Generative AI for Improving Operations

Generative AI is set to do for Business Process Management what the smartphone did for the internet: Make it accessible to all. And that matters. For years, engaging with processes meant navigating EPCs, BPMN diagrams or similarly complex modelling notations and languages, typically confined to a Centre of Excellence. Generative AI changes that paradigm. Suddenly, any employee can pose direct, meaningful questions such as: Which order-to-cash variant costs us the most? Which ten workflows have the longest cycle times? or How should I book my paid time off? Instead of interpreting visual diagrams, teams can access data-driven insights through natural-language queries, meaning no specialist training required. Why this matters to the C-suite? Democratised process intelligence When process data becomes conversational, every team, from customer support to finance can explore it independently, accelerating insight and action. Work flows through connected teams People see how their contributions align with broader strategic goals, deepening engagement and shared purpose. Leaders can make sharper decisions Executives gain real-time, end-to-end visibility, moving beyond fragmented data to drive confident action. Unlocks latent efficiency AI-powered mining highlights bottlenecks and opportunities for automation before they impact performance or margins. But the most profound shift isn’t technical. It’s cultural. When employees understand how work flows and why it matters, performance isn’t mandated; it emerges organically. Generative AI + Process Intelligence + Human Capital = a self-optimising organisation. As organisations revisit their operating models, the question is no longer if generative AI will be integrated into the process stack, but how soon leaders will equip their people with these new capabilities. Curious to learn more? Just ask your AI companion. #GenAI #BPM #ProcessMining #DigitalTransformation #EmployeeEngagement #CustomerExperience

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

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.

Unlocking Operational Excellence with GenAI, LLMs, and RAG Deploying cutting-edge technologies like Generative AI (GenAI), large language models (LLMs), and Retrieval-Augmented Generation (RAG) is no longer a luxury—it’s a necessity. These tools transform organizations' operations, enabling more intelligent decision-making, streamlined processes, and enhanced customer experiences. What does this look like in action? Customer Support Revolution Imagine a customer service team equipped with an LLM fine-tuned on your company’s knowledge base. With RAG, the model retrieves real-time, context-specific data to provide accurate answers. This reduces resolution times and boosts customer satisfaction.  Knowledge Management At Fokker, an aerospace company, RAG-powered LLMs allow employees to instantly access critical contract details or warranty terms, saving hours of manual searching and ensuring accuracy.  Predictive Maintenance: In manufacturing, AI-driven systems analyze equipment data to predict failures before they occur, minimizing downtime and cutting costs.  How to Get Started:   1️⃣ Define Your Goals: Identify areas where AI can drive the most value—customer service, operations, or innovation.   2️⃣ Leverage Existing Data: Use RAG to integrate proprietary data into LLMs, ensuring relevant and actionable outputs.   3️⃣ Start Small, Scale Fast: Pilot projects in specific departments to demonstrate ROI before scaling across the organization.  The future of operational efficiency is here. Are you ready to lead the charge? #Leadership #AI #Innovation #OperationalExcellence  

