Understanding Technological Evolution

AI is rapidly moving from passive text generators to active decision-makers. To understand where things are headed, it’s important to trace the stages of this evolution. 1. 𝗟𝗟𝗠𝘀: 𝗧𝗵𝗲 𝗘𝗿𝗮 𝗼𝗳 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗙𝗹𝘂𝗲𝗻𝗰𝘆 Large Language Models (LLMs) like GPT-3 and GPT-4 excel at generating human-like text by predicting the next word in a sequence. They can produce coherent and contextually appropriate responses—but their capabilities end there. They don’t retain memory, they don’t take actions, and they don’t understand goals. They are reactive, not proactive. 2. 𝗥𝗔𝗚: 𝗧𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗖𝗼𝗻𝘁𝗲𝘅𝘁-𝗔𝘄𝗮𝗿𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Retrieval-Augmented Generation (RAG) brought a major upgrade by integrating LLMs with external knowledge sources like vector databases or document stores. Now the model could retrieve relevant context and generate more accurate and personalized responses based on that information. This stage introduced the idea of 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝗰𝗰𝗲𝘀𝘀, but still required orchestration. The system didn’t plan or act—it responded with more relevance. 3. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: 𝗧𝗼𝘄𝗮𝗿𝗱 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Agentic AI is a fundamentally different paradigm. Here, systems are built to perceive, reason, and act toward goals—often without constant human prompting. An Agentic system includes: • 𝗠𝗲𝗺𝗼𝗿𝘆: to retain and recall information over time. • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: to decide what actions to take and in what order. • 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: to interact with APIs, databases, code, or software systems. • 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆: to loop through perception, decision, and action—iteratively improving performance.    Instead of a single model generating content, we now orchestrate 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀, each responsible for specific tasks, coordinated by a central controller or planner. This is the architecture behind emerging use cases like autonomous coding assistants, intelligent workflow bots, and AI co-pilots that can operate entire systems. 𝗧𝗵𝗲 𝗦𝗵𝗶𝗳𝘁 𝗶𝗻 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 We’re no longer designing prompts. We’re designing 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 capable of interacting with the real world. This evolution—LLM → RAG → Agentic AI—marks the transition from 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 to 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲.

AI is becoming a make-or-break factor for banks. But success will not depend on their ability to offer #AI, but on their competence in integrating it. Let’s take a look.   Banking is forecasted to feel the biggest impact from generative AI among sectors and industries as a percentage of their revenues with the additional value calculated between $200 bn and $340 bn annually (source: McKinsey). But why is the impact so powerful? One of the main reasons is because the abrupt surge of gen AI is exponentially increasing the speed with which #banking is being transformed. That is not to say that the transformation has started with or due to AI. On the contrary: during the past 10 to 15 years banking was already in the middle of transforming from a human-based, relationship-first industry to a more automated and technology-driven business following the #fintech revolution and the ascend of nimbler and more innovative competitors. But AI now does 2 things: —  It brings the transition to a new level, across 3 dimensions: speed, outcome and impact. —  It turbo-charges one of the biggest challenges in modern FS: the combination of AI and data that brings under the same roof two inherently opposing forces: mass and customization. In other words, AI seems to find a credible answer to achieving hyper-personalization. In a recent report Deloitte has provided realistic examples on how this is done across both cost efficiency and income growth: Cost efficiency: —  Workforce acceleration efficiencies across the board: 0–15% of total staff cost —  IT development and maintenance acceleration: 10–20% of IT staff cost —  Improved credit-risk assessment leading to 10-15% savings in impairment charges —  Improved FinCrime/fraud detection reducing litigation/redress charges and fraud losses Income growth: —  Next generation market analysis / predictive trading algorithms: 5–7% uplift on trading income —  Improved customer retention: 1–2% uplift on fees & commissions —  Improved customer acquisition through hyper-personalised marketing: 5-10% uplift from interest income and fees & commissions —  Tailored loan pricing based on credit risk assessment: 2–3% increase on net interest income Despite all the excitement around these estimated benefits, success will not be a walk in the park. It will depend on the banks’ ability to integrate AI in a seamless way into their day-to-day operations. Going forward AI will be re-writing much of the scenarios and use cases of the banking value chain. That doesn’t necessarily mean that they will all be different, but most will certainly be enhanced with impact spanning both across the back-end and the front-end. Given that resources are limited, one of the main challenges will be how to identify the ones to focus on. Factors such as #strategy, potential impact and a match with the existing skillset should be guiding the selection process.   Opinions: my own, Graphic source and use cases: Deloitte

