Business Trends

Over two years ago, I wrote about the emerging synergy between LLMs and ontologies - and how, together, they could create a self-reinforcing loop of continuous improvement. That post struck a chord. With GPT-5 now here, it’s the right moment to revisit the idea. Back then, GPT-3.5 and GPT-4 could draft ontology structures, but there were limits in context, reasoning, and abstraction. With GPT-5 (and other frontier models), that’s changing: 🔹 Larger context windows let entire ontologies sit in working memory at once.   🔹 Test-time compute enables better abstraction of concepts.   🔹 Multimodal input can turn diagrams, tables, and videos into structured ontology scaffolds.   🔹 Tool use allows ontologies to be validated, aligned, and extended in one flow. But some fundamentals remain. GPT-5 is still curve-fitting to a training set - and that brings limits: 🔹 The flipside of flexibility is hallucination. OpenAI has reduced it, but GPT-5 still scores 0.55 on SimpleQA, with a 5% hallucination rate on its own public-question dataset.   🔹 The model is bound by the landscape of its training data. That landscape is vast, but it excludes your private, proprietary data - and increasingly, an organisation’s edge will track directly to the data it owns outside that distribution. Fortunately, the benefits flow both ways. LLMs can help build ontologies, but ontologies and knowledge graphs can also help improve LLMs. The two systems can work in tandem.   Ontologies bring structure, consistency, and domain-specific context.   LLMs bring adaptability, speed, and pattern recognition that ontologies can’t achieve in isolation.   Each offsets the other’s weaknesses - and together they make both stronger. The feedback loop is no longer theory - we’ve been proving it:   Better LLM → Better Ontology → Better LLM - in your domain. There is a lot of hype around AI. GPT-5 is good, but not ground-breaking. Still, the progress over two years is remarkable. For the foreseeable future, we are living in a world where models keep improving - but where we must pair classic formal symbolic systems with these new probabilistic models. For organisations, the challenge is to match growing model power with equally strong growth in the power of their proprietary symbolic formalisation. Not all formalisations are equal. We want fewer brittle IF statements buried in application code, and more rich, flexible abstractions embedded in the data itself. That’s what ontologies and knowledge graphs promise to deliver. Two years ago, this was a hopeful idea.   Today, it’s looking less like a nice-to-have…   …and more like the only sensible way forward for organisations. ⭕ Neural-Symbolic Loop: https://lnkd.in/eJ7S22hF 🔗 Turn your data into a competitive edge: https://lnkd.in/eDd-5hpV

This morning, we released a preview to the press of the TechnoVision Top 5 Tech Trends to Watch in 2025. Technology is advancing at an unprecedented pace, and our teams have diligently worked to highlight the transformative technologies that we anticipate will reach a pivotal point next year. In addition to leveraging insights from our best experts across all technology domains, we conducted a global study of 1,500 top executives and 500 venture capitalists this year to gain a clear perspective on emerging technology trends. What stands out for us in 2025 is that AI (and Gen AI) are leading the pack. Their ripple effects are also accelerating advancements in adjacent domains, including robotics, cybersecurity, supply chains, and even the energy sector. Drawing from our research and the views of our top experts, here’s a brief snapshot of the Top 5 technology trends set to shape the business landscape in 2025: 🤖 Generative AI: From copilots to reasoning agents, AI systems are evolving into specialized, interconnected agentic systems, enabling autonomous and efficient decision-making. 🔐 Cybersecurity: New threats, new defenses – AI is reshaping the landscape, driving increasingly sophisticated cyber threats and equally advanced defenses to counter these new risks. 🦾 AI-Driven Robotics: Robots powered by advanced AI are blurring the lines between humans and machines, with the promise to transform entire industries. ⚛️ Energy: AI driving the Nuclear Agenda – The growing energy demands of technology in the AI era are driving major tech companies to make significant investments in nuclear energy, potentially accelerating both the deployment of nuclear projects and advancements in reactor technology. 🚚 Next-Gen AI-Assisted Supply Chains: Agile, AI-assisted, and sustainable supply chains are becoming the backbone of modern business resilience and innovation. The full study on these top 5 trends, along with other emerging technology trends, will be released in a global report available at the opening of CES in Las Vegas next year. Stay tuned! https://lnkd.in/e3SWs4iN #top5techtrends Robert (Dr Bob) Engels Marco Pereira Sally Epstein Laurent BROMET Paul Shoemaker Emmanuelle BISCHOFFE CLUZEL🌍

