Generative AI for Improving Operations

Continuing with my series on Gen AI, we had recently assisted a leading global company in unlocking cognitive insights generation at scale. The client faced significant obstacles in accessing and analysing critical performance metrics and market intelligence. They relied on disparate data sources—including multiple tables, external datasets, and competitor insights from websites and news articles—which made the process slow and complicated. Business leaders spent significant time gathering data and insights, often requiring help from tech teams leading to delays in decision-making and reduced agility.   Recognising the need for transformation, we collaborated closely with the client to design, deploy, and scale a GenAI-driven platform, empowering business leaders to track the performance of business divisions. The platform was based on a module with two kinds of datasets: structured KP datasets and unstructured textual datasets. Our GenAI solution enabled the client to conduct real-time computations, extract insights, and generate visual answers from both structured tabular data and unstructured text—allowing users to “converse” with the data. Leveraging advanced LLM models and text embeddings, the system performs at least eight distinct computations in response to queries, while summarising information from multiple sources seamlessly.   The impact of this solution has been significant. Leaders can now access critical information in seconds, changing their decision-making process from reactive to proactive. The client realised key benefits such as:   - Rapid access to critical insights: The solution reduced the effort for business managers to generate insights by 90%, while also minimising the risk of missed insights, enabling accurate and timely data-driven decisions. - Accelerated decision-making: The rapid analysis of data augmented by textual insights has led business leaders to make timely decisions, enabling them to respond to market dynamics instantly - Significantly improved operational efficiency: By automating routine tasks such as calculations and data summarisation, operational efficiency has improved significantly, with a reported 30% reduction in time spent on manual data gathering - Conversational interface: By enabling users to interact directly with the underlying data and insights, the organisation has fostered a self-service culture, significantly improving access to information across all levels   This case is a compelling case of how Generative AI could transform the insights generation process, delivering business decision support. Currently, the solution supports business leadership and has been scaled up across almost all global business units, with plans to cover most of the organisation in the future.   #GenAI #GenAISeries #Innovation #Consumer #GenAIInnovation #InsightGeneration #ConversationalAI

Remember the last time you tried to map a business process? You probably started with optimism, sticky notes, and endless coffee. Hours turned into days. Stakeholder interviews stretched on forever. The whiteboard filled up, got photographed, and then came the dreaded task of transcribing everything into a proper diagram. For decades, this has been the reality of process mapping – a bottleneck rather than a driver of innovation. But what if you could skip the painful parts? What if a simple conversation, rough whiteboard sketch, or pile of old procedure documents could become a perfect, formal process diagram in minutes? This isn't science fiction. It's Generative AI transforming how we understand and improve business operations. The traditional approach is broken: → Endless interviews with subject matter experts → Manual transcription prone to human error → Specialized tools requiring technical expertise → Long review cycles and frustrating revisions The result? Companies are left with outdated diagrams that don't reflect how work actually happens. Generative AI flips this model completely. It acts as the perfect translator between how people talk about their work and the technical language of process diagrams. You provide the raw material – interview transcripts, SOPs, emails, even whiteboard photos. The AI identifies actors, actions, systems, and decision points. Then it connects the dots using semantic analysis to understand logical flow. In seconds, you get a clean, structured, accurate process model in BPMN 2.0 format. This level of speed represents a competitive advantage. Instead of waiting months to identify bottlenecks, you spot them in an afternoon. Instead of one improvement project per quarter, you can run several. Full blog: https://lnkd.in/e5meRWn6 What's been your biggest challenge with traditional process mapping? #ProcessMapping #BusinessProcessManagement #ArtificialIntelligence #DigitalTransformation #ProcessImprovement

