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Alameda, California, United States
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Articles by James
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The Hidden Cost of Custom Rate Cards
The Hidden Cost of Custom Rate Cards
Custom rate cards feel like a smart way to win deals. In many cases, they are.
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Most Pricing Teams Aren’t Failing — They’re Set Up to FailMar 19, 2026
Most Pricing Teams Aren’t Failing — They’re Set Up to Fail
If you spend any time talking to pricing professionals, you’ll hear a familiar frustration: “We’re doing good work… but…
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The Mix Problem — When Higher Prices Reduce RevenueMar 13, 2026
The Mix Problem — When Higher Prices Reduce Revenue
One of the strangest meetings you can be called into as a revenue or yield leader goes like this. Every team reports:…
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The Mix Problem — When Higher Prices Reduce RevenueMar 12, 2026
The Mix Problem — When Higher Prices Reduce Revenue
One of the strangest meetings you can be called into as a revenue or yield leader goes like this. Every team reports:…
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4K followers
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James Deaker shared thisCustom rate cards feel like a smart way to win deals. And early on… they are. But over time, they often lead to: • Inconsistent pricing • Operational complexity • Constraints on product changes The tricky part? You don’t see the damage immediately. There’s a better way to structure flexibility—without losing control. I break it down here: https://lnkd.in/gJtBv6Gs
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James Deaker shared thisHow to approach requests for Custom Rate Cards from advertisers
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James Deaker shared thisIf you spend any time talking to pricing professionals, you’ll hear a familiar frustration: “We’re doing good work… but no one listens.” The issue usually isn't capability. It's the structure of the organization. In this week's article I talk through how to address these challenges.
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James Deaker shared thisIssue 1 of the Yield Report - Understanding Mix DynamicsThe Mix Problem — When Higher Prices Reduce RevenueThe Mix Problem — When Higher Prices Reduce RevenueJames Deaker
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James Deaker shared thisToday I launched a LinkedIn newsletter called The Yield Doctor Report. I’ll be sharing practical frameworks for understanding the economics of digital advertising — pricing, yield management, retail media, and monetization strategy. The first issue explains a strange phenomenon many revenue leaders have seen: Every product improved price realization… and yet revenue fell. You can read the first issue here: Subscribe on LinkedIn https://lnkd.in/gyHpXUEjThe Mix Problem — When Higher Prices Reduce RevenueThe Mix Problem — When Higher Prices Reduce RevenueJames Deaker
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James Deaker shared thisMy first LinkedIn Newsletter - The Mix Problem in Yield Management explainedThe Mix Problem — When Higher Prices Reduce RevenueThe Mix Problem — When Higher Prices Reduce RevenueJames Deaker
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James Deaker shared thisI just got back from RampUp 2026, where AI dominated almost every session and hallway conversation. But what struck me most wasn’t the technology itself — it was how early the industry still is in figuring out how AI will actually be implemented. After two days at the conference, here are three takeaways that stood out. 1️⃣ The biggest thing missing from AI in advertising right now is standards. Scott Howe, CEO of LiveRamp, spoke about waves of innovation in history. He referenced the U.S. railroad system, where network effects accelerated once standards were introduced — things like standardized time zones and track gauge. At RampUp, it became clear that we are still very early when it comes to standards for AI. Protocols are still emerging or competing — for example AdCP (Yahoo, Scope3, PubMatic) and AAMP (LiveRamp and the IAB). My takeaway: It won’t be the speed of technology that slows the rise of AI agents in advertising. It will be the speed at which protocols and standards develop and are adopted. 2️⃣ AI in AdTech currently doesn’t have an organized resistance. In most innovation waves, one group pushes forward while another pushes back. Programmatic buying is a good example. Supporters emphasized efficiency, while critics — often publishers and agencies — warned it would drive down prices and hurt advertiser outcomes. But that dynamic hasn’t really appeared with AI. Maybe the industry learned from programmatic that you can’t hold back the tide. My takeaway: AI may be the first major innovation wave in advertising with almost no organized resistance. 3️⃣ The conversation about AI is far ahead of the implementation. Almost every post and conference session is about AI. But the gap between talk and action is still large. In one session, the moderator asked how many companies had run campaigns through an agentic workflow. Only about 20–25% of the room raised their hands. Even panelists struggled to provide concrete examples. My takeaway: The conversation around AI is far ahead of the operational reality. One final observation. AI is accelerating execution at the same moment that privacy laws are increasing constraints across U.S. states. The companies that succeed will likely be the ones best at governance, control, and structured data. Curious what others who attended RampUp saw.
