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79 posts about Automation. Every summary links to the original.
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Apache Camel is shown as the integration path that brings application actions into one request workflow. The diagram clarifies real-time versus scheduled ingestion, mapping, merging, authorization, and audit.
Adoption moves through experimentation, measurement, and reinforcement loops. Learn how leading companies build momentum from pilot programs into enterprise-wide habits and measurable business impact.
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An analysis of 166 interviews examines how customer service roles, workflows, and team structures changed after adopting Fin or similar AI agents. Using interviews with Intercom customers or prospects, the researchers cleaned conversation transcripts with Python and coded them through the OpenAI API against a closed codebook. Workflow disruption was reported by 94.58% of participants, while 82.53% reported changed responsibilities; 27.71% reported headcount or hiring effects, and 6.02% reported changed team structures or reporting lines. The findings describe hybrid human–AI operations in which AI handles triage, routing, translation, repetitive responses, and some refunds, while people increasingly oversee quality, testing, metrics, and complex or sensitive interactions, although organizational strategies and required skills remain uneven.
The post frames AI as a new “miracle material” that could reshape knowledge work as steel and semiconductors reshaped earlier eras. It argues that coding agents already let one programmer orchestrate several AI coding agents, but broader knowledge work remains constrained by fragmented context across tools and weak verifiability. At the organisational level, it compares AI with steel and steam, suggesting agents could preserve workflow context, surface decisions, and reduce dependence on meetings and hierarchical approvals; Notion is described as using more than 700 agents alongside 1,000 employees for repetitive tasks. At economy scale, it predicts organisations spanning humans and thousands of agents, with continuous workflows and new rhythms, while acknowledging that this transition may be disorienting and requires redesign rather than simply adding chatbots to existing tools.
AI-first customer service makes capacity planning less predictable because automation changes volume, work mix, human responsibilities, and performance over time. The proposed planning model puts automation rate—AI Agent involvement rate multiplied by AI Agent resolution rate—alongside inbound volume, human output for complex remaining work, and occupancy. It recommends ambitious automation assumptions only when backed by named ownership, content and systems investment, work-type targets, and staged improvement, while modeling lower cases closed per person and more time for off-queue work. Finance should treat the plan as a revisable set of assumptions, review it at least quarterly, and avoid cutting headcount before automation is proven. Surplus capacity should be redirected to AI improvement, product feedback, new channels, or proactive customer experience work rather than automatically reduced.
After defining team roles, an operating model is needed to make progress part of customer-support work and keep an AI Agent improving over time. The stated goal is that “the first time you answer a question should be the last,” with each resolution improving the system so fewer issues repeat, quality compounds, and support scales. The post emphasizes clear ownership, risk-aware guardrails, feedback loops, and a culture that values improvement work; it identifies ambiguity as a common cause of performance plateaus. An AI ops lead, often located in support operations or developed from an existing role, owns performance and drives change, illustrated by Dotdigital’s dedicated specialist after resolution volume held at about 2,800 conversations per month for three months; lightweight governance is presented as a way to keep iterating without excessive bureaucracy.
AI performance in customer support can drift without clear ownership, even when the underlying model is capable. This part of a 2026 planning series presents four roles: AI operations lead, knowledge manager, conversation designer, and support automation specialist. The roles cover monitoring and guardrails, structured and compliant knowledge, conversational tone and flow, and reliable action through backend systems, with the AI operations lead coordinating reporting and improvement. The post describes an operating loop in which performance patterns drive knowledge fixes, interaction changes, and expanded automation, while phased ownership moves from part-time assignments to formal roles and hiring. It says dedicated specialization becomes essential when AI handles 50–70% of incoming volume, and frames these roles as the backbone of an AI-first support organization.
Ramp spent the last year building what operations and AI product leader Ben Levik calls an “AI operating system” for the company, with the goal of becoming more productive. Rather than waiting for tools to mature, Ramp promotes a “Builder” mindset and organizes adoption around Prompt, Knowledge, and Workflow. Employees refine vague requests by having AI ask clarifying questions, while a Notion knowledge base connected to other tools uses feedback loops in which AI drafts corrections and humans approve them. AI agents and connectors then automate tasks such as summarizing project updates from five sources and researching prospects for review-ready sales emails. About 90% of 1,200 employees use Notion AI monthly, and Ramp reports shipping 270 features in the first half of 2025, more than during all of 2024, shifting the bottleneck toward choosing what to automate next.
