GenAI Implementation and Impact

This week MIT dropped a stat engineered to go viral: 95% of enterprise GenAI pilots are failing. Markets, predictably, had a minor existential crisis. Pundits whispered the B-word (“bubble”), traders rotated into defensive stocks, and your colleague forwarded you a link with “is AI overhyped???” in the subject line. Let’s be clear: the 95% failure rate isn’t a caution against AI. It’s a mirror held up to how deeply ossified enterprises are. Two truths can coexist: (1) The tech is very real. (2) Most companies are hilariously bad at deploying it. If you’re a startup, AI feels like a superpower. No legacy systems. No 17-step approval chains. No legal team asking whether ChatGPT has been “SOC2-audited.” You ship. You iterate. You win. If you’re an enterprise, your org chart looks like a game of Twister and your workflows were last updated when Friendswas still airing. You don’t need a better model - you need a cultural lobotomy. This isn’t an “AI bubble” popping. It’s the adoption lag every platform shift goes through. - Cloud in the 2010s: Endless proofs of concept before actual transformation. - Mobile in the 2000s: Enterprises thought an iPhone app was strategy. Spoiler: it wasn’t. - Internet in the 90s: Half of Fortune 500 CEOs declared “this is just a fad.” Some of those companies no longer exist. History rhymes. The lag isn’t a bug; it’s the default setting. Buried beneath the viral 95% headline are 3 lessons enterprises can actually use: ▪️ Back-office > front-office. The biggest ROI comes from back-office automation - finance ops, procurement, claims processing - yet over half of AI dollars go into sales and marketing. The treasure’s just buried in a different part of the org chart. ▪️Buy > build. Success rates hit ~67% when companies buy or partner with vendors. DIY attempts succeed a third as often. Unless it’s literally your full-time job to stay current on model architecture, you’ll fall behind. Your engineers don’t need to reinvent an LLM-powered wheel; they need to build where you’re actually differentiated. ▪️Integration > innovation. Pilots flop not because AI “doesn’t work,” but because enterprises don’t know how to weave it into workflows. The “learning gap” is the real killer. Spend as much energy on change management, process design, and user training as you do on the tool itself. Without redesigning processes, “AI adoption” is just a Peloton bought in January and used as a coat rack by March. You didn’t fail at fitness; you failed at follow-through. In five years, GenAI will be as invisible - and indispensable - as cloud is today. The difference between the winners and the laggards won’t be access to models, but the courage to rip up processes and rebuild them. The “95% failure” stat doesn’t mean AI is snake oil. It means enterprises are in Year 1 of a 10-year adoption curve. The market just confused growing pains for terminal illness.

No bank will remain untouched by the  #GenAI tsunami. But there is great confusion on the impact and on what to prioritize. Here is my take on best practices. The first step to clear the confusion is to understand what are the main areas of impact and why. Not so much in terms of use cases but of disruption and transformation. This is my top list: 1.   The back-office will be disrupted the most. Most of the processes and repetitive tasks will be completely replaced by GenAI. 2.   The front-office will be both replaced and enhanced by  #AI. Lower added-value tasks will be replaced to a bigger extent (i.e. chatbots for 1st- level customer support), whereas more customer-facing ones will mostly be enhanced. 3.   Hyper-personalization (deliver highly personalized experiences on a massive scale) is one of the biggest opportunities, only because it was so far the bank’s biggest weakness. GenAI will not only allow banks to customize customer interactions but also their outcome: offers, pricing, the whole experience. Marketing will never be the same again. 4.   Decisioning will be turbo-charged but not replaced by GenAI: i) the more complex the decision, the higher the degree of enhancement ii) the more at stake, the bigger the need for involving people at the end. 5.   