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James Deaker posted thisMost executives think AI will fix their data problems - It won’t. If anything, AI is exposing how weak most data foundations actually are. In my latest conversation with Brian Silver (EVP, Global Marketing Solutions at TransUnion), we discussed a statistic that should make every publisher, ad tech company, and CMO pause: ~60% of AI initiatives fail. Not because of the model. Not because of compute. Because of the data. Fragmentation. Weak identity resolution. Shallow enrichment. Disconnected measurement. AI is a multiplier. If your inputs are flawed, it scales error. If your identity infrastructure is strong, it becomes a revenue engine. Three themes stood out in our discussion: 1️⃣ Identity resolution is now infrastructure — not a targeting enhancement. 2️⃣ Enrichment matters more than raw scale — depth of signal drives predictive power. 3️⃣ Closed-loop connectivity wins — resolve → enrich → activate → measure → optimize. We also touched on something more forward-looking: What happens when AI agents — not humans — begin generating traffic at scale? How do we verify identity? How do we measure ROI? How does monetization evolve? The power dynamics between demand and supply may shift faster than many expect. If you’re investing in AI — or being asked to define your AI strategy — this conversation is worth your time. Link to the full episode in the comments. Curious - Are you seeing AI improve performance in your organization — or just increase complexity?
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James Deaker shared thisMost executives assume stronger data controls, privacy processes, and governance frameworks slow a business down. In practice, the opposite is often true. Weak data practices quietly create friction across product, sales, and leadership teams. Decisions get delayed. Escalations happen late. Teams hesitate — not because they’re risk-averse, but because they lack confidence in how data is actually being used. I sat down with Devan Brua, Founder & CEO of Privacywise, LLC, to unpack why stronger data controls don’t block innovation — they create the shared visibility and clarity that lets organizations move faster, especially as AI and automation scale. We talk about: • Why many companies believe they’re “fine on data” — until they’re not • How data governance issues surface after key product or partnership decisions • Why AI amplifies existing data weaknesses rather than creating entirely new risks • How better data governance reduces hesitation and speeds up execution This isn’t a conversation about box-checking compliance. It’s about operational confidence. We also touch on EDGE, PrivacyWise’s diagnostic tool for understanding data exposure and governance gaps: 👉 https://lnkd.in/gC7xd_-7 If your business depends on data for growth, this conversation may challenge a few assumptions. https://lnkd.in/gbDhtUzH
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James Deaker liked thisJames Deaker liked thisScooplet: Yahoo DSP is hiring a dedicated team to build out a range of AI-powered tools designed to serve mid-market, performance-focused advertisers—many of whom have historically transacted through Google DV360 or The Trade Desk. New features are expected to arrive later this year. For ADWEEK: https://lnkd.in/ekdPqqVHEXCLUSIVE: Yahoo Moves Downmarket With DSP Performance PushEXCLUSIVE: Yahoo Moves Downmarket With DSP Performance Push
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James Deaker liked thisJames Deaker liked thisI am beyond thrilled to share that I've been promoted to EVP of Global Sales at Yext! 🎉 Eight months back, I rejoined this incredible company with a deep sense of belief that this was our time and it is proving to be right in ways big and small. We are living through one of the most profound shifts in buyer behavior any of us has ever witnessed. The pace of change is staggering. And in moments like these, the companies that win aren't just the ones moving fast — they're the ones built for exactly this moment. Yext is that company. Here's why I believe it so deeply: 🏆 Our Client Base — We work with some of the world's most iconic brands, many for ten years plus. They trust Yext to help ensure they do not become invisible in this new world. 🔭 Our Vision — We have always been ahead of the curve on where search, AI, and the customer journey are headed. That clarity matters now more than ever, the new AI world is just an extension of what we have been doing for over a decade, helping businesses be found wherever buyers are searching. 