In my work with back-office and customer support teams in logistics and other B2B service areas, I find they’re often overwhelmed.   They’re fielding endless “Where’s my shipment?” emails, entering the same data into multiple systems, and chasing updates across siloed platforms. Most of these teams are doing heroic work — but they’re stuck in reactive, manual workflows.   Over the past decade, traditional AI tools — including machine learning models, regression analysis, and optimization algorithms — have made operations smarter and more efficient. We’ve seen them drive real value through things like: * Route optimization * Demand forecasting * Inventory and network planning * Load consolidation Dynamic pricing   But generative AI opens a new frontier — one that transforms how customer-facing teams interact, communicate, and respond.   We now have AI tools that can:   * Instantly answer tracking and status questions * Generate proactive updates before the customer even asks * Interpret and summarize internal systems in plain language * Draft accurate, personalized responses at scale   This isn’t just automation — it’s a fundamental shift in how we serve customers. It improves response times, eliminates bottlenecks, and significantly reduces the cost to serve.   The organizations that adopt these tools thoughtfully — and integrate them into real workflows — will gain a serious competitive advantage.   Are you rethinking how your teams serve customers? #LogisticsTech #GenerativeAI #DigitalTransformation #CustomerExperience #AIinBusiness #Automation #OperationsExcellence

🔧 Rewiring Maintenance with Generative AI: The Next Industrial Revolution? 🔧 Maintenance operations are evolving rapidly as industries face increasing complexity, aging workforces, and pressure to maximize uptime. Generative AI is emerging as a game-changer, transforming traditional maintenance practices into proactive, data-driven strategies that reduce downtime, optimize resources, and preserve institutional knowledge. How Gen AI is Reshaping Maintenance: 🚀 Enhanced Efficiency – AI-driven automation of routine tasks and data analysis is freeing up skilled workers for higher-value activities. ⚙️ Predictive Maintenance – Instead of reacting to failures, AI is now predicting them before they happen, significantly reducing unplanned downtime. 📚 Knowledge Retention – AI-powered assistants are capturing and sharing expertise, addressing the challenge of workforce retirements and skill gaps. Real-World Impact: 🔹 An oil and gas company used Gen AI to automate Failure Modes and Effects Analysis (FMEA)—cutting equipment downtime and improving operational efficiency. 🔹 A consumer goods manufacturer implemented an AI-powered troubleshooting assistant, leading to faster issue resolution and minimized production disruptions. What’s Holding Companies Back? Despite these benefits, many organizations struggle with AI adoption. The most common barriers include: ❌ Lack of AI-ready data – Maintenance data is often unstructured, siloed, or incomplete. ❌ Change resistance – Technicians and engineers may be hesitant to trust AI-driven recommendations. ❌ Integration challenges – Legacy systems weren’t designed for AI, requiring significant investment in modernization. Critical Questions for Business Leaders: 💡 How can companies effectively integrate Gen AI into their existing maintenance processes without overhauling legacy systems? 💡 What strategies can organizations use to upskill their workforce and drive AI adoption among frontline technicians? 💡 Will Gen AI fully replace human decision-making in maintenance, or is its true power in augmenting human expertise? The potential for AI-driven maintenance transformation is massive, but the real challenge lies in execution. Organizations that successfully leverage Gen AI, predictive analytics, and human expertise together will gain a significant edge in operational resilience and efficiency. 🚀 Is your company exploring AI-powered maintenance solutions? What challenges or successes have you seen? #GenerativeAI #PredictiveMaintenance #Industry40 #AIInnovation #Manufacturing #SupplyChain #DigitalTransformation