🌳 Design Patterns For Building Trust. With practical guidelines for designers on how to make products — AI and non-AI — more trustworthy, reliable and honest. In the noisy and polluted world today, trust doesn’t come for free. It doesn’t emerge by default. It must be earned and meticulously preserved — by being reliable, accountable and treating customers with respect. This holds true for people but it also for software. According to Anyi Sun, there are 5 psychological foundations of user trust: 1. Reliability 🔰 The degree to which the product consistently behaves as expected. It's a sense that that the product is dependable — based on a track record of past actions. Reliability comes from promising what you do, and doing what you promised. 2. Technical competence ⚡ Perceived intelligence, sophistication and capability of the product. It's user's belief that the product can successfully perform what they are being trusted to do. It's about trusting product's capability. 3. Understandability 🧠 The extent to which users feel they can understand how the system works or why it made a certain decision. The product must be able to articulate how a decision came along, with references to fragments that underpin a decision. 4. Faith and Care 🌱 Emotional, almost "blind trust" in the product, especially when users don't understand the underlying logic. It's a belief that the trusted party actually cares about the positive outcome for you, and intends to do good. 5. Personal attachment 🌳 A sense of rapport, connection or emotional engagement with the product. Typically it emerges when a user feels that they get meaningful value from the product, and from interactions with people supporting it. Personally, I would also add the value of repeated positive experiences that build confidence in the quality of the product, and hence its reliability. --- With AI products, hitting all these psychological foundations is extremely hard. Surely some people trust AI almost instinctively, others are more critical. But people's attitude often changes dramatically once they realized that they've made severe mistakes because of AI. Recovering from it is very hard. We can help with some design patterns: 1. Avoid "Ask me anything" → push for scoping and constraints 2. Slow down users in prompting → request specific details 3. Present multiple viewpoints, explain that experts disagree 4. Allow users to manage “memory”, profiles personalization 5. Highlight what is AI-generated and what isn't (AI disclosure) 6. Allow users to override AI-generated suggestions manually 7. Allow users to tweak AI output and refine it for their needs 8. Adapt AI's tone depending on the severity of user's task Trust is why people stay or leave. It builds long-term loyalty and helps users overcome hesitation. But it must be designed and retained — across all psychological foundations and with thoughtful UX work. I think designers will be quite busy for years to come. #ux #design

