Exploring Technology Innovations

Who would have thought that broken glass could become a canvas for breathtaking art? Simon Berger, the Swiss artist, transforms shattered glass into mesmerizing portraits, proving that creativity thrives where others see only destruction. His work is a testament to the boundless ingenuity of the human mind. But what if the next leap in creativity isn’t just about what we do with our hands, but how we collaborate with machines? Recent research reveals that when artists collaborate with AI, their brains show increased connectivity, suggesting that the creative process itself is being rewired and expanded. These real-world results are staggering: ✅ Creative output can rise by up to 300% when humans and AI work together, compared to working alone. ✅ Over 80% of top creators now leverage AI in some part of their workflow, blending human intuition with machine precision. ✅ AI is no longer a novelty, it’s becoming the connective tissue of modern creative work, scaffolding human imagination rather than replacing it. The partnership between human and AI is not about replacement, but amplification. AI can suggest, iterate, and inspire, but it’s the human touch, our intuition, emotion, and willingness to see art in the unexpected - that turns shattered fragments into masterpieces. This invites us to consider: ✅ If broken glass can become art, what new forms of beauty might emerge when we break the boundaries between human and artificial creativity? ✅ As AI becomes a creative partner, are we witnessing the birth of a new kind of artist - one that is part human, part machine? ✅ Could the next Simon Berger be a duo: a person with a hammer and an AI with a vision? What will you create when you let go of old limits and embrace the new tools at your fingertips? Creativity is about daring to make something beautiful from what others overlook. In the age of AI, our capacity to imagine, adapt, and collaborate is more powerful than ever. To Stay Ahead in #Technology and #Innovation:  👉 Subscribe to the CXO Spice Newsletter: https://lnkd.in/gy2RJ9xg  📺 Watch us on CXO Spice YouTube: https://lnkd.in/gnMc-Vpj

The Dubai Art Museum has embraced cutting-edge AI technology to animate paintings, creating an immersive and dynamic experience for visitors. This innovative approach merges traditional art with modern tech, allowing AI to breathe life into static images. Through deep learning algorithms and neural networks, AI systems analyze the brushstrokes, colors, and composition of a painting, predicting how the artwork might move or change over time. This process creates a fluid animation that preserves the artist's original vision while adding a new dimension of interactivity. The technology often integrates with augmented reality (AR) devices, enabling museum-goers to use their smartphones or wearables to view these dynamic interpretations, bringing historical and contemporary art to life in real time. Visitors can watch landscapes ripple with motion, figures in portraits blink or shift, or abstract shapes evolve organically, transforming how they engage with art. This fusion of AI and art at the Dubai Art Museum highlights how technology can enhance creativity, offering a futuristic glimpse into the evolving world of cultural experiences. It redefines the role of museums, turning them into spaces where art is no longer confined to walls but interacts with viewers in profound new ways. Video credit and rights are reserved for the respective owner (s). #honestai #honestaiengine HonestAI - Generative AI, Machine Learning and More

Embracing Modern Solutions for Big Data 👩💻 As a data engineer, I've seen how data management has evolved over the years, moving from traditional systems to modern architectures. Here's a simple breakdown of the key developments in managing today's data explosion: 1. Data Warehouse Traditional data warehouses have been the go-to for business intelligence. They’re great for structured data and reporting but have some limitations. Strengths: Fast querying, reliable for structured data, and consistent reporting. Limitations: Struggles with unstructured data, and scaling can get expensive. 2. Data Lake Data lakes emerged to handle unstructured and semi-structured data that warehouses couldn't manage well. Strengths: Stores raw data, highly scalable, and flexible. Challenges: Can turn into a "data swamp" without governance and requires strong metadata management. 3. Data Lakehouse This hybrid combines the best of data warehouses and data lakes, offering a unified solution for analytics and machine learning. Strengths: Handles multiple data workloads, better performance than lakes, supports SQL and ML. Considerations: Still a new concept, and teams might need training to adapt. 4. Data Mesh Data mesh introduces a decentralized, domain-focused approach to data. It's as much about culture as it is about technology. Strengths: Decentralized ownership, treats data as a product, and supports self-service. Challenges: Requires major organizational changes and robust governance. 🔑 Key Steps for Transitioning Assess your current setup: Identify pain points in your existing architecture. Define your goals: Align data strategies with business objectives. Understand your data: Look at the volume, variety, and sources of your data. Evaluate your team: Address skill gaps through training or hiring. Start small, scale fast: Test with pilot projects and expand based on results. Adopt hybrid solutions: Combine tools like a data lake for raw storage and a lakehouse for analytics. 💡 What’s Your Story? Have you faced unique challenges or found creative solutions while working with big data? Share your experiences below! ➖ Image Credits: Brij Kishore Pandey

