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Marketing e Vendas no mesmo time? Marketing gera leads. Vendas reclama que os leads não são qualificados. Marketing diz que vendas não dá follow-up. Vendas diz que marketing não entende o cliente. Essa história é velha. Mas o dado concreto é que 27% dos líderes industriais não têm visibilidade clara do próprio funil. Quando marketing e vendas operam em separados, cada área enxerga uma parte do problema e ninguém vê o quadro completo. No B2B, a falta de alinhamento entre as duas áreas não é só um problema de relação entre times. É um problema de receita. Leads que poderiam ser convertidos voltam pra nutrição? Leads descartados por vendas têm motivo registrado? Existe um SLA mínimo de tempo de contato? E tem ainda a base inativa. A maioria dos times não tem nem a base segmentada para reativação. Clientes que já compraram. Leads que já demonstraram interesse. Tudo parado, enquanto o time prospecta lead frio do zero. O checklist que organizei tem um bloco inteiro sobre integração marketing-vendas e reativação de base. Cinco perguntas que ajudam a ver se o alinhamento existe no papel ou na prática. Se quiser dar uma olhada, comenta "Checklist" que envio o link.

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The way we buy and sell has changed, but most sales enablement platforms haven't evolved. Today, we're relaunching Dock as an AI revenue enablement platform built for the modern revenue team. Legacy tools were built for slide decks, certifications, and content dumps. But today's reps move fast, sell to buying committees, and collaborate across Marketing, CS, RevOps, and SEs. Meanwhile, buyers do most of their decision-making offline, where reps aren't even in the room. That's why we're evolving Dock into an AI-powered platform that guides reps in real-time. What this looks like: - Collaborative buyer workspaces that evolve from first call through renewal - AI Enablement Agent that answers reps' questions in real time - AI Documents that generate personalized business cases and other follow-up using live CRM data and call transcripts - Content libraries that sync with Google Drive and SharePoint and organize themselves with AI - And soon: a learning management platform that connects to the rest of Dock Dock started as deal rooms, expanded to customer lifecycle management, and now we're becoming the system of collaboration for AI-native revenue teams. We partner with over 300 amazing customers like Lattice, Zip, Postscript, and who are already seeing this impact. I strongly believe the future of enablement is AI-powered, buyer-facing, and cross-functional. We're building it.

We just launched something that solves a problem I've watched plague sales teams for years: AI Documents in Dock. Here's the problem we solved: Every sales leader knows reps should write business cases, mutual action plans, and executive summaries. But they don't, because personalization takes hours. So deals stall without the documentation buyers need to make decisions internally. AI Documents change this completely. Now reps can generate personalized customer documents in seconds, pulling directly from CRM data, call transcripts, and deal files. No copy-pasting from ChatGPT. No starting from scratch. Enablement builds the prompt templates once. Reps get customer-ready output instantly. Early results are promising. Lattice saw a 25% jump in late-stage win rates after rolling out AI business cases. Every opportunity now gets a polished business case without the rep grind. The best part? It works with your existing tools—Salesforce, HubSpot, Gong, Zoom, other call recorders. Everything integrates out of the box. If you're tired of watching good processes get skipped because they're too manual, this might be worth a look. Want to see AI Documents in action with your own deal data? Leave a comment or drop me a DM.

💰 Hidden cost alert: How much are you spending on custom API development that could be automated? In an era of tight budgets, too many enterprises still pour resources into hand-coding APIs for each new project. It’s like paying top engineers to lay pipeline, which might be important work, but extremely time-consuming and not a differentiator. For example, a manufacturing firm recently realized they spent over $500K last year building and maintaining ad-hoc API connectors between legacy systems and new cloud apps. That’s half a million on digital plumbing, not on innovation. There’s a better approach: automate the API generation. Instead of engineers grinding out boilerplate code in FastAPI or Java for every data source, DreamFactory provides a platform to generate those APIs on-demand: - Connect to any database or system and get a full, documented REST API instantly - Standardize security and governance across every endpoint (no drift or omissions) - Free up development teams to focus on strategic features, not repetitive integration tasks The ROI is tangible: faster project delivery, lower development costs, and more secure, reliable integrations. Why invest months and $$$ on something you can get at the click of a button? Stop paying premium prices for custom-built APIs and start redirecting that budget into innovation.