🔴 Banks are facing a compounding problem of interconnected risks. Risks are not checklists. They are dynamic and elusive. When banks describe their operating environment as "off the map," something fundamental has shifted. ABA Banking Journal's 2026 risk survey reveals simultaneous disruptions that don't fit traditional risk frameworks - a stress test of institutional assumptions. 𝐓𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫𝐬: 48% of institutions are updating risk appetite statements, 62% investing in scenario analysis. 𝗕𝗮𝗻𝗸𝘀 𝗮𝗱𝗺𝗶𝘁 𝘁𝗵𝗲𝘆 𝗰𝗮𝗻'𝘁 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁 𝘄𝗵𝗮𝘁 𝗰𝗼𝗺𝗲𝘀 𝗻𝗲𝘅𝘁 𝘄𝗵𝗲𝗻 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝘁𝗿𝗮𝗻𝘀𝗮𝗰𝘁 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆, 𝗱𝗲𝗲𝗽𝗳𝗮𝗸𝗲𝘀 𝗱𝗲𝗳𝗲𝗮𝘁 𝗮𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝘀𝘄𝗶𝗻𝗴 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗲𝘅𝗽𝗮𝗻𝘀𝗶𝗼𝗻 𝗮𝗻𝗱 𝗿𝗼𝗹𝗹𝗯𝗮𝗰𝗸. 𝐓𝐡𝐫𝐞𝐞 𝐜𝐨𝐧𝐯𝐞𝐫𝐠𝐞𝐧𝐭 𝐩𝐫𝐞𝐬𝐬𝐮𝐫𝐞𝐬: ‣ 𝐓𝐡𝐞 𝐫𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐥𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞 𝐢𝐬 𝐟𝐫𝐚𝐠𝐦𝐞𝐧𝐭𝐢𝐧𝐠. Federal deregulation (CRA rollback to 1995, reconsidering CFPB rules) meets state attorney general enforcement. Banks navigate 50 different enforcement philosophies while documenting every account decision to withstand scrutiny. ‣ 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐜𝐫𝐞𝐚𝐭𝐞𝐬 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐟𝐚𝐬𝐭𝐞𝐫 𝐭𝐡𝐚𝐧 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐤𝐞𝐞𝐩𝐬 𝐩𝐚𝐜𝐞. AI introduces ambiguity—who's liable when an AI agent executes a Reg E transaction? Legacy platforms force M&A among regionals and community banks who can't compete without scale. ‣ 𝐓𝐡𝐞 𝐭𝐡𝐫𝐞𝐚𝐭 𝐬𝐮𝐫𝐟𝐚𝐜𝐞 𝐡𝐚𝐬 𝐞𝐯𝐨𝐥𝐯𝐞𝐝 𝐛𝐞𝐲𝐨𝐧𝐝 𝐩𝐞𝐫𝐢𝐦𝐞𝐭𝐞𝐫 𝐝𝐞𝐟𝐞𝐧𝐬𝐞. Cyber intrusions arrive through vendor pathways, deepfake audio convinces staff to override protocols, sophisticated phishing defeats caller-ID trust. The old playbook of "trust but verify" breaks when verification signals themselves can be synthesized. Here's what makes 2026 different: these aren't isolated risks you can address sequentially. They're interconnected pressures that amplify each other in a dynamic system. AI deployments require clean data, but legacy systems produce messy data. Regulatory uncertainty discourages the technology investments needed to modernize those legacy systems. Cyber threats exploit the integration gaps between old and new infrastructure created during half-finished modernization efforts. Each pressure makes the others harder to solve. This is a compounding problem, not a checklist. Banks that treat 2026 as "business as usual with complications" are misreading the moment. The institutions updating their risk appetite statements are acknowledging something more fundamental—the rules of the game are being rewritten in real time, and nobody handed out the new rulebook. #banking #AI #riskmanagement Link to the article that triggered my analysis in the comments

One of the questions I get most often from FS CEOs and Board Directors is whether rising technology investment is translating into lasting change and improved ROI.      We've just completed research examining how some of the world's largest banks are investing in technology. Average spend rose by 11% in 2025 - and a few things stood out for me: 1️⃣ The scale of investment is not the issue, it’s how it’s spent.     The largest banks now spend on average over US$4bn a year on technology – with some spending multiples of this – but only around 12% goes into revenue-driving transformative change. Most spend is absorbed by keeping existing systems running and meeting mandatory requirements.     2️⃣ The way investment is approved affects what gets delivered.     Most banks operate a 2-year return-on-investment cycle, and 88% say unclear ROI for tech spend makes it difficult to secure approval for longer term projects – even when they’re critical to delivering the bank’s strategic plans.    3️⃣ Legacy systems and skills gaps constrain impact.     More than 80% of banks told us that legacy systems stand in the way of lowering day-to-day spend, and 86% cited them as the primary cause of IT project failures. On talent, 71% of banks spoke of capability gaps in key areas such as cybersecurity and GenAI, which is further constraining transformation.    These findings paint a picture. Technology investment is rising, but high levels of BAU spend, governance structures, the approach to ROI measurement, and legacy systems are limiting the impact of that spend. To unlock more value from technology spend, global banks need to move beyond incremental change – redefining how they prioritise investment, measure value, and build the capabilities to scale transformation.   You can read more here ➡️ https://lnkd.in/eEkHwxAD   #Banking #Transformation #ShapeTheFutureWithConfidence

