How GenAI is transforming self-service: Verint's perspective

GenAI is reshaping self-service—but are your tools built to keep up? Verint’s Jacob Murray-White explains why flexibility, control, and ROI matter more than ever. 📖 https://bit.ly/4nYzc1V

In the AI era, tech can’t be your moat. Features can’t be your moat. Even product can’t be your moat. Building has never been easier, and it’s only getting easier. If you’re betting on tech to protect you, you’re already losing. AI is flattening the field. Tech is getting commoditized. Your product isn’t 10x better, it’s just 10x easier to copy. In a world where anyone can build anything, the real moat will be the most human things: The story. The message. The customer insight. The community. The brand. The trust. The relationships. The nuance. The irony? As the world becomes more AI-first, the only lasting edge will be being deeply, unmistakably human.

In the AI era, tech can’t be your moat. Features can’t be your moat. Even product can’t be your moat. Building has never been easier, and it’s only getting easier. If you’re betting on tech to protect you, you’re already losing. AI is flattening the field. Tech is getting commoditized. Your product isn’t 10x better, it’s just 10x easier to copy. In a world where anyone can build anything, the real moat will be the most human things: The story. The message. The customer insight. The community. The brand. The trust. The relationships. The nuance. The irony? As the world becomes more AI-first, the only lasting edge will be being deeply, unmistakably human.

Different Generative AI Optimization Techniques — Choosing the Right One for Your Use Case A bit of an older video I had, but still very relevant given all the conversations lately around scaling LLMs in production. We’ve all heard about Agents, RAG, Multi-Agents, Fine-Tuning, and Full-Scale Training — but when it comes to actually adopting these solutions in enterprise settings, I’ve found a few core factors determine success: 1️⃣ Data Availability Your model is only as good as the data it learns from. Fine-tuning requires structured, high-quality examples that a model can truly learn patterns from. Without that foundation, even the best model won’t perform as expected. The quality and type of data you have access to can determine whether you should be going the Fine-Tuning or Agentic/RAG based route. 2️⃣ Data Science / Developer Experience Your team’s skillset matters. Techniques like RAG or Agent-based systems don’t require deep ML expertise, whereas fine-tuning and custom training demand experience with recipes, hyperparameter tuning, and evaluation workflows. Example: Do we go with GRPO, SFT based fine-tuning, what's the right technique here to optimize for performance? 3️⃣ Price & Scalability Considerations Fine-tuning and training can get expensive fast. Even for Model Deployment, it's important to balance the trade-offs between self-hosting on GPUs and using serverless or managed experiences. Knowing where your cost-performance inflection point lies can save thousands at scale. In the video below, I break down these trade-offs in more detail — and emphasize why the simplest solution that solves your problem is often the best one, not necessarily the flashiest. 🎥 Watch here: https://lnkd.in/epkuW4xv #GenerativeAI #LLM #RAG #FineTuning #AIInfrastructure #MachineLearning #AWS #SageMaker #ModelOptimization

📊 In every gold rush, a few find gold—but the shovel sellers always win. In 2024, AI “shovels” are: Agent frameworks Data storage + organization Automation infrastructure The future belongs to those building the systems others depend on—not the ones chasing headlines. 📹 This video explains where the real AI leverage lives. #AIAdoption #SmartBusiness #FutureOfWork #K8HVentures #BenchmarkHousing #BuildToLast #AIInnovation

Business moves slowly, tech moves fast. So don’t assume the "tech can’t do that" Chances are, it’s been possible for the past year if not longer. Today I showed a potential client how we could generate their technical document in minutes Similar to what we did for Soil & Rock Consultants They were blown away. But what really surprised them? We actually built that AI solution a year ago. The gap between what’s possible and what’s adopted is bigger than most realise.

Everyone is talking about AI agents, but few discuss what happens after the demo, when it is time to make them actually work.   That is when the real challenges appear. Unclear ROI, tool mismatches, and security risks can quickly turn “wow” into “wait… how do we scale this?”   At SoftServe, we've learned that success with agentic AI is not about the smartest model, but having the right setup with people, processes, and platforms that work together.   Here is what really makes the difference: ✅ Clear roles for those who manage and monitor agents ✅ Simple and repeatable workflows so nothing gets lost when things go wrong ✅ Frameworks that make scaling easier and safer   We’ve shared what we’ve learned along the way: the wins, the roadblocks, and what truly makes agents production-ready.   👉 Explore our new white paper to see what truly makes them production-ready: https://lnkd.in/eU7RQa9Z 

The trade-off: 👉🏻 Traditional RAG = Speed & simplicity (customer support, FAQs) 👉🏻 Agentic RAG = Intelligence & control (research, financial analysis, debugging) ✅ Real talk: Most production systems don't need agents. But when your problem requires reasoning over multiple sources or dynamic decision-making? That's where agentic RAG shines. #AIAgent #AI

In Alaska, Kindred Post uses Gemini in Google Sheets to help create complex inventory tracking sheets. See how AI is helping small businesses accomplish big wins and get started today at sheets.new! #50States50Stories

How close are we to self-optimizing products? Tools like Kameleoon, Amplitude or Fibr AI are already automating parts of experimentation and optimization workflow, suggesting variants, improvements, running tests, even implementing changes. But there’s still a human in the loop for final decisions. It seems a logical next step toward closing the optimization loop: What’s happening? How can we improve? Let’s make that change Did it work? Quick note: By “optimization,” I mean pushing towards a local maximum, which is not always what the product needs.