Establishing AI Usefulness in the Workplace with SEB

‎ ‎Mastering the Iterative Edge: Why "One-Shot" AI is a Recipe for Sub-optimal Results ‎ ‎I have found that the most transformative aspect of AI isn’t just its raw output. Rather it is the iterative collaboration it demands. If you treat AI like a vending machine, you get generic results. If you treat it like a trainee, you get excellence. ‎ ‎My Framework: The "Teach-as-you-Go" Method ‎ ‎I approach content development in deliberate phases rather than requesting a final product upfront. The secret lies in the transition between Prompt n and Prompt n+1: ‎ ‎1. Phase-Based Development: Break complex topics into modular segments. Never ask for the whole report at once. ‎ ‎2. The Feedback Loop: When moving from Prompt 1 to Prompt 2, your new instruction must contain two critical components: ‎ ‎The Correction Layer: Explicitly critique the previous output. Tell the AI what it missed or where the tone drifted. This "teaches" the model your specific preferences for the remainder of the session. ‎ ‎The Expansion Layer: Only after correcting the previous step you provide the requirements for the next section. ‎ ‎3. Avoid the "All-at-Once" Trap: Rushing for immediate results is a risk to content development. It leads to shallow, "hallucinated" content. Steer the AI toward your vision through incremental guidance. ‎ ‎The Strategic Guardrails ‎ ‎While the creative potential is vast, my ongoing prompting into AI capabilities highlights four critical "bottlenecks" that every professional must manage: ‎ 1. ‎Hallucination: The tendency for models to generate confident but false information. ‎ 2. ‎Data Privacy: The vital need to ensure sensitive institutional data is never fed into public models. ‎ 3. Logical Reasoning Errors: AI can struggle with complex, multi-step deductions required in high-level content development tasks. 4. ‎Temporal Decay (Outdated Data): Most LLMs have a "knowledge cutoff," making them unreliable for real-time market shifts without RAG (Retrieval-Augmented Generation) tools. ‎ ‎The takeaway? Don't just use AI, rather audit, educate and use it. Success isn't found in the first prompt. It’s found in the third, fourth, and fifth. ‎ ‎#AI #DigitalTransformation #PromptEngineering #GenerativeAI ‎ ‎

𝗧𝗵𝗲 𝗔𝗜 𝗠𝗮𝘁𝘂𝗿𝗶𝘁𝘆 𝗖𝘂𝗿𝘃𝗲 Most organizations go through the same stages when adopting AI. But the difference between companies experimenting with AI and companies 𝗴𝗮𝗶𝗻𝗶𝗻𝗴 𝗿𝗲𝗮𝗹 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗼ften comes down to how quickly they move through these stages. 𝗦𝘁𝗮𝗴𝗲 𝟭: 𝗖𝘂𝗿𝗶𝗼𝘀𝗶𝘁𝘆 Employees experiment with AI tools like chatbots, copilots, and generators. Use cases are informal: • writing drafts • summarizing documents • generating ideas This stage creates awareness, but impact is still limited. 𝗦𝘁𝗮𝗴𝗲 𝟮: 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 Teams begin using AI intentionally to 𝘀𝗽𝗲𝗲𝗱 𝘂𝗽 𝘁𝗮𝘀𝗸𝘀. Examples: • marketing teams generating first-draft content • developers using AI coding assistants • analysts summarizing reports and research faster Productivity improves, but AI is still used 𝗮𝘁 𝘁𝗵𝗲 𝗶𝗻𝗱𝗶𝘃𝗶𝗱𝘂𝗮𝗹 𝗹𝗲𝘃𝗲𝗹. 𝗦𝘁𝗮𝗴𝗲 𝟯: 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 This is where real transformation begins. AI becomes embedded directly into 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗮𝗻𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀: • AI automatically analyzing customer feedback • AI assisting support teams with response suggestions • AI summarizing meetings and generating action items • AI helping prioritize leads or opportunities At this stage, AI is no longer just a tool. It becomes part of 𝗵𝗼𝘄 𝘄𝗼𝗿𝗸 𝗴𝗲𝘁𝘀 𝗱𝗼𝗻𝗲. 𝗦𝘁𝗮𝗴𝗲 𝟰: 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 The most advanced stage. AI systems begin supporting 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 by: • identifying patterns in large datasets • forecasting outcomes • recommending actions Leaders are no longer just asking teams for insights. They are working with 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗹𝗮𝘆𝗲𝗿𝘀. Right now, most organizations are still somewhere between 𝗦𝘁𝗮𝗴𝗲 𝟭 𝗮𝗻𝗱 𝗦𝘁𝗮𝗴𝗲 𝟮. The companies that will win are not necessarily the ones experimenting the most. They are the ones 𝘀𝘆𝘀𝘁𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗺𝗼𝘃𝗶𝗻𝗴 𝘁𝗼𝘄𝗮𝗿𝗱 𝗦𝘁𝗮𝗴𝗲 𝟯 𝘄𝗵𝗲𝗿𝗲 𝗔𝗜 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗽𝗮𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗻 𝗶𝗻𝗱𝗶𝘃𝗶𝗱𝘂𝗮𝗹 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝘁𝗼𝗼𝗹. That’s where the real advantage starts. Let 10Alytics Business help your organization attain new heights with AI #AI #ArtificialIntelligence #TechStrategy #DigitalTransformation #FutureOfWork #Innovation