Top 6 Sustainability Tech Trends for 2025 🌎 The rapid evolution of sustainability-focused technologies is reshaping industries and redefining best practices. In 2025, six key trends are driving the integration of advanced tools to address environmental, social, and governance challenges more effectively. Data analytics for ESG reporting is enhancing transparency and compliance. Real-time tracking of emissions, energy, and water use enables precise monitoring and reporting, meeting increasing regulatory demands and stakeholder expectations while driving operational efficiencies. AI for risk modeling is emerging as a critical tool for predicting climate risks and navigating regulatory changes. By providing data-driven insights, it supports the development of resilience strategies and long-term planning, reducing vulnerability to disruptions. Blockchain for supply chains is ensuring traceability, ethical sourcing, and circular economy practices. Its role in creating transparent supply chains enhances accountability and supports sustainability commitments across industries. Technologies such as IoT for resource efficiency and AI for circular design are streamlining operations and advancing sustainable practices. IoT optimizes energy, water, and waste management in real time, while AI facilitates sustainable product lifecycles by improving reuse, remanufacturing, and recycling processes. These advancements, alongside digital risk platforms for integrating compliance and resilience planning, are driving systemic change. The focus is not just on addressing immediate challenges but on building long-term strategies for a sustainable future. #sustainability #sustainable #business #esg #climatechange #tech

Clayton Christensen announced it — product managers are underestimating the disruption caused by Large Language Models (LLMs) for the reasons described in The Innovator's Dilemma. Incumbent organizations often focus on what new technologies CANNOT do, highlighting their limitations and risks instead of embracing the low-cost and scalability benefits that are emerging. Every profession has an implicit Return On Investment (ROI). If you're rejecting LLMs because they can only accomplish tasks with 80% quality, you're missing the point. A machine that can accomplish 80% of a task (= return) with merely 1% of the effort (= investment) offers a much much much better ROI than a human everything manually. Adding to this, there exists an absurd subconscious belief among some product managers that their lack of adoption will somehow slow down the inevitable tsunami of disruption. Combined with natural organizational inertia, this mindset results in a profession that clings to internal debates—such as the distinction between a product manager and a product owner—when it should be focusing on learning how to surf this lava-wave. Product managers should be obsessed with: 1. Breaking down their jobs into huge lists of tiny tasks; 2. Exploring how each task could be done slightly more rapidly thanks to LLMs; 3. Figuring out what new investments or habits need to happen to accelerate the tango — starting by abandoning ChatGPT and hopping onto LLMs that tap into private databases, your most important asset moving forward. Here's the beautiful part: LLMs are an amazing piece of technology, but the actual products remain to be invented on top of it. What's holding you back?

LLMs massively empower individuals. Used well, they augment thinking and intentions to an extraordinary degree. The impact is far more muted and delayed for large organizations, which have entrenched ways of working that will take years to shift through careful negotiation of culture and governance. AI doyen Andrej Karpathy has neatly laid out how genAI results, quite simply, in: Power to the people. Transformative technologies have usually been developed and used by governments and the military, and then diffused to companies and individuals. For LLMs, everyone has access to the same quality AI, largely free, in every language, to be applied immediately to whatever users want to do. In contrast, there are many reasons why it will be far slower for organizations to get value: ➡️ LLMs offer broad but shallow capabilities, which are less valuable to organizations already equipped with deep domain experts. ➡️ Organizations already consolidate specialized expertise, so LLMs typically enhance existing workflows rather than enabling entirely new capabilities. ➡️ The improvements LLMs provide are incremental, making organizations slightly more efficient at tasks they already perform well. ➡️ Integrating LLMs into complex legacy systems and existing processes is technically challenging and resource-intensive. ➡️ Strict security, privacy, and regulatory requirements limit how freely LLMs can be used in corporate and government environments. ➡️ The risk of errors or hallucinations from LLMs is unacceptable in high-stakes or legally sensitive organizational contexts. ➡️ Organizational culture can resist the adoption of new tools, especially when they disrupt established roles or processes. ➡️ Decision-making in large organizations is often slow, with multiple layers of approval and governance slowing experimentation. ➡️ Retraining employees to use LLMs effectively at scale is a significant undertaking with cost and coordination challenges. ➡️ Bureaucracy, turf wars, and political dynamics within organizations often create resistance to rapid technological adoption. Take advantage of power flowing to the people!

