Multimodal

As we wrap up the week, here’s the latest AI Lab Newsletter. We’re kicking things off with our new partnership with Swisscom, where we’re tackling document classification and extraction in finance and insurance. You’ll also find insights into emerging fintech trends from investor Lulu Chang, along with the full launch of our Unstructured AI product, which helps companies process unstructured data for GenAI applications. Get all the details below.

Every hour spent on manual documents is an hour wasted—ready to change that? Consider this your essential blueprint for success. Here's what you need to know: Document automation uses AI to streamline creation, processing, and management of docs. It's not just for big enterprises - SMBs, legal teams, finance departments, and startups can all benefit. Key advantages: increased efficiency, major cost savings, improved accuracy, easy scalability, better compliance, and enhanced customer experience. But there are challenges too. Initial investment can be high, integration can get complex, and data quality is crucial. Want to implement doc automation? Start by identifying processes to automate, prepare your data, set up extraction and retrieval systems, and continuously monitor performance. Choosing the right tools is critical. Consider your specific needs, integration capabilities, scalability, AI features, and ease of use. Remember, successful implementation requires clear objectives, team training, and ongoing adjustments. Curious to learn more about how AI can transform your document workflows? Let's chat. https://lnkd.in/eWNCCYsB

What advice would you give to entrepreneurs just starting out in AI? Lauren "🤖" Vriens shares the 3 easiest ways to get started: 1. Reading the reports and studies that are coming out 2. Going to the source material and trying to understand emerging breakthroughs 3. Not just ingesting the news, but diving deeper into the actual research It's not about following trends. It's about spotting opportunities before everyone else. I highly recommend watching this if you're new to AI and looking to see how to get started. Check out the full episode with Lauren Vriens. We dive into everything from scaling AI startups to the future of enterprise AI adoption. https://lnkd.in/ejnpB4UH What's your take? Are you reading research papers or sticking to headlines? Share your thoughts!

We’re asking the wrong question about AI and job loss. It's not about jobs disappearing, but how roles are being redefined. The fear of AI taking jobs is still out there, but it's evolving. We're starting to understand that AI is more likely to displace tasks, not entire roles. Take loan officers, for example. AI can handle document reading, data extraction, and calculations. But it can't replace the human touch in customer interactions and complex decision-making. This shift allows loan officers to focus on what they do best: 1. Spending more time with borrowers 2. Collaborating closely with underwriters 3. Handling nuanced cases that require human judgment The key is change management. Business leaders and technologists need to help employees understand how AI will affect their roles. It's not about job loss, but about enhancing job quality. Here's the exciting part: As AI improves customer experiences, we might see increased demand for loans. This could actually lead to more loan officer positions, not fewer. The nature of work will change, but roles won't disappear. We're in the early stages, but I'm optimistic about the future of work alongside AI. Want to explore more this topic? Check out my full video breakdown.

Lauren "🤖" Vriens, VP of Growth Strategy at Accenture, drops some serious knowledge on the common pitfalls startups face when trying to become AI-native. She covers everything from mindset shifts to user experience design. Check out this sneak peek from my conversation with her in the latest episode of the Pioneers podcast—just released! One key insight? The importance of user experience in AI integration. Lauren contrasts Notion's confusing implementation with Instagram's smart intent parsing approach. It's not just about adding AI; it's about seamless integration that enhances user interaction. But that's just the tip of the iceberg. In our full episode, Lauren shares her expertise on: - How AI-native startups are rethinking hiring and efficiency - The challenges enterprises face in AI adoption - Strategies for building products that improve over time - The power of community-driven growth - Why founder-led marketing is crucial in the AI era Lauren's insights come from her impressive track record, including scaling Revel from $500K to $50M in just 18 months. Her perspective is invaluable for anyone looking to navigate the AI landscape successfully. The full episode is available here: https://lnkd.in/e4Yd8pqQ

Our comprehensive blog post on "Building AI Agents: 9 Lessons We Learned Since 2023" is packed with valuable lessons. If you've been following our journey in the AI space, you know we've been hard at work developing and deploying AI AGENTS for various enterprise applications. Here are 9 key takeaways we've learned since 2023: 1. Proper guardrails are non-negotiable, especially in high-stakes industries 2. Context is everything - AI needs the right data at the right time 3. Fine-tuning actually improves agent performance, not worsens it 4. Balance broad and specialized LLMs for optimal results 5. Seamless integration with existing systems is critical 6. The magic happens in both the agent and the infrastructure 7. Open source frameworks are just the starting point 8. Adaptability and human-in-the-loop are essential 9. Build for scalability from day one This post distills our key insights, from the importance of proper guardrails and context-specific knowledge to the balance of broad and specialized LLMs. We explore why fine-tuning improves agent performance, the critical nature of seamless integration, and why adaptability and human-in-the-loop processes are non-negotiable. Want to learn more about building scalable, effective AI Agents? Check out the full article here: https://lnkd.in/e6wYM47V As always, we're committed to sharing our experiences and helping businesses navigate the exciting world of AI. Let me know your thoughts!