Across our workflows at RCM, we’re seeing a clear rise in adoption of ��𝗜 𝘁𝗼𝗼𝗹𝘀 + 𝘃𝗶𝗯𝗲-𝗰𝗼𝗱𝗶𝗻𝗴 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 and the impact is very real: faster execution, sharper documentation, quicker iterations, and automation of tasks that used to consume hours. Here’s what we’re leveraging AI for: ✅ 𝗙𝗿𝗼𝗺 𝗶𝗱𝗲𝗮 → 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 𝗶𝗻 𝘄𝗲𝗲𝗸𝘀, 𝗻𝗼𝘁 𝗺𝗼𝗻𝘁𝗵𝘀 Vibe-coding platforms are helping us turn business requirements into usable internal tools rapidly - test, refine, deploy. ✅ 𝗣𝗥𝗗𝘀 𝘁𝗵𝗮𝘁 𝗱𝗼𝗻’𝘁 𝘁𝗮𝗸𝗲 𝗳𝗼𝗿𝗲𝘃𝗲𝗿 AI-assisted documentation is making requirement clarity faster: structured PRDs, better alignment, fewer back-and-forth cycles. ✅ 𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 Call reviews, ticket patterns, field feedback - AI can scan volumes and surface signals that help teams act faster and more consistently. ✅ 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗰𝗿𝗲𝗮𝘁𝗶𝗼𝗻 With structured research and content workflows, learning modules can be produced significantly faster while staying aligned with business context. ✅ 𝗦𝗽𝗲𝗲𝗱𝗶𝗻𝗴 𝘂𝗽 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 & 𝗴𝗿𝗮𝗽𝗵𝗶𝗰 𝗱𝗲𝘀𝗶𝗴𝗻 𝗼𝘂𝘁𝗽𝘂𝘁 AI is supporting our design team with faster concept exploration, creative variations, quick iterations, and production acceleration — helping us move from brief to high-quality creatives in less time. The biggest advantage isn’t “using AI.” It’s 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝘄𝗵𝗲𝗿𝗲 𝘀𝗽𝗲𝗲𝗱 + 𝗰𝗹𝗮𝗿𝗶𝘁𝘆 + 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗻𝗼𝗿𝗺𝗮𝗹 and AI simply becomes the engine behind it. At RCM, we’re using AI responsibly, practically, and at scale to make execution smoother, decisions faster, and teams more focused on what truly needs human intelligence. #AI #Automation #Productivity #VibeCoding #Innovation #RCM

AI Is No Longer a Tool — It’s Becoming the Operating System of Work For years, we’ve treated AI as a feature. A tool to improve productivity, automate tasks, or assist decision-making. That mindset is now outdated. 👉 AI is no longer sitting on top of workflows. It is starting to replace and redefine them. 🔄 The Shift: From Assistance to Execution Early AI helped us: Write emails Analyze data Generate content Now, AI is beginning to: Execute multi-step workflows Coordinate across systems Make decisions with minimal human input 👉 The transition is clear: From “helping humans work” → “working alongside humans” ⚙️ Systems, Not Features The biggest misunderstanding today is thinking AI is just another capability. In reality: AI is becoming the interface The decision layer And increasingly, the execution engine We’re moving toward systems where: Data flows automatically Actions are triggered without manual input Outcomes are continuously optimized 👉 This is not software evolution. It’s system transformation 🧠 The New Bottleneck: Integration The challenge is no longer access to AI models. It’s: Connecting AI to real business systems Ensuring reliability across workflows Managing cross-functional dependencies 👉 The real problem is not intelligence. It’s integration at scale 🌍 AI Will Reshape Roles — Not Just Tasks We often talk about jobs being replaced. That’s the wrong framing. What’s actually happening: Roles are becoming more orchestration-focused Less execution, more decision-making More system thinking, less siloed work 👉 The future belongs to people who can design, guide, and trust intelligent systems ⚠️ The Hidden Risk: Over-automation Without Control As AI systems become more autonomous: Errors scale faster Dependencies become opaque Accountability becomes unclear 👉 Speed without control is not progress. It’s risk. 🔮 What Leaders Should Focus On If you’re leading teams today, the priorities are shifting: Build systems, not isolated tools Invest in integration, not just models Design workflows where humans and AI collaborate 👉 The winners won’t be the ones using AI. They’ll be the ones restructuring how work gets done 🧠 Final Thought We are not adding AI to work. We are rebuilding work around AI. And that changes everything. #AI #ArtificialIntelligence #FutureOfWork #DigitalTransformation #Leadership #Innovation #TechTrends