Scenario planning and forecasting including financial analysis will be greatly automated by using GenAI data-driven models that can learn from large and diverse data sources. The big change here is the accuracy of the predictions: traditional models were based on historical  #data, whereas AI can incorporate dynamic market movements. 6.   HR will see a massive transformation. People will not only have to be re-trained and up-skilled but will also see their job descriptions and time allocation adjusted. Once the big picture is clear, prioritizing the how can be daunting task. The biggest - and most common - mistake is to start with the use cases. Priority should instead focus on getting 4 areas right using a top-down approach: data, culture, IT and governance: 1.   Bring data in an AI-capable format 2.   Focus on the people and on the culture 3.   Understand how to bridge existing IT infrastructure (many parts of which can be legacy) with GenAI 4.   Re-assess your governance model (and make sure it can handle AI challenges, i.e. stemming from regulation) Opinions: my own, Graphic sources: World Economic Forum, Accenture Subscribe here to my newsletter: https://lnkd.in/dkqhnxdg

🚨 MIT Study: 95% of GenAI pilots are failing. MIT just confirmed what’s been building under the surface: most GenAI projects inside companies are stalling. Only 5% are driving revenue. The reason? It’s not the models. It’s not the tech. It’s leadership. Too many executives push GenAI to “keep up.” They delegate it to innovation labs, pilot teams, or external vendors without understanding what it takes to deliver real value. Let’s be clear: GenAI can transform your business. But only if leaders stop treating it like a feature and start leading like operators. Here's my recommendation: 𝟭. 𝗚𝗲𝘁 𝗰𝗹𝗼𝘀𝗲𝗿 𝘁𝗼 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵. You don’t need to code, but you do need to understand the basics. Learn enough to ask the right questions and build the strategy 𝟮. 𝗧𝗶𝗲 𝗚𝗲𝗻𝗔𝗜 𝘁𝗼 𝗣&𝗟. If your AI pilot isn’t aligned to a core metric like cost reduction, revenue growth, time-to-value... then it’s a science project. Kill it or redirect it. 𝟯. 𝗦𝘁𝗮𝗿𝘁 𝘀𝗺𝗮𝗹𝗹, 𝗯𝘂𝘁 𝗯𝘂𝗶𝗹𝗱 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱. A chatbot demo is not a deployment. Pick one real workflow, build it fully, measure impact, then scale. 𝟰. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗵𝘂𝗺𝗮𝗻𝘀. Most failed projects ignore how people actually work. Don’t just build for the workflow but also build for user adoption. Change management is half the game. Not every problem needs AI. But the ones that do, need tooling, observability, governance, and iteration cycles; just like any platform. We’re past the “try it and see” phase. Business leaders need to lead AI like they lead any critical transformation: with accountability, literacy, and focus. Link to news: https://lnkd.in/gJ-Yk5sv ♻️ Repost to share these insights! ➕ Follow Armand Ruiz for more

As AI efficiency, privacy, and cost become central to deployment decisions, Indian startups are increasingly adopting Small Language Models (SLMs) instead of Large Language Models (LLMs), Vaibhavi Khanwalkar reports for The Economic Times. Startups across fintech, healthtech, and legaltech are shifting toward compact AI systems to address high cloud expenses, patchy internet infrastructure, and stricter data privacy requirements in India, the report says. For instance, wealthtech firm Stockgro and trading platform Dhan are using compact models for market analysis and financial intelligence, while legaltech startup August deploys on-premise AI to safeguard client data. Healthcare company Qure.ai runs lightweight models directly on diagnostic devices for offline clinical analysis, and construction tech firm Powerplay is using homegrown models to improve accuracy in project workflows. Gnani AI, Shunya Labs, and Adya AI are all developing smaller, real-time voice and enterprise AI systems that reduce computing costs while supporting localisation and data sovereignty needs, the report says further. Unlike LLMs that rely on vast cloud-hosted datasets processed through offshore GPU data centres, SLMs operate on smaller, specialised datasets and can run locally, enabling stronger control over sensitive information, the report adds. While global LLMs perform well on broad use cases, smaller models deliver greater accuracy for domain-specific applications, note industry experts. “Most LLMs cannot do deep fundamental or technical