AI field note: Reducing the 'mean time to ah-ha' (MTtAh) is critical for driving AI adoption—and unlocking the value. When it comes to AI adoption, there's a crucial milestone: the "ah-ha moment." It's that instant of realization when someone stops seeing AI as just a smarter search tool and starts recognizing it as a reasoning and integration engine—a fundamentally new way of solving problems, driving innovation, and collaborating with technology. For me, that moment came when I saw an AI system not just write code but also deploy it, identify errors, and fix them automatically. In that instant, I realized AI wasn’t just about automation or insights—it was about partnership. A dynamic, reasoning collaborator capable of understanding, iterating, and executing alongside us. But these "ah-ha moments" don’t happen by accident. Systems like ChatGPT or Claude excel at enabling breakthroughs, but it really requires us to ask the right questions. That creates a chicken-and-egg problem: until users see what’s possible, they struggle to imagine what else is possible. So how do we help people get hands-on with AI, especially in enterprise organizations, without relying on traditional training? Here are some approaches we have tried at PwC: 🤖 AI "Hackathons" or Challenges: Host short, low-stakes events where employees can experiment with AI on real problems. For example, marketing teams could test AI for campaign ideas, while operations teams explore process automation. ⚙️ Sandbox Environments: Provide low-friction, risk-aware access to AI tools within a dedicated environment. Let users explore capabilities like text generation, workflow automation, or analytics without worrying about “messing something up.” 🚀 Pre-built Use Cases: Offer ready-to-use templates for specific challenges, such as drafting a client email, summarizing documents, or automating routine reports. Seeing results in action builds confidence and sparks creativity. At PwC we have a community prompt library available to everyone, making it easier to get started. 🧩 Embedded AI Mentors: Assign "AI champions" who can guide teams on applying AI in their work. This informal mentorship encourages experimentation without formal, structured training. We do this at PwC and it's been huge. ⚡️ Integrate AI into Existing Tools: Embed AI into everyday platforms (like email, collaboration tools, or CRM systems) so users can naturally interact with it during routine workflows. Familiarity leads to discovery. Reducing the mean time to ah-ha—the time it takes someone to have that transformative realization—is critical. While starting with familiar use cases lowers the barrier to entry, the real shift happens when users experience AI’s deeper capabilities firsthand.

12 ways to generate crazy valuable ideas: 1. Time Travel Imagine tackling your problem from different time periods. The further back or forward you go, the more your assumptions get challenged. • Example: How would you design a communication system in 10,000 BC vs. 10,000 AD? 2. Iconic Figures Channel the mindset of remarkable individuals. Their unique perspectives can unlock new approaches. • Example: How would Steve Jobs redesign the public education system? 3. Medici Effect Look for intersections between seemingly unrelated fields. That's where the most interesting ideas often emerge. • Example: What can a chef learn from a software engineer about process optimization? 4. Variable Brainstorming Identify the key variables in your goal, then systematically explore their permutations. • Example: For a website, consider traffic sources: organic, paid, social, referral, direct. 5. Reverse Thinking Do the opposite of what's expected. It's surprising how often this leads to valuable insights. • Example: Instead of adding features to a product, what if you removed them? 6. Unlimited Resources Temporarily ignore constraints. It's amazing what you can come up with when limitations vanish. • Example: If money were no object, how would you solve homelessness? 7. Exaggeration Amplify or shrink your problem to absurd proportions. New perspectives often emerge. • Example: How would you run a company with 1 million employees? 8. Rolestorming Step into someone else's shoes. Different roles bring different priorities and insights. • Example: How would a 5-year-old redesign a smartphone? 9. Framestorming Reframe the problem entirely. The right question is often more valuable than the right answer. • Example: Instead of "How do we increase sales?", ask "How do we make our product indispensable?" 10. Attribute Change Alter fundamental attributes of yourself or your target audience. It forces you to challenge deep-seated assumptions. • Example: How would your product change if your users were all centenarians? 11. The Third Door Approach Look for unconventional, overlooked solutions. There's often a less obvious but more effective path. • Example: Instead of competing for job listings, create a unique role that a company didn't know they needed. 12. 10X Thinking Push for exponential rather than incremental improvements. It forces you to rethink your entire approach. • Example: How could you make your software 10 times faster, not just 10% faster? What would you add? ——————————— P.S. Click the link below when you read this to sign-up to my newsletter Faster Than Normal. It's read by 70,000 people including Fortune 500 execs, founders, operators, creatives, and investors from Silicon Valley to Wall Street. https://lnkd.in/gEbFuTFf

