---
title: "AI"
description: "492 posts about AI, summarised, each linking to the original."
---

# AI
> 492 posts about AI, summarised, each linking to the original.

## Articles

### [How we turned support into a revenue engine at Intercom](https://yomu.fyi/post/how-we-turned-support-into-a-revenue-engine-at-intercom.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Franka Martinovic
- Published: May 8, 2026
- Type: Problem & solution

After Fin improved resolution rates and efficiency at Intercom, support gained capacity and began testing a consultative role focused on customer value and company goals. The team generated campaign ideas through a support town hall, then assigned a small volunteer group to proactively help self-serve customers with Fin trials, optimization, and adoption. Volunteers were trained to probe customer context rather than only resolve immediate issues, while sales and success teams provided guidance on timing and outreach. Over six months, engaged accounts grew roughly twice as fast in usage and expansion as accounts contacted without a response; the team also tracked resolutions, automation, and influenced expansion ARR. The initiative expanded into cross-functional partnerships and a ~16-person volunteer group, positioning support as a contributor to adoption, retention, and expansion revenue.


### [Why talent transformation is the missing focus of enterprise AI](https://yomu.fyi/post/why-talent-transformation-is-the-missing-focus-of-enterprise-ai.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Christy Seto, Pratyarth Rao
- Published: May 7, 2026
- Type: Announcement

Enterprise AI adoption is presented as a talent problem as much as a technology or strategy problem: insufficient worker skills are identified as the biggest barrier to integrating AI into work, while hiring alone cannot fill demand. The proposed response is continuous upskilling for both technical practitioners and line-of-business users, replacing one-time enablement with learning tied to current work and changing platform capabilities. Databricks Academy Pro is an annual, per-seat subscription combining self-paced courses, weekly live reviews, unlimited public instructor-led classes, hands-on labs in hosted Databricks environments, and one certification exam voucher per user each year. Starter, Growth, and Enterprise tiers add private online community access, a custom Academy portal, and a designated Talent Transformation program manager as seats increase. The offering is available today, alongside private instructor-led training and standalone certification vouchers.


### [The AI scaling gap hiding in digital native companies](https://yomu.fyi/post/the-ai-scaling-gap-hiding-in-digital-native-companies.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Madelyn Mullen
- Published: May 5, 2026
- Type: Explainer

An Economist survey of 1,220+ executives across eight industries finds that digital native companies lead in ambition and breadth of AI deployment, but not in full operational maturity. At 18%, they are the most likely group to prioritize embedding AI across core processes at scale, and nearly 92% report AI ROI ahead of plan. Yet they lead fully embedded AI—defined as use by 100+ users, SLAs, and performance and impact monitoring—in only one of eight functions, R&D/product development; they rank seventh in finance and sixth in operations and supply chain. Telecom, media and entertainment, manufacturing, and energy outperform them in selected functions despite lower stated scaling priority. The proposed response is to build shared, production-grade foundations for data, governance, workloads, models, agents, and applications, with security, lineage, monitoring, and performance measurement treated as reusable capabilities.


### [Peril predicts: Precision payouts for a volatile world](https://yomu.fyi/post/peril-predicts-precision-payouts-for-a-volatile-world.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Anindita Mahapatra, Timo Roest, Justin Monaldo
- Published: May 5, 2026
- Type: Explainer

Parametric insurance pays automatically when objective thresholds—such as wind speed, rainfall, or earthquake magnitude—are met, replacing lengthy loss assessments with event-based payouts. Modern catastrophe modeling combines geospatial data, weather observations, engineering insights, and historical loss records to estimate extreme-event probability and impact and define reliable triggers. Operationalizing these programs requires near-real-time processing of satellite imagery, weather feeds, exposure datasets, and model outputs. Databricks’ Geospatial Lakehouse unifies those sources on Delta Lake while Spark runs spatial joins and catastrophe modeling pipelines. When thresholds are crossed, the system identifies eligible policies, calculates tiered payouts, and surfaces results through dashboards, Lakehouse Apps, and Genie; aerial imagery and multimodal AI can support damage validation and fraud detection, while Unity Catalog governs access and Delta Sharing supports controlled data exchange.