📊 Our Scale of Data — In the AI era, data is the differentiator. Full stop. Yext's breadth and depth of structured data is a genuine, durable competitive advantage. ⚙️ Our Product & Engineering Teams — They move fast, they build with purpose, and they never stop innovating. ❤️ Our People — Everything else is possible because of this. The talent, the grit, the heart in this organization is something special. A special thanks goes to my amazing teammates in EMEA, we have made a real difference in how we approach the market, lead with our customers and work together! You are all so special to me! And to Michael Walrath — I have had the privilege of working alongside you for nearly 20 years. Thank you for your trust, your leadership, and for always seeing around corners and being the leader you are. This one means a lot. To our clients, partners, and the entire Yext team — Let's GO. The best is ahead. 🚀 #Yext #Promotion #GlobalSales #Leadership #AI #Grateful #EMEASales #EMEA
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James Deaker liked thisJames Deaker liked thisI've heard the same line from VCs three times in my career. Every time, they were wrong. In 1999: "Cool idea but I'm pretty sure Oracle is going to do that." In 2009: "Smart but I'm certain Google's got that locked." In 2026: "Really? I'm positive Anthropic has it handled." Same fallacy, new logos. When I was building Krux, I saw slides from Google in 2010 describing exactly what we were building. VCs saw them too and moved on. A lot of them told us to pack it in. We built it anyway, scaled it, and grew it into the leading data management platform for marketers before Salesforce acquired the company in 2016. Now listen, the velocity of AI is genuinely different. You've gotta be more paranoid than ever about whether there's real daylight between what you're building and what the model does (or will soon do) on its own. I wrote about this a few weeks ago. That threat is real. But the lazy consensus that two or three AI companies will simply inhale all of tech? The long arc of innovation says otherwise. The oligopoly always fractures. The behemoths never have it all on lock. And uppity founders who refuse to mind their place keep showing up to spoil the oligopolists' best-laid plans.
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James Deaker liked thisJames Deaker liked thisBefore investing in another compliance tool, it’s worth asking a simple question: 👉 Are we actually solving the right problem? In a recent conversation with James Deaker, Devan Brua explains why the EDGE diagnostic was built to reverse the usual sequence: Many organizations start with, “What should we buy?” The better starting point is, “What is actually happening inside our organization today?” A diagnostic approach forces clarity: • How does data really move through the organization? • Where is the actual risk? • Where are we trying to go? Without that clarity, it’s easy to end up with polished policies that don’t reflect reality, tools that don’t quite fit the operating model, and audits that uncover surprises. When organizations start with a clear picture of their environment, governance becomes much more practical. Policies align with how teams actually work, tools support real needs and audits start validating maturity instead of exposing gaps. With AI adoption accelerating and state privacy laws continuing to expand, reactive compliance spending quickly becomes expensive. Clarity about how your organization actually operates is a far better place to start. Watch the full interview → https://lnkd.in/gbjZwnAb #PrivacyWise #AIGovernance #DataPrivacy
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Pricing Award - Media and Telecom
Pricing Week by SV Pricing Recruiting
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Allant Group's AMP+ unifies 16+ sources of demographic, transactional, behavioral, and digital intent data into a single composable data environment. Each additional source improves audience accuracy and coverage by 3–17%. Why does that matter? Because when you're only using one file, you're leaving entire audience segments behind. Our new benchmark paper, “A New Era in Composable Data Intelligence,” shows how multi-source orchestration leads to: cleaner identity resolution, more predictive modeling, and better-performing segments [DOWNLOAD] https://lnkd.in/gnYqPzZk #DataQuality #AudienceManagement #MultiSourceData #AudienceIntlligence #DataActivation #CustomerAcquisition
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Wiard Vasen
PGLLAB • 336 followers