#Generative_AI has unlocked powerful capabilities, from generating content and code to summarizing knowledge and accelerating decision making. The challenge for most organizations is not access to the technology, but identifying where it creates the most value. One practical approach is the Generative AI #Creative_Matrix, a simple framework that helps organizations systematically identify AI opportunities. The idea is pretty straightforward. On one side are the core capabilities of generative AI, such as creating, summarizing, transforming, or extracting information. On the other side are business #workflows across the organization operations, customer service, marketing, product development, legal, or internal knowledge management. By combining these two dimensions, organizations can quickly surface practical use cases. Applying summarization to customer service may reduce ticket resolution time, while content generation can accelerate documentation or marketing development. The value of this approach lies in the structure it introduces. so instead of random experimentation, teams explore AI opportunities in a disciplined way, directly linking capabilities to business processes. Once potential opportunities are identified, prioritization becomes critical and business driven. Effective organizations evaluate initiatives based on business impact and implementation complexity, focusing first on those that deliver meaningful value with manageable effort. Generative AI is ultimately a creativity engine. But turning that creativity into measurable business outcomes requires structure, cross functional collaboration, and disciplined prioritization.

🐾 Everyone is racing to learn AI tools. But very few are learning how value actually gets created. AI is the engine. But an engine alone doesn’t create a journey. You still need: 🧭 Strategy to choose the destination 🧠 Design thinking to understand human needs ⚙️ Process knowledge to embed intelligence into workflows 🎮 Gamification to drive behaviour change 📖 Storytelling to move organisations to act 🔁 Automation sense to scale outcomes Without these, AI remains powerful… but directionless. This isn’t a fringe view. Across the industry, leaders are saying the same thing: • Satya Nadella has repeatedly emphasised that AI creates value only when it is integrated into workflows. • Andrew Ng often reminds organisations that AI transformation is fundamentally about process redesign. • McKinsey research shows that real AI impact comes from operating model change, not just technology deployment. • BCG summarises it succinctly: AI + Human + Process = Impact. In most organisations, the real challenge isn’t AI capability. It’s integrating intelligence into actual workflows and decision systems. That’s exactly what I’m seeing firsthand while working on a Generative AI rollout for a Fortune 100 bank. The technology is impressive. But the real work lies in reimagining processes, decisions, and behaviours around it. The real advantage will belong to people who understand the entire value chain of intelligence — not just the newest tool. Learn the system, not just the engine. 🐾 #Xponential #StrategyVectors #Valuecreation #Consulting #MBB #Big4 #Partners #AI #Strategy #DigitalTransformation #FutureOfWork #GenAI #Automation #DesignThinking #Storytelling

Over the past year, we’ve had hundreds of conversations with executives trying to move AI forward inside their organizations. And the pattern is always the same. The interest is there. The ideas are there. But execution breaks down. Because deploying AI is a series of interconnected decisions: • How do we structure and access our data? • Do we need RAG, fine-tuning, or both? • Where should AI live — chat, workflows, or both? • What can actually be automated safely? • How do we ensure outputs are secure, compliant, and trusted? This is where most organizations get stuck. because it’s not one solution. It’s an ecosystem. That’s exactly how we built Accelyst. A full AI deployment layer across the enterprise. What we’re actually implementing with teams today: • LLM Fine-Tuning → tailoring models to your business, not generic outputs • RAG & Knowledge AI → connecting AI directly to your internal data and documentation • AI Chatbots & Assistants → giving teams conversational access to systems and insights • AI Agents → automating workflows, decisions, and repetitive operations • Intelligent Document Processing → extracting and structuring data from complex documents • Predictive Analytics → turning historical data into forward-looking decisions • Responsible AI → ensuring everything is secure, compliant, and enterprise-ready Because the shift is building an organization where: AI is embedded into how decisions are made, how workflows run, and how teams operate. We just launched our new site to better reflect this full picture of what enterprise AI actually looks like in practice. If you’re exploring AI this year, I’m curious: Where are you right now • Experimenting • Piloting • Or actually deploying across the business?