The AI software landscape is evolving at breakneck speed, and I'm seeing some fascinating trends emerge. Companies are facing critical decisions at every level - from infrastructure choices to the application layer. Here's what's I'm seeing: 1. The rise of specialized foundation models 2. The increasing complexity of the tooling layer 3. The shift towards application-layer platforms But the big question on my mind: How will this impact businesses across industries? I believe we're on the cusp of a major transformation in how companies adopt and implement AI. The days of building everything from scratch are numbered. Think about it - would you assemble a computer from individual components, or buy a pre-built machine? The AI software ecosystem is heading in a similar direction. I predict 2025 will be the year of fully-baked AI solutions. We'll see a battle between horizontal platforms tackling common use cases and vertical-focused offerings tailored for specific industries. As a vertical platform provider ourselves, I'm both excited and anxious about what's coming. How will the landscape shift? Where will the next wave of innovation come from? One thing's for sure - the race is on to make AI truly accessible and valuable for businesses of all sizes. Want to learn more about this rapidly evolving ecosystem? Check out my full breakdown in this clip. What are your thoughts on the future of AI software? I'd love to hear your perspective.

The #1 question I get about AI Agents is, "How do I actually USE them in my business?" Here are 3 ways companies are leveraging AI Agents right now: 1. Automating complex workflows in regulated industries 2. Boosting sales team efficiency with automated prospect research 3. Streamlining data extraction and processing from unstructured sources I recently analyzed 15 top AI Agent companies transforming industries. A few key trends emerged: - Combining multiple specialized AI Agents to handle end-to-end processes - Integrating with existing systems and apps for seamless workflow automation - Focusing on highly regulated sectors like banking, insurance, and healthcare - Aiming for significant cost reduction and productivity gains (80%+ in some cases) The most innovative companies are developing AI Agents that can execute multi-step tasks autonomously, adapt to dynamic environments, and make decisions independently. At Multimodal, we're leading the charge in this space. Our AI Agents are designed to automate complex workflows in highly regulated industries like banking and insurance. We offer a suite of specialized Agents that can be stacked together to handle end-to-end processes such as underwriting and claims handling. What sets us apart is our focus on combining advanced AI capabilities with deep industry expertise. Our Agents can extract data from unstructured documents, make accurate decision recommendations, access company databases, and even draft business reports - all while adhering to strict regulatory requirements. The impact? Some of our clients have seen up to 40x increases in their user base and 97% workflow automation. We're talking about transformative changes in how businesses operate. If you want to explore how AI Agents could redefine your business operations, I highly recommend checking out the full analysis here: https://lnkd.in/g_xYn_Uv P.S. Curious about the ROI potential? The numbers don't lie - 80% cost reduction, 40x client base increase, 97% workflow automation. The future of business is here, and it's powered by AI Agents.

I had an incredible conversation with Lauren "🤖" Vriens , VP of Growth Strategy at Accenture, about how startups can effectively integrate AI into their core business model. Lauren shared two primary ways: 1. On the operations side - becoming more AI native by rethinking processes and leveraging automation. This requires adjusting hiring strategies and headcount planning. 2. On the revenue generation side - using AI to create better content, optimize sales efforts, and accelerate the path to revenue. But here's the real gold: Lauren dives deep into how AI-native startups are reshaping traditional growth strategies. They're prioritizing AI optimizers early, focusing on automating processes instead of just adding headcount. This approach reduces overhead and ensures scalability. We also tackle the enterprise perspective, exploring the challenges of AI adoption in larger organizations. Lauren breaks down the trade-offs between custom AI solutions and off-the-shelf tools, and how to balance control with innovation speed. There's so much more packed into this episode - from retention strategies that leverage AI to the power of community-driven growth for startups. If you're building, scaling, or transforming a business in the AI era, you won't want to miss this. Full episode drops soon. I'll keep you posted. Keep building.

📰 Today in AI Lab! What do early-stage investors really think about generative AI in fintech? Letizia Royo-Villanova of Plug and Play Tech Center shares insider insights on why industry-specific solutions are leading the charge, the promise (and pitfalls) of automation, and the potential shift from traditional banking to multi-agent frameworks. Don’t miss this edition—read now!