Ontology-Driven Orbs: PGLLab as a Decision System PGLLab’s orbs gain real meaning when they are modeled not just as devices but as ontology objects: concepts with properties, relationships, and actions. This follows the Palantir vision of ontology as a decision-centric model, where data, logic, and actions come together to drive real operations. An Orb Ontology describes each sphere with attributes (position, energy, sensors, health), connections (in-contact-with, in-formation-with), and permitted actions (roll, hop, cluster, sample, relay). Logic and permissions live inside the model: which formations are safe on a slope, which orbs can transfer power, which actions require quorum. Instead of scattered data and ad-hoc code, PGLLab exposes a unified surface where developers, AI agents, and human operators interact through shared concepts. The ontology becomes the operating language of the swarm: Concepts: Orb(id, pose, energy, sensors[], health, role) ContactEdge(orbA, orbB, force, bandwidth) Formation(id, type, members[], stability, purpose) TerrainCell(id, slope, temp, iceProb) Mission(id, objective, zone[], policy) Actions: Move(orb, targetCell) Cluster(formation, goal) TransferEnergy(src, dst, amount) Sample(orb, cell, method) Relay(src, dst, payload) This simple schema unlocks five unique swarm capabilities: 1. Composable formations: lines, triangles, lattices, or shells are modeled as types, with stability and coverage built in. 2. Policy-aware autonomy: mission rules and safety checks run at the action boundary. 3. Explainable planning: every action ties back to named objects (“Formation-12 stabilized CraterEdgeCell-A17”). 4. Scenario simulation: the same ontology runs in sim and field, making rehearsal seamless. 5. Secure collaboration: permissions bind directly to concepts and actions, enabling multi-party use without leakage. For the orbs themselves, contact edges aren’t just physical—they are decision triggers: a touch can raise bandwidth, allow energy transfer, or validate a new formation. A hex lattice isn’t only geometry; it is an ontology object with defined roles, policies, and mission functions. By binding physics, governance, and purpose into one structure, PGLLab turns geometry into governance. Crucially, this also defines the interface between LLMs and swarm control. LLMs generate intent (“Deploy hex lattice over all cells with iceProb > 0.7”), but execution runs through ontology actions with policy enforcement. That division—language for goals, ontology for execution—keeps the system both creative and safe. In short: PGLLab’s orbs evolve from rolling machines into a decision system, where each cluster is a concept, each contact is an action, and each mission is an ontology graph. What emerges is not a swarm of robots but a thinking organization of orbs, able to observe, reason, and act together on alien ground.
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Dominic Cross
GTM Engine • 3K followers
This hits a nerve. Most teams don’t lose deals because they lack data, they lose them because the why disappears. Six months later everyone’s arguing opinions instead of evidence. Decision traces feel like the missing link if AI is going to be more than another reporting layer. This is exactly the problem we keep running into in real revenue teams.
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Adam McCabe
Convictional • 1K followers
We wrote a new essay detailing our journey towards AI powered analytics in Convictional: https://lnkd.in/gX4QCgqm TLDR: With LLMs, true self-serve analytics seemed finally within reach. No more over-busy dashboards built for everyone and exactly right for no one. Even better, the LLM could help answer questions about the data. However, a serious trust problem is introduced. Most business users are already skeptical of data and its interpretations, and when you throw in LLM hallucinations (bad SQL, incorrect join logic, or poor interpretation of data definitions) the margin of trust shrinks even further. One or two errors and the solution is written off. Data is a crucial input to decision making, so we’ve been committed to finding a solution that can provide analytics at the levels of accuracy needed. Over the last year we tried a number of approaches, with our initial achieving an unacceptable ~50% accuracy rate, and our current productionized technique approaching 100% accuracy. The key was acknowledging the human role in the solution. We found that using a semantic layer, defined by humans and queries by the LLM was the unlock. By no longer asking the LLM to write the SQL, and instead rely on robust pre-defined metrics and configurations, it could focus on actually answering the user’s questions instead of resolving SQL. If you’re using dbt’s semantic layer (or plan to) and interested in trying it out, just touch base and we can help get you set up! cc Jake Beresford Matthew H. Chequers, Ph.D.
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SiteSpect, Inc.
