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How did you get to this article? Maybe you opened a link in an email, or you navigated from the Fast Company home page. Perhaps you Googled agentic AI and this figured in the results. The point is, you almost certainly clicked, scrolled, tapped, or typed your way here, because thats the digital grammar that shapes nearly every online experience. But that 30-year-old paradigm is about to change. Agentic AI is ripping up the rulebook, by creating a new layer of intelligent, autonomous mediation between us and the digital world. Personal shopping agents will handle routine purchases, while in the workplace, agents will automate workflows and streamline procurement. Investors are excited, of course. Earlier this year, more than half of the Y Combinator startups chosen for its accelerator program were working in agentic, while the three biggest Q1 acquisitions in the AI sector involved companies building agentic enterprise technologies. This is because agents are about to transform how work gets done, how businesses operate, and how systems interact. And nowhere are the consequences more profound than in e-commerce. CLICKLESS COMMERCE Today, e-commerce success depends on online visibility, with search driving half of website traffic and fuelling the $75 billion search engine optimization (SEO) industry. But agentic AI upends the traditional business model. For agents, displays and brand content are less important. Merchants must focus on making product data machine-legibleaccurate, structured, and accessiblebecause machines wont browse sites. Theyll connect directly to sales platforms and databases via APIs, creating a parallel e-commerce track that serves agents rather than human shoppers. Building out that machine track will be a critical job for merchants, fintechs, software companies, and financial institutions. For merchants, an early task is compiling and structuring data in a way thats visible and relevant to agents. Another is creating seamless connections between merchant systems and agent systems. The good news is that the connective tissue is already here. New protocolsmodel context, agent-to-agent, and agentic paymentsnow enable agents to connect, communicate, and transact autonomously. MARKETING TO MACHINES: A NEW INDUSTRY For retailers, one question looms: How do I market to machines? As SEO evolves for human audiences, new disciplines are emerging to optimize digital environments for AI agents. Agent engine optimization helps make digital spaces easier for agents to understand and use. Agent interaction design focuses on how they communicate with platforms, APIs, and other agents to get things done for users. These areas connect with generative engine optimization, which improves content so AI systems can better generate recommendations and make decisions. Taken together, they signal a new ecosystemone that will create fresh roles, unlock new value streams, and redefine how businesses compete in an AI-driven economy. Another way of attracting machine buyers is to reward them. This is the agentic version of e-commerces affiliate system, where publishers, partners, and influencers are paid a commission for driving sales. Theres feverish speculation as to how an attribution mechanism may work, and who will get paid. There are plenty of challenges toonot least verifying agents identities, knowing with confidence which agent influenced the sale, and preventing fake agents from gaming the system. But the opportunities are apparent. The affiliate market is predicted to double in size by 2030, even without agents. FACE THE HEADWINDS Certain issues will require regulatory clarity. Agentic referral will have to be transparent, for example, to ensure fair competition. Then theres the matter of trust. Well need confidence in the agents and the ecosystems they operate in. Giving an autonomous bot spending authority is a big commitment, but getting it right is key to scaling agentic commerce. Ensuring agents can operate across different payment systems, platforms, and legal jurisdictions will also pose technical and regulatory challenges. That said, were about to experience the biggest transformation in e-commerce since it began. This promises to be an era of great innovationa time to build new tools, new ecosystems, and new ways of creating value. Agentic commerce isnt just a tech shift; its a reimagining of how we buy, sell, and connect in a digital world. Ken Moore is chief innovation officer at Mastercard.
