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When people think about artificial intelligence, they often picture sleek start-ups or futuristic labs. But what happens when AI meets a company that has been innovating for over 100 years? Unilever is one of the worlds largest consumer goods companies, home to brands like Dove, Hellmanns and Vaseline, with products used by 3.4 billion people every day. And behind those everyday items is a deep and evolving commitment to science. From soap and margarine in the early 20th century to todays breakthroughs in sustainable packaging and personalized skincare, Research and Development (R&D) has always been our engine of progress. But now, that engine is being transformed by AI. AI is not just a new tool in our labs, it is a new way of thinking. And for a company with a centurys worth of scientific data, that is a game-changer. AI is reshaping every industry, but the companies that will be the most successful are the ones that know how to adapt, learn, and build on what they already know. While many legacy companies are exploring how to modernize through AI, the real opportunity lies in how they harness their institutional memory: the decades of research, product development, and consumer insights that can often sit untapped. This requires deep domain expertise, robust data stewardship, and a culture that values learning as much as legacy. When those elements align, AI can become a catalyst for transformation, by revealing the full potential of what has come before. Unilever was born in the Victorian era, shaped by the industrial and scientific revolutions. Over the decades, we have evolved by responding to cultural shifts; from the transformation of domestic life in the mid-20th century to todays shifting expectations around skin health, beauty, and wellbeing to the growing urgency of sustainability. When new materials like Formica and stainless steel became common in mid-century kitchens, our scientists developed products tailored to these surfaces. This was not just chemistry, it was a scientific response to a changing way of life. That same mindsetscience in service of real lifestill drives us today. But the questions were asking have become more complex: How do we support the skins natural microbiome? How do we clean homes without disrupting the ecosystems that live on our surfaces? How do we design products that are both effective and sustainable? These are not simple problems, and they require new ways of doing science. Thats where AI comes in. With machine learning, we can uncover patterns that would take human researchers hundreds of years to detect. We are using AI to understand how microbes interact with our products, how skin responds to environmental stressors, and how we can personalize formulations for different needs and regions. But here is what makes our approach uniquewe are not starting from scratch. Like many legacy companies our R&D archives stretch back over 100 years. We have records of every formulation, every trial, and every consumer insight. This historical depth gives our AI models something incredibly rare: context. While many companies are just beginning to build their data sets, established companies like ours are standing on a foundation that has been carefully constructed for generations. Our scientists can unlock proprietary knowledge that was once siloed, scattered across teams, or locked in an archive. A century of skincare expertise is now structured, searchable, and ready to be applied. We are using AI to connect the dots across decades of research, accelerating discovery in new materials while simultaneously optimising formulations for specific needs, like different skin types. Were moving from research and discovery to formulation design and refinement in a single, integrated process, helping us respond faster and more precisely to peoples needs around the world. This is not about replacing scientists with algorithms. It is about creating the conditions where human talent can thrive. Agentic AI systems give our teams the ability to ask better questions, explore more possibilities, and unlock insights from our data. By amplifying human creativity and empathy not automating it, were enabling our scientists to focus on what they do best: imagining, experimenting, and designing products that meet real human needs. So why should this matter to anyone outside Unilever? Because it shows what is possible when legacy meets learning. In an era where AI is reshaping every industry, the companies that thrive will not just be the newest or the loudest, they will be the ones that know how to adapt, how to learn, and how to build on what they already know. AI rewards data maturity. It rewards curiosity. And it rewards companies that see technology not as a threat to tradition, but as a way to reimagine it. We do not have all the answers. But we have learned that staying curious, being a learn-it-all not a know-it-all, is what keeps a company relevant for a century. AI is helping us stay curious at scale. We believe the next 100 years of innovation will be driven by companies that embrace the partnership between human talent and agentic AI: hybrid systems that augment creativity, empathy, and scientific intuition. This is not just a story about technology. It is a story about legacy, learning, and the enduring power of science to shape the everydayonly now with a little help from artificial intelligence. Alberto Prado is global head digital & partnerships at Unilever.