4K followers
If your A/B tests and CRO metrics don’t map to business goals, you might be leaving more strategic experiments on the table. 😵💫 This post explores how CRO teams can: 🚀 Demonstrate ROI with metrics like LTV, CAC, and RPV 🚀 Uncover surprising patterns using behavioral segmentation 🚀 Turn test results into decisions that drive long-term growth Whether you’re refining your program or scaling experimentation across teams, this guide will help you select the most important metrics: https://hubs.la/Q03kHjN30 #CRO #CROmetrics #abtesting #experimentation
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Brian Bickell
TextQL • 3K followers
There’s really nothing like taking a new major feature release out to the market on the first day of two back to back conferences. When we planned to roll out Cube D3, our agentic analytics platform, built on our market leading universal semantic layer, I was excited to get the in-person feedback. I spent more time working the booth at both Snowflake Summit and Databricks Data + AI Summit than most partnerships guys would, watching to see what resonated and what we still needed to refine. At first, most understood what we were doing, or at worst kind of disinterestedly said “oh another chatbot”. D3 being able to build and expose visual assets, as well as answer questions and provide result sets caught many folks' attention. That part clicked, because they could get from the demo we had on offer, to D3 being able to rapidly prototype visualizations that could be kicked out into popular front-end frameworks and hosted however they liked. What connected with everyone and pulled in even the most cynical was when we explained our semantic SQL. Semantic SQL is the rather simple looking SQL that D3 (or any consumer via our SQL API) is writing that Cube is rewriting into the complex warehouse SQL that eventually hits your data source of choice. Complex business metrics are defined once upstream providing for trust, governance and consistency. Compared to traditional text-to-sql approaches, we are breaking apart the place where things typically go wrong - generation of highly complex analytical SQL, without context for *exactly* what a user means when they ask for a metric. The result is the user can still ask for ad-hoc analysis built upon these metrics, but they are always going to get compiled down to the approved metric definitions under the hood without any LLM guesswork. We also expose a reasoning trace every step of the way so you can inspect why D3 did what it did. This is becoming standard for AI applications and we think it’s a great practice to incorporate. Cube D3 is currently in preview but if you’re interested drop me a line and I’ll help you get access.
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Leah van Zelm
3K followers
Another important movement in advertising is getting back to the basics of marketing... Marketing fundamentals like human attention, memory and persuasion that has made consumer experiences better, and businesses more successful in establishing a connection with consumers. thank you Joseph Meehan for calling out these critical signals is driving performance!!
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Kate Parker
Transcend • 3K followers
Earlier today we released Transcend's third annual Data Rights Unwrapped, and it’s a clear signal of where enterprise use of consumer data is heading. The numbers from 2025 tell a massive story of scale and maturity in the face of the AI supercycle. Here is what leading the way looked like this past year: ⏱️ 𝗧𝗵𝗲 𝗔𝗜 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗿𝗮𝗰𝗲 𝗶𝘀 𝗼𝗻. There was a staggering 1150% (12x) growth in responsible AI workflows this year. Companies are racing to build the guardrails needed to manage consumer data. 🏗️ 𝗧𝗿𝘂𝘀𝘁 𝗮𝘁 𝘂𝗻𝗽𝗿𝗲𝗰𝗲𝗱𝗲𝗻𝘁𝗲𝗱 𝘀𝗰𝗮𝗹𝗲. Our customers unlocked new revenue opportunities by securing over 14 billion consent opt-ins. 📈 𝗨𝗻𝗶𝗳𝘆𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝘁𝗼 𝘂𝗻𝗹𝗼𝗰𝗸 𝗮𝗱𝗱𝗿𝗲𝘀𝘀𝗮𝗯𝗹𝗲 𝗮𝘂𝗱𝗶𝗲𝗻𝗰𝗲𝘀. Personalization is huge, with companies investing right now in the infrastructure to responsibly scale. Six of this year's top 10 Transcend integrations, including Adobe Experience Platform, Rokt mParticle, Salesforce, Braze, Intercom and HubSpot, are all key to strategic, AI-driven personalization initiatives. 💼 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗶𝘀 𝘁𝗵𝗲 𝗯𝘆𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗼𝗳 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻. By automating this complex work, our customers reclaimed 40 million working hours for strategic initiatives and realized nearly $1 billion in savings. Thank you to all of our customers and partners on such a monumental year! We can't wait to see what we're able unwrap together next!
