How AI is Transforming Supplier Management

AI in supply chain and procurement has shifted from experimental pilots to operational necessity, transforming workflows and team structures across the supply chain function. Operations that experimented with AI tools a few years ago now face a different reality. The technology has moved from optional pilot projects to standard operational requirements. AI systems are transitioning from copilot assistance to autonomous agents that execute multi-step workflows within procurement platforms. This change affects every area of the function and its connection to supply chain operations. AI applications in procurement Systems now read contracts, assess supplier risk, route requests according to policy and resolve data discrepancies between platforms. These tasks often require minimal human oversight and integrate directly into enterprise systems. Work that previously required days can be completed in hours. Procurement's capacity to identify patterns and anomalies across large volumes of data continues to expand. Supply chain visibility could improve as AI tools process more supplier information and transaction data. The technology may enable faster responses to disruptions and better coordination across procurement and logistics teams. Barriers to implementation A large percentage of procurement leaders are implementing or plan to implement AI agents within the next 12 months. Data quality remains the single biggest barrier to adoption. Privacy and compliance concerns follow as the second obstacle. Technology and process complexity present additional challenges. Internal skills gaps also limit implementation. Teams need new capabilities to work alongside AI systems and interpret their outputs. Legacy systems could hold back organisations that do not invest in data foundations and updated infrastructure. The gap between early adopters and those relying on older platforms may widen. Changing team structures Most industry voices agree AI is not replacing procurement professionals. The technology is elevating them by removing low-value, manual work. AI is expected to increase efficiency and free procurement professionals to focus on higher-impact activity. This includes strategic supplier relationship management and supply chain risk assessment. Teams that build appropriate structures, skills and data foundations now will be better positioned to capture value. Supply chain resilience could benefit from procurement teams equipped to use AI for supplier monitoring and demand forecasting.

This Font Looks Perfectly Normal to Humans but Wreaks Havoc on AI

A new “AI-proof” font was designed to be hard for AI agents to scrape, but you can’t tell by just looking at it. Unlike other anti-AI fonts that use letters that are difficult for bots to read, ShieldFont swaps out words behind the scenes to poison the data that automated scrapers take without permission. ShieldFont was designed as part of a project created by a group of professionals including the Brazilian creative studio Seneda & Abrucio and the Danish type foundry PlayType. It shields text from large language models (LLMs) by garbling sentences in the HTML source code, leaving automated scrapers to sift through sentences filled with decoy words that make a sentence incoherent. Thanks to a custom font on a backend, though, the real text is displayed for a human reader to see. For example: A sentence that originally reads “The knight rode his horse into battle” is altered in the source code so it’s scraped by a bot to read “The knight rode his engine into battle.” Since LLMs group words and phrases that are likely to be used together, the quality of its output is degraded if it scrapes a lot of jumbled text like this. The goal, according to two of ShieldFont’s creators, Isaque Seneda and Gabriel Abrucio, is to push back against unauthorized LMM scraping by making it harder and costlier to do so. How it works ShieldFont works using ligatures, the technical term in typography when two letters next to each other in a word are combined into a single glyph. Ligatures are designed for aesthetics, so letter combinations like fi in “fish” or fl in “flow” look naturally spaced instead of visually cluttered. When a program sees these specific letters next to each other, it swaps two characters for one that combines the letters into a single glyph. ShieldFont works in a similar way, except instead of letters, it swaps out whole words. “We didn’t invent a new font capability, just pointed to an old one that hadn’t been used this way before,” Felipe Petroni, a creative director who was part of the project’s leadership, tells Fast Company. Determining which words to swap out was tricky, since doing so at random results in gobbledygook phrasing that bots reject outright. The key was changing the meaning of sentences and phrases, not just words, so bots would still accept the text, resulting in a “poisoned” version of the scraped data. The thinking goes that if there are enough of these sorts of digital speed bumps, it will raise the cost of illegal scraping and those who build LLMs will opt to pay for what they take instead. To determine which words to swap, ShieldFont’s creators made a do-not-swap list of 113 words that included things like pronouns, articles, conjunctions, prepositions, negations, quantifiers, and every form of be, have and do. Instead, it swaps out adjectives, adverbs, nouns, and verbs. Dates and numbers also get scrambled. Automated scrapers can get around ShieldFont by taking a picture of the text to view it as a human does, but that also raises the price of scraping at scale. Rather than scraping plain text files quickly and cheaply, it has to photograph the page and conduct image recognition. Seneda and Abrucio’s white paper notes that ShieldFont introduces some friction for humans too, as search engines index the decoy text, so publishers would have to use ShieldFont for content that doesn’t depend on search traffic. The substitute words also show up in translation tools and screen readers, and when users copy and paste. Petroni and his team began prototyping ShieldFont in October 2025, and they partnered with the type foundry PlayType this March to develop a ready-to-use typeface with the ligatures called ShieldFont Optik. The protocol for ShieldFont is open-source and type-face agnostic. Petroni says they made implementation for ShieldFont as low-friction as possible, and it comes with three public mappings, so the word “wrote,” for example, becomes either “sang,” “wrought,” or “labeled,” depending on whether it’s using its alpha, beta, or gamma dictionary. “That’s deliberate; a decoder built for one mapping doesn’t work for another, and private mappings have to be identified and reversed one by one,” he says. “Scrapers can’t know in advance whether a site uses ShieldFont or which mapping it uses. Across millions of pages, that added cost is the point.” ShieldFont is a font system designed for human reading, not bots. Instead of using visual tricks to make it harder to read, it confuses the bots in the code and leaves the human reading experience alone. By Hunter Schwarz

