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The AI industry is growing up fast. New model releases are now a regular event and premium AI features are quickly overtaken by free or freemium alternatives. Exhibit A: OpenAI unveiled its Deep Research tool, which can write reports on complex topics in minutes, as part of its $200-a-month Pro package, but rival Perplexity gives non-subscribers some access to its Deep Research assistant free of charge. (Yes, Google Geminis agentic research assistant is also called Deep Research.) With fewer fundamental breakthroughs, the likes of OpenAI, Anthropic, and xAI are slugging it out over incremental improvements in search and reasoning performance. As AI pricing falls and performance gaps close, the focus has shifted from novelty applications to finding real business value. Its a new era for AI Agentic AI is the game-changer. Gartner forecasts that 33% of enterprise software applications will include agentic AI by 2028, a drastic increase from less than 1% in 2024. Some 15% of day-to-day work decisions could be made autonomously by AI agents, hiking business productivity and freeing up workers for more strategic tasks. Its probably no surprise, then, that OpenAIwhich famously took 4.5 years to launch ChatGPT without any idea of who our customer was going to be, according to CEO Sam Altmanis releasing its first-ever product roadmap. Nothing says maturing market like a product roadmap. As Finn Murphy, a founder and venture capitalist, posted on X from the AI Action Summit in Paris, where the EU said it would mobilize 200 billion for AI investment: It really feels like the era of interesting technical breakthroughs being announced is over and the era of policy, partnerships, and money announcements is here. Security matters Growing up brings responsibilities, of course, especially at the enterprise level. Among the 1,803 C-suite executives surveyed for the Boston Consulting Group (BCG) AI Radar, published in January, 76% recognized that their AI cybersecurity measures need further improvement. If anything, that number should be closer to 100%. Execs ranked data privacy and security as the top AI risk. Regulatory challenges and compliance also featured strongly. Their fears are not unfounded: AI applications open up a new attack surface for threat actors and security researchers have already succeeded in breaking all of the world-class AI models to some extent. Still, it took the shock arrival of Chinas DeepSeek to properly push AI security into the mainstream. It is notable that consumers and corporates have concerns about a Chinese entity having their data but seem content that U.S. and Europe-based entitieswhich impose almost identical terms and conditionswill keep it secure. Security must be a key consideration for all AI models, not just those built (or hosted) outside the US. History shows us that bad actors are often the earliest adopters of new technology, from wire fraud to phone, text and email phishing scams. In an agentic world, where AI agents have been given access to critical business information and in-house applications, the blast radius from any attack may be exponential. Think like an attacker Its often said that the best defense is to think like an attacker. Today, that means using Agentic Warfare to comprehensively test AI-driven systems for vulnerabilities long before they see the light of day. Automated red-teaming is the new standard in testing AI with speed, complexity, and scale. At every step, security has to sit alongside performance in choosing AI, rather than coming as an afterthought when something goes wrong. As much as cost, security-to-performance will be a key metric in model and app selection; this is a one-way-door decision for safe and successful AI implementations. Interestingly, the BCG survey reports that the intuitive, friendly feel of GenAI masks the discipline, commitment, and hard work required to introduce AI in the workplace. It is hard work but the rewards should be significant. Just as software led to era-defining leaps in innovation and productivity, agentic AI promises great advances in all sectorsas long as security is baked in from the beginning. Donnchadh Casey is CEO of CalypsoAI. The Fast Company Impact Council is a private membership community of influential leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual membership dues for access to peer learning and thought leadership opportunities, events and more.
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E-Commerce
In todays whiplash business environment of change and uncertainty, there are a few simple, timeless strategies that consistently rank among the best for accelerating growth. No reinventing the wheel required. One such strategy is test-to-scaleclose cousin of the venerable test-and-learn approach thats long been a startup staple. Both can play a key role, depending on the stage of your company, industry, size, growth curve, andimportantlyinternal culture. Basically, test-and-learn uses small scale, iterative experiments to see what works best. Testing different messaging in a marketing campaign, for example, or perhaps different product features. The idea is to gather data, analyze the results, and learn quickly what worksor doesnt. Test-to-scale ups the ante by taking things to another level. These tests aim to discover whether a product, system, or process can withstand the stress test of large-scale rollout or production. This might include manufacturing capacity, supply chains, distribution channels, sales transactions, user adoption, and data collection. The benefits of making test-to-scale part of your go-to strategic arsenal will accrue from multiple directions, including: Faster time to market: By testing concepts early, you can identify solutions and innovations most likely to succeed, and launch them quickly and confidently. Data trove: Experimentation generates valuable real-time customer data that is super helpful for making quick scaling decisions. Innovation and agility: Testing fosters an entrepreneurial mindset that enables continuous improvement, better informed decisions, and rapid adjustments. Risk reduction: Experiments also lower risk by identifying possible pitfalls and pointing to alternative solutions early. Entrepreneurial DNA In a hypergrowth environmentlike the one Liquid I.V. has been navigating for yearsthings must happen quickly. The entrepreneurial spirit baked into our rapidly growing companys DNA is a major factor in helping us achieve the kind of growth and expansion we could scarcely have imagined 5 years ago. For us, one way this entrepreneurial spirit plays out is how we choose to invest and what we prioritize. This is where test-to-scale enters the picture. We follow the 80/20 rule. While about 80% of our investments are proven and measurable, the other 20% goes to experimentation. You never know where that experimental fifth will take you. In our case, the answer has been A long, long way! Our experimentation with TikTok Shop, for example, resulted in a wealth of e-commerce knowledge and data that helped our team open a new, high-potential channel. Experimentation