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Michael Ni
Constellation Research, Inc. • 5K followers
Constellation Research’s sneak peak at 2026 http://bit.ly/48YJ2MP we @ConstellationR analysts were split on whether we're in an AI bubble or not, recapped 2025 in AI agents, and gave a hint of what's to come in 2026 around decision automation driving decision velocity, the shift to platforms, the impact on the future of work, and exponential efficiency. #CCE2025
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David Kohl
Morgan Digital Ventures • 3K followers
I periodically stumble on Michael Kaminsky's musings, blogs and research, which are consistently well-grounded and seemingly really good science. When I read his most recent article in AdExchanger, his career-long data science track record came through loud and clear. Folks ... AI has tremendous potential. There is no doubt that the advancements over the last few years are nothing short of revolutionary. But buyer beware ... hundreds of "powered by AI" technology companies have popped into the market, but most remain heavy on promise and light on reality. Please don't take this as being a Debbie Downer. Quite the opposite. We are still in the nascent stages of AI in marketing and media. Some use cases are further along than others. But holistically, we still have a long way to go before many of us will clock measurable AI-powered improvements to our businesses. Now is the time for organizations to figure out which business functions will most benefit from AI -- for competitive differentiation, speed, cost reduction -- and gain a comfort level with AI's capabilities to achieve these goals. Start learning the tool-sets so that you can be ahead of the pack when AI shifts from experimental to real utility and value.
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Lenny Murphy
Greenbook • 12K followers
Friday must-read summary and analysis of news and thought leadership for insights leaders. "This week’s news continued clarifying the trend that Agents and AI infrastructure are now maturing into a full ecosystem around measurement, governance, and vertical specialization, even as new research shows just how chaotic and inconsistent AI‑mediated recommendations can be. This week’s stories deepen the ongoing themes of agentic workflows and trust, but add sharper focus on how we track AI visibility, modernize ad and outcomes measurement, and rewire leadership and M&A around AI‑first strategies." https://lnkd.in/eEGTh4ri
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Yoav Goldhorn
Stealth • 958 followers
There are very few things that excite me more than applying my experience in new ways. The Master of Analytics program in NC State University allows me to do just that, applying lessons I've learned in intelligence research to data science. While there are endless topics in #machine_learning I am eager to write about, one topic that felt especially close to heart was how language is shaping our decision-making. In "Why Lingo Matters in Analytics", I chose to tackle one specific aspect of this: how our framing of the data must be as accurate and formal as our actual data. Our framing must be accurate and formal, because all throughout history people were hearing and reading ideas via *words*, not numbers; and thus we evolved to make decisions based on *words* and not based on numbers. When a data analyst/scientist makes a recommendation to a stakeholder, the actionable part is always in the words, never in the numbers. The numbers are there only to show rigor and make our claims verifiable. To quantify. To answer the "how much", but never the "what" or "why". The true benefit of AI is allowing us to reach conclusions much faster (and make funny animal videos faster too, I guess). But to reach these conclusions safely, and make the right decisions, we need the right framing to judge the numbers by. Have a fun read!
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David Eisenberg
LiveRamp • 6K followers
We’re excited to announce a new strategic partnership between LiveRamp and Scowtt AI performance platforms like Meta Advantage+, Google Performance Max, and TikTok Smart+ are transforming how campaigns are executed and optimized. It’s the signals that drive performance. LiveRamp’s data collaboration network enables brands to securely access and activate highest-value signals. With Scowtt, those signals become predictive purchase (conversion values) that flow directly into advertising platforms, enhancing how models learn, optimize, and scale performance. The result: +30% incremental purchases across Google, Meta, and TikTok, turning data connectivity into measurable growth. Excited for what’s ahead w Eduardo Indacochea Eric Schwartz and great work Jimmy Ren Matt Kilmartin !
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Sean M. Kerner
5K followers
I've been a believer in streaming data for a long time...but so much of the AI-data conversation has been about static data. That's now changing and real-time data streaming that bakes in Apache Kafka and Apache Flink from Confluent is bringing the context. "The part that we're unlocking for businesses is the ability to essentially serve that structural context needed to deliver the freshest version," Sean Falconer told VentureBeat.