Let’s Use Generative AI to Build American Business, Not Tear It Down The same technology businesses have been using to disassemble their companies they could be using to expand and accelerate them. Generative AI, as it exists today, can almost always unsuccessfully, or it can be used to empower individuals and entire organizations to level up. When approached with imagination and courage, AI becomes an augmentation platform, a way to partner with people and make them infinitely greater at what they do. It’s not just about improving bottom-line efficiency. It’s about unlocking top-line growth by creating smarter, more adaptive, and more human-centered businesses. Here are 3 ways to build your business with GenAI: 1. Build Organizational Infrastructure for Augmentation Forget isolated AI pilots. Think integrated ecosystems where AI supports every function—marketing, sales, ops, HR, R&D, creating seamless cross-functional collaboration and strategic clarity. McKinsey found that companies using integrated AI strategies see *35% higher cross-functional productivity and 25% faster time-to-market.¹ 2. Level Up Human Brilliance Generative AI isn’t here to replace human intelligence, it’s here to multiply it. By augmenting creativity, decision-making, and knowledge-sharing, companies can unlock new ideas and solutions. BCG reports that AI-augmented teams achieve *3x faster innovation cycles and 20% higher revenue growth.² 3. Operationalize Ethics and Monetize Shared Values Embedding ethics into AI operations isn’t just a safeguard, it’s a strategic differentiator. Companies that align AI initiatives with shared values (including those of employees and customers) build trust and drive sustainable advantage. PwC found that organizations with strong AI governance outperform peers by *20% in long-term shareholder returns.³ The truth is, we don’t need more technology in American business. We need more imagination, and a belief that by working together, we can build a much stronger, more human-centered future. ******************************************************************************** The trick with technology is to avoid spreading darkness at the speed of light Stephen Klein is Founder & CEO of Curiouser.AI, the only Generative AI platform and advisory focused on augmenting human intelligence through strategic coaching, reflection, and values-based decision-making. He also teaches AI Ethics at UC Berkeley. Learn more at curiouser.ai or connect via Hubble https://lnkd.in/gphSPv_e Footnotes McKinsey & Company, "The State of AI in 2024" Boston Consulting Group, "AI as a Driver of Innovation" PwC, "AI Governance as a Business Advantage" Gartner, "AI-Driven Foresight in the Future Enterprise" Forrester Research, "AI and Employee Engagement"

When we think about Generative AI, it’s tempting to focus solely on efficiency gains—cost savings, faster processes, and streamlined operations. While these are critical, they tell only half the story. To truly harness Gen AI’s potential, we must look at effectiveness—its role in enhancing decision-making, driving innovation, and aligning organizational goals with future opportunities. Here’s a breakdown of both aspects: Efficiency: Doing Things Right Gen AI excels at optimizing how organizations operate, enabling them to do more with less. Key highlights include: 1. Cost Optimization: Automating workflows, predictive maintenance, and intelligent supply chain management to trim operational expenses. 2. Resource Utilization/ Optimization: Maximizing the productivity of human and physical resources by matching talent and tasks effectively. 3. Operational Automation: Eliminating repetitive tasks to reduce human error and free up time for strategic priorities (think Agentic). Efficiency gains are undoubtedly essential—they enable organizations to sustain competitiveness and improve the bottom line. Effectiveness: Doing the Right Things Efficiency alone isn’t enough. Gen AI’s power lies in its ability to transform organizations into future-ready, adaptive entities. Here’s how: 1. Data-Driven Decision-Making: Providing predictive insights for smarter, proactive leadership choices. 2. Innovation Acceleration: Using insights from customer feedback and market data to drive product development and market differentiation. 3. Strategic Integration: Breaking down silos and fostering collaboration for aligned, agile teams. 4. Strengthened Risk Management and Compliance: Automating compliance checks, detecting anomalies, and identifying potential risks to safeguard organizations against regulatory and financial pitfalls. Effectiveness ensures organizations not only survive but thrive in an ever-changing landscape. As organizations embrace Gen AI, leaders must resist the urge to view it solely as an efficiency enabler. Effectiveness—making the right decisions, fostering innovation, right AI governance and aligning culture and teams—is equally critical for sustainable success. How are you balancing these two dimensions in your AI strategies? #GenAI #FutureOfWork #DigitalTransformation #OrganizationalDesign #Efficiency #Effectiveness #OrganizationoftheFuture