### [The foundation of AI scalability: One team, one platform, one operating model](https://yomu.fyi/post/the-foundation-of-ai-scalability-one-team-one-platform-one-operating-m.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Aly McGue
- Published: May 4, 2026
- Type: Explainer

Albertsons Companies describes a centralized AI strategy for scaling decisions across merchandising, labor, supply chain, and customer experience across approximately 2,300 stores. The model combines one central AI core, the Databricks Data + AI Platform, and a shared operating model spanning data engineering, ML, governance, and analytics. Reusable ingestion pipelines, templates, feature-store patterns, model monitoring, performance observability, and governance wrappers support local execution, while a company-wide governance committee sets shared standards. Albertsons reports accepting 1.38 million lines of AI-generated code in nine months, with more than 90% of engineers using AI tools, and it provides low-code dashboards, prompt libraries, and conversational agent generation for nontechnical teams. Success is measured through reuse rates, time to deployment, responsible AI compliance, and business outcomes linked to AI uplift, with initiatives required to demonstrate impact before scaling.


### [Building a new enterprise AI services company](https://yomu.fyi/post/building-a-new-enterprise-ai-services-company.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: May 4, 2026
- Type: Announcement

Anthropic, Blackstone, Hellman & Friedman, and Goldman Sachs announced a new AI services company that will help mid-sized organizations bring Claude into core operations. The firm will combine its engineering team with Anthropic Applied AI engineers to identify high-impact opportunities, build custom Claude-powered systems, and support customers over the long term; it is backed by additional alternative asset managers including General Atlantic, Leonard Green, Apollo Global Management, GIC, and Sequoia Capital. Engagements will start with a small team working with customer staff, then tailor systems to existing workflows, such as documentation, medical coding, prior authorizations, and compliance reviews in healthcare. The company will also join Anthropic’s Claude Partner Network, extending delivery capacity alongside systems integrators that serve large enterprises.


### [LLM Vs AI: A Practical Guide to Differences, Use Cases, and Tools](https://yomu.fyi/post/llm-vs-ai-a-practical-guide-to-differences-use-cases-and-tools.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: May 1, 2026
- Type: Explainer

This guide distinguishes artificial intelligence, a broad field covering systems that perform tasks associated with human intelligence, from large language models (LLMs), specialized generative AI models for language and code. It places LLMs within generative AI and deep learning, and explains how transformer self-attention processes tokens while training data, parameter scale, and context windows shape capability and limitations. Examples include content drafting, code generation, translation, customer-service chatbots, sentiment analysis, extraction, summarization, and tool-connected agentic workflows, while traditional machine learning remains suited to structured labels and numeric predictions. It recommends retrieval-augmented generation, human review, bias testing, privacy controls, and evidence-based pilots with defined workflows, metrics, budgets, realistic data, and logged outputs.


### [AI Applications: Tools, Use Cases, and Platforms](https://yomu.fyi/post/ai-applications-tools-use-cases-and-platforms.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: May 1, 2026
- Type: Explainer

The guide maps AI applications for data scientists, machine learning engineers, and technical leaders, covering predictive AI, generative AI, conversational AI, and autonomous agents across consumer, developer, and enterprise settings. It distinguishes consumer-facing tools from developer platforms and describes production concerns including model lifecycle management, vector search, data lineage, deployment, monitoring, governance, and evaluation. Generative systems create text, images, code, audio, and video from prompts, while large language models and mixture-of-experts architectures are presented as important foundations for enterprise applications; open models offer control over weights, governance, and deployment. The guide recommends defining use cases, assessing data readiness, and building privacy, bias-auditing, and monitoring controls before production, while noting that agents coordinate multi-step workflows across tools, APIs, and databases.


