understanding that a human employee would, to help them function side-by-side with our team and minimize the frustration that comes with false or limited context. Our agents are tailored to specific roles, not catchalls from an outside vendor that shoehorns a base agent into a wide variety of situations. We ensure theyre trained with high-quality, high-volume, company-owned data. We spent four-plus years building a world-leading data platform, ensuring more than 40 terabytes of data was instantly available as it began building AI agents, giving our digital teammates the best possible training. 4) Invest in the right foundations AI-first isnt what works; data-first is. This is how you ensure your AI colleagues have that employee-like context. More than four years ago (pre-LLMs!) we made the decision to invest in a next-generation data engine that would ensure everything that happened on our platform (from a single text message to a full underwriting analysis) would be stored and could be queried by anyone and anythingtraditional AI models, human analysts, and, yes, even LLMs via vectorization. With a single monorepo and an event-based system that logs everything, we have nearly perfect context: 50 terabytes of clean, searchable data. If you dont own your stack (i.e., control your data and event logs) you will rent your advantage to a vendor. Set your AI-native team up for success by logging everything, and reap the benefits of a database that can be read by humans and AI alike. 5) Celebrate adoption Reward employees usage of AI by celebrating adoption rates, cycle-time reduction, and defects avoided. This year, our AI initiatives saved upwards of $500,000 in annual operating costs. For lean teams where a startups success is their teammates success, these metrics (and transparency) matter. That $500K isnt a bottom-line cut; its $500K back into the pockets of our employees in the form of raises, better benefits packages, and profit sharing. Tie budgets to solved tickets, minutes saved, merchants activatedthen compound wins into subsequent quarters. That mindset of AI gains are your gains is why AI can comfortably power half of our legal and coding throughput, a big chunk of CX, and critical onboarding flows. In Summary Train your people to be AI-native and give them the infrastructure to thrive. The models will change. The muscle you build wont. This approach is how weve been able to launch more products more quickly while maintaining a generally lean teamand its why Im confident the best AI ROI stories are still to come.
Category:
E-Commerce
For decades now, we have been told that artificial intelligence systems will soon replace human workers. Sixty years ago, for example, Herbert Simon, who received a Nobel Prize in economics and a Turing Award in computing, predicted that machines will be capable, within 20 years, of doing any work a man can do. More recently, we have Daniel Susskinds 2020 award-winning book with the title that says it all: A World Without Work. Are these bleak predictions finally coming true? ChatGPT turns 3 years old this month, and many think large language models will finally deliver on the promise of AI replacing human workers. LLMs can be used to write emails and reports, summarize documents, and otherwise do many of the tasks that managers are supposed to do. Other forms of generative AI can create images and videos for advertising or code for software. From Amazon to General Motors to Booz Allen Hamilton, layoffs are being announced and blamed on AI. Amazon said it would cut 14,000 corporate jobs. United Parcel Service (UPS) said it had reduced its management workforce by about 14,000 positions over the past 22 months. And Target said it would cut 1,800 corporate roles. Some academic economists have also chimed in: The St. Louis Federal Reserve found a (weak) correlation between theoretical AI exposure and actual AI adoption in 12 occupational categories. Yet we remain skeptical of the claim that AI is responsible for these layoffs. A recent MIT Media Lab study found that 95% of generative AI pilot business projects were failing. Another survey by Atlassian concluded that 96% of businesses have not seen dramatic improvements in organizational efficiency, innovation, or work quality. Still another study found that 40% of the business people surveyed have received AI slop at work in the last month and that it takes nearly two hours, on average, to fix each instance of slop. In addition, they no longer trust their AI-enabled peers, find them less creative, and find them less intelligent or capable. If AI isnt doing much, its unlikely to be responsible for the layoffs. Some have pointed to the rapid hiring in the tech sector during and after the pandemic when the U.S. Federal Reserve set interest rates near zero, reports the BBCs Danielle Kaye. The resulting hiring set these firms up for eventual workforce reductions, experts saida dynamic separate from the generative AI boom over the last three years, Kaye wrote. Others have pointed to fears that an impending recession may be starting due to higher tariffs, fewer foreign-worker visas, the government shutdown, a backlash against DEI and clean energy spending, ballooning federal government debt, and the presence of federal troops in U.S. cities. For layoffs in the tech sector, a likely culprit is the financial stress that companies are experiencing because of their huge spending on AI infrastructure. Companies that are spending a lot with no significant increases in revenue can try to sustain profitability by cutting costs. Amazon increased its total CapEx from $54 billion in 2023 to $84 billion in 2024, and an estimated $118 billion in 2025. Meta is securing a $27 billion credit line to fund its data centers. Oracle plans to borrow $25 billion annually over the next few years to fulfill its AI contracts. Were running out of simple ways to secure more funding, so cost-cutting will follow, Pratik Ratadiya, head of product at AI startup Narravance, wrote on X. I maintain that companies have overspent on LLMs before establishing a sustainable financial model for these expenses. Weve seen this act before. When companies are financially stressed, a relatively easy solution is to lay off workers and ask those who are not laid off to work harder and be thankful that they still have jobs. AI is just a convenient excuse for this cost-cutting. Last week, when Amazon slashed 14,000 corporate jobs and hinted that more cuts could be coming, a top executive