Chris Bassila’s Post

Excited to share that I had the privilege of being interviewed by Bloomberg’s Caroline Hyde and Edward Ludlow! 🎙️ During our conversation, we delved into the transformative potential of AI. Here's a sneak peek into what I believe AI will change: 🔍 Search/Synthesis of Information  🖋️ Content/Asset Creation  🧠 Decision Making/Taking Action These core work behaviors are on the brink of a revolution thanks to AI. We'll see AI making significant strides in streamlining simple, repeatable processes like Customer Support and Data Analysis before moving into more complex business domains. But what about non-AI and consumer-focused companies? Will AI change the way they run? My answer: ABSOLUTELY! The key to success isn't about whether a company is or isn’t leveraging AI - it's about delivering solutions that genuinely work for your customers. This universal truth transcends technology. I believe that some of today's emerging companies will become the iconic giants of the next generation. 🌟 If you want to read everything I shared with Bloomberg, I’ll link to the full interview in the comments below. 

𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗪𝗼𝗿𝗹𝗱 𝗼𝗳 𝗧𝗲𝗰𝗵: 𝗔 𝗗𝗲𝗲𝗽 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗧𝗿𝗲𝗻𝗱𝘀 𝗦𝗵𝗮𝗽𝗶𝗻𝗴 𝗢𝘂𝗿 𝗙𝘂𝘁𝘂𝗿𝗲 The rapid evolution of technology is a spectacle to behold, but what makes it truly fascinating? It's the unpredictable journey from skepticism to widespread adoption, a pattern observed across numerous tech innovations, reminiscent of our own expansion story. When we discuss technology trends, particularly those like artificial intelligence, big data, and large language models, it's essential to recognize this ebb and flow. Each innovation starts with doubts and cautionary tales. Yet, as we've seen time and time again, perseverance coupled with adaptability leads to success. The key lies in understanding the underlying potential and applying it creatively to solve real-world problems. Take artificial intelligence as an example; initially viewed with apprehension and portrayed as a distant future or even a threat, it has now become integral in various industries, enhancing efficiency and driving growth. This shift from skepticism to acceptance underscores a fundamental truth about innovation: it thrives on challenge and evolves through continuous learning. Drawing parallels with our venture's journey into new markets showcases the broader dynamic at play in the tech world. Despite initial resistance or doubts – whether it be launching in a new country or adopting a new technology – success is attainable. It requires belief in the product or concept, an understanding of the market or application area, and an unwavering commitment to adapt and innovate. In conclusion, whether expanding a brand internationally or navigating the latest tech trends, the path is seldom straightforward. Yet, those willing to embrace uncertainty, learn from diverse experiences, and innovate relentlessly will not only adapt but thrive. The future belongs to those who see beyond the scepticism surrounding new ventures or technologies and who are prepared to explore uncharted territories with curiosity and confidence. ________________________ 👋 Hi, I'm Yves. Thanks for giving my post a read. Want to know more about Generative AI for your business ? ⬇️ 📖 Grab a copy of our BOOK "𝙂𝙚𝙣𝙚𝙧𝙖𝙩𝙞𝙫𝙚 𝘼𝙄 𝘽𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝘼𝙥𝙥𝙡𝙞𝙘𝙖𝙩𝙞𝙤𝙣𝙨 : 𝘼𝙣 𝙀𝙭𝙚𝙘𝙪𝙩𝙞𝙫𝙚 𝙂𝙪𝙞𝙙𝙚 𝙬𝙞𝙩𝙝 𝙍𝙚𝙖𝙡-𝙇𝙞𝙛𝙚 𝙀𝙭𝙖𝙢𝙥𝙡𝙚𝙨 𝙖𝙣𝙙 𝘾𝙖𝙨𝙚 𝙎𝙩𝙪𝙙𝙞𝙚𝙨" https://lnkd.in/eh9ghjB8