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E-Commerce
In a world where leadership is often mistakenly understood as a position of control, Ive found that true impact comes from serviceleading not from above, but alongside your team to achieve success. This point was driven home about 15 years ago, when I made a resolution to read more books. Since starting this journey, one book stood out and shaped my leadership style more than any other: The Way of The Sheperd. It resonated with me so much that I even named my youngest son, Shep, after it. This book takes readers through core principles of servant leadership and outlines a leadership plan that showcases how true influence comes not from authority, but from trust, empathy, and a genuine devotion to the growth and well-being of your team. I reread this book once a year to remind myself that the most effective leaders are ones who lead with compassion, understand what motivates each team member, and empower everyone to hold themselves accountable. It underscores how a leader cant manage what they dont know, and how too many well-intentioned leaders focus solely on performance rather than their people. These lessons have influenced the way I lead, inspiring me to prioritize building trust and authentic connections with our team. At Kendra Scott, I make it a point to ensure everyone has direct access to memy email is always openand I encourage team members to reach out or schedule time to discuss anything on their minds. Company connections Weve also established a tradition of celebrating the meaningful connections within our company. For the past 9 years, weve passed down the shepherds staff. The tradition involves the current holder receiving the staff and keeping it for a set period of time before nominating someone else at the next family meeting. They share how this person has impacted them, add a meaningful token to the staff, and pass it on to the new nominee. The cycle continues from there. This simple yet powerful practice has reinforced our culture and the importance of the connections we share with one another. However, this modern leadership philosophy wasnt one that always came naturally to me. Growing up, my life was all about sports, which helped me become the first in my family to go to college. The competitive mindset required for sports can sometimes be too focused on the individual. But the real lesson I took away, doing whatever it takes with your team to win, shaped my early approach to leadership. 3 things I know As I moved into the business world and started working with founders like Ralph Lauren and Kendra Scott, I began to see how personal their connection to their companies was. That shifted my approach to leadership, and I started to focus more on building trust and creating a more supportive, nurturing environment for my team. Here are three things I know now: Fostering a sense of purpose leads to stronger performance Investing in your team and infusing every position with importance helps to instill passion and purpose in your employees. Every employee should be empowered to feel as if they represent a brands vision. This authentic connection fuels productivity and drives success. Making mistakes can be your biggest asset You learn the most through your mistakes. In those moments, the best lessons come from acknowledgement and accountability. As a leader, being transparent and vulnerable about your own missteps can set a tone that not only enhance company culture, but also make employees feel secure in taking risks. The importance of leading with a learners heart Great leaders are great learners. No one has all of the answers, regardless of their position in an organization. Thats why its important to maintain a culture of continuous learning and collaboration. The more you learn, the more tools and opportunities you have. In the end, leaders cant be successful without their team, and leadership isnt about control, power, or having all the answers. Its about showing up for your people, creating space for them to grow, and demonstrating humility through it all. Whether its by reading a book about servant leadership or establishing supportive methods, Ive learned that true leaders are those who cultivate intentional connections with their employees. There is still much to learn, and my journey is still unfolding, but one thing I know to be true is that leading with trust and empathy is the type of legacy worth leaving. Tom Nolan is CEO of Kendra Scott.