noted the current generation of AI is enabling companies to innovate much faster than ever before. Shortly thereafter, another Amazon rep anonymously admitted to NBC News that AI is not the reason behind the vast majority of reductions. On an investor call, Amazon CEO Andy Jassy admitted that the layoffs were not even really AI driven.” We have been following the slow growth in revenues for generative AI over the last few years, and the revenues are neither big enough to support the number of layoffs attributed to AI, nor to justify the capital expenditures on AI cloud infrastructure. Those expenditures may be approaching $1 trillion for 2025, while AI revenuewhich would be used to pay for the use of AI infrastructure to run the softwarewill not exceed $30 billion this year. Are we to believe that such a small amount of revenue is driving economy-wide layoffs? Investors cant decide whether to cheer or fear these investments. The revenue is minuscule for AI-platform companies like OpenAI that are buyers, but is magnificent for companies like Nvidia that are sellers. Nvidias market capitalization recently topped $5 trillion, while OpenAI admits that it will have $115 billion in cumulative losses by 2029. (Based on Sam Altmans history of overly optimistic predictions, we suspect the losses will be even larger.) The lack of transparency doesnt help. OpenAI, Anthropic, and other AI creators are not public companies that are required to release audited figures each quarter. And most Big Tech companies do not separate AI from other revenues. (Microsoft is the only one.) Thus, we are flying in the dark. Meanwhile, college graduates are having trouble finding jobs, and many young people are convinced by the end-of-work narrative that there is no point in preparing for jobs. Ironically, surrendering to this narrative makes them even less employable. The wild exaggerations from LLM promoters certainly help them raise funds for their quixotic quest for artificial general intelligence. But it brings us no closer to that goal, all while diverting valuable physical, financial, and human resources from more promising pursuits.
Category:
E-Commerce
Dole invented a new fruit. The Dole Colada Royale Pineapple is sweet and tangy with notes of coconut and, as the name suggests, pia colada. Unlike its golden yellow counterpart, the Colada Royale has a cream-colored pulp with a green-to-golden shell. It also took more than 15 years to get it just right. The suggested recipes the company released with the new fruit include snacks like a pineapple and coconut carpaccio and a basil-wrapped pineapple with pine zest. Clearly this is meant to be a luxury pineapple experience. The fruit, which is now available in select grocery stores in the U.S. and Canada, is 100% non-GMO and naturally bred. The company didn’t share its suggested retail price, but the Colada Royale comes amid a wider trend toward “designer” pineapples. Just last year, Fresh Del Monte released a pink pineapple it called the Pinkglow, which it followed up with a $400 Rubyglow. [Photo: Dole] A new growing process Developing new pineapples requires patience since the natural process can stretch out for nine years or more. “You have to go through thousands of pollinations and develop thousands of seeds and then have the capacity to pinpoint that particular plant that combines what you are looking for,” says Roberto Young, director of pineapple breeding at Doles farm in La Ceiba, Honduras. He led the team that developed the Colada Royale variety. The new pineapple also had be grown in different seasons, since temperature can affect the taste of the finished product. All in all, that means it takes thousands of attempts that go wrong in hopes of getting one that goes right. Dole pineapple breeder Roberto Young [Photo: Dole] “Usually, you have to discard most of the fruit because it could taste very good during the summer, but in the winter you cannot really taste it because it’s too tangy, it’s very acidic,” Young tells Fast Company. Plant breeders consider factors like size, productivity, and color as they’re developing a new product, but taste, of course, is the most important. “It doesn’t help if the fruit is a good size, good productivity, but doesn’t taste like pineapple,” Young says. Dole’s new pineapple had the right taste, but its cream-colored pulp was at first a concern since consumers today are used to yellow pulp in pineapples. At the produce and floral trade show in Anaheim, California, where Dole unveiled the Colada Royale in October, Young says people were hesitant about the fruituntil they tasted it. Then, he says, their reaction was Wow, this is something different. [Photo: Dole] Developing a new market The goal from the beginning was to develop a unique flavor and bring something new to the market. Pineapple is genetically very variable, Young says, and the biggest challenge was consistency. Plant breeding doesn’t have a high success rateIf you are a plant breeder, you might be successful, or you might not,” he concedesand pineapple is especially tricky since it has a relatively long harvest cycle. The process requires first planting parents, which take about a year to produce flowers that can be pollinated. From there, it’s about five more months until the fruit can be harvested. The seeds from that harvest are then planted to get all new plants, repeating the cycle. [Photo: Dole] The results need to be repeatable to ensure the fruit can be mass-produced, so it takes at least three generationsroughly nin yearsto develop a new product. The Colada Royale took longer, and Young, a Honduran native who’s been with Dole for 28 years, has been on the project from the start. He considers it his legacy. “I feel really very, very grateful,” he says. Dole is also looking at the new fruit as a legacy play of its own. The company plans to reinvest a portion of the proceeds of every box of the pineapple sold to create a community center in La Ceiba that will provide healthcare services, language classes, and vocational training. In its most recent earnings report, Dole said its second-quarter 2025 revenue was $2.4 billion, an increase of 14.3% over the same period in 2024. The company is expected to report third-quarter financials on November 10. Designer pineapples may sound like a novelty, but since they can be upsold, fruit growers and grocers alike may find they’re a sweet addition to the produce section.
Category:
E-Commerce
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