I believe disruption isn’t a threat. It’s a signal. A catalyst. With the right intelligence layer, the right tools, and a culture of continuous reinvention, we’re not just navigating volatility. Predict Disruption. Fuel Growth. In the logistics industry, we operate in a world where disruption is constant. Geopolitical instability, climate volatility, and economic uncertainty can cripple operations overnight. Traditional playbooks can’t keep up. But what if, instead of reacting to volatility, we could anticipate it—and use that foresight to drive growth? We’re entering a new phase in supply chain leadership: one defined by intelligent orchestration powered by generative AI, cloud-native infrastructure, and real-time data. This isn’t theoretical. It’s already reshaping how the most forward-thinking organizations operate—and we intend to lead from the front. From Reactive to Predictive: Enabling AI Decision Support In the Supply Chain industry, we’re leveraging generative AI not just to answer questions but to inform decisions. AI copilots are helping our teams process vast volumes of structured and unstructured data in real time, surfacing high-value insights from across our network. Need to know which supplier is driving delays? What external risk—weather, macroeconomics, labor, transport—is most likely to impact a lane or warehouse? AI assistants can pull those signals instantly and suggest next-best actions. This is how we reduce cycle time from insight to execution. Operational Intelligence at Scale Our strategy goes beyond dashboards. We’re embedding gen AI directly into our operational layer. These AI agents don’t just observe—they act. They automate routine workflows, flag anomalies, and suggest process redesigns based on transaction history, past outcomes, and evolving KPIs. This creates a self-optimizing loop—one where supply chain intelligence is continuous, and workflows dynamically adjust to changing realities on the ground. Simulating the Future, Not Just Reporting the Past Through virtual modeling and digital twins, we can simulate scenarios before they occur. Picture this: real-time data flowing in from drones, robotics, IoT, and WMS systems, visualized across a geo-aware orchestration layer. We can watch disruptions unfold in real time—or simulate future disruptions and test mitigation strategies in advance. This capability is invaluable not just for fulfillment accuracy but also for product lifecycle visibility, waste reduction, and meeting sustainability targets. GXO isn’t just optimizing for today—we’re engineering the supply chain of tomorrow. Putting Disruption to Work So what do we do with this capability? We operationalize it. We define what success looks like (not vanity metrics—true operational impact). We identify friction points between analysis and action. We evaluate architectural gaps continuously. We align AI-powered supply chain transformation with commercial outcomes & customer expectations.

The most valuable AI use case no one is talking about 80-90% of enterprise content is invisible to AI because no one wants to do the boring metadata work. The irony? That’s the key to scaling all the cool stuff. It's time we talk about the AI use case everyone’s ignoring. We hear it constantly... "80% of AI projects fail, twice as many as traditional IT initiatives”… "Only 25% meet ROI expectations”… We get it. AI is struggling. Think about your organization right now. Millions of documents, images, and datasets sit idle in silos, effectively invisible to any AI system. They lack the basic metadata and understanding needed to be useful, which is exactly why most AI initiatives can't scale beyond pilot programs. For decades, organizing this data meant slow, expensive, manual work. So, teams naturally focused only on their most mission-critical information, leaving the rest in limbo. And, has always been a key reason AI moved at a snails pace... well, before ChatGPT. But what if we flipped the script? Instead of chasing the flashy use cases like customer service bots, content generators, agentic workflows, what if we used Generative AI to prepare the data foundation that would make all those other use cases actually work? This is where it gets interesting. Generative AI can now do what legacy systems never could: ✔️ Intelligent tagging at scale: Analyze thousands of documents or data sets at once, surfacing themes and connections human catalogers would miss. ✔️ Dynamic ontology development: Build and evolve taxonomies based on real content patterns, not rigid, predefined categories. ✔️ Contextual relationship mapping: Reveal non-obvious links between disparate content and data, creating rich, navigable knowledge networks. And, this isn't just prep work for future AI projects. It immediately transforms how your organization accesses knowledge: 👉 Teams uncover insights buried across disconnected reports, emails, and databases, without manual digging. 👉 Sensitive information is automatically identified and tracked, reducing compliance risk without slowing down operations. 👉 Patterns and trends emerge across years of unstructured content, enabling smarter decisions and faster innovation. 👉 ritical institutional knowledge is captured and made searchable, before it’s lost to turnover or forgotten in archives. Here’s the reality. Leaders are moving beyond experimentation and looking for practical, scalable applications of AI. Yet, this foundational use case remains largely overlooked because it has no immediate, direct ROI and isn’t flashy enough for the headlines. The organizations that recognize this opportunity will gain an advantage, and be better positioned to unlock every other AI use case in their repertoire, including the high-value use cases currently failing to deliver ROI. #AI #ArtificialIntelligence #GenerativeAI #DataQuality #AIStrategy