### [Agentic AI vs Generative AI: Comparing Autonomy, Workflows, and Use Cases](https://yomu.fyi/post/agentic-ai-vs-generative-ai-comparing-autonomy-workflows-and-use-cases.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: May 1, 2026
- Type: Explainer

Agentic AI and generative AI differ primarily in whether a system autonomously pursues a multi-step goal or produces content in response to a prompt. The post defines agentic systems through a perceive-plan-act cycle: agents maintain memory and state, decompose goals, call tools or sub-agents, evaluate conditions, and recover from errors, while generative AI typically performs bounded, reactive inference. It presents workflow examples such as sales follow-up and market-intelligence summarization, showing how APIs connect agents to CRMs, databases, communication platforms, and news services while LLMs provide text generation or reasoning at individual steps. RAG can ground generative outputs in external knowledge, but agentic deployments add operational concerns around repeated inference loops, human oversight, provenance logging, and access controls. The conclusion recommends choosing by task structure: generative AI for single-turn creation or summarization, agentic AI for autonomous coordination, and both together for complex enterprise workflows.


### [Unlocking SAP business context in Databricks with semantic metadata Delta Sharing](https://yomu.fyi/post/unlocking-sap-business-context-in-databricks-with-semantic-metadata-de.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Akram Chetibi, Katie Cummiskey, Moe Derakhshani, Abhijit Chakankar
- Published: Apr 30, 2026
- Type: Announcement

SAP Business Data Cloud now offers generally available semantic metadata synchronization with Databricks Unity Catalog for mounted SAP BDC Delta Shares. When a table is accessed, Unity Catalog receives table-level business-friendly display names, descriptions, context, and governance tags, while SAP BDC remains the single source of truth and subsequent changes are reflected. The capability builds on BDC Connect and Delta Sharing, allowing governed SAP data products to be discovered and combined with other enterprise sources without recreating business context or governance separately. Column descriptions, table relationships such as primary and foreign keys, and PersonalData namespace tags give Databricks AI Assistant and AI/BI Genie explicit context for natural-language questions and join-ready queries. The stated result is more understandable, discoverable, and AI-ready SAP data, with automated classification signals supporting compliance, access control, and responsible AI.


### [Shipping faster isn't learning faster](https://yomu.fyi/post/shipping-faster-isn-t-learning-faster.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Madelyn Mullen
- Published: Apr 30, 2026
- Type: Problem & solution

Product organizations can ship features in days while taking weeks to understand their behavioral impact, because fragmented analytics stacks depend on analysts, BI expertise, or SQL skills. The post argues that this architectural bottleneck slows the insight-to-ship cycle, causing roadmap decisions to rely on instinct, anecdotes, and lagging indicators. It presents Databricks AI/BI Genie as a conversational interface to event-level behavioral data, with experiment integration, cohort analysis, and product-specific growth-metric definitions. According to the post, product leaders can ask questions in plain language without filing analyst requests, while governed data access supports faster follow-up and feature-impact decisions. It reports that Genie users across 3,300+ Databricks customers cited a 49% productivity gain, 41% faster speed to market, and 5x faster ad-hoc analysis, though these are reported customer results.


### [Why your OEE dashboard is lying to you](https://yomu.fyi/post/why-your-oee-dashboard-is-lying-to-you.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Caitlin Gordon
- Published: Apr 30, 2026
- Type: Problem & solution

Manufacturing OEE dashboards can look healthy while omitting recurring downtime that supervisors recognize, because relevant facts are split across SCADA, MES logs, maintenance tickets, and shift reports. OEE combines Availability, Performance, and Quality, but extracting those inputs often requires SQL or an analyst, delaying root-cause analysis after throughput drops. Databricks Genie is presented as a conversational AI layer over a unified data platform that leaves MES and SCADA in place while allowing business leaders to ask plain-language questions such as OEE by line against maintenance windows. Its semantic-layer awareness maps terms to actual fields, while governed access and logged, source-cited answers support different visibility levels and traceability. The proposed shift is from static reporting to faster operational questioning about production status, forecast risk, line performance, and quality signals.