This Deep Dive edition of Fintech Wrap Up explores the great bank unbundling offering a comprehensive analysis of how the financial services industry has evolved through technological innovation and regulatory shifts. Analyses by Contrary Research, break down fintech's transformation into three major phases: Digitization – The transition from traditional banking to online services, driven by innovations like online banking in the 1990s and early digital financial tools. Disintermediation – Post-2008 financial crisis distrust in large banks and the rise of smartphones led fintech startups to disrupt traditional banking with digital payments and simplified infrastructure. Embedded Infrastructure – Platforms like Stripe and Plaid enabled fintechs to deliver financial services more efficiently, fueling the growth of Banking-as-a-Service (BaaS). The article also highlights how community banks partnered with fintechs to stay competitive, taking advantage of regulatory changes like the Durbin Amendment. Companies like Uber leveraged embedded finance to unlock new revenue streams and improve customer retention, while BaaS providers empowered non-bank companies to launch financial products faster and more affordably. However, the piece also underscores the growing regulatory scrutiny and compliance challenges in BaaS, stressing the importance of balancing innovation with regulatory compliance. #fintech #banking #baas Prasanna Thomas Richard Panagiotis Tony Nicolas Arjun Dr Ritesh Sandra

It’s Sunday night, and that means it’s time for my weekly online grocery shop. Fifty or so items need to be searched for, selected, and added to the basket one by one. It’s tedious and time-consuming, but necessary. Now, imagine if the retailer had an AI-powered shopping assistant embedded in its website. The technology for this already exists. Instead of manually selecting each product, I could simply write out my shopping list, take a picture of it, upload it, and let the AI agent populate my basket. I’d review it, confirm the order, and be done in a fraction of the time. Or, even better, I could dictate the items I need, and the AI, learning from my previous brand preferences, would automatically select my usual choices. This technology exists. This would dramatically streamline my weekly shopping experience. But let’s think beyond groceries. Consider your own job. How many of your daily tasks involve repetitive data entry, administrative processes, or decision-making based on predictable patterns? Now, imagine an AI system handling those tasks, boosting your efficiency, freeing up your time for higher-value work. For roles composed largely of these repetitive elements, AI isn’t just an assistive tool, it’s a transformative one. Businesses could deploy multiple AI agents, each specialised in different aspects of operations, working in tandem to reshape workflows. AI is on the cusp on not being a task replacement technology, but a role replacement technology. Recently, I used an analogy with a client that I think captures the stage we are in with AI. The explosion of generative AI in the past two years, and the increasing prevalence of AI-driven automation, are merely the tremors that are felt before a volcanic eruption. The real paradigm shift - the true societal transformation that AI represents - hasn’t yet happened. We are still in AI’s early nascent stages. The technology we have now is the Model T of AI. Our great great grandchildren will list out ChatGPT alongside the spinning jenny as key early technologies of their respective revolutions. But signs of this shift are already emerging. In the United States, reports suggest that recent forced redundancies in the public sector will be filled by AI-driven systems. This is one of the first tangible examples of AI replacing human roles. And it’s only the beginning. Businesses must prepare for this dramatic shift, not just in commerce but in society as a whole. Those that fail to anticipate and adapt will be left behind. Many of our clients at WILLIAM FRY LLP are among the first to recognise this reality. They are already deeply integrating AI into their operations, positioning themselves at the forefront of this new era. Helping businesses navigate this transformation is the most exciting aspect of my work as a lawyer. Together with our clients we are ploughing the furrows of a brave new world. It’s such a privilege to be working at the crest of this wave.