with gaming yielded Twitch as a high ROI media channel for us. Those are just two among many mainstream Liquid I.V. products, processes, sales channels, and marketing tactics that began as tests, but are now integral to our success. Learn from test-to-scale Below are three key learnings, ingredients, and benefits of test-to-scale that can help any company use this simple strategy to supercharge growth: 1. Commitment to learning: The means and ability to test are, of course, a requirement. But more importantly, you must also be willing and able to learn from the results. This is more difficult than it sounds. Theres a natural tendency to bury failure rather than learn from it. The learning side of the equation is valuable payoff. Remember, finding out that something doesnt work is just as important as learning that something does. 2. Relentlessly prioritize: At Liquid I.V., we invest considerable time and effort into prioritizing our chosen experiments and potential lessons. Theres an endless list of things we could test, but only a few we truly should. Areas we prioritize include go-to-market capabilities and capacity, customer relationship management (CRM) engagement and personalization, R&D and innovation in new product development, omni-channel demand generation, and different combinations of in-house/outsourced resourcing that prioritize speed and expertise. The areas you choose may be totally different. The important thing is to make the hard choices. 3. JTBD: The jobs-to-be-done way of approaching experimentation is based on research showing that people buy products and services mainly to get some type of job done. Jobs-to-be-done might include staying properly hydrated, booking travel, building a deck, or thousands of other tasks that consumers need to regularly accomplish. Centering your test-to-scale approach around JTBD helps make innovation more predictable and marketing more effective. At Liquid I.V., its been one of the key drivers helping our brand awareness and household penetration numbers skyrocket. Theres no such thing as failure in a culture that values experimentation. There is only feedback. Your odds of success will directly corollate with your ability to embrace that feedback. Mike Keech is CEO of Liquid I.V. The Fast Company Impact Council is a private membership community of influential leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual membership dues for access to peer learning and thought leadership opportunities, events and more.
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E-Commerce
Its the dynamic pulse that surges through our electrical lines, the unseen force that illuminates the dark, and the silent but commanding engine propelling the future of artificial intelligence. Though intangible, its influence is omnipresent in our daily lives. This invisible force is power. As the world shifts from fossil fuels to embrace renewable energy, power is emerging as an ultimate finite resource. The ever-growing demands on our electrical grid, driven by the explosion of data centers, electric vehicles, and the proliferation of AI are creating a race against time where needs outpace supply. Consequently, our insistence on tech innovation may be hindering efforts to combat climate change, as it forces us to continue relying on fossil fuels to meet the rising energy requirements. Power system innovation Solving this energy crisis is essential for next-generation economies to successfully adopt AI and electrification. Innovation in power systems is essential to achieving this, enabling smarter, more sustainable energy solutions. Integrating AI data centers with advanced power systems can drastically reduce energy consumption and costs while meeting growing energy demands. As AI continues to expand, power system innovation will play a crucial role in reducing its environmental impact to foster a more sustainable future. Supporting a sustainable future by adopting energy-efficient technologies and practices has become crucial over the last decade. Customers are increasingly demanding that enterprises and their supply chains meet stringent sustainability benchmarks, underscoring the importance of these technologies and practices. From a marketing perspective, companies that work to innovate and prioritize sustainable solutions not only contribute to a greener planet but also gain a competitive edge by aligning their brand values to the values of environmentally conscious consumers and partners. In fact, Deloitte research indicates that a major shift is happening in consumption patterns, where sustainability is considered a baseline requirement for purchase rather than a nice-to-have. Technological progress advances AIs surge began nearly 4 years ago, with headlines predicting its potential impact. AI has continued to evolve and its capabilities have become increasingly fine-tuned. It is now seen as a transformative force, anticipated to revolutionize global economies and industries, specifically in sectors like healthcare and manufacturing. For example, regions such as the United Kingdom are planning to leverage AIs capabilities across its public and private sectors to cut costs and spur economic growth. However, harnessing this advanced technologys power brings considerable challenges, specifically in energy consumption and its environmental impact. AI’s computational requirements are increasingly energy-intensive, especially as data centerswhich power all AI functionsrequire more power to manage the boundless amounts of data flowing in. This surge in energy demand strains the electrical grid, creating a tipping point for power that risks progress made over the last decade in reducing our carbon footprint. As we look toward the future, energy efficiency is the key to ensuring that this technological progress doesnt advance at the expense of our environment. Power system innovation, for example, can significantly reduce energy consumption and operational costs while still meeting the growing AI demands. Additionally, integrating renewable energy sources and improving energy management systems can help mitigate AIs overall environmental impact. The sustainable path forward We stand at a pivotal moment where innovation and collaboration are essential to improving efficiency through new technologies, all while maintaining a strong focus on the common sustainability theme. To overcome today’s challenges and achieve a sustainable future, we must explore and cooperate across various industries. It’s crucial that we support AIs continued growth without compromising the significant progress individuals and enterprises have made in reducing carbon emissions for more sustainable supply chains and a more sustainable world. Felicity Carson is SVP and chief marketing officer at onsemi. The Fast Company Impact Council is a private membership community of influential leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual membership dues for access to peer learning and thought leadership opportunities, events and more.
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E-Commerce
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