What's AI’s Secret Weapon.. Data isn’t just a byproduct of business anymore—it’s the fuel driving AI innovation. Think about it: AI relies on data to power everything from smarter recommendations to game-changing predictions. But with unstructured data growing faster than ever, managing it has become a real challenge. That’s where Unstructured Data ETL comes in. The Data Explosion: Challenges and Opportunities By 2025, the world’s data will hit a staggering 175 zettabytes, according to IDC. Yet, only 10% of this data will be stored, and even less will be analyzed. 📊 What’s driving this growth? Enterprise data is predicted to double between 2020 and 2022, reaching 2 petabytes per organization (Seagate). Mobile and WiFi transmissions now account for over 60% of global IP data traffic (Cisco). Despite this growth, managing unstructured data—emails, PDFs, images, videos—remains a monumental challenge. Without proper tools, this untapped goldmine of information becomes a liability instead of an asset. Building Data Muscle: The Foundation for AI Innovation In a world where AI thrives on data, quality is as critical as quantity. Capital One’s approach highlights three principles to tackle data challenges: 1️⃣ Standardization: Clear rules for metadata and data governance ensure consistency. 2️⃣ Automation: Reduce manual tasks like metadata management to focus on innovation. 3️⃣ Centralization: Create modular tools that streamline data management across platforms. Without these pillars, scaling data for AI becomes unsustainable. 📌 What is Unstructured Data ETL? Unstructured Data ETL (Extract, Transform, Load) : 1️⃣ Data Sources: Pull data from PDFs, emails, presentations, or websites. 2️⃣ Extract: Automate the extraction of relevant content from these diverse formats. 3️⃣ Transform: Clean and structure the data for downstream use. 4️⃣ Load: Deliver the transformed data into databases, APIs, or BI tools. Why It Matters Traditional ETL processes were built for structured data—rows and columns neatly stored in databases. But today’s challenges demand tools that can handle the messiness of unstructured data. 🔑 Key Benefits of Unstructured Data ETL: Scalability: Process vast amounts of data with minimal human intervention. Accuracy: Improve data quality through automated cleaning and transformation. Speed: Reduce time-to-insight by delivering ready-to-use data for AI and BI tools. Looking Ahead: A Data-Driven Future Unstructured Data ETL isn’t just a tool—it’s a strategic enabler for businesses navigating the complexities of the data explosion. 💡 What’s Next? Seamless integration with AI to generate insights in real-time. Adoption of cloud-native ETL pipelines for greater flexibility and scalability. The question isn’t whether you’ll adopt Unstructured Data ETL—it’s how soon you’ll realize its potential to unlock the next wave of innovation. Let’s shape the future of data together. ♻️ Share 👍 React 💭 Comment

Here’s one reason to think big and try new things: With AI, the cost of experimentation goes down. This allows business operators to think about risk adjusted returns differently. In the past, certain costs and dependencies have kept many projects from moving forward because teams couldn't get support for the ‘cost of consideration’: the capital required to formulate a compelling (and as importantly, believable) risk-return model for an initiative. Countless solid ideas have failed to graduate beyond the idea phase.  Now, there’s a huge opportunity for businesses to benefit from a new technology. We can now use AI to drive down the cost of research and, potentially, experimentation. More robust research done at a fraction of the cost will help underwrite investments that would otherwise not even receive a second look.   This means individuals, teams, and organizations are in a position to try more things at more reasonable price points. Put another way, they can place a greater number of bets with equal or higher yield. Another thing to consider is whether you have adjusted your mark for what constitutes something being researched well enough to warrant investment. Has your posture been adjusted to the AI enabled world?  What used to cost you $50,000 may only cost you $500 today.  What idea haven’t you funded in the past year because you were still applying an old model to the new world? #AI #innovation 