AI today is where electricity was in factories a century ago: powerful, misunderstood, and mostly constrained by old operating models. When electricity first entered factories, most leaders treated it as a technical upgrade. They replaced the steam engine with a single large electric motor — and changed nothing else. The factory still revolved around a central drive shaft powering every machine through belts. As a result: - every machine — and every worker — ran at the same imposed rhythm - if the central shaft failed, the entire factory stopped - machines could not be relocated, locking inefficient layouts in place - roles focused on maintaining the mechanical system, not optimising work This was incremental change: new energy, old organisation. And productivity barely improved. The real breakthrough came when manufacturers redesigned the system around electricity. - Each machine received its own motor. - Workflows were reorganised around products and outcomes, not power constraints. - Roles, skills and safety standards evolved. - Resilience, flexibility and productivity followed. The lesson for AI is clear. Step-change impact comes from rethinking processes end-to-end, redefining roles and skills, and evolving the operating model to reflect what AI now makes possible. Technology creates potential. Organisation design determines whether that potential is realised.

#AI didn’t just change how work gets done. It changed who we trust. For decades, trust was earned through people. Credentials. Experience. Reputation. Now trust is quietly shifting to systems. If the dashboard says it’s fine, we relax. If the model recommends it, we comply. If the workflow moves forward, we assume someone checked. Medicine has lived through this transition. Clinical judgment slowly gave way to protocol. Protocol gave way to automation. And automation changed behavior long before outcomes were measured. AI accelerates that shift. The most dangerous moment is not when AI is wrong. It’s when AI becomes the default authority. Because authority without accountability feels efficient. And efficiency is seductive. Leaders need to ask a hard question. Who does my organization trust more, humans or models? If the answer is unclear, the system is already deciding for you. Best practices for preserving human authority in AI systems: Make recommendations explainable, not just accurate Force deliberate pauses before irreversible actions Design interfaces that invite questioning, not compliance Train people to challenge AI, not defer to it Tie accountability to humans, not tools AI should inform judgment, not replace it. Trust should be earned, not automated. The future belongs to organizations that understand this early. #AI #ArtificialIntelligence #AIGovernance #ResponsibleAI #Leadership #TrustInAI #HumanCenteredAI #DigitalTransformation #RiskManagement #DrGPT

The Convergence of Intelligent Technologies: Shaping the Autonomous Future pt.1 We are at the dawn of a technological revolution where the convergence of intelligent technologies is reshaping industries and societies. In a 2023 Forbes article I co-authored with Sarwant Singh, we explored the rise of the ‘Autonomous World’—a world powered by hyper-connectivity, intelligent machines, and continuous innovation. Today, these shifts are accelerating, driven by advances in AI, robotics, edge computing, and interconnected networks. FROM HARDWARE TO SOFTWARE INTEGRATION IN ROBOTICS Historically, robotics innovation has been centered around hardware improvements in motors, sensors, and physical components. However, as hardware matures and becomes commoditised, the future of robotics is moving toward software-driven intelligence. AI, machine vision, and multi-agent orchestration platforms now empower fleets of diverse robots—ranging from drones to forklifts—to navigate unpredictable environments and collaborate in real time. While software is leading this new wave, custom hardware remains critical for high-stakes industries such as healthcare, defense, and advanced manufacturing, where performance, reliability, and durability cannot be compromised. EDGE AI: BRINGING INTELLIGENCE CLOSER TO THE SOURCE AI is also evolving from cloud-reliant systems to intelligent, edge-based operations. As NVIDIA CEO Jensen Huang highlighted at GTC 2025, the future of AI is not just about generating data—it’s about enabling physical AI, where embodied machines learn, reason, and act autonomously. Edge AI brings these capabilities closer to the source, improving data security, reducing costs, and enabling real-time decision-making without relying on the cloud. This shift is crucial as enterprises strive to scale AI securely and efficiently across various industries, including logistics, mining, healthcare, and finance. THE HUMAN FACTOR: AUGMENTING, NOT REPLACING A key misconception is that autonomous technologies will replace humans. In reality, they are designed to augment human capabilities, reduce operational risks, and create new opportunities for reskilling and higher-value work. As these technologies take over hazardous or repetitive tasks, they can extend the working life of an aging workforce, support diversity, and improve work-life balance. However, this requires ethical foresight and leadership that embraces system thinking—integrating AI, robotics, human capital, and sustainability into a holistic strategy. Part 2 of this post series will explore the power of converging S-curves, which illustrate how various technological advancements interlink and complement one another to create connected ecosystems and drive further innovation. #autonomy #intelligenttech #convergence