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Don't Dip Your Toe, Dive In: How to Go Big on AI Many companies are taking a cautious approach to AI, with small-scale pilots and experiments. While these can offer insights, they often fall short of unlocking the true potential of AI: large-scale transformation. Here's the truth: testing the waters won't bring the big wins. To truly leverage AI's power, businesses need to embrace a bolder approach. Learning from the AI Frontrunners Researchers have identified 30 companies that have achieved significant success by going "all in" on AI. Here are the 10 key actions these companies took: Define Your Goals: Clearly identify what you want to achieve with AI. What problems can it solve, or what opportunities can it create? Build Your Ecosystem: Partner with experts and technology providers to fill any knowledge or resource gaps. Master Data Analysis: Data is the fuel for AI. Invest in building strong data analytics capabilities. Embrace Flexibility: Create an IT infrastructure that can adapt and evolve alongside your AI initiatives. Integrate Seamlessly: Don't let AI exist in a silo. Integrate it into existing workflows for a smooth user experience. Think Big: Don't restrict AI to a single department. Explore how it can benefit your entire organization. Lead the Way: Establish clear governance and leadership structures to guide your AI journey. Centers of Excellence: Create dedicated teams with the expertise to push the boundaries of AI within your company. Continuous Investment: AI is an ongoing journey. Be prepared to invest in its development over time. Data is King: Never stop seeking new and better data to power your AI initiatives. The AI Advantage: Beyond Tech Giants You might think AI is reserved for tech giants like Google or Amazon. But here's the secret: no company was born "AI-powered." The successful companies we see today all took the same essential steps: dedicated leadership, resource investment, and an aggressive approach to building AI capabilities. The bottom line: AI isn't just for the tech elite. By taking a bold approach, any company can harness the transformative power of AI and revolutionize the way they do business. So ditch the timid experiments and dive headfirst into the exciting world of large-scale AI implementation.

Are you as tired as I am reading about generative AI? Enough already, right? But not with this article... it’s the exception because it’s so relevant to the implications of how AI affects business. Amidst all the AI buzz, it's crucial not to lose sight of core business essentials and the need for strategic rewiring. Kudos to Kate Smaje and Rodney Zemmel from McKinsey, who, with this article, remind us to focus on ten critical areas shaping the modern business landscape: -         Embrace the builder's mindset - innovate and scale with agility. -         Scaling innovations is as crucial as inventing them. -         Leverage the digital "More law" for compounding value. -         Continuous digital and AI transformation is non-negotiable. -         Data strategy is foundational for business and AI success. -         Empower your workforce with AI "superpowers" for a human breakthrough. -         Adopt an agile, neural business model for rapid innovation. -         Transition IT to a service-oriented model to foster distributed innovation. -         Never lose sight of creating value - aim for significant EBITDA improvement. -         Excel in testing to adapt quickly in a fast-changing world. Why read this article? It offers a comprehensive blueprint for business leaders to navigate the generative AI wave without overlooking the fundamental shifts required for sustained success and value creation. 🔗 Dive deeper into these insights and prepare your business for the transformative journey ahead. Read the article here: https://mck.co/4bkwyO1 #GenerativeAI #BusinessTransformation #DigitalInnovation #McKinseyInsights

In the rapidly evolving landscape of digital transformation and AI, businesses are constantly seeking new strategies to stay ahead. A recent McKinsey report highlights ten key, yet often overlooked, digital and AI ideas that are shaping the future of business. These insights challenge conventional thinking and underscore the importance of innovation, scalability, and value creation in the digital age. 1. Value Creation Over Technology Mastery: The primary goal of digital and AI isn't just technological advancement but building substantial value for the company. 2. Knowledge Economy Integration: Every company is now part of the knowledge economy, with every worker becoming a knowledge worker, emphasizing the need to harness data effectively. 3. Generative AI's Impact: Despite the buzz around generative AI, businesses must not lose focus on core imperatives that drive strategic growth and transformation. 4. Builders Are Shaping the Future: The ability to build and adapt quickly using digital tools is becoming increasingly crucial, with an estimated $30 trillion in corporate revenues arising from yet-to-be-launched products. 5. Scaling Innovation: Successful digital transformation involves not just innovation but also the ability to scale effectively, requiring a deep integration of technology into the organization's fabric. 6. The Digital 'More Law': A play on Moore's Law, this concept highlights how leading companies leverage digital and AI for compounding value, setting them apart from competitors. 7. Architecting for Agility: The importance of designing businesses in a way that allows for continuous testing and adaptation, ensuring resilience and responsiveness. 8. The AI Copilot: Envisioning a future where every worker has their own generative AI assistant, enhancing productivity and decision-making. 9. Focus on Value: Amidst the technological hype, maintaining a tight focus on creating real value remains a fundamental business principle. 10. Continuous Transformation: Recognizing that digital and AI transformation is a journey, not a destination, requiring ongoing adaptation and learning. These insights from McKinsey not only highlight the unsung ideas driving business today but also serve as a roadmap for leaders looking to navigate the complexities of digital and AI transformation. As we look to the future, embracing these principles will be key to unlocking new avenues of growth and innovation.