Must-read piece on Responsible AI for Philanthropy published on the Center for Effective Philanthropy blog by Rachel Kimber, MPA, MS Joanna Drew Ravit Dotan, PhD Mark Greer II, MBA, CAP® It is a call to action to philanthropy to invest in responsible AI adoption for the social sector. "We are all individually called to experiment and learn about AI, warts and all, but funding is the critical element to ensure that AI is harnessed for good. And we all know nonprofit infrastructure is perennially and abysmally under-funded. So, take this as a call-to-action, dear philanthropic partners, as your role is crucial in moving the sector forward and, equally as crucial, in helping with responsible AI development and adoption." Includes four concepts for a framework for philanthropy to spearhead a collaborative approach to developing and adopting responsible AI. * Funders should support social sector in adopting the tools, but also use the tools themselves in a responsible, innovative way. * Invest in solutions to help the social sector and civil society access innovative technology, build capacity to manage its adoption and usage, and to share knowledge across the sector to improve the efficacy of the tools. * Create collaborative learning playing fields to help develop governance and guardrails to accelerate the responsible use of AI. * Invest in equitable innovation by supporting research and advocacy that diversifies AI training data and promotes transparency in AI decision-making processes. Beyond protection, philanthropy should also focus on supporting AI applications that directly benefit vulnerable communities. (Examples: healthcare AI that accounts for diverse medical needs and historical health disparities, AI-driven environmental justice initiatives that help monitor and address pollution in marginalized areas, and AI tools that aid in equitable resource distribution in areas like education, housing, and business development.) https://lnkd.in/guDPQFkT

🔎 The latest WEF report on enterprise AI adoption is incredibly detailed and well-researched! It’s one of those reports that feels more like a story than just numbers & numbers. ⛳ Some patterns that stood out to me 👉 GenAI adoption is led by human-centric industries like healthcare, finance, media, and entertainment—not just tech companies. These industries are using AI for automation, personalization, and content creation, shifting the focus from pure tech to human-centered applications. 👉 Scaling AI is *still* a major challenge—74% of enterprises struggle to move beyond PoCs, and only 16% are truly prepared for AI-driven transformation. Many remain stuck in early adoption phases with fragmented experiments and no clear strategy. 👉 The most successful AI adoption relies on "fusion skills"—where AI augments human intelligence, not replaces it. Organizations that combine critical thinking, judgment, and collaboration with AI see far better results than those pushing pure automation. 👉 Workforce concerns are a real barrier. Many employees fear job displacement and burnout, leading to resistance. Companies that focus on reskilling and AI literacy will see smoother adoption and long-term success. 😅 These are unprecedented times, and learning from others’ experiences is invaluable. The key patterns keep seeing in multiple reports: ⛳ Start with the problem first: A solid strategy that prevents AI PoCs from getting stuck. ⛳Augment before automating: Don’t rush to replace humans, make them more powerful. ⛳ Invest in upskilling employees: AI adoption is smoother when people feel equipped, not threatened. ⛳ A good strategy is everything: Without one, AI initiatives fail before they even start. Link: https://lnkd.in/gsRJT2D5

Get curious! Last staff meeting I gave my team at Duke Innovation and Entrepreneurship an assignment--explore how generative #ai tools can increase our #productivity. Everyone presented their best use case. Here's what we learned... 🤖It's not perfect. Don't expect to turn 100% of a task over to (the current version) of ChatGPT; you still need to apply your knowledge and cross-check the output (hallucinations do happen). But, don't throw the baby out with the bathwater. There's a lot of efficiency that can be gained, so explore and see where there's value add. 🤖Tools can help you stimulate new ideas and serve as a creative partner. For example one of our team members asked for ideas for the name of a program. There were several suggestions that were obvious or overused but there were also a couple no one on our team had ever thought of and we will certainly consider mixing and matching the ideas in our marketing this year. 🤖Tools can help you get 80% of the way there but you'll need to personalize it. For example, one team member used a generative AI tool to the narrative of a new teaching case study while another draft logo ideas for a new program. Both needed additional work and changes, but taking advantage of getting 80% of the way there will make each of us (and collectively our team) more efficient. 🤖One team member even used ChatGPT as a technical consultant, having it write a script that would connect our Airtable database to another database for real-time data integration. Brilliant. Q: What was the reason I devoted an hour of our team time to this exploration? A: I wanted to replace the fear (or avoidance) of AI with #curiosity. One team member even vocalized their concern about the potential of AI to replace their function, but noted that after playing around with it, they found the tool to be a great complement to the work they are doing. Success 💥 What are you doing to push your teams to be curious learners about using AI to drive organizational potential? https://lnkd.in/g9QPBYxc