– Implementing Generative AI requires more than deploying technology—it demands a strategy aligned with your association’s mission, goals, and stakeholder needs. – Begin by assessing current processes to identify where AI can deliver the most value, such as automating membership renewals or providing personalized recommendations. – Define clear, measurable objectives that connect AI adoption to strategic priorities, like improving member services or reducing operational costs. – Engage stakeholders early and form a cross-functional team to oversee implementation, ensuring ethical, technical, and operational alignment. – Develop a phased roadmap, starting with pilot projects, and select AI tools and partners with proven success in the association sector. – Measure outcomes against your objectives, tracking metrics such as reduced administrative hours, improved engagement, and member satisfaction. – Treat AI adoption as an ongoing process, using regular feedback, updates, and training to sustain success and drive continuous improvement. What’s the biggest operational challenge that you think AI could solve in your organization?

The Future of Generative AI in Enterprise Decision Making Generative AI has moved from novelty to necessity. Beyond content creation, it is reshaping how executives make decisions, allocate capital, and manage risk. Boards must understand where this technology is heading and how it will transform governance and strategy. Generative AI as a Decision Partner Generative AI is evolving into a decision support layer that synthesizes complex data, generates scenarios, and surfaces tradeoffs aligned to executive workflows. Rather than replacing judgment, it accelerates insight when paired with clear human‑in‑the‑loop controls, shortening the path from data to decision while preserving accountability. Strategic Use Cases Emerging Now Four high‑impact use cases are moving rapidly from pilot to production: 1. Board Reporting & Insights: AI can synthesize large volumes of operational and financial data into concise, decision‑ready summaries, improving the quality of board discussions. 2. Scenario Planning:  Leaders can test “what if” scenarios across supply chain, pricing, workforce, and M&A, enabling faster iteration and continuous contingency planning. 3. Policy Simulation:  AI can model the downstream effects of regulatory or geopolitical shifts, helping boards stress‑test strategy under multiple regimes. 4. Customer & Market Intelligence:  Real‑time analysis of market signals and sentiment helps leadership detect inflection points earlier and align capital allocation accordingly. Risks Boards Must Anticipate Generative AI introduces material governance risks: 1. Hallucinations that require verification 2. Model bias that can reinforce blind spots 3. Data leakage from poorly governed integrations 4. Over‑reliance on automation that erodes accountability Boards must ensure AI‑augmented decisions remain transparent, auditable, and aligned with the enterprise risk framework, supported by documented data lineage, versioned models, human approval gates, and routine audits. Case Example: Strategic Planning with AI A global logistics company embedded generative AI into planning and forecasting, achieving faster scenario modeling, more accurate demand projections, and stronger cross‑functional alignment. The key insight: when paired with strong data pipelines and governance, AI turns planning into a continuous strategic capability with 30% faster model scenario cycles. What Boards Should Do Now Boards should require: 1. A clear Generative AI governance framework 2. Explicit human‑in‑the‑loop decision guidelines 3. An integration roadmap across planning, forecasting, and reporting 4. Regular model performance and risk reporting Executive Takeaway Within the next 24–36 months, generative AI will become a core component of enterprise decision making. Organizations that combine disciplined data, strong governance, and sustained human oversight will gain a durable advantage in speed, insight, and strategic agility.