### [The turbine that tried to tell you it was failing](https://yomu.fyi/post/the-turbine-that-tried-to-tell-you-it-was-failing.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Caitlin Gordon
- Published: Apr 30, 2026
- Type: Problem & solution

Gas turbines produce millions of daily readings—vibration, temperature, pressure, flow rates, and electrical output—but maintenance teams may learn about warning signals only when an unplanned outage occurs. The post argues that predictive maintenance has struggled less because of model capability than because decision-makers lack fluid access to model findings and operational context. Databricks Genie provides a conversational interface to sensor, maintenance, dispatch, cost, and regulatory data, allowing questions about elevated vibration against maintenance baselines and timing maintenance around outage cycles. It is presented as decision support, not automation, with time-series analysis, maintenance history, integrated costs, and compliance context helping asset managers investigate fleets and act with more confidence.


### [Companies winning with AI built the data layer first](https://yomu.fyi/post/companies-winning-with-ai-built-the-data-layer-first.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Aly McGue
- Published: Apr 29, 2026
- Type: Explainer

Trinity Industries’ experience is presented as a case for treating the data layer, rather than models or dashboards, as the foundation of enterprise AI. The railcar manufacturer migrated 95% of its enterprise data to a single Databricks lakehouse, adopted Medallion architecture, moved transformations upstream, and retired legacy dashboards containing nearly 600 measures. That consolidation supports streaming ETA predictions, procurement agents, and Genie conversational analytics: the ETA model is reported as 50% more accurate than industry ETAs, agents helped increase on-time material delivery by 15%, and Genie handles more than 1,000 questions monthly. The migration took close to a year, followed by six to eight months of additional work, but the account argues that trusted, standardized data enables experimentation, automation, and broader employee access to analysis.


### [Built in, not bolted on: What AI-native actually means in cybersecurity](https://yomu.fyi/post/built-in-not-bolted-on-what-ai-native-actually-means-in-cybersecurity.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Aly McGue
- Published: Apr 28, 2026
- Type: Explainer

The discussion defines AI-native cybersecurity applications as systems architected with intelligence at their core, rather than traditional products with AI added later. It links tool sprawl to slower threat detection and response and argues that proprietary, context-rich security telemetry is the foundation for adaptive defense. Barracuda uses Databricks to consolidate fragmented data, normalize schemas, support real-time streaming detection, run ML operations through MLflow, and power natural-language log search across billions of security events with strict data isolation. The work began by defining customer outcomes, then progressed through incremental delivery; normalized data enabled models and agents to use cross-domain context. This approach extended across WAF-as-a-service, automated configuration, API security, and bot protection while shared outcomes aligned product, data science, engineering, and business teams.


### [Powering KPMG UK Audit's AI future with Databricks](https://yomu.fyi/post/powering-kpmg-uk-audit-s-ai-future-with-databricks.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Mark Wallington, Greta Nasai
- Published: Apr 27, 2026
- Type: Problem & solution

KPMG UK is evolving its Audit data platform with Databricks to support AI-enabled analytics while preserving governance, rigour, and professional standards. The program converges structured data, advanced analytics, and AI on a unified cloud-native Lakehouse, with Databricks SQL replacing core SQL Server workloads and Delta underpinning the platform. Lakebridge assessed migration complexity, while Databricks-hosted large language models including Claude Sonnet and Genie Code helped convert T-SQL, refactor stored procedures, modularize queries, and suggest Delta-based optimizations under engineer review. This reduced refactoring time by around 60% and enabled modernization of more than 400 scripts and stored procedures in roughly three months. Databricks SQL Serverless provides elastic compute for spiky workloads, while Genie offers traceable, version-controlled SQL and Delta Sharing supports governed data exchange.