Focusing on AI’s hype might cost your company millions… (Here’s what you’re overlooking) Every week, new AI tools grab attention—whether it’s copilot assistants or image generators. While helpful, these often overshadow the true economic driver for most companies: AI automation. AI automation uses LLM-powered solutions to handle tedious, knowledge-rich back-office tasks that drain resources. It may not be as eye-catching as image or video generation, but it’s where real enterprise value will be created in the near term. Consider ChatGPT: at its core, there is a large language model (LLM) like GPT-3 or GPT-4, designed to be a helpful assistant. However, these same models can be fine-tuned to perform a variety of tasks, from translating text to routing emails, extracting data, and more. The key is their versatility. By leveraging custom LLMs for complex automations, you unlock possibilities that weren’t possible before. Tasks like looking up information, routing data, extracting insights, and answering basic questions can all be automated using LLMs, freeing up employees and generating ROI on your GenAI investment. Starting with internal process automation is a smart way to build AI capabilities, resolve issues, and track ROI before external deployment. As infrastructure becomes easier to manage and costs decrease, the potential for AI automation continues to grow. For business leaders, identifying bottlenecks that are tedious for employees and prone to errors is the first step. Then, apply LLMs and AI solutions to streamline these operations. Remember, LLMs go beyond text—they can be used in voice, image recognition, and more. For example, Ushur is using LLMs to extract information from medical documents and feed it into backend systems efficiently—a task that was historically difficult for traditional AI systems. (Link in comments) In closing, while flashy AI demos capture attention, real productivity gains come from automating tedious tasks. This is a straightforward way to see returns on your GenAI investment and justify it to your executive team.

2025 Predictions: What’s Next for Business, Culture, and Innovation As we just entered 2025, here are the trends I believe will shape the year ahead. Some are already bubbling under the surface, while others are set to make waves. 1. Talent-Led Brands Will Steal the Spotlight People want real connections. The brands led by individuals with authentic stories, rather than faceless corporations, will win big. Influence will matter more than reach, and trust will be the ultimate currency. 2. AI Gets Personal AI is everywhere, but the focus is shifting to making it feel more human. From personalised healthcare to smarter shopping experiences, AI will get better at understanding you. 3. Mental Health Becomes the New Fitness The wellness industry is evolving. This year, mental health will take centre stage—whether it’s through apps, functional drinks, or better workplace support. It’s about time. 4. Retail Reinvents Itself High streets aren’t dying; they’re transforming. Shops will double as content studios, experiential hubs, and places where the digital world meets the physical. Shopping is about to get exciting again. 5. Diversity as a Must-Have, Not a Tick-Box Businesses that treat diversity as an afterthought will be left behind. The ones embedding it into their DNA—from leadership to campaigns—will be the ones we’ll remember (and buy from). 6. Sustainability Becomes Non-Negotiable Customers are demanding it, and businesses can’t ignore it. Sustainability will stop being a buzzword and start being a baseline—especially in industries like fashion, food, and energy. 7. Creators Build Empires The creator economy is growing up. This year, we’ll see more creators launching businesses, partnering with brands, and building long-term legacies. It’s less about quick viral moments and more about real, sustainable impact. 8. Africa Takes the Stage From tech in Lagos to the global influence of African music and fashion, the continent’s impact is undeniable. It’s not just an emerging market—it’s the future. 9. Purpose Over Profit More businesses will focus on purpose and values. Whether it’s faith, community, or making a difference, the most successful entrepreneurs will build businesses that align with what they truly believe in. 10. Collaboration Beats Competition The future is collaborative. Whether it’s joint ventures, co-branded products, or unexpected partnerships, working together will be the secret to scaling and staying relevant.