Vorige week was ik op HRtech 2026 🚀 Meestal ga ik met lage verwachting naar zo’n beurs, want let’s be honist ‘HR tech beurzen’ en ik … dat is niet altijd liefde op het eerste gezicht. Maar het viel best mee! Eén ding viel me wel op: iedereen heeft het over AI. Echt iedereen. En ik snap het, het is 2026 en als je AI niet in je salespitch hebt, besta je schijnbaar niet. Maar de manier waarop stoorde me een beetje. Ik zag waren vooral bestaande oplossingen met een AI-laagje eroverheen of erin. Een chatbot hier, een slimme aanbeveling daar. Puntoplossingen. Handig misschien, maar geen fundamenteel andere manier van werken. Terwijl dat toch is waar het om zou moeten gaan. Niet AI als feature, maar AI als manier om HR-processen écht anders in te richten. Dat was schaars. Wat wél positief verraste: een paar echte niche oplossingen. Een ademhalingsplatform. Een neuropath wearable. Dingen waarbij je denkt: bestaat hier echt genoeg markt voor? Blijkbaar wel. En dat vind ik eigenlijk mooi. Het laat zien dat je niet breed hoeft te zijn om waardevol te zijn. Liever meerdere services met een goed datamodel ten grondslag ipv. all-in-one solutions. Al met al een leuke beurs. Genoeg om over na te denken. #peopletech #hrtech #softwarevoormensen #techforgood

💡 Up to 𝟴𝟬% 𝗼𝗳 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗮𝗶𝗹! Almost nobody is talking about 𝗵𝗼𝘄 𝘁𝗼 𝘀𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹𝗹𝘆 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝘁𝗵𝗲𝗺. Here's how 👇 Market statistics are brutal: up to 𝟴𝟬% 𝗼𝗳 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗮𝗶𝗹 to deliver real business value. But this isn't because the underlying technology is broken. They fail due to false expectations, lack of preparation, or lack of data. 🤖 Companies buy a bot and expect miracles. But an 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 isn't a plug-and-play widget, it's a digital worker. And just like any human employee, it requires clean data, a well-defined role, API integrations, and systematic onboarding. At Daktela, we've been deploying 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 in real contact centers, handling real customer conversations. And we've learned that the difference between success and expensive failure is rarely the technology! 🤔 It's the process and the people around it. 👍 👉 So we decided to share everything we know. To help 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝗰𝗲𝗻𝘁𝗿𝗲 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 and business owners skip the trial-and-error phase and see what a professional, enterprise-grade AI deployment actually looks like, we have put together a free, comprehensive guide: 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗶𝗻 𝟮𝟬𝟮𝟲: 𝗔 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗕𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗳𝗼𝗿 𝗦𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻. 💪 𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲: 👉 Why specialized AI agents and generic (chat)bots are fundamentally different tools 👉 𝟱 𝘀𝘁𝗲𝗽𝘀 to get your organization ready before day one 👉 The proven implementation roadmap 👉 𝟰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that actually measure success 👉 What separates a good implementation partner from a bad one If you're considering using 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 (chatbots, voicebots or emailbots) for customer service, this is a great starting point for you. 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝘁𝗵𝗲 𝗳𝗿𝗲𝗲 𝗴𝘂𝗶𝗱𝗲: 📥 English: https://lnkd.in/dCKSbeSB 📥 Czech: https://lnkd.in/d28fqZUr #AIAgents #ContactCenter #CustomerExperience

APIs designed for human developers often create real friction when used by AI agents and MCP servers. Shifting toward outcome-oriented design is quickly becoming a strategic necessity, not just a technical improvement.

De hoeveelheid data binnen organisaties groeit razendsnel. Digitalisering, samenwerking, Microsoft 365 en AI-toepassingen brengen kansen, maar ook één grote uitdaging: hoe houd je grip op je informatie? Vorig jaar werd het me - onder meer tijdens de Microsoft AI Tour, DigiBouw 2025 en in de gesprekken die ik voerde - opnieuw duidelijk: technologie is pas echt waardevol als je zelf bepaalt waar je data staat, wie erbij kan en hoe je ermee werkt. In dit kennisartikel deel ik waarom ik vind dat flexibele opslagstrategieën, data governance en data-soevereiniteit niet alleen een IT-keuze en -aangelegenheid zijn, maar een bestuurlijk thema dat elke directie op de agenda zou moeten hebben.