analysis with key market signals, so we decided to build an SLM. What’s more, it costs less and has fewer hallucinations”, said Ajay Lakhotia, Founder of Stockgro. Meanwhile, India’s push to develop sovereign AI models is gaining early validation, suggests another report by businessline. Healthcare and education institutions are emerging as key adopters of locally tailored AI solutions under the India AI Mission, even as enterprise uptake remains nascent, the report says. Companies including Tech Mahindra and Fractal Analytics have already reported strong interest from domestic and overseas institutions seeking linguistically and culturally contextualised AI applications, with use cases ranging from education-focused LLMs to healthcare chatbots and diagnostic support tools, the report adds further. What does this rising interest in compact AI models suggest for India's tech landscape? Share your thoughts in the comments section. ✍: Nakul Ghai 📷: Getty Images Source: The Economic Times https://lnkd.in/dPgjfkZE businessline: https://lnkd.in/dzCx4V4M #AI #AImodels #Startups #Technology

Generative AI (GenAI) is transforming DevOps by addressing inefficiencies, reducing manual effort, and driving innovation. Here's a practical breakdown of where and how GenAI shines in the DevOps lifecycle—and how you can start implementing it.  Key Applications of GenAI in DevOps  𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀   - Automatically generate well-defined 𝘂𝘀𝗲𝗿 𝘀𝘁𝗼𝗿𝗶𝗲𝘀 and documentation from business requests.   - Translate technical specifications into simple, 𝗵𝘂𝗺𝗮𝗻-𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 to improve clarity across teams.  𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁   - Automate 𝗯𝗼𝗶𝗹𝗲𝗿𝗽𝗹𝗮𝘁𝗲 𝗰𝗼𝗱𝗲 generation and unit test creation to save time.   - Assist in debugging by analyzing 𝗰𝗼𝗱𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 and suggesting potential fixes.  𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁   - Generate test cases from 𝘂𝘀𝗲𝗿 𝘀𝘁𝗼𝗿𝗶𝗲𝘀 𝗮𝗻𝗱 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 to ensure robust testing coverage.   - Automate deployment pipelines and 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗼𝘃𝗶𝘀𝗶𝗼𝗻𝗶𝗻𝗴, reducing errors and deployment times.  𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀   - Analyze 𝗹𝗼𝗴 𝗱𝗮𝘁𝗮 in real-time to identify potential issues before they escalate.   - Provide actionable insights and 𝗵𝗲𝗮𝗹𝘁𝗵 𝘀𝘂𝗺𝗺𝗮𝗿𝗶𝗲𝘀 of systems to keep teams informed.  How To Implement GenAI: A Step-by-Step Approach  𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗮𝗶𝗻 𝗣𝗼𝗶𝗻𝘁𝘀   Start by pinpointing 𝘁𝗶𝗺𝗲-𝗰𝗼𝗻𝘀𝘂𝗺𝗶𝗻𝗴, 𝗿𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲, 𝗼𝗿 𝗲𝗿𝗿𝗼𝗿-𝗽𝗿𝗼𝗻𝗲 𝘁𝗮𝘀𝗸𝘀 in your DevOps workflow. Focus on areas where GenAI can deliver measurable value.  𝗖𝗵𝗼𝗼𝘀𝗲 𝗧𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗧𝗼𝗼𝗹𝘀   Explore GenAI solutions tailored for DevOps use cases. Look for tools that integrate seamlessly with your existing CI/CD pipelines, testing frameworks, and monitoring tools.  𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻   Ensure your data is 𝗰𝗹𝗲𝗮𝗻, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱, 𝗮𝗻𝗱 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 to the GenAI models you're implementing. Poor data quality can hinder GenAI's performance.  𝗣𝗶𝗹𝗼𝘁 𝗦𝗺𝗮𝗹𝗹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀   Start with a 𝘀𝗶𝗻𝗴𝗹𝗲 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲 in a controlled environment. Measure the outcomes and gather feedback before scaling up across your organization.  𝗠𝗼𝗻𝗶𝘁𝗼𝗿 & 𝗥𝗲𝗳𝗶𝗻𝗲   Continuously evaluate your GenAI implementation for accuracy, efficiency, and impact. Be ready to retrain models and refine your approach as needed.  𝗧𝗵𝗲 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀  ✅ Faster development and deployment cycles.   ✅ Improved collaboration through simplified communication.   ✅ Enhanced system reliability with proactive monitoring.   ✅ Reduced manual effort, enabling teams to focus on innovation.  By adopting GenAI in DevOps strategically, you can unlock its potential to create a faster, more efficient, and innovative development environment.  𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝘁𝗮𝗸𝗲?   How do you see GenAI reshaping the future of DevOps in your organization?