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E-Commerce
Amaras Law, coined by the American scientist and futurist Roy Amara, says humans tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run. If the first half of 2025 is anything to go by, in the AI era, the runs are getting shorter, and the effects of the technology will be larger than weve seen in a generation. In a matter of months, the conversation in companies has accelerated far beyond if AI is a useful productivity tool, to where and when it can be applied. Across industries and geographies, executives are acknowledging that AI is a general-purpose business solution, not just a technical one. Despite widespread workplace adoption, the focus on cybersecurity has not kept pace. In the rush to adopt AI systems, applications and agents, companies are failing to consider that rapid deployment of these new technologies could lead to data breaches and other security risks. That matters because AI models are not only getting more powerful but also more useful for enterprises. More enterprises are using AI agents As of early June, OpenAIs base of paying business users reached 3 million, up from 2 million in February. In a move for that market, ChatGPT can now connect to popular business apps such as Google Drive, Dropbox, and Sharepoint, allowing workers to quickly access answers that are locked in dispersed documents and spreadsheets. Confusion, and even fear, about AI agents has given way to exploration and adoption. Among US-based organizations with annual revenues of $1 billion or more, 65% were piloting AI agents in the first quarter of this year, up from 37% in the space of a single quarter. Microsofts Azure AI Foundry, its platform for building AI agents, processed 100 trillion tokens in the first three months of 2025 (with one token representing the smallest unit of text that an AI model processes)a five-fold increase year-on-year. At the same time, the cost per token more than halved, spurring higher use and creating virtuous cycles of innovation. As John Chambers, the former CEO of Cisco, says, AI is this generations internet revolution but at five times the speed, with three times the outcome. Beyond the hype that haunts the sector, there are signs of enterprise AI adoption everywhere. In his latest letter to shareholders Alex Karp, CEO of Palantir Technologies, describes a ravenous whirlwind of adoption of AI. IBM, which has rolled out its AI strategy to 270,000 employees, reports that AI already handles 94% of routine human resources tasks. At Shopify, the e-commerce group, AI usage is now a baseline expectation, CEO Tobias Lütke said in an employee memo. The workplace automation company Zapier, which took steps to embed AI across its workforce, says that 89% of employees actively use AI in their daily work. The list goes onand its not just technology companies. JP Morgan, the worlds largest bank, has rolled out GenAI tools to 200,000 staff members, and says employees have each gained one-to-two hours of productivity each week. AI acquisitions are plentiful The shift from novel to mass-market tech is reflected in the business strategies of the main AI model makers, which are reimagining themselves as application companies. In the space of two weeks, OpenAI, the ChatGPT parent, appointed a CEO of Applications and then acquired IO, the AI device startup founded by former Apple designer Jony Ive, for $6.5 billion. Meta, perceived to be behind in the AI race, has invested $14.3 billion in Scale AI, which provides data and evaluation services to develop applications for AI. Meanwhile, Apple is reported to have had internal talks about buying Perplexity AI, a two-and-a-half year-old AI model maker. AI app security is rarely discussed Companies are naturally focused on the potential and performance of AI systems, but it’s striking how rarely security is part of the story. The reality is that the speed of deployment of AI apps and agents is leaving companies at risk for breaches, data loss, and brand impact. For example, an AI system or agent that has access to employee HR data or a banks internal systems leaves a company open to possible cyberattacks by bad actors. In business-critical applications, risks emerge at every stage of the development cycle, from choosing which AI model to use and what systems to give it access to, right through to deployment and daily use. In our work on testing the security of AI models with simulated attacksknown as red-teamingand creating the CalypsoAI Model Security Leaderboards, we have discovered that, despite performance improvements, new or updated AI models are often less secure than existing ones. At the same time, existing models can see their security score slip over time. Why? Because the attacks keep progressing and bad actors learn new tricks. More techniques and capabilities of breaking or bypassing AI model securities keep being invented. Simply, the attack techniques are getting better and they’re causing AI models that have only recently launched to become less secure. That means that organizations that begin using an AI system or agent today, but don’t stay up to date with the latest threat intel, will be more vulnerable as attack techniques increase in capability and frequency. As corporate AI systems gain autonomy and access to sensitive data, what is safe today may not be safe tomorrow. The research firm Gartner has forecast that 15% of day-to-day business decisions will be made autonomously by agents by 2028, though that percentage may increase by then. Against that backdrop, virtually all the security protocols and permissions in enterprises are built for human workers, not for AI agents that can roam through company networks and learn on the job. That mismatch opens up vulnerabilities, such as the ossibility of agents accessing sensitive information and sharing it inappropriately. Poorly secured agents will be prime targets for hackers, particularly where they have access to valuable data or functions such as money transfers. The consequences include financial loss and reputational damage. Final thoughts Securing these new systems will be critical to AI adoption and to successful return on investment for the companies involved. A new security paradigm, using the capabilities of agentic AI to secure enterprise AI, is needed to allow innovation to thrive and agents to reach their potential. While the development of AI models and systems so far can reasonably be summarized as better, cheaper, less secure, the final part of that equation must improve significantly as the emerging application-first AI era accelerates. Once that happens, Roy Amara seems certain to be proven right once again. Donnchadh Casey is CEO of CalypsoAI.
Category:
E-Commerce
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