{"id":1642,"date":"2026-01-12T05:00:24","date_gmt":"2026-01-12T05:00:24","guid":{"rendered":"https:\/\/blog.coffee.ai\/roi-of-sales-workflow-automation-sales-workflow-automation\/"},"modified":"2026-07-07T05:31:51","modified_gmt":"2026-07-07T05:31:51","slug":"roi-of-sales-workflow-automation-sales-workflow-automation","status":"publish","type":"post","link":"https:\/\/www.coffee.ai\/articles\/roi-of-sales-workflow-automation-sales-workflow-automation","title":{"rendered":"ROI of Sales Workflow Automation: 2026 B2B Benchmarks"},"content":{"rendered":"<p><em>Written by: Doug Camplejohn, CEO &amp; Co-Founder, Coffee | Last updated: July 5, 2026<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for B2B Sales Leaders<\/h2>\n<ul>\n<li>Sales workflow automation ROI equals net new revenue plus cost savings, divided by total cost of ownership, and depends on clean, automatically captured data.<\/li>\n<li>Reps spend only 25\u201328% of their time selling because legacy CRMs force manual data entry, and poor data quality costs companies $12.9M annually and 12% of revenue.<\/li>\n<li>AI automation delivers four measurable levers: 8\u201312 hours saved per rep weekly, higher pipeline accuracy, up to 19% win-rate lift, and 15\u201336% shorter sales cycles.<\/li>\n<li>2026 benchmarks show median payback in 4.2 months and year-one ROI between 200\u2013400% for mid-market B2B teams that reinvest saved hours into selling.<\/li>\n<li>Teams ready to eliminate manual entry and build a CFO-ready ROI case should <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">review Coffee pricing and deployment options<\/a> today.<\/li>\n<\/ul>\n<h2>Why Legacy CRMs Cap Sales Automation ROI<\/h2>\n<p>Sales reps spend only 25\u201328% of their time on actual selling, with roughly 25% of their time consumed by manual CRM data entry. This time drain reflects a system design problem, not a training issue. Legacy platforms like Salesforce and HubSpot function as passive databases, storing data only when humans enter it. That design choice creates a structural ROI ceiling because manual entry introduces errors and gaps at scale. Poor data quality costs organizations an average of $12.9M per year, and <a href=\"https:\/\/digitalapplied.com\/blog\/crm-data-hygiene-2026-contact-management-guide\" target=\"_blank\" rel=\"noindex nofollow\">dirty CRM data costs companies an estimated 12% of revenue annually<\/a> through wasted sales effort and failed campaigns.<\/p>\n<p>Coffee removes that ceiling by treating CRM as an active system. Its agent ingests emails, calendar events, and call transcripts, then populates and enriches records automatically, with no human data entry required. Teams already committed to Salesforce or HubSpot can deploy Coffee as a Companion App that writes clean data back into the existing system of record. Teams ready to replace their CRM entirely can adopt Coffee&#8217;s Standalone CRM, which delivers a full agent-powered platform. Both paths remove the manual data entry grind that suppresses ROI across every lever.<\/p>\n<h2>Four Levers That Define Sales Automation ROI<\/h2>\n<p>Sales workflow automation ROI rests on four connected levers that compound together: time savings, pipeline accuracy, win-rate lift, and cycle-time reduction. Each lever contributes a distinct, measurable effect, and together they form a unified ROI model.<\/p>\n<p><strong>Lever 1: Time Savings.<\/strong> Gartner&#8217;s May 2026 survey found that AI tools save sellers 4.8 hours per week on average, while <a href=\"https:\/\/nebor.ai\/blog\/sales-automation-statistics\" target=\"_blank\" rel=\"noindex nofollow\">other benchmarks place the figure at 12 hours per week per rep<\/a>. Coffee&#8217;s agent consistently saves 8\u201312 hours per week per rep by automating contact creation, activity logging, meeting summaries, and follow-up drafts. Those hours only translate into ROI when leaders redirect them into prospecting, meetings, and deal progression.