I read a paper from NVIDIA Research last month that made a strong case for shifting from giant large language models (LLMs) to leaner, more specialized small language models (SLMs). I couldn’t agree more. https://lnkd.in/gbBNd_Bm Here are my top three takeaways: 1. Efficiency First – Models under 10B parameters consume fewer tokens, run faster, and cost significantly less to operate. Lower latency, reduced infrastructure demands, and greener AI. 2. Specialized Power – While large models excel at general conversation, small models shine in narrowly scoped tasks. Fine-tuning for a specific job can often match or exceed the performance of much larger models. 3. Better Fit for Agentic Systems – Most AI agents repeat structured, tool-based actions. SLMs are easier to fine-tune, deploy on-device, and integrate into modular multi-agent workflows, resulting in faster, cheaper, and more aligned systems. To test the theory, I built a specialized agent that generates a typical energy model based on building type and climate zone. I swapped between Qwen3:14B and Qwen3:4B on my local computer (M3, 18GB RAM). Running the same user query to generate results: Qwen3:14B – Input tokens: 3,052 | Output tokens: 2,070 | Duration: 164.24 s Qwen3:4B – Input tokens: 2,048 | Output tokens: 619 | Duration: 8.34 s That’s about 30% fewer tokens and 20× faster — achieving the same result. Sometimes, the future of AI is not about going bigger, but about going smaller, smarter, and faster. #AI #ArtificialIntelligence #MachineLearning #LLM #SLM #SmallLanguageModels #LargeLanguageModels #AgenticAI #MultiAgentSystems #EdgeAI #OnDeviceAI #NaturalLanguageProcessing #EnergyModeling #BuildingPerformance #EfficiencyInAI #TokenOptimization #ModelOptimization #AITesting #AIResearch

At NTT DATA, we see massive promise in GenAI but only if we meet it with equal parts of ambition and responsibility. Today, NTT DATA, Inc. released our newest report on GenAI, focused on #Manufacturing leaders across 34 countries. What we found is both exciting and urgent.     𝑷𝒍𝒂𝒚𝒕𝒊𝒎𝒆 𝒊𝒔 𝒐𝒗𝒆𝒓  Nearly all manufacturers now view GenAI as a critical enabler to smarter factories, more resilient supply chains, and faster innovation. 95% of respondents said GenAI is already improving efficiency and bottom-line performance. From quality control to R&D and inventory optimization, GenAI is already driving long-term use cases that are reshaping business performance, workplace culture, compliance, safety and sustainability.    𝑮𝒆𝒏𝑨𝑰 𝒏𝒆𝒆𝒅𝒔 𝒕𝒐 𝒃𝒆 𝒉𝒖𝒎𝒂𝒏-𝒄𝒆𝒏𝒕𝒓𝒊𝒄  Manufacturers should follow a human-centric, ethical-level approach to knowledge transfer, technical training, and workforce development. 81% of those surveyed say it is very important to have the required skills in-house to deliver a GenAI strategy, yet two-thirds say their employees lack the necessary skills to use GenAI effectively, creating functional and operational disadvantages and risks.    𝑰𝒕’𝒔 𝒏𝒐𝒕 𝒋𝒖𝒔𝒕 𝑮𝒆𝒏𝑨𝑰...𝒃𝒖𝒕 𝒄𝒐𝒎𝒃𝒊𝒏𝒂𝒕𝒐𝒓𝒊𝒂𝒍 𝒊𝒏𝒏𝒐𝒗𝒂𝒕𝒊𝒐𝒏 GenAI doesn’t act alone. The real breakthrough lies in combinatorial innovation, where GenAI works hand-in-hand with digital twins, IoT, additive manufacturing, and private 5G to unlock entirely new possibilities across the value chain.    𝑻𝒉𝒆𝒓𝒆 𝒊𝒔 𝒂 𝒓𝒆𝒔𝒑𝒐𝒏𝒔𝒊𝒃𝒊𝒍𝒊𝒕𝒚 𝒈𝒂𝒑 It takes governance to grow. Nearly all executives (99%) agree leadership must guide how to balance innovation with responsibility–yet 65% acknowledge a gap between the two.    