More tools do not make a better AI agent. Across 30 tests, lean MCP setups were the cheapest and the most reliable, and the surface area you expose mattered more than the model. Here's what that means for scaling MCP integrations.

ISO gecertificeerd. Maar wat heb je daar nou eigenlijk aan? Een certificering is geen doel op zich. Het is een middel. Het heeft alleen waarde als je het echt leeft binnen je organisatie. De processen, de afspraken, de manier waarop je omgaat met data en incidenten. Niet als logo op je website, maar als onderdeel van hoe je dagelijks werkt. Zeker in sectoren als zorg, finance, HR en ecommerce, waar veel gevoelige data is en mensen afhankelijk zijn van de software die wij bouwen, is dat geen nice-to-have. Het is gewoon onderdeel van je verantwoordelijkheid. Compliance is niet sexy. Maar het is wél het kussen waarop je 's nachts slaapt 😴

Today, we are launching the first ever API Gateway with a built-in Agent Mode. And I am genuinely excited about what this changes for API teams. Software development has become dramatically faster with AI. But configuring an API Gateway is still manual, platform-specific and full of unnecessary learning curves. We wanted to change that. With Agent Mode, you can bring an API spec, or simply describe what you want to build. > Secure it > Add rate limits > Configure routing and transformations > Deploy it All through a conversational experience. You bring the API knowledge and the agent handles the rest. That means shipping APIs faster than ever and a simpler path to consolidate, migrate and prepare APIs for developers, partners and agents. We have been heads down building this for months. Today, we are sharing it with you all. Agent Mode is free to try. Build your first API in minutes. #APIGateway #APIs #AgenticAI #MCP

This is the future of Contact Centers and agentic customer service. It's a loop. Imagine every customer interaction instantly making both your AI smarter and your human agents sharper. That’s the loop. The real differentiator isn’t just automation, it’s the feedback cycle where automation itself evolves from every human conversation. And spoiler alert, there ain't gonna be 100% agentic automation. There's no AGI coming (or at least no proof it's coming). A large majority of customer interactions will be automated, but you will always need humans in the loop. This loop though is when AI becomes self-aware, terminator style. Just kidding, but it will enable self-learning where the AI agents automatically get better by humans training the AI without the human even realizing it. And it's not just one direction, the AI agent will also be able to train the human agent from self-learning from it's own conversations with customers. The AI agent will be a live assistant for the human agent and humans will be a teacher for the agentic AI. The future has the full customer journey in a single pane of glass. The future has AI learning from humans and humans learning from AI. The future is multi-modal and multi-channel with every form of customer communication in one platform with both AI agents and human agents. The future is both inbound and outbound with AI agents automating anything you can think of in support, sales, or anything in business. The future launches next month with Dialpad. #Agentic #AiAgent #CX #ContactCenter

Anthropic uses Workday. OpenAI uses Slack. Think about that. They have the best AI models in the world and some of the smartest people alive. If anyone were going to use AI to wipe out SaaS, it would be them. And yet, they still use Workday and Slack. The “SaaS apocalypse” mostly hits small business tools. Maybe a handful of mid-to-large tech companies get creative at the edges, but that’s not what 90 percent of businesses want or need. You’re not just paying for software. You’re paying for expertise, reliability, and the ability to not think about a problem that is not your core competency. Companies should focus on what they are uniquely good at and use best-in-class tools for everything else. I guarantee that when companies try to vibe-code their internal systems, they might save money in the short term, but they will lose far more in distraction, complexity, and slowed revenue over time. Add Dialpad to the list of companies that will never try to replace something like Workday. We will happily pay them so we do not have to think about it, because our job is to be the best human and automated customer service and sales platform in the world. Anything else is a distraction. Distraction is expensive.