Unlocking the Potential of Generative AI for Business Innovation 🚀 As the Founder and President of Thoughtwave, I’ve witnessed firsthand how technology continues to transform the way businesses operate. Today, I want to dive into Generative AI—a game-changing innovation reshaping industries across the globe. What is Generative AI? Generative AI is a subset of artificial intelligence that uses machine learning models to create new content—be it text, images, code, or even entire workflows. Unlike traditional AI that analyzes data to make decisions, Generative AI produces entirely new outputs, mimicking human creativity. Applications Across Industries Generative AI has unlocked unparalleled opportunities for businesses to innovate and optimize. Here are just a few examples: Content Creation: • Automating blog writing, social media posts, and marketing collateral • Generating creative assets like product designs or videos Customer Support: • AI-powered chatbots offering 24/7 personalized assistance • Automated ticket resolution, improving response times and customer satisfaction Process Automation: • Streamlining repetitive tasks like invoice generation and data entry • Enhancing decision-making with predictive models that learn and adapt over time How Businesses Can Leverage Generative AI Integrating Generative AI into enterprise operations may seem daunting, but with the right strategy, it can drive significant value: 1. Identify Key Use Cases: • Assess areas where automation or innovation could yield the highest ROI, such as customer interactions or supply chain optimization 2. Invest in the Right Talent: • Partner with staffing experts (like Thoughtwave!) to source top-tier AI and machine learning professionals on a C2C, C2H, full-time, or project outsourcing basis. 3. Choose the Right Engagement Model: • Leverage onsite, nearshore, offshore, or hybrid models to suit your organizational needs. • Consider partial teams for specific projects or dedicated teams for larger-scale, ongoing AI initiatives. 4. Adopt an Iterative Approach: • Start small, test extensively, and scale successful pilots 5. Prioritize Data Quality: • Generative AI thrives on high-quality, well-organized data. Ensure your data pipelines are robust and secure The Future is Now At Thoughtwave, we specialize in connecting businesses with skilled IT talent who can lead initiatives like Generative AI implementation. Whether you need expertise for contractual (C2C), contract-to-hire (C2H), full-time, or project outsourcing models, we’re here to ensure your organization is ready to harness the power of innovation. 📢 How is your business preparing to embrace Generative AI? Let’s discuss! #GenerativeAI #BusinessInnovation #Thoughtwave #ITStaffing #AITransformation #DigitalInnovation

When asked to identify where generative AI capabilities are supporting security operations, practitioners most frequently cite automation via agentic AI (40%). Automating aspects of detection, analysis or response, including outside tool coordination and data retrieval, can streamline repeatable incident response tasks in chronically understaffed security operations centers (SOCs). A close second is using GenAI assistants to correlate current activity with past activity or known threat actor tactics, techniques and procedures (38%), a key part of threat hunting. The remainder of the top five responses highlight efforts to boost efficiency in some of the most complained-about, time-intensive SOC tasks: summarizing incidents in write-ups, automating reporting, and making remediation recommendations. The most damning finding in the SecOps study over the past few years remains the percentage of alerts that SecOps staff are aware of and simply cannot address due to a shortage of person-power. In the 2025 study, the average proportion is 45%, steady with 43% in 2024 and an improvement over 2023’s 54%. This number helps explain the enthusiastic response to capabilities that automate important but repeatable tasks, as outlined in the paragraph above: Any improvement that allows for added investigation of known anomalous or problematic activity is likely to be welcomed. While correlation should not be confused with causation, one cannot ignore that GenAI tool integration accompanied the 10-percentage-point drop in average unaddressed alerts between 2023 and 2024, after years of continual increase. In the 2025 study, generative AI assistants (44%) are the most commonly cited technology integrated into SIEM or security analytics, ahead of supplemental threat detection and response (38%) and threat intelligence tools (37%). As noted above, enterprises are applying GenAI capabilities for a variety of purposes, led by agentic automation.

Hot off the press! 📰 Exciting updates from the latest report by Capgemini Research Institute on generative AI reveal significant shifts in organizational investments and operations. The report highlights that 80% of organizations have increased their investment in generative AI since 2023, with 24% integrating it into some or most locations/functions, a notable surge from just 6% a year ago. This trend underscores the transformative impact of generative AI on operational paradigms across sectors. Organizations embracing generative AI are reaping benefits such as improved operational efficiency, enhanced customer experience, and increased sales. Notably, there's been a 6.7% boost in customer engagement where generative AI is in play, prompting strategic adaptations and innovative explorations to harness its full potential. Furthermore, the report sheds light on AI agents poised to revolutionize automation and productivity. With 82% of organizations planning to integrate AI agents in the next 1-3 years for tasks like email generation and data analysis, ensuring transparency and accountability in AI-driven decisions is key. To elevate your GenAI journey, consider these recommendations: - Cultivate expertise through strategic training and talent development - Establish a robust framework for benefit management & data governance - Deploy a generative AI platform for scalable use case management - Fortify against legal, compliance & cybersecurity threats Stay ahead of the curve in the evolving landscape of generative AI! 🚀 #AI #GenerativeAI #Innovation Capgemini Invent