𝑨𝒏𝒅 𝒊𝒕 𝒕𝒂𝒌𝒆𝒔 𝒑𝒂𝒓𝒕𝒏𝒆𝒓𝒔𝒉𝒊𝒑  88% worry about AI-related cybersecurity, while just 18% of CISOs feel equipped. This is why it’s important to partner with trusted providers who can deliver both strategic guidance and end-to-end implementation.    This is a human-centric transformation, and its success hinges not only on machines, but on how we prepare people. I call this moment: 𝑹𝒆𝒔𝒑𝒐𝒏𝒔𝒊𝒃𝒍𝒆 𝑹𝒆𝒊𝒏𝒗𝒆𝒏𝒕𝒊𝒐𝒏. It’s about boldly embracing transformative technology while building the guardrails–ethical, organizational, and technical–that ensure innovation is durable, inclusive, and built to last.    We have a once-in-a-generation opportunity to transform global manufacturing. Let’s make sure we do it right.    Read the press release here: https://lnkd.in/daVBtkcF   #SmartManufacturing #GenAIinManufacturing #ResponsibleAI #DigitalTransformation #NTTDATA    

If you are an AI engineer, thinking how to choose the right foundational model, this one is for you 👇 Whether you’re building an internal AI assistant, a document summarization tool, or real-time analytics workflows, the model you pick will shape performance, cost, governance, and trust. Here’s a distilled framework that’s been helping me and many teams navigate this: 1. Start with your use case, then work backwards. Craft your ideal prompt + answer combo first. Reverse-engineer what knowledge and behavior is needed. Ask: → What are the real prompts my team will use? → Are these retrieval-heavy, multilingual, highly specific, or fast-response tasks? → Can I break down the use case into reusable prompt patterns? 2. Right-size the model. Bigger isn’t always better. A 70B parameter model may sound tempting, but an 8B specialized one could deliver comparable output, faster and cheaper, when paired with: → Prompt tuning → RAG (Retrieval-Augmented Generation) → Instruction tuning via InstructLab Try the best first, but always test if a smaller one can be tuned to reach the same quality. 3. Evaluate performance across three dimensions: → Accuracy: Use the right metric (BLEU, ROUGE, perplexity). → Reliability: Look for transparency into training data, consistency across inputs, and reduced hallucinations. → Speed: Does your use case need instant answers (chatbots, fraud detection) or precise outputs (financial forecasts)? 4. Factor in governance and risk Prioritize models that: → Offer training traceability and explainability → Align with your organization’s risk posture → Allow you to monitor for privacy, bias, and toxicity Responsible deployment begins with responsible selection. 5. Balance performance, deployment, and ROI Think about: → Total cost of ownership (TCO) → Where and how you’ll deploy (on-prem, hybrid, or cloud) → If smaller models reduce GPU costs while meeting performance Also, keep your ESG goals in mind, lighter models can be greener too. 6. The model selection process isn’t linear, it’s cyclical. Revisit the decision as new models emerge, use cases evolve, or infra constraints shift. Governance isn’t a checklist, it’s a continuous layer. My 2 cents 🫰 You don’t need one perfect model. You need the right mix of models, tuned, tested, and aligned with your org’s AI maturity and business priorities. ------------ If you found this insightful, share it with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content ❤️