🚀 𝟴𝟬% 𝗼𝗳 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗮𝗶𝗹. We just 𝗿𝗲𝗯𝘂𝗶𝗹𝘁 𝗼𝘂𝗿 𝗔𝗜 𝗢𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴 so our customers aren't in that number. 💪 This week I walked our sales teams through something we've spent the last months rebuilding: how customers start with AI at Daktela. 🎯 Our goal is to 𝗿𝗲𝗱𝘂𝗰𝗲 𝘁𝗵𝗲 𝗮𝗺𝗼𝘂𝗻𝘁 𝗼𝗳 𝘄𝗼𝗿𝗸 our customers have to do to onboard an AI Agent to a minimum. At Daktela, we're ready to take on the hard work for our customers. 💡 The thinking behind it is simple. Your Daktela contact centre already holds thousands of real customer conversations. That is the best possible input for an AI Agent. 🤖 So we rebuilt the whole process around it: ✅ Preparation starts from your real conversations, not from a workshop and guesswork ✅ The AI Agent gets tested against realistic scenarios before it ever talks to a customer ✅ After go-live it keeps improving from real traffic – always with a human approving ✅ And you get numbers showing what it actually resolved, not just how many messages it handled It's a different way of delivering AI at Daktela. And we're already running it with our first customers. 🤖 If you're planning to use AI in your customer care, get in touch with our sales team and we'll help you launch it quickly and start seeing real results soon. #Daktela #AI #ContactCenter #CustomerExperience

Everyone keeps saying token prices are about to rise... who you gonna believe, the naysayers or your lyin' eyes??? I mean, OpenAI slashing prices by 80!? Since the price change their usage has apparently spiked on OpenRouter as well, proving that price does matter. Both Anthropic and OpenAI are locked in an intense fight against: 1. Cheaper Chinese model inference purchased directly or from any number of model hosting providers 2. The ability of clients to run those Chinese models locally, on smaller and smaller hardware - see the latest Qwen 3.8 drop OpenAI is directly fighting back with price cuts. Anthropic views themselves as the Mercedes of the category - but they are quietly cutting prices too. How? Anyone else notice they've stopped all the threats about kicking you off Claude Max if you run your agents on it? The real story here is - AI is here to stay and is going to keep getting cheaper, faster, and more ubiquitous. It's on you to figure out how to use it - or to ask someone who knows!

I don't think the old SaaS multiples are going to come back. So what's the move for those of us still in/around software? First, let me frame the dilemma: - The TCO (total cost of ownership) of a small software product has fallen from 500k/year to 100k per year for a wide range of use cases. - You used to need a team of multiple folks - now arguably you can get away with less than one full salary. - This still sounds expensive compared to SaaS, but if that product is customized to the precise needs of one firm, it can replace a half-dozen old SaaS products. B2C might be more defensible today, because no one is going to build software to replace a single $10 subscription. But as the models continue to improve, what's the endgame for B2B software, even AI native software? I think there's a new market structure coming, with roughly the following choices: 1. SaaS: Prices will fall and discounts will rise 2. Build option: more and more teams will build on their own 3. AI Native Players: this is just SaaS 2.0 - why can't your customer build this? Long term they will be subject to the same forces as SaaS but with more embedded token costs and lower margins New Options: 4. Custom SaaS: Vendors will build custom SaaS with N=1, hyperspecialized hosted solutions. Cheaper than building, better than buying? 5. Software Factories? Provide the client with the hard parts (auth, security, integrations) and guardrails (agents watching for security holes etc), and let them build whatever features they want atop it. (This is DevHawk) It's Mad Max right now in the Post-SaaSpocalypse world - What other options do you think will emerge?

“We’re processing 40% more data streams than last quarter, but our GPU costs went up 180%. Our cost-per-alert has nearly doubled.” An engineering leader at a real-time AI monitoring platform company told me that last week. That's the kind of red flag I can’t ignore. When infrastructure spend grows faster than actual workload, you've got an efficiency problem. We dug into their Kubernetes cluster together. The numbers told the story... On paper: 96 A100 GPUs fully allocated across 3 availability zones In reality: - Average GPU utilization: 23% - 12 model replicas handling real-time social media ingestion - 31 replicas sitting idle, allocated for "peak traffic" that happens 2 hours per day - Memory requests set to 32GB per pod when most workloads used <8GB - Zero visibility into which models were actually processing events vs. sitting empty 𝘊𝘭𝘢𝘴𝘴𝘪𝘤 𝘰𝘷𝘦𝘳-𝘱𝘳𝘰𝘷𝘪𝘴𝘪𝘰𝘯𝘪𝘯𝘨 𝘥𝘳𝘪𝘷𝘦𝘯 𝘣𝘺 𝘧𝘦𝘢𝘳. The team had been burned by an outage during a major news event, so they allocated worst-case resources everywhere. When we implemented DCGM telemetry with proper Grafana dashboards, the waste became impossible to ignore: - 47 GPU replicas hadn't processed a single inference request in 72 hours - Memory utilization averaged 12% across active pods - Queue depths showed most models could handle 3x current load After right-sizing: - Reduced active replicas from 43 to 28 during normal hours - Implemented HPA scaling for the 2-hour daily peaks - Result: 34% cost reduction while maintaining sub-200ms p99 latency The real win? Cost-per-processed-event dropped 40% while actually improving reliability through better resource allocation. If you're running real-time AI workloads on K8s, you're probably seeing similar waste patterns. The economics don't work unless you can see what's actually happening under the hood. Join me and Mubeen Syed from NVIDIA for a practical workshop on measuring and eliminating GPU waste in production ML systems. 👉 Save your spot here: https://hubs.ly/Q03KzxB50