Change Management & Generative AI Diving into the world of Gen AI is exciting, but let's talk about the real deal—how to make this change not just a buzz but a scalable reality. Companies are exploring, tossing out proofs of concept, but the game-changer is what happens next. The big question revolves around the end intervention. 1) How do we tweak organizations to truly reap Gen AI benefits? 2) What do humans need to do differently to accept, adopt, and scale Gen AI? On the tech side, organisations need to understand , it's not just about capabilities - 1) It's about having necessary data foundation. 2) It's about setting up the stage for massive model production and data fueling. I feel one should - 1) Understand before you Execute 2) Start Small, Start With Value and 3) Finding A Balance. Remember human experience and expertise aren't always noticeable , but they can leave a gaping hole in your business once they're gone. #GenAI #FutureTechTalks #DigitalInnovation #changemanagement #ocm

Evaluate existing business processes to identify where generative AI can enhance operations, focusing on areas with the potential for significant improvement and efficiency gains. StartSmallAndIterate IdentifyImpactAreas PrioritizeProjects FutureReadyWorkforce ResponsibleAIGuidelines Learn more about this topic here: https://lnkd.in/epNMfKBP At Grow Scale Win™, we're all about using AI to enhance productivity and efficiency. That's why we've built the AI Growth System™, a truly holistic approach to growing your business. You get a personalized growth strategy and a full suite of AI powered tools to generate leads, nurture prospects, and close sales. Learn more at www.growscalewin.com. DM us with any questions.

Every Transformation need TIME. Capabilities and technologies are catalyst for change. Once change is triggered then it's tries to mix and match in different combination before it's stabilizes into fix form which can scale. GenAI is catalyst which has triggered a change and now it's under reaction/stabilization phase where new approaches are coming up every few months like prompt, RAG. We need to be proud that we are part of this change but we need to keep working and give sufficient time to the process. It's quite similar to Chemical reaction triggered by Catalyst A chemical reaction is a process that leads to the chemical transformation of one set of chemical substances to another.[1] When chemical reactions occur, the atoms are rearranged and the reaction is accompanied by an energy change as new products are generated.

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Great points. Successful AI starts with getting the data right. Without accurate data, AI models won’t scale or deliver results, regardless of trends.   We've seen clients who focus on data quality achieve sustainable, scalable AI. For example, one client prioritized data management and saw significant, consistent results from their AI models. Multi agentic approaches have been giving us the most flexibility to address a wide range of complete solutions! Glad to share our experiences Nitin - how do you suggest we do this in a meaningful way?

In the fast-evolving world of AI, we've seen a rapid shift in focus: “Prompt engineering is the future,” “RAG engineers are the next big thing,” and now, “Learn agentic flow and do anything in the Enterprise". Even though it’s tempting to follow these trends, it takes time to implement any new methodology, and it’s not as simple as switching gears. It's not just about implementation; it’s about ensuring the entire ecosystem adapts. GenAI adoption is THE BIGGEST challenge for enterprises. These days, launching a new capability is easier than solving a real-world problem with it. It is for this reason that many people are skeptic about the tangible impact of Generative AI. Constantly changing methodologies without fully understanding or integrating the previous ones only adds to the noise and confusion. This shift in messaging isn’t driven by getting the best out of the technology, but often by poor problem formulation. It’s been said that “RAG will solve hallucinations," but is that truly the case? It’s only one example. You might have heard about a lot more. We need more insights from teams solving real business problems with GenAI, not just from those launching the latest tools. The former is far more crucial than the latter. Don’t fall into the trap of thinking “GenAI is just in a standard hype cycle.” If we keep changing methodologies without grasping the fundamentals and getting incremental benefits, we risk never reaching the plateau of productivity. #ExperienceFromTheField #WrittenByHuman #EditedByAI

So true. Focusing on the business outcome you are trying to drive with GenAI helps the technical solution fall into place. Don't chase the hype! I see some customers trying to boil the ocean with GenAI and they get paralyzed with the technical complexity of solving for a plethora of use cases all at once. Those organizations that are very specific about their goals tend to do much better. For example, we are working with a retailer whose stated goal is to improve responses to natural language questions from consumers. They want to be able to provide a seamless experience for a shopper who asks a question like: "I want to hang my 85" Sony TV on the wall. What mounts work best?" Being clear on the problem you are trying to solve helps immensely with the tech selection. (In this case we helped the retailer select an agentic flow to solve their use case.)

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