### [For Teams – Tools & craft – Notion](https://yomu.fyi/post/for-teams-tools-craft-notion.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Published: Apr 27, 2026
- Type: Explainer

The page presents a collection of Notion content about teams, tools, workplace practices, and the use of AI in organizational work. Its entries cover Custom Agents for automating repetitive busywork, teamwork skills, launch orchestration, and the changing role of team size. Several pieces focus on disconnected company knowledge, describing Notion AI as a way to bring information together and turn knowledge into action, while other entries examine AI operating systems and workflows. The page also includes survey-based perspectives from Notion, including research involving more than 3,000 global knowledge workers, 85-plus interviews, and 650 European business leaders, alongside customer stories from companies such as Merge, Ramp, Chronicle, Toyota, Affirm, and Mercury.


### [OpenAI GPT-5.5 + Codex, now available and fully governed on Databricks](https://yomu.fyi/post/openai-gpt-5-5-codex-now-available-and-fully-governed-on-databricks.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Patrick Wendell, Hanlin Tang, Ahmed Bilal, Arnav Singhvi, Ivan Zhou, Harish Gaur
- Published: Apr 24, 2026
- Type: Announcement

Databricks announces native support for OpenAI’s GPT-5.5, making it available for coding workflows with Codex, enterprise agents, document pipelines, and data-driven employee workflows. Access is governed through Unity AI Gateway, which provides permissions and rate limits, configurable guardrails for PII, prompt injection, and content safety, MCP tool-call auditing, failover, and request-level observability for model and Codex interactions. The announcement describes GPT-5.5 use with Genie for natural-language analytics, Agent Bricks Custom Agents for multi-step workflows, and Lakeflow Spark Declarative Pipelines for document-focused GenAI ETL involving summarization, extraction, and classification. GPT-5.5 is available on Databricks across AWS, Azure, and GCP, with entry points including AI Playground, governed Codex endpoints, Agent Bricks, and the Foundation Model API.


### [Guide to BI Reporting and Maximizing Intelligence Effectiveness](https://yomu.fyi/post/guide-to-bi-reporting-and-maximizing-intelligence-effectiveness.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Apr 23, 2026
- Type: Explainer

Business intelligence (BI) reporting turns data from operational systems and analytical repositories into structured reports, dashboards, and visualizations for organizational decision-making. The guide explains managed and ad hoc reporting, then traces a typical workflow through data sources, ETL, deduplication, normalization, validation, data modeling, visualization, and automated distribution. It compares established platforms such as Microsoft Power BI, Tableau, SAP BusinessObjects, Qlik, and Looker with newer AI-native tools, while noting evaluation criteria including connectivity, scale, governance, usability, and cost. It also addresses dashboard overload, stale semantic models, and specialist bottlenecks, and cites reports of 10x faster query creation from AI-assisted natural-language querying alongside recommendations to start with one decision, one primary source, and three to five KPIs.


### [Announcing the Public Preview of Lakeflow Designer](https://yomu.fyi/post/announcing-the-public-preview-of-lakeflow-designer.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Jason Messer, Emanuel Zgraggen, V Maharajh, Matt Jones, Tracy Yang
- Published: Apr 23, 2026
- Type: Announcement

Databricks announces the Public Preview of Lakeflow Designer, a visual, no-code, AI-native experience for data preparation and analytics built directly into the Databricks platform. It targets analysts, domain experts, and other less technical users with drag-and-drop workflows and natural-language authoring, while representing each transformation as an operator with previews. Genie Code can use Unity Catalog metadata, descriptions, lineage, popularity, and example queries to generate or modify workflows, inspect transformation results, and iterate when needed. Every visual transformation produces production-ready Python code that can be reviewed, versioned in Git, and scheduled through Lakeflow Jobs, while users pay for compute rather than per-user licenses. The preview is available in all workspaces, although an administrator may need to enable Visual data prep in the preview portal.


[Newer posts](https://yomu.fyi/topic/ai/page/18.md) · [Older posts](https://yomu.fyi/topic/ai/page/20.md)