Sparq

Dr. Ram Bala says most enterprise AI evaluations are grading the wrong test. Bala, Founder & Chief AI Scientist at samvid.ai and Professor of AI at Santa Clara University, argues that benchmarks built for solving math problems don't tell you whether an AI system works for a financial analyst or a lawyer. In Episode 4 of the AI Friction Files, he breaks down the ABC of enterprise AI evaluation (Accuracy, Brevity, Consistency), why data silos are the top obstacle to building the context layer AI agents need, and why enterprise AI automates specific tasks inside a job while leaving the role intact, which means the total volume of work keeps climbing rather than shrinking. The close: as AI takes over the grunt work, human judgment over the story being told becomes the most valuable skill left. "If AI can do the actual grunt work, then it's all about the storytelling." 🎙️ Dr. Ram Bala, Founder & Chief AI Scientist, Samvid.ai | Professor of AI, Santa Clara University 📍 IIT2026 | Long Beach, CA Catch Episode 4 and the rest of the AI Friction Files series: https://lnkd.in/eQtWJUsW Sujatha Padmanabhan (Su), MBA #AIFrictionFiles #EnterpriseAI #AgenticAI #SemanticLayer #TeamSparq

“85% is good enough.” Sparq CEO Ingrid Curtis joined the Innovators Inside podcast and made the case for putting AI products in front of users sooner. Endless iteration can become a form of “doom scrolling,” with teams continuing to build long after they have something useful enough to test. The episode also explores how Sparq helps clients choose a high-impact business problem and take a focused solution into production within 90 days. Listen here: https://lnkd.in/eVFtx5pt

There's a version of this story where AI adoption is easy. Our CEO Ingrid Curtis told the real one to Ian Bergman on the Innovators Inside Podcast: OpenAI and Anthropic are building their own services firms because consumption isn't coming fast enough on its own. Capital-intensive businesses don't build consulting arms for fun. They build them because demos don't ship themselves into production. Hear the full conversation: https://lnkd.in/gVGwCN9D

Three roles open at Sparq right now, all focused on making AI production-ready instead of demo-ready. A Principal Platform Engineer (Remote LATAM) to build the control plane wiring our LLM platforms, APIs, and dev tools together—audit logging, approval gates, kill switches, the works. 🔗 https://lnkd.in/gdU7YSzb A Senior Cloud Engineer (Remote U.S.) to build governance-as-code across AWS and Azure, so access controls and compliance don't depend on someone remembering to check. 🔗 https://lnkd.in/gf8mQ2ZG A Senior Elixir Developer (Contractor, Remote Uruguay, Costa Rica, or Colombia) to build event-driven systems on Kafka and PostgreSQL that hold up under production load. 🔗 https://lnkd.in/gkDWAv4q Learn more and apply via the links above.

An interesting read from Meredith Shubel at The New Stack on Anthropic’s reflection dashboard and the tension behind using AI to assess our AI use: https://lnkd.in/eRctgg3b At Sparq, we see value in any prompt that makes developers pause and examine their habits. The most useful insights go beyond activity counts and reveal which workloads we overlook and what our usage says about the role we think we play. A vendor dashboard can sharpen judgment, but it can't replace it. Engineering leaders still need to guard against cargo culting, vendor lock-in, rising costs, and engagement metrics that distract from customer outcomes. Mirrors are not without opinions. The opportunity lies in using them to reclaim our agency and recognize the work where human judgment delivers greater value.

A dashboard and an AI agent walk into a bar. They order the same drink, split the same check, and somehow can't agree on what they owe. In this month's Rewired, we're getting into the semantic layer: the piece of architecture that decides whether "revenue," "active customer," or "on-time" means the same thing everywhere it gets asked. Skip it, and every AI initiative eventually rediscovers an argument your teams have been having for years. Also inside: what MIT and AtScale just confirmed about why this argument is now a boardroom problem, the number that shows exactly what a shared definition is worth, and a diagnostic for spotting the gap before an agent finds it for you. Read the full edition below 👇 Ingrid Curtis | Barry Newton | Brian Carter | Christa Patrylak (Creeger) | Derek Perry | Jason Paru | Robin Stenzel | Scott Monnig

AI is no longer competing for attention. It’s competing for budget. Every AI investment now needs to demonstrate measurable business value. That’s why we published From AI Experiments to Earnings Impact. The paper examines how organizations can move beyond implementation and build the governance, accountability, and measurement needed to understand AI’s impact over time. Get the whitepaper here: https://lnkd.in/dyHU_yxj

We're hiring for three roles that will shape how Sparq builds and delivers. Principal Data Architect (Snowflake) - Remote U.S: Own medallion and lakehouse architecture, governance, and RBAC for regulated data at enterprise scale. https://lnkd.in/gpb67P_S Principal Data Architect (Azure) - Remote LATAM: Architect lakehouse and semantic layers on Microsoft Fabric, and lead migrations off legacy SQL Server environments. https://lnkd.in/gY_iXB_u Associate Engineering Manager, The Shop - Remote U.S.: Run the weekly rhythm of Sparq's proving ground, from Monday kickoff to Friday demo. https://lnkd.in/gWfnerjt If you're someone who wants real ownership over enterprise data architecture, or who makes chaos manageable and teams sharper, we're into that. #Hiring #DataArchitecture #Snowflake #Azure #TheShop #TeamSparq

Enterprise confidence in AI is growing. According to KPMG’s quarterly AI research, organizations with clearly defined accountability for AI outcomes are three times more likely to achieve ROI. That shouldn’t come as a surprise. As AI moves from experimentation into day-to-day operations, success depends on knowing who owns the outcome, how success will be measured, and what happens after deployment. At Sparq, we often see the conversation ending when an AI solution goes live. But that's when the work should begin. We’re seeing the greatest success in organizations that treat AI as an ongoing business capability, with governance, measurement, and continuous improvement built in from day one. How is your organization measuring AI success beyond deployment? https://lnkd.in/e22Has8y

Ranveer Chandra, Vice President of Frontier Tuning at Microsoft, has a name for why enterprise AI performance feels inconsistent: "jagged behavior." It's a term he borrows from Andrej Karpathy's observation that a model can perform like a PhD student on one task and a ten-year-old on the next. Generic models are trained on general, synthetic data. That doesn't transfer cleanly once a model hits a specific enterprise's workflows. Ranveer leads the team building the product for exactly that problem: giving every enterprise its own personalized model, tuned on that enterprise's own data and highest-value workflows. In Episode 3 of the AI Friction Files, Ranveer covers what it takes to tune a model to your enterprise: eval sets and rubrics, hill climbing with harness engineering, and the three phases every enterprise moves through on the way to what Microsoft calls a "frontier firm." The close: most organizations are still in phase one, where an agent answers a question and stops. Phase three is when every employee manages a team of agents, or what Satya Nadella calls "macro delegation, micro steering." "One can do the job of 10. But ten can do the job of 100." 🎙️ Ranveer Chandra, Vice President of Frontier Tuning, Microsoft 📍 IIT2026 Global Conference | Long Beach, CA Catch episodes 1 and 2 of the AI Friction Files series here: https://lnkd.in/gRtQea_S Sujatha Padmanabhan (Su), MBA #AIFrictionFiles #AgenticAI #EnterpriseAI #TeamSparq