Cut through the hype and unlock AI's true potential. Tired of empty promises and buzzwords surrounding AI? This blog post dives deep into practical strategies for building an AI-ready organization that delivers tangible results in 2024. Dive into the blueprint for success: Transcend the hype: Separate genuine AI applications from fleeting trends. Define your unique AI strategy: Align your goals with real-world solutions. Build a future-proof foundation: Lay the groundwork for successful implementation. Unlock hidden value: Discover actionable use cases across your organization. Empower your workforce: Prepare your team to thrive in the AI era. Stop waiting, start building. Click the link below to access this essential guide and position your organization for AI-powered success in 2024 and beyond. https://lnkd.in/gGEfjYew #AI #artificialintelligence #business #digitaltransformation #strategy #futureofwork P.S. Share your thoughts on building an AI-ready organization in the comments! What challenges and opportunities do you see?

Are you as tired as I am reading about generative AI? Enough already, right? But not with this article... it’s the exception because it’s so relevant to the implications of how AI affects business. Amidst all the AI buzz, it's crucial not to lose sight of core business essentials and the need for strategic rewiring. Kudos to Kate Smaje and Rodney Zemmel from McKinsey, who, with this article, remind us to focus on ten critical areas shaping the modern business landscape: -         Embrace the builder's mindset - innovate and scale with agility. -         Scaling innovations is as crucial as inventing them. -         Leverage the digital "More Law" for compounding value. -         Continuous digital and AI transformation is non-negotiable. -         Data strategy is foundational for business and AI success. -         Empower your workforce with AI "superpowers" for a human breakthrough. -         Adopt an agile, neural business model for rapid innovation. -         Transition IT to a service-oriented model to foster distributed innovation. -         Never lose sight of creating value - aim for significant EBITDA improvement. -         Excel in testing to adapt quickly in a fast-changing world. Why read this article? It offers a comprehensive blueprint for business leaders to navigate the generative AI wave without overlooking the fundamental shifts required for sustained success and value creation. 🔗 Dive deeper into these insights and prepare your business for the transformative journey ahead. Read the article here: https://mck.co/4bkwyO1 #GenerativeAI #BusinessTransformation #DigitalInnovation #McKinseyInsights

Explore the future of #AI beyond the trend! Qlik’s Chief Strategy Officer James Fisher shares how last year's investments in #genAI can deliver lasting impacts for your business. Read more for insights on empowering your team, managing data quality and more. #Qlik

🚀 Is your AI stuck in the mud? 🚀Recommendations for #Advisoryboard on AI.   I came across a fantastic BCG article outlining five game-changing rules to guide AI development. Let's break it down:   1️⃣ Ditch the Tech Hype for Business Impact: Align your AI goals with business needs. Use the 10-20-70 formula: 10% on the model, 20% on data quality, and 70% on integrating AI into your business processes.   2️⃣ Think Outside Your Data Silo: Don't hoard data! Seek insights from diverse sources – industry benchmarks, public datasets, or even (ethically!) from competitors. Enrich your models by embracing external perspectives.   3️⃣ Divide and Conquer: ⚔️ Break down your AI model into smaller, modular units with specific functionalities. This agile approach makes development a breeze, simplifies troubleshooting, and makes upgrades seamless.   4️⃣ Turn Insights into Action: ⚡ Machine learning isn't just a crystal ball. Use predictions to fuel real-world business decisions. For example, Automate tasks, personalize customer recommendations or set dynamic pricing based on AI insights.   5️⃣ Embrace the Unexpected: The world changes, and so should your AI. Build flexible systems that learn and adapt to evolving conditions. Stress-test your models and consider "what-if" scenarios to be future-proof. ⏳   Collaboration is key! Bridge the gap between business leaders, data scientists, and engineers to create an AI culture that thrives. By following these rules, you can overcome common pitfalls and unlock the power of AI to transform into a business powerhouse. Let's ditch the AI hype and get down to the real work! 🚀   Interested in the full article? Check it out here: https://lnkd.in/gtMqw4xK   #AI #BusinessTransformation #DataScience #Leadership #FutureOfWork What are your biggest AI challenges? Let's share our experiences in the comments! 👇