Claude’s New Watermarks Will Follow Text Even After It’s Copied. The Era of Secret AI Use May Be Ending

Using AI can be empowering, but it can also spark controversy when people try to pass off AI-generated content as actually being human-made. The European Union is wary of the implications of deepfakes and other AI-fashioned material, so a new law mandates that AI-generated content be clearly labeled as such. Reacting to this legal change, Anthropic just announced that it’s adding markers to text written by Claude, starting with the latest generation model and extending, soon, to earlier versions of the chatbot; the change applies, for now, to users inside the EU. It sounds like a subtle shift, but it may have large, long-term implications and affect businesses across the globe. In a blog post announcing the news, Anthropic noted that from now on, in the E.U., “new models will mark AI-generated content” with “machine-readable marking.” This means any text you generate with Claude, for personal or business use, will “carry embedded watermarks” and any generated files will “include digitally signed provenance metadata where supported.” The company explains that it’s conscious that as “AI-generated content becomes commonplace, greater transparency and signals about where content comes from can give people useful context about the information they consume.” So while its move to watermark content is following E.U. law, it’s really a transparency push that’ll help consumers and perhaps the public in general. As well as marking any generated files with relevant metadata, Anthropic notes that “when a supported Claude model generates text, it weaves an imperceptible watermark directly into the text itself.” It’s invisible, so “you won’t see it,” and the company stresses it “doesn’t change the meaning, quality, or readability of Claude’s response.” To ensure that people don’t try to circumvent these protections, Anthropic explains that since these invisible marks are part of the text, they’ll “travel with the text when it’s copied and pasted elsewhere, and may persist through some editing.” Anthropic’s new watermarks will change how businesses worldwide use Claude—and how transparent they are about it.