Managing multiple social accounts makes consistency, timing, content mix, and collaboration difficult, so the guide presents a nine-step scheduling process. It recommends auditing active and dormant channels, defining goals and KPIs, studying audience behavior and platform differences, selecting a tool, and building a calendar around content pillars, campaigns, roles, and a two-to-four-week planning horizon. The process then covers batch creation, reusable assets, platform-specific captions, approval workflows, analytics, time zones, and flexibility for timely content; Brandwatch Publish is presented as an example supporting TikTok, Instagram, Facebook, X, LinkedIn, and Pinterest. It concludes that scheduling should create capacity for engagement and strategy without sacrificing authenticity, and advises regular audits, experimentation, and readiness to pause or adapt queued posts. The guidance also warns against excessive promotion, ignoring engagement, identical cross-platform posts, weak visuals, and over-reliance on automation.
Fin 3 is presented as an AI Agent for customer service focused on increasing automation rate by handling complex queries end-to-end across more channels. Procedures combine natural-language instructions with data connectors, conditional steps, code snippets, and approval checkpoints, allowing teams to encode standard operating procedures with varying levels of control. Simulations provide multi-turn testing for different customer segments and scenarios, while Fin Voice, Slack, and Discord expand deployment options. Insights upgrades add CX Score Reasons, Topic Trends, and AI-powered Suggestions that surface quality changes, topic shifts, and content improvements. The announcement says Fin 3’s broader goal is to increase the share of overall support workload it handles, beyond a resolution-rate metric that had reached 66% on average across more than 6,000 customers.
Support leaders are being asked to preserve customer experience and operational performance while becoming AI experts and leading high-lift, cross-functional implementations. Before AI, support was often treated as a cost center; now executives may demand aggressive AI metrics without understanding the resources, risks, and work still required. The proposed response is to harness the change by learning AI deeply, testing multiple vendors through trials, choosing a durable partner, and defining benchmarks and phased implementation plans. Support leaders should also explain why deflecting 50% of inquiries does not justify cutting staffing by 50%, given coverage, service-level redundancy, growth, attrition, and work beyond direct inquiries. The article ultimately argues that visible ownership, concise project management, and weekly or bi-weekly executive updates can reposition support as a value driver rather than a cost center.
Notion’s certified Solutions Partners help organizations replace fragmented tools, connect workflows, and build systems that can scale. At Affirm, Notion State used a five-phase deployment strategy across multiple departments, helping achieve 95% knowledge-management adoption, reduce tool seats, and create a unified workspace. NorthSand helped Toyota’s Future Creation Center add custom automations, approval workflows, and centralized scheduling, making key workflows three times faster and cutting meeting-preparation time in half. MegazoneCloud supported JOYCITY’s migration from Jira, Confluence, and Microsoft by running parallel systems, focusing new projects in Notion, and providing on-site training, while Optemization archived more than 63,000 of Mercury’s pages and established a standardized information architecture. The examples position partner expertise and ongoing support as ways to improve adoption, trust, collaboration, and scalability.
Heroku Postgres 17 is now available, and the platform introduces a simplified, self-service path for upgrading managed databases. The new heroku pg:upgrade:* commands cover preparation, manual execution, cancellation, dry runs, and progress tracking, while email notifications report major stages and detected issues. Essential-tier databases can be upgraded directly with pg:upgrade:run; Standard-tier and higher databases can use a dry run, schedule an upgrade for the next maintenance window, or run it manually, with followers upgraded automatically. The in-place workflow uses an internal replica, leaves DATABASE_URL and other config vars unchanged, and typically reduces downtime to about five to ten minutes. The legacy heroku pg:upgrade command will be deprecated soon, although the traditional follower approach remains available.
eBay has introduced a simplified, mobile-friendly listing flow that integrates its “magical listing” AI technology to reduce the effort required to create high-quality listings. Sellers start with photos and a title, while AI uses image matching and inference to suggest item specifics and categories through guided pages where sellers can review and approve the content. Early testing in the U.K. found a 50% reduction in total listing steps and faster listing times, while eBay says item-specific accuracy will continue improving through updates made during the year. The tool is available in the eBay mobile app for private sellers across all categories in the U.S., U.K., and Germany, and eBay plans to expand it to sellers using business tools.