Category:
E-Commerce
How would a school shooting affect your employees? Its something that most employers never want to think about, but its a horrifyingly real threat to any communityand the companies and organizations that do business there. Following the death of my youngest son, Dylan, in the 2012 Sandy Hook School shooting, I can tell you first-hand about the lasting trauma that occurs when your child is injured or killed in this type of tragedyand how that ripples through the entire community. In October, we held Americas Safe Schools Week, a national initiative to raise awareness about school violence and promote safety. Its also a time for companies to recognize they have a major role to play in preventing school violenceand a lot to gain by doing so. When we invest in the safety and well-being of our children, we are also investing in our workforce, and in turn, the long-term health of our businesses. WHY THIS MATTERS FOR CEOS Our employees dont leave their lives at the office door. Their childrens safety directly impacts their focus, mental health, and productivity. A tragedy in a school can create anxiety and shake an entire companybecause our companies are made up of parents, caregivers, neighbors, and friends. Taking a role in vioence prevention isnt just an act of compassion or show of goodwill and support. Its a sound business strategy that strengthens your workforce, your brand, and your reach into communities. KNOW THE WARNING SIGNS People intending to harm themselves or others will often exhibit a range of telling behaviors. This might include expressing threats or a plan, bragging about access to weapons, becoming socially withdrawn, or experiencing chronic social isolation, among other signals. In most mass shootings and school shootings, someone knew something was off before it happened. Often, a peer, a friend, or a parent are among the first to notice a problem. The same is true for youth suicides, which are the second-leading cause of death for children and teens in the United States. Getting proactive about violence prevention begins with learning to recognize the warning signs that lead to violence and self-harm. For parents and caregivers, these signals offer some of our best opportunities to intervene and save a child. The ability for you and your employees to recognize warning signs will go far beyond your company’s walls. Ultimately, this type of impact will create safer schools, homes, and communities, leading to a stronger business environment where your company can thrive. Thats why its essential to learn these warning signs and know how to get help. THE PROTECT OUR KIDS PLEDGE One impactful way to take action is by signing the Protect Our Kids Pledge, a first-of-its-kind corporate initiative developed by Sandy Hook Promise. The pledge provides actionable training and tools to employees and their families to recognize warning signs of potential violence and self-harm, and knowledge of what to do next. Lessons from the Protect Our Kids Pledge will not only keep employees and their children safer, they will also help companies stand out in their industries by taking an active stand against youth violence and self-harm, prioritizing safety in innovative ways. It shows that a company cares about people, and signals to employees and customers alike that it values safety and social responsibility. OTHER IMPORTANT RESOURCES FOR EMPLOYERS In the last few years, legislation has provided additional violence prevention resources for companies and school communities. The Bipartisan Safer Communities Act (BSCA), passed in 2022, included many nationwide investments that have been creating a positive impact. The Act included funding for states to implement crisis intervention programs and extreme risk protection orderssometimes referred to as red flag or temporary transfer laws. These laws allow family members, law enforcement, and others to petition courts to temporarily remove firearms from a home when an individual is deemed a threat to themselves or others. The BSCA also expanded access to community health clinics that provide mental health crisis services and substance abuse treatment. This gives more employees and their families, particularly in rural areas, options to access the care they need. These resources are designed to save lives while protecting rights. Companies should ensure they understand the laws in their states, help educate their employees, and connect them to local resources when needed. TAKE ACTION TODAY Since the Sandy Hook tragedy, Ive worked alongside leading experts and business leaders who understand that ending the epidemic of gun violence requires a holistic, public health approach focused on prevention. And when we make prevention a priority, we can also create safe, healthy communities that allow our businesses to thrive. Business leaders can demonstrate their commitment by signing on to the Protect Our Kids Pledge and encouraging other leaders to do the same. Share that prevention is possible, and its both a moral and business imperative to deliver that for our communities, our employees, andmost importantlyour children. Nicole Hockley is cofounder and CEO of Sandy Hook Promise.