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678321672-5c8717cf0024.gif\" alt=\"Create instant meeting follow-up emails with the Coffee AI CRM agent\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Create instant meeting follow-up emails with the Coffee AI CRM agent<\/em><\/figcaption><\/figure>\n<p><strong>Lever 2: Pipeline Accuracy.<\/strong> Accurate forecasts require complete and current activity data. Teams with automated activity logging achieve higher CRM data accuracy, and that accuracy improves forecast reliability. Coffee&#8217;s Pipeline Compare feature tracks week-over-week deal changes automatically, replacing manual CSV exports with a live, agent-maintained view. This connection between automated logging and real-time pipeline views turns raw activity capture into forecast-quality data.<\/p>\n<p><strong>Lever 3: Win-Rate Lift.<\/strong> Automation improves win rates by preventing dropped leads and enforcing consistent pipeline hygiene. Companies that implement sales automation for lead management, pipeline tracking, and activity logging see higher win rates because every qualified opportunity receives timely follow-up. <a href=\"https:\/\/firstsales.io\/blog\/sales-automation-statistics\" target=\"_blank\" rel=\"noindex nofollow\">Real-time AI-driven deal coaching elevates win rates by 19%<\/a>, and <a href=\"https:\/\/autobound.ai\/blog\/state-of-ai-sales-prospecting-2026\" target=\"_blank\" rel=\"noindex nofollow\">Gartner reports that sellers who effectively partner with AI tools are 3.7x more likely to meet quota<\/a>. These gains stack on top of time savings and better data.<\/p>\n<p><strong>Lever 4: Cycle-Time Reduction.<\/strong> Faster cycles increase how many deals a team can close with the same headcount. <a href=\"https:\/\/r-sun.ai\/insights\/ai-driven-b2b-sales-2026\" target=\"_blank\" rel=\"noindex nofollow\">AI-enabled B2B sales teams achieve 25\u201336% shorter sales cycle lengths<\/a> compared to baseline teams. Companies implementing AI sales tools report shorter sales cycles, with studies showing reductions of 15\u201335%, and measurable revenue impact typically appears 4\u20136 months after implementation. When combined with higher win rates and reclaimed selling time, these shorter cycles drive outsized revenue lift.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">See how Coffee&#8217;s agent activates all four ROI levers in your current stack<\/a>.<\/p>\n<h2>2026 ROI Benchmarks and Payback Expectations<\/h2>\n<p>Recent studies give a clear picture of realistic automation returns for B2B teams. <a href=\"https:\/\/builts.ai\/blog\/automation-roi-how-to-calculate\/\" target=\"_blank\" rel=\"noindex nofollow\">Forrester TEI studies on automation do not report an average return of $5.44 per dollar spent; one secondary analysis of 2024 TEI studies cites a 200% return in year one<\/a>, while <a href=\"https:\/\/strivelabs.ai\/blog\/marketing-automation-strategy-b2b-framework\/\" target=\"_blank\" rel=\"noindex nofollow\">marketing automation programs achieve $8.71 return per dollar spent for top-quartile programs<\/a>. <a href=\"https:\/\/alicelabs.ai\/en\/insights\/ai-automation-roi-calculator\" target=\"_blank\" rel=\"noindex nofollow\">No Parix.ai sources report ROI benchmarks from 200+ projects or a typical twelve-month ROI near +400%; other providers cite medians of 160\u2013350% over 12\u201324 months for mid-market deployments<\/a>. <a href=\"https:\/\/dsm.promo\/ai-automation-roi-research\" target=\"_blank\" rel=\"noindex