Okta invites SaaS application builders to Oktane 2024, held at Caesars Forum in Las Vegas from October 15–17, to discuss how identity and user lifecycle platforms can support secure B2B SaaS applications. The announcement frames identity as increasingly complex, with a growing protection surface expected to influence SaaS roadmaps and developer priorities. Attendees can join three breakout sessions, visit the Dev Hub, attend hands-on labs, and connect at a B2B SaaS builders happy hour or the Okta Workflows community meetup. Lab topics include moving Okta app configurations from PowerShell into Terraform, signing users out across apps with one API, and identifying inactive users with Okta Workflows. Some activities require Oktane attendance, while labs have limited, first-come-first-served capacity and the happy hour is invitation-based.
For eBay sellers, social media promotion can make listings more visible, but composing effective captions may be difficult. Its new generative AI feature adds a “Use AI Caption” button in the Social page of Seller Hub, where it drafts item-focused posts with hashtags and emojis. Sellers can ask for rewrites, manually keep or remove those extras, and publish posts containing listing links and item images. The capability is fully available in the US, UK, Germany, and Australia for sharing individual listings, categories, promotions, and stores, while an announced auto-posting option will later automatically publish new listings when they go live. The company reports a positive response from early users.
This tutorial shows how to extend a SaaS Todo application with a weekly organizational report using Okta Workflows, a no-code automation platform. It adds an API service to the application, exposes the local app through a tunnel, and configures a scheduled flow to retrieve all todo items. A helper flow processes the returned list with a Reduce card, carrying accumulated text in a memo field before producing one summary for the Gmail Send Email card. The report is sent automatically by email after the items are combined. The workshop concludes by enabling both flows and testing the automation, while noting that the local setup is for demonstration and that a real-world application would be deployed in the cloud.
This installment in a Devday23 presentation series addresses how to move an enterprise-ready SaaS application beyond its MVP. It focuses on the functionality needed to help an application stand out to enterprise customers, including user provisioning and automation that can scale. The stated goal is to support growth from a working MVP toward enterprise adoption. Aaron Parecki shared material on Okta Workflows, provisioning, SaaS application maturity, and SCIM, with links to a SCIM developer video and Workflow Connector Builder tutorials. The broader series also points to upcoming coverage of authentication mechanisms and deployment automation, positioning enterprise readiness as an ongoing progression rather than a single feature.
What makes a SaaS application ready for enterprise customers when identity is involved? This Devday23 keynote introduces enterprise readiness as a concern spanning application security, user onboarding, scalable authentication, identity, user management, and automation. Arnab Bose, Chief Product Officer for Workforce Identity Cloud at Okta, leads the presentation’s “Why” and “What” questions in a half-day virtual conference hosted at WIC Devday 23. The keynote is positioned as the opening of a series covering enterprise customer expectations, building an enterprise-ready MVP, moving beyond MVP, authentication considerations, user management, and stronger authentication mechanisms; a related “Setting the Bar for Enterprise-Grade Identity” whitepaper provides benchmarks for secure SaaS management.
Incident #2105 began during a production deployment of a Go-based Git server after a configuration variable had been set but not transmitted to production instances. Staging deployment completed successfully before the same change was attempted in production. The deployment sent a new code version to one batch, whose unauthenticated calls to a private API produced HTTP 401 responses that were returned publicly; the author paused before deploying the remaining batches and resolved the issue by synchronizing configuration, killing affected processes, and allowing them to restart. The post argues that the failure was enabled by a process requiring separate, infrequent manual actions rather than being solely an individual omission. Its proposed remedy is to combine configuration synchronization with code deployment, or alternatively warn when deployment begins with unsynchronized variables, supported by a blameless retrospective.
On 27 November 2018, Eric Fung, a Senior Data Scientist, presented “How the Command Line Can Empower You,” a 45-minute talk followed by 15 minutes of questions and answers. The presentation addresses work that often happens outside an IDE, including interacting with APIs, creating screenshots, and massaging data, and shows how command-line tools can simplify, improve, or automate those tasks. Using real-world examples, Fung covers typing less in the terminal, searching files, manipulating images and JSON files, and generating code automatically without point-and-click interaction. The presentation focuses on software for macOS, while noting that Linux and Windows users can benefit because many mentioned tools are cross-platform; the presentation can be watched online and speaker notes can be downloaded.
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