I’m hosting a workshop with Mubeen Syed (NVIDIA) to dig into why most GPU and CPU clusters are run at just 15-25% utilization. We’ll show you how to measure what’s actually being used, uncover why utilization lags, and implement fixes that boost performance while cutting waste. Bring your questions and join us here: https://hubs.la/Q03MqNgn0

Editing used to take me HOURS 😅 …AI is fabulous when you know how to use it to make your life better. I’ve never loved video editing. Which made my content creation process so much slower, because I am so particular about quality. So when Underlord, Descript’s new AI co-editor started editing my videos exactly how I would have done it (only faster), I was sold. It finds the best moments, trims the fluff, and adds captions within seconds. No hours of editing needed. You can get access here → https://lnkd.in/dfhurQWZ You’ll know what I mean when you try it. PS: how fun is this #AD ?! I loved making this video.

After five years in the creator economy, I realized that the how matters just as much as the what. How we collaborate, how we repurpose content, how we communicate our value… that’s creator etiquette. I’m thrilled to share that I’m partnering with Descript to launch my new series, Creator Etiquette, where I’ll be breaking down what to do (and what not to do) in the creator world from pitching to publishing, with empathy, strategy, and transparency. The best part is that I’m producing it all myself thanks to Descript. It’s wild to me that one tool lets me script, record, edit, caption, and publish all in one place. Descript isn’t about replacing video editors or producers, it’s about giving creators and marketers their time back, freeing us to focus on what really matters which is our value and message. ✨ In this series, I’ll be using Descript to help me: → Repurpose key creator tips into short-form educational clips → Record and clean up my talking head videos in minutes (thanks, Studio Sound!) → Automatically remove filler words (so yes, goodbye “ums”) → Create branded, captioned content without opening six other apps And at this point you know that I believe creators aren’t just posting online… creators are business owners and mastering tools like this means working smarter, not harder. 🔗 Try Descript yourself: https://lnkd.in/esWNCffy #Descript #CreatorEconomy #CreatorEtiquette #ContentMarketing #VideoEditing #itsgigirobinson #hostsofinfluence #partner

Een CRM hier, een ERP daar, productinfo in een PIM en de rest in Excel-bestanden die niemand meer durft aan te raken. Herkenbaar? Dan snap je ook waarom AI bij de meeste organisaties blijft steken bij mooie demo's. Het probleem zit zelden in de AI zelf, maar in de data die eronder ligt. Losse systemen, eilandjes van informatie, geen enkele bron die de hele waarheid vertelt. Zonder die basis heeft zelfs het slimste AI model weinig om mee te werken. Wat de meeste organisaties missen is een verbindende laag. Iets wat je bestaande systemen niet vervangt, maar samenvoegt. Dat is wat een Data Management Platform doet. Misschien niet het eerste waar je aan denkt als je het over AI hebt, maar wel de stille kracht achter AI-toepassingen die écht werken. En dan is er nog een tweede stap. Je hebt ook een manier nodig waarop AI-agents je data kunnen bevragen. MCP (Model Context Protocol) is daarvoor de open standaard. Ik vergelijk het altijd met een universele reisstekker: je dataplatform is het stopcontact, MCP zorgt dat de stekker past, en AI-agents zijn de apparaten die je inplugt. Wat ik als operations manager altijd direct zeg: bouw beveiliging in vanaf dag één, als onderdeel van de architectuur. Waar loopt jouw AI-initiatief op vast? Laat het me weten en ik denk graag mee 🙂 Cube