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Thomas Been
16K followers
The gap between an agentic AI prototype and a production system isn't a model problem. It's a governance problem. Domino Data Lab's Winter Release introduces the first fully governed end-to-end platform for operationalizing agentic AI systems, with a new agentic development lifecycle (ADLC) experience to build, evaluate, deploy, and monitor AI agents. Read the news: https://gag.gl/Tlzr8n
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Andre Christopher Avanessian
RAPP • 1K followers
AI frontier vision models delivered a breakthrough I didn’t see coming. I’ve been prototyping a Databricks native AI pipeline to explore how AI frontier vision models interpret creative assets. Using UC Volumes and multiple model families (GPT‑5, Gemini 2.5, Claude, Llama, Gemma), the workflow converts visuals into structured creative attributes. The most interesting part has been comparing how different models interpret the same asset and what that means for creative intelligence and pattern analysis. Seeing the differences across models surfaced an insight that reshaped how I think about this space. Early signals point to meaningful opportunities for future analytics workflows: ✅ More consistent creative metadata ✅ More interpretable model outputs ✅ More opportunities for pattern discovery Curious what others are seeing as they push deeper into AI vision work. #analytics #ai #databricks #visionmodels #creativeintelligence #machinelearning
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Joann Coffey
KGU International • 2K followers
🤖 The Collusion Boogeyman; Can Performance AI Resist Rage Bait? The #adexchanger explores the complex dynamics of contemporary AI technology and its impact on today's digital landscape. This insightful piece presents an in-depth analysis of the programmatic news, views, and data gaps in CTV. With the increase of AI and Automation in various industries and sectors, it's important that businesses understand how to leverage AI to their advantage. Beyond the hype and controversy, there's the potential for substantial ROI and transformative effects on business operations. Want to learn how Automation and AI could benefit your business? 🔍💡KGUInternational.com is here to help! Offering a deep well of knowledge on these fast-moving technologies, KGUInternational.com can guide your business through the AI landscape, ensuring you make the most out of its potential. If you're interested in exploring how AI can impact your business, feel free to sign up for a free consultation with JC Coffey at: https://lnkd.in/g28iUjVn [https://lnkd.in/g28iUjVn]. Let's reshape the future of your business, together. 💼🚀🌐 #AI #Automation #BusinessGrowth #KGUInternational #DigitalTransformation #LinkedInPost
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Amir Hartman
Studio CX • 13K followers
🔥 AI and CX leaders are no longer experimenting. They are scaling what works Last week, Jeb Dasteel, Michael Hinshaw and I, hosted a deep dive on AI for CX. We expected good discussion. And indeed that's what we got. What we saw instead was a clear shift. Executives are moving from pilots to performance. From playbooks to measurable outcomes. From curiosity to capability. And now the full research and assets are live for you to use. 📊 Explore the full dataset from 200+ organizations Download here → https://lnkd.in/gFttCaMe 🎥 Watch the full webinar and grab the slides Replay + deck → https://lnkd.in/ghimPiHp 💡 What stood out to me • AI in CX is now about real operational gains, not hype • The gap is no longer technology, it is enablement, adoption, and leadership alignment • Teams that win are building internal AI confidence, not outsourcing thinking to vendors • The next wave is already visible: proactive service, adaptive personalization, and early agentic orchestration 👀 If you care about how AI is transforming experience, service models, and customer outcomes, this is worth your time 📥 Download the assets ✔️ Save the frameworks ✔️ Share with your team ✔️ Use the data to sharpen your 2025 CX plan The playbook for AI and CX is not emerging. It is here. And leaders are already separating themselves. #AIforCX #CustomerExperience #CXStrategy #AI #DataAnalytics #WebinarReplay #CXLeaders
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Gaurav Verma
Kanerika Inc • 7K followers
🚀 The gap between enterprise AI hype and reality is MASSIVE. Fortune 500s are spending millions on "AI transformation" projects that never see production. 💸 What eventually ends up happening is that people try to put AI on existing processes instead of designing new AI native processes. Here's what I'm seeing after working with 20+ enterprise clients: 🔥 The Real Problem: • Orgs build AI tools in silos • Internal teams lack deployment expertise. • Beautiful strategy decks ≠ Working solutions • Most projects die in "pilot purgatory" 💡 What Actually Works: ✅ Start small, ship fast ✅ Focus on ONE workflow first ✅ Partner with founders who understand enterprise pain ✅ Measure business impact, not AI accuracy 🎯 The Opportunity: Startups who can bridge this gap are sitting on gold mines. While big corps struggle with internal bureaucracy, nimble founders can: → Understand specific use cases deeply → Ship solutions that actually integrate → Scale what works, kill what doesn't ⚡ Bottom line: Don't chase the AI hype wave. Chase the implementation gap. That's where the real money is. 💰 What's your take? Are you seeing similar patterns in your industry? #AI #EnterpriseAI #Startups #B2BSaaS #TechTrends #Innovation #DigitalTransformation #Entrepreneurship
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