FeatureByte

Predictive AI just took a leap forward. FeatureByte – a Gooder AI strategic partner – has advanced the state-of-the-art for predictive AI model development. Its groundbreaking approach supercharges the discovery of predictive features within your data by leveraging the power of LLMs. Your predictive models just got a whole lot better. But what's the true value of a better model? What does improved prediction deliver – in business terms such as profit, cost savings, returns, or other KPIs? To answer this question – and ensure that better tech means more value – FeatureByte and Gooder AI have partnered to offer a focused professional-services engagement called Model Reality Check. https://lnkd.in/gD7qQzPb

Delays are eroding agentic AI ROI. New research from FeatureByte, Databricks, and CIO Online shows it takes many enterprises up to six months to move an agentic AI use case from concept to production. 92% of leaders are concerned about these delays. More than half cite rising costs, and nearly half point to diminished ROI. Read the blog to explore the hidden cost of AI delays and how automating the full data science lifecycle helps teams move faster and protect ROI: https://lnkd.in/g_x74VKe 

What’s cutting through the noise in AI right now? This week’s PredAI Pulse highlights why structured data, analytics discipline, and predictive AI are quietly driving real enterprise value: 1️⃣ From Text To Tables: Why Structured Data Is AI’s Next $600 Billion Frontier by Rocio Wu Dianoux via Forbes As GenAI captures attention, the real enterprise opportunity lies in structured, tabular data where business decisions actually live. This piece reinforces why organizations that operationalize predictive signals from core data will have the advantage. https://lnkd.in/eyf5UVrT 2️⃣ Why Foundation Models for Tabular Data Need Agentic Data Science by Xavier Conort Foundation models alone aren’t enough. In this article by FeatureByte’s CPO, Xavier argues that agentic workflows are essential to turn tabular models into production-ready systems. https://lnkd.in/ggk73ZNm 3️⃣ Why Your Digital Investments Aren’t Creating Value by Prabhakant Sinha, Arun Shastri, and Sally Lorimer via Harvard Business Review The article highlights how value emerges when reusable analytical models are embedded directly into day-to-day decision-making. Without operational integration, even significant digital investments struggle to translate into measurable business impact. https://lnkd.in/ecdph7uD 4️⃣ Why CIOs need analytics capability to scale AI by Dr Ranko Cosic via CIO Online Scaling AI depends on strong analytics foundations, data readiness, and operational maturity. CIOs who invest in analytics capabilities today are better positioned to industrialize AI tomorrow. https://lnkd.in/g8DJHGbG 5️⃣ Predictive AI Thrives, Despite GenAI Stealing The Spotlight by Eric Siegel via Forbes While GenAI dominates headlines, predictive AI continues delivering measurable business impact where it matters most. This piece underscores that forecasting and decision intelligence remain core to enterprise AI success. https://lnkd.in/gJD9rBts

Most ML projects don’t stall because the models are weak. They stall because the value was never made clear. In the latest episode of A Tale of Two AIs, Razi Raziuddin talks with Eric Siegel, CEO of Gooder AI, about why evaluation ≠ valuation in enterprise AI. If predictive AI isn’t tied to business impact upfront, it rarely gets prioritized or deployed. 🎙️ Listen in here: https://lnkd.in/gtE3mXAz

"Any data science team that’s not thinking about automation is just going to be left behind." Razi Raziuddin joined Greg Michaelson and Zerve AI to explore why this is the case. Manual data science isn't feasible at scale in the age of AI: "It’s just like coding, if you’re not using one of the coding assistants or LLMs to help you code better, faster…it’s not an option anymore." Tune into the conversation ⬇️

How do leading teams turn predictive models into real-world impact today? In the episode of A Tale of Two AIs, Razi sits down with Benjamin J. Miller, VP of Applied AI at Forian, to explore how his team designs, builds, and scales predictive models that actually drive business outcomes. They unpack what it means to move toward agentic systems, how to structure data science work for impact, and the patterns that separate successful AI initiatives from stalled experiments. 🎙️ Tune in here, or wherever you listen to podcasts: https://lnkd.in/gmee29dW

It’s hard to know how good your team’s models actually are. The FeatureByte Data Science Challenge is a rare opportunity to make sure they’re delivering the most value for your organization. Benchmark your models with zero financial commitment—and have a shot at $10k. Need help convincing your manager? Shoot us an email challenge@featurebyte.ai with the subject line “What’s in it for my org?" https://lnkd.in/g4-SWN79

Last chance to register for the FeatureByte Data Science Challenge info session, happening tomorrow! There’s $10k on the line for Challenge registrants whose models beat FeatureByte. Join the session to get answers to questions like:  - What kind of data and models are eligible for the Challenge?  - What is the model evaluation process like?  - How can I convince my manager to allow me to participate? Register to join live & submit your questions for Q&A: https://lnkd.in/gTK-gd-8 

What does data science look like in an agentic world? In our new blog, we walk through the fundamentals of Data Science Agents, what today’s early agents can and can’t do, and how deeper business context becomes essential as agentic workflows expand. You’ll also see how predictive models play a central role in helping these systems make smarter decisions. If you’re exploring where data science is headed and how agentic AI will influence real-world workflows, this is a good place to dive in. Read the full post here: https://lnkd.in/g-Ti8jR9

FeatureByte improves models by 4-15% in 92% of customer tests. Think your models can outperform a Data Science Agent? Now’s your chance to put them to the test ⬇️ Who: You and up to 5 teammates What: Pit your best production model against FeatureByte When: Register by January 16th, the Challenge kicks off in February  Where: https://lnkd.in/g4-SWN79  Why: Benchmark your models, earn serious bragging rights, and—we almost forgot—compete for a $10k cash prize Register now: https://lnkd.in/gYn8fbwF