95% of organizations report no measurable ROI from GenAI. MIT Media Lab / NANDA’s new report (300+ initiatives, 50+ org interviews, 150+ leaders surveyed) finds: ⏱️ Big firms lead in pilot volume but lag in scale-up. Successful rollouts by mid-market firms averaged 3 months; enterprises took 9+ months. 💵 The highest AI budgets were allocated to (board-friendly) marketing/sales use cases, while the best ROI came from automating back-office tasks. 🛠️ Internal builds fail at 2x the rate of external partnerships. 👻 Official LLM purchases cover only 40% of firms, yet 90% of employees use personal AI daily (“shadow AI”). So the tech is here. The value isn’t. Why? Most leaders are still using a “one right answer” playbook for a “many right answers” technology. Based on the report, here are 6 tips for winning at GenAI: 🔹 Keep up: Adaptive tools evolve with workflows; static ones flatline. 🔹 Go narrow: Start with a high-value, bounded use case, then expand. 🔹 Embed: Integrate into existing systems with minimal friction. 🔹 Win trust: Show deep process understanding, protect data, deliver results fast. 🔹 Show quick wins: Prove value in weeks, not quarters. 🔹 Measure usefulness: Assess contextual impact, not abstract accuracy. 💡 In my latest essay, I argue this last shift is the real leadership test and most organizations aren’t ready. Read it here: Why GenAI Leadership Requires an Alien Mindset https://lnkd.in/evU92fTc If you found this useful, a repost ♻️ makes my heart happy. And a subscription to my newsletter decision.substack.com makes my day.

In this latest Forbes article, I draw a compelling line from Ada Lovelace’s 19th-century foresight to today’s AI-driven enterprise transformations. Lovelace envisioned machines augmenting human creativity—a vision now realized as #generativeAI reshapes industries. Accenture's experience with over 2,000 gen AI projects reveals that only 13% of companies achieve significant enterprise-wide value, while 36% are scaling AI for industry-specific solutions. Success in this new era hinges on more than just technology investment. Companies must also invest in their people, prioritize industry-specific AI applications, and embed responsible AI practices from the outset. Organizations adopting agentic architecture - digital teams comprising orchestrator, super, and utility agents—are 4.5 times more likely to realize enterprise-level value. Here are five key lessons we’ve learned: 1. Lead with value from the top: Executive sponsorship is crucial. Companies with CEO sponsorship achieve 2.5 times higher ROI from their #AI investments.  2. Invest in people, not just technology: Empower your workforce with the skills to harness AI. Organizations excelling in AI transformation invest in broad AI upskilling, adopt dynamic workforce models, and enable human + agent collaboration.  3. Prioritize industry-specific AI solutions: Tailor AI applications to your sector’s unique needs. Companies creating enterprise-level value are 2.9 times more likely to have a comprehensive data strategy to support their AI efforts.  4. Design and embed AI responsibly from the start: Ensure ethical and effective AI integration. Organizations creating enterprise-level value are 2.7 times more likely to have responsible AI principles and governance in place across the AI lifecycle.  5. Reinvent continuously: Stay adaptable in the face of ongoing change. Companies with advanced change capabilities are 2.1 times more likely to achieve successful transformations. These lessons should serve as a practical playbook for navigating the complexities of #AI integration and achieving sustainable growth. Please read the full article to explore how Lovelace’s visionary ideas are shaping the future of business through #generativeAI. https://lnkd.in/gEVzQeRA