One of the biggest misconceptions about AI in enterprises is that success depends on building better models. In reality, the success of AI initiatives depends far more on implementation and integration into business workflows. While working on AI-driven projects like predictive maintenance and intelligent document systems, I’ve observed a common pattern: The challenge is rarely technical. The real challenges are: • Aligning business stakeholders with AI capabilities • Ensuring data readiness and governance • Integrating AI into existing enterprise systems • Driving adoption across teams For example, in one of the AI-led transformation initiatives I worked on, the biggest breakthrough didn’t come from improving the model — it came from redesigning the workflow around the AI system. That’s when the real business impact started to show. AI is not just about algorithms. It’s about how effectively it is embedded into business processes. As AI adoption continues to grow, the role of project and program leaders becomes critical in bridging the gap between innovation and real-world value. #AI #DigitalTransformation #TechnologyConsulting #EnterpriseAI #ProjectManagement #AIImplementation #BusinessTransformation

THE REAL ROI OF AI ? It’s not just productivity... 👾 It’s what helps your teams think better, learn faster, and grow stronger together ! 🦋 #HybridVibes is a collective #HybridIntelligence framework designed to couple Humans × AI — not just to produce deliverables, but to create robust, shared meaning and turn it into action. ✅️ What makes it different ? 👉 It’s an #intraface, not just an interface. AI is not something we passively “use.” We actively put it under tension — through doubt, criteria, verification, and reframing. 👉 It’s built to prevent "over-reliance". Micro-check rituals, organized dissent, traceable hypotheses: AI becomes a high-powered accelerator under human oversight — not an oracle. 👉 It’s a hybrid SENSEMAKING system. Its purpose is not just to generate content, but to help groups surface meaning, test it, and avoid the illusion of understanding just because something sounds polished. ✅️ A stable backbone, not a loose prompt stack 1. Gather and model insights: signals, frictions, blind spots 2. Put them under tension: competing hypotheses, counterarguments, “what if this is wrong?” scenarios 3. Stabilize progressively: maps, patterns, meaning territories, decisions 4. Build traceability: versions, criteria, trade-offs, sources → The result: collective intelligence that is actionable, auditable, and decision-ready ✅️ Flexible by design Prompt Factory + RAG power modular, repeatable sequences grounded in your real-world context: strategy, field feedback, operational data, documentation, regulation. Why does that matter? Because it shifts AI from improvising in the abstract to reasoning inside your reality. ✅️ Multiple use cases 👾 Strategic facilitation — align, clarify, arbitrate, converge 👾 Open innovation — explore fast without falling into solutionism 👾 Strategic foresight — weak signals, scenarios, bifurcation points 👾 Scenario planning — options, robustness criteria, blind spots ✅️ The real ROI: transferable cognitive capabilities 》 Metacognition — knowing what we know, what we delegate, and what remains uncertain 》 Critical thinking — calibrating trust vs. verification, reducing automation bias 》 Collective sensemaking — building shared frames without rushing into premature consensus 》 Durable learning — because producing with AI does not automatically mean understanding; in fact, some studies suggest comprehension can decline when too much is delegated Combined with augmented facilitation capabilities, 🦋#HybridVibes directly addresses the hardest part of AI transformation : adoption, governance, rules, learning, and engagement — precisely where many AI initiatives stall. 🎯 In one line : 🦋 #HybridVibes is not AI thinking for your teams. It is hybrid intelligence helping your teams think better with AI. People first... In Principo The Regenerative Agency #HybridIntelligence #AI #Strategy #OpenInnovation #Transformation #Foresight #ScenarioPlanning #Sensemaking #FutureOfWork #CognitiveDesign

Spending time recently with the Frontier Firm AI Initiative, I've been thinking just how quickly no-code or low-code approaches can deliver reimagined work processes. The professional term I'm using to describe that experience to others is "scary good". It has me wondering what a Centre of Excellence needs to be excellent at if we are going to create ongoing value from (rapidly changing) AI functionality. I've boiled it down to three CoE capabilities I think stand out for me right now. First and foremost is the capability to undertake some enhanced form of workflow analysis. Understanding how work actually happens (including analysis of non-digital cognitive processing and behaviour) and then knowing how to map this well is going to be critical, because that’s the detail we need to be able to make competent redesign decisions with some of the available technology. A second CoE capability I think has to be ideation. The ability to imagine and prototype genuinely different ways of working once constraints loosen is key, because otherwise we run the danger of just validating incremental improvements. Being too familiar with existing processes is probably a barrier here - solid ways to enhance individual and team metacognition will definitely be needed if we are going to reliably and continually be able to surface "old" assumptions. And third, at least right now I think, is agentic management. As AI agents begin to take on real tasks, CoEs need to be both comfortable and competent in designing and governing work that is shared between humans and agents, including questions of accountability, escalation, and effectiveness over time. I don't think any of this replaces the traditional rigour CoEs bring. But perhaps AI changes where that rigour is going to be most valuable. For example, one of my team this week, tasked with understanding what other companies are doing as best practice in this area described this as a need to "not look backward to best practice, but to look forward to what could be possible." That's the sort of redesign conversation I think we'll increasingly be pulled into. If this is something you’re thinking through as well, as always I’d be interested to compare notes. #FutureOfWork #AIatWork #WorkDesign #Talent