What are the Trends for the C-Suite in 2025? As we reflect on the challenges of 2024, businesses faced unprecedented disruptions and rapid changes. From navigating the complexities of remote work to addressing supply chain vulnerabilities and adapting to evolving consumer behaviours, C-suite executives had to demonstrate resilience and agility. The lessons learned from these challenges are shaping the strategic priorities for 2025. Top Five Trends for C-Suite in 2025: Digital Transformation and AI Integration: Embracing advanced technologies and integrating artificial intelligence into business operations will be crucial. This trend will drive efficiency, enhance customer experiences, and provide valuable insights for decision-making. Sustainability and ESG (Environmental, Social, and Governance) Initiatives: Companies are increasingly prioritising sustainability and ESG factors. C-suite leaders must focus on creating sustainable business models that not only meet regulatory requirements but also resonate with stakeholders and consumers. Workforce Evolution and Hybrid Work Models: The future of work continues to evolve, with hybrid work models becoming the norm. Executives need to foster a flexible and inclusive work environment that supports employee well-being and productivity. Cybersecurity and Data Privacy: As cyber threats become more sophisticated, ensuring robust cybersecurity measures and data privacy protocols is paramount. C-suite leaders must invest in advanced security technologies and foster a culture of vigilance. Customer-Centric Innovation: Staying competitive requires a relentless focus on innovation that meets customer needs. Executives should prioritise customer feedback and leverage data analytics to drive product and service enhancements. In 2025, C-suite executives will need to navigate a landscape shaped by digital transformation, sustainability, workforce evolution, cybersecurity, and customer-centric innovation. Leaders can drive business growth and advance their careers by staying ahead of these trends. If you need help with hiring or navigating your career journey, please feel free to get in touch with me. Let's work together to achieve your goals.

Large language models (LLMs) can improve their performance not just by retraining but by continuously evolving their understanding through context, as shown by the Agentic Context Engineering (ACE) framework. Consider a procurement team using an AI assistant to manage supplier evaluations. Instead of repeatedly inputting the same guidelines or losing specific insights, ACE helps the AI remember and refine past supplier performance metrics, negotiation strategies, and risk factors over time. This evolving “context playbook” allows the AI to provide more accurate supplier recommendations, anticipate potential disruptions, and adapt procurement strategies dynamically. In supply chain planning, ACE enables the AI to accumulate domain-specific rules about inventory policies, lead times, and demand patterns, improving forecast accuracy and decision-making as new data and insights become available. This approach results in up to 17% higher accuracy in agent tasks and reduces adaptation costs and time by more than 80%. It also supports self-improvement through feedback like execution outcomes or supply chain KPIs, without requiring labeled data. By modularizing the process—generating suggestions, reflecting on results, and curating updates—ACE builds robust, scalable AI tools that continuously learn and adapt to complex business environments. #AI #SupplyChain #Procurement #LLM #ContextEngineering #BusinessIntelligence

For years, fine-tuning LLMs has required large amounts of data and human oversight. Small improvements can disrupt existing systems, requiring humans to go through and flag errors in order to fit the model to pre-existing workflows. This might work for smaller use cases, but it is clearly unsustainable at scale. However, recent research suggests that everything may be about to change. I have been particularly excited about two papers from Anthropic and Massachusetts Institute of Technology, which propose new methods that enable LLMs to reflect on their own outputs and refine performance without waiting for humans. Instead of passively waiting for correction, these models create an internal feedback loop, learning from their own reasoning in a way that could match, or even exceed, traditional supervised training in certain tasks. If these approaches mature, they could fundamentally reshape enterprise AI adoption. From chatbots that continually adjust their tone to better serve customers to research assistants that independently refine complex analyses, the potential applications are vast. In today’s AI Atlas, I explore how these breakthroughs work, where they could make the most immediate impact, and what limitations we still need to overcome.