"AI agentic workflows will drive massive AI progress" said Andrew NG in Sequoia Capital's AI Ascent event last week. So today, I wanted to document how we approach things at Naas to make this work for everyday business. → MoA (Mixture of Assistants) is a business-knowledge-centric framework aimed at generating interconnected business intelligence by combining multiple specialized AI assistants. Its focus is on technical implementation - integrating domain-specific AI models into a unified data pipeline and knowledge graph. The goal is to provide comprehensive, customized insights tailored to an organization's operational needs, where human oversight is central. → AAS (Agentic AI System) takes another perspective on the responsible development of AI systems that can pursue complex, open-ended goals with limited human oversight. While MoA concentrates on an AI-driven business intelligence, AAS provides a more comprehensive structure for the responsible development of advanced AI systems that can pursue autonomous, unbounded objectives. Naas's mixture of focused assistants with defined roles is ultimately limited to well-scoped business use cases. In contrast, OpenAI's work recognizes the broader challenges and risks posed by the creation of AI systems with wide-ranging agentic capabilities that require rigorous governance and controls. In essence, Naas's MoA represents an applied business solution, while OpenAI has proposed a more generic framework to ensure the safe and responsible development of autonomous AI that could one day possess intelligence surpassing humans across most domains. I don't think there is going to be only one framework to build multi-agent AI systems, and some approach may be complementary like MoA and AAS. What do you think? If you think our approach can be useful for your organization, let's chat!

Generative AI (Gen AI) has captured the imagination of many organizations, promising to revolutionize content creation, automate tasks, and unlock novel insights. However, as with any emerging technology, initial excitement can lead to a period of experimentation that overshadows the need to achieve tangible business impact. A recent McKinsey report highlights several key considerations for CIOs and IT leaders to bring Gen AI from experimentation to value creation: 1. Prioritizing measurable impact - Initial Gen AI projects often involve extensive experimentation, but prioritizing applications that address real business challenges and deliver quantifiable value is essential. 2. Seamless integration - The infrastructure should facilitate a smooth flow of information between Gen AI applications, the IT ecosystem, various organizational domains and workflows, thereby delivering true benefit by complementing and enhancing existing processes. 3. Resource optimization - Large Language Models can be resource-intensive, and although cloud-based solutions provide flexibility and scalability, cost management remains crucial. Change management can be a significant cost factor, potentially exceeding the running costs of Gen AI projects themselves. Continuously exploring options for optimizing resource utilization is essential to ensure cost-effective scaling. 4. Focused innovation - Organizations should prioritize a focused set of Gen AI applications that align with core business goals and existing skill sets. Additionally, building a flexible platform that allows for rapid provider switching can be highly advantageous. 5. Building multidisciplinary teams - Scaling Gen AI requires a multi-disciplinary team with expertise spanning various domains, e.g. business analysts, engineers, data governance specialists. 6. Prioritizing data quality - The focus should be on identifying and acquiring data that's most relevant to the chosen Gen AI application, rather than the pursuit of “perfect” data. "Good enough" data can often lead to significant improvements. 7. Leveraging reusability - Break down core functionalities into reusable components, rather than build custom models from scratch per project. With pre-trained modules, organizations can rapidly assemble new applications, streamlining development time and resource allocation. TLDR version: stop dreaming, start working! #GenAI #AI #MachineLearning #DigitalTransformation #CloudComputing #BusinessTechnology #Leadership #FutureOfWork #ScaleUp #AIAdoption

Design innovation has the potential to move faster than ever with the use of AI enhanced practices. We're leading the forefront by helping our clients educate their teams around GenAI, and working with them to define use cases that builds on their unique data. Curious? Read how you can integrate a GenAI innovation layer in this article by our co-founder, Dan Kraemer.