This Single ChatGPT Prompt Can Do Hours of Market Research in Minutes—Here’s How

Market research can be a slow, fragmented, and difficult process, often involving tedious internet searches, questionable data sources, and time-consuming manual synthesis. This makes it a great candidate for some assistance from AI. What’s more, an update to a popular feature on ChatGPT has made it even better at doing this kind of work. Imagine that you have a potential business idea but still need to validate how viable it actually is, identify primary competitors in your market, and develop an ideal customer persona. Instead of spending hours collating data, explains Dan McCarthy, an associate professor of marketing at the University of Maryland, you can use Deep Research, a ChatGPT feature that directs an AI agent to develop a comprehensive, well-cited report on any topic. Last week, OpenAI upgraded Deep Research with some new abilities. The feature now runs on GPT-5.2, one of the company’s most recent models (previously it ran on a much older o3 model), and can now prioritize specific websites in its search process. Deep Research is available for all paid ChatGPT users. Here’s how to use it to get some thorough market research done quickly. Step 1: Get your prompt right To test out how this feature could help with market research, I pretended that I wanted to start a digital transformation firm based in Denver with a focus on upgrading bars with mobile, bar-to-table ordering capabilities. All I needed to do in order to get started was click the plus button next to the text box, select More, then Deep Research, and enter a prompt. This prompt will determine the information that ChatGPT prioritizes in its search, so it helps to be verbose. If you need help developing a lengthy prompt, try using ChatGPT to help write it. McCarthy, who uses AI tools extensively, says that an easy way to develop a comprehensive prompt is to activate the chatbot’s voice mode and simply have a conversation with it. Once you’ve explained what you want, McCarthy says, you can ask ChatGPT, “Given all this that I’m telling you, what do you think would be the best thing that I should even be asking you?” That should help clear up any blind spots you might’ve missed. According to McCarthy, this method should produce a solid prompt that you can give to the Deep Research agent. When I asked ChatGPT to help expand my prompt, the platform generated a 673-word result. This prompt (which you can view here) defined the agent as a market research analyst and gave it objectives to determine the business idea’s viability, map out the competition, and define my ideal customer’s persona. Additionally, it provided details on the scope of the research, and information for how the agent should format its report. I also used ChatGPT to develop a list of specific websites for the Deep Research agent to prioritize in its search. Step 2: Start the research I entered my ChatGPT-created prompt, selected the Deep Research feature, and pressed return. Before getting to work, the agent broke down its objectives into the following bullet points: Collect primary vendor docs and pricing pages starting with user-preferred sites. Survey industry, local Denver sources, and hospitality reports for market context. Compile POS integration lists, local competitors, and implementation partners in Denver. Analyze demand, model ROI scenarios, and estimate Denver bar counts and adoption rates. Draft recommendations, ICP personas, GTM plan, and cite sources with confidence ratings. Over the next 21 minutes, the agent searched through hundreds of web pages. It found liquor license databases, census information, and data regarding competitors in Denver’s hospitality-focused digital transformation market. It compiled all this information into a multi-section report. Step 3: Read the report That report (which you can view here) ended up being roughly 4,000 words. It included an overview of the market, identified customer pain points, and listed out my potential competitors. The report also included recommendations for how to position my business, strategies to break into the Denver hospitality scene, and even identified a small business that would likely be my direct competitor: a Denver-based POS integrator called Megabite. ChatGPT found that while my business idea had potential, it wouldn’t fully meet the needs of Denver-based bar owners, who have reported that bar-to-table ordering can actually lead to fewer sales and tips. Instead, the report suggested, I should consider a system that can sit on top of popular POS in which diners don’t need to pay for every new drink they order, and can instead open a digital tab. What the expert thinks of the result McCarthy told me he was impressed by the report that Deep Research produced. In particular, he was pleasantly surprised by the agent’s cleverness in using liquor licenses to get a sense of the market size, and its thoughtfulness in calling out disruption to bar culture as a potential blocker to the business. But the report wasn’t perfect. McCarthy said much of what was included was unnecessary or needlessly complex. An easy prompt to fix this? “Just tell it, ‘Explain it to me like I’m an idiot.’” McCarthy adds, “I do that all the time.” He says that a solid market research report should also answer questions regarding the scope of adoption and how often repeat purchasing is expected. McCarthy also says that users should direct the Deep Research agent to be very upfront about the data it attempted to get but couldn’t. Many websites block AI agents from engaging with their content to prevent data scraping, which can hinder the research process. By telling your agent to list out the sites that it couldn’t access, you can manually obtain that data and add it to the analysis. Our bar-to-table digital transformation firm will have to remain a pipe dream for now, but it’s clear that AI has made the process of taking an idea from zero to one easier and faster than ever. If you have an idea for a new business or are planning on an expansion or pivot in your current business, consider giving Deep Research a spin. It might unearth something that makes you think in a different way. BY BEN SHERRY @BENLUCASSHERRY