Category:
E-Commerce
Every C-suite executive I meet asks the same question: Why is our AI investment stuck in pilot purgatory? After surveying over 200 AI practitioners for our latest research, I have a sobering answer: Only 22% of organizations have moved beyond experimentation to strategic AI deployment. The rest are trapped in what I call the messy middleburning resources on scattered pilots that never reach production scale. In my 20-plus years helping companies solve complex problems with open-source AI and machine learning, I’ve watched this pattern repeat across industries. Companies get excited about AI’s potential. They fund pilots. They hire data scientists. But when it comes to production deployment and measurable ROI, they hit the same wall: over 57% take more than a month to move from development to production. That’s not innovation velocitythat’s friction eating your competitive advantage. The problem isn’t enthusiasm or investment. The problem is they’re building on quicksand. Without shared standards, every team reinvents the wheel. Tools fragment. Governance gaps widen. Trust erodes. What should take days stretches into months. Here’s what business leaders need to understand: The companies escaping this trap aren’t using better AI models. They’re using better foundations by using open-source software. Standards create a competitive advantage Standards might sound like bureaucracy, but in AI they separate companies that scale from companies that stall. Our research reveals the real barriers: 45% of teams cite data quality and pipeline consistency as their top production obstacle. Another 40% point to security and compliance challenges. These aren’t technical problemsthey’re coordination problems. When every team speaks a different technical language, you can’t share work, build trust, or scale effectively. Think about it this way: Imagine if every department in your company used different email systems that couldn’t talk to each other. That’s essentially what’s happening with AI tools today. Open standards solve this by creating shared languages for AI development. When everyone uses compatible tools and formats, collaboration becomes automatic. Integration that used to take months happens in days. The result? Faster deployment cycles and measurable ROI. Companies are starting to get the message: 92% of AI practitioners use open-source tools, and 76% say their organization has increased its open-source priority this year, according to our research. Three standards that drive results Not all standards matter equally. Based on what I’ve seen transform organizations, here are three that deliver immediate impact: Ways to move AI models between systems without rebuilding. Standards like Open Neural Network Exchange prevent vendor lock-in and eliminate reworkthe silent killer of innovation velocity. When teams can deploy the same model across different environments, development speeds up dramatically. Protocols that let AI services communicate seamlessly. Instead of building custom integrations for every new tool, teams can assemble complex AI systems from standard components. This turns months of integration work into days of configuration. Frameworks for responsible AI governance. With 53% of organizations lacking comprehensive AI policies, standardized approaches to model documentation and validation turn governance from a blocker into an accelerator. Teams move faster because they know exactly what compliance looks like. The pattern I see repeatedly is this: Each standard reduces friction. Together, they create an ecosystem where innovation compounds instead of fragmenting. Open source is your competitive edge Some executives worry that open source means chaos. They think standards need central authority. But AI moves too fast for traditional standardization. By the time a formal standards body publishes specifications, the technology has evolved. Open source solves this through evolutionary design. Standards emerge from real-world use, spread through community adoption, and adapt at market speed. This keeps them relevant in ways top-down standards can’t match. There’s another crucial factor: Transparency builds trust. Our research shows less than half of AI practitioners feel confident explaining model decisions to executives or regulators. When standards are open, you can inspect how they work, verify their claims, and adapt them to your needs. This transparency accelerates adoption and regulatory approval. What surprised me most in our research was the community insight: People distinguish between using open-source software and building on open-source foundations. True acceleration requires shared standards that let teams move independently while still moving together. Escaping the messy middle Here’s my core advice for C-suite leaders: Stop treating AI as a technology problem and start treating it as a systems problem. The messy middle exists because organizations approach AI as isolated projects. Teams pick different tools, build separate pipelines, and create individual governance processes. This works for pilots but kills scalability. Strategic AI requires a foundation built on compatibility. Here are three ways to achieve it: 1. Simplify your toolchain around core platforms that work together. You don’t need 47 different AI tools. You need a unified approach where teams can share models, data pipelines, and deployment processes without starting from scratch. 2. Choose solutions you can inspect and verify. This reduces risk and builds stakeholder confidence. Trust accelerates adoption, and adoption accelerates value creation. 3. Measure deployment cycles, not just model accuracy. Track time from prototype to production. Track how many AI projects deliver measurable business outcomes. These metrics reveal whether your foundation is working. Our work with large corporations shows that organizations moving from fragmented approaches to unified platforms see dramatic improvements: faster deployment, higher success rates, and clearer ROI measurement. Standardization and innovation are partners The gap between strategic AI deployers and pilot-trapped organizations will only widen. The winners won’t be those with the most experiments; they’ll be the ones who turn experiments into value fastest. According to McKinsey research, organizations are seeing material benefits from AI deployment, with a majority reporting cost reductions and revenue increases in busines units using the technology. The good news? The foundations you need are being built right now by the open-source community. Your job as a leader is recognizing their strategic value and committing to building on them. This means making architectural decisions that prioritize compatibility over proprietary lock-in. It means investing in platforms that combine the innovation velocity of open source with the governance requirements of enterprise deployment. Most importantly, it means understanding that in AI, standardization and innovation aren’t oppositesthey’re partners. Standards create the stable foundation that lets innovation flourish at speed. Start with one diagnostic question: Can your teams share AI models and data pipelines across projects without rebuilding them? If not, you’re building on quicksand. The companies that can answer yes will set the competitive pace for the next decade. Peter Wang is cofounder and chief AI and innovation officer at Anaconda.
Category:
E-Commerce
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