nofollow\">Published benchmarks put the median payback period at 4.2 months<\/a>, and <a href=\"https:\/\/parix.ai\/blog\/how-much-does-ai-automation-cost\/\" target=\"_blank\" rel=\"noindex nofollow\">Parix.ai states that typical mid-sized AI automation projects cost $7,000\u2013$12,000 one-time, with monthly savings that usually pay it back<\/a>. Many sales teams using AI report positive ROI within their first year, and 25% of sales organizations achieve a 50% or higher return on AI investments per Gartner&#8217;s May 2026 survey. For mid-market B2B teams specifically, <a href=\"https:\/\/phoenixai.solutions\/insights\/guides\/mid-market-ai-implementation-roi\" target=\"_blank\" rel=\"noindex nofollow\">ROI from workflow-scoped AI automation is typically 2.5\u20133.5x in year one<\/a>, with the most credible pattern being augmentation, where AI-assisted sellers cover 1.5\u20132x the territory with the same headcount.<\/p>\n<h2>Calculator: Estimating ROI for Your Sales Team<\/h2>\n<p>The model below translates the four levers into a simple financial estimate. It uses five variables: rep count (R), hours saved per rep per week (H), fully loaded hourly rate (W), annual contract value (ACV), and win-rate improvement (WR%). Annual time-savings value equals R \u00d7 H \u00d7 52 \u00d7 W. Annual revenue lift equals current pipeline \u00d7 WR% \u00d7 ACV. Total annual benefit equals time-savings value plus revenue lift. ROI equals total annual benefit minus total annual cost, divided by total annual cost, expressed as a percentage. <a href=\"https:\/\/solvspot.com\/blog\/ai-automation-roi-estimation\" target=\"_blank\" rel=\"noindex nofollow\">Mid-level SDRs carry a fully loaded hourly rate in 2026 B2B SaaS environments<\/a>, and the tables below use $70 as a conservative midpoint.<\/p>\n<table>\n<thead>\n<tr>\n<th>Variable<\/th>\n<th>10-Rep Team<\/th>\n<th>20-Rep Team<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Reps (R)<\/td>\n<td>10<\/td>\n<td>20<\/td>\n<\/tr>\n<tr>\n<td>Hours saved\/rep\/week (H) <a href=\"https:\/\/nebor.ai\/blog\/sales-automation-statistics\" target=\"_blank\" rel=\"noindex nofollow\">*<\/a><\/td>\n<td>10<\/td>\n<td>10<\/td>\n<\/tr>\n<tr>\n<td>Fully loaded hourly rate (W)<\/td>\n<td>$70<\/td>\n<td>$70<\/td>\n<\/tr>\n<tr>\n<td>Annual time-savings value<\/td>\n<td>$364,000<\/td>\n<td>$728,000<\/td>\n<\/tr>\n<tr>\n<td>Win-rate lift applied to pipeline <a href=\"https:\/\/logiclot.io\/docs\/sales-automation\" target=\"_blank\" rel=\"noindex nofollow\">*<\/a><\/td>\n<td>12%<\/td>\n<td>12%<\/td>\n<\/tr>\n<tr>\n<td>Estimated annual revenue lift<\/td>\n<td>~$180,000<\/td>\n<td>~$360,000<\/td>\n<\/tr>\n<tr>\n<td>Total annual benefit<\/td>\n<td>~$544,000<\/td>\n<td>~$1,088,000<\/td>\n<\/tr>\n<tr>\n<td>Estimated Year 1 total cost <a href=\"https:\/\/tommasomariaricci.com\/blog\/ai-for-sales-guide\" target=\"_blank\" rel=\"noindex nofollow\">*<\/a><\/td>\n<td>~$55,000<\/td>\n<td>~$95,000<\/td>\n<\/tr>\n<tr>\n<td>Year 1 ROI<\/td>\n<td>~889%<\/td>\n<td>~1,045%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Revenue lift is modeled on a $1.5M annual pipeline per team using a conservative 12% win-rate improvement. <a href=\"https:\/\/tommasomariaricci.com\/blog\/ai-for-sales-guide\" target=\"_blank\" rel=\"noindex nofollow\">Licensing for conversation intelligence, lead scoring, and outreach automation for a 10\u201320 person team runs $2,500\u2013$8,000 per month, with first-year implementation adding $15,000\u2013$40,000<\/a>. Saved hours contribute to ROI only when leaders reinvest them into selling activities. Gartner&#8217;s 2026 survey found that sales organizations reinvesting AI time savings into high-value activities are 2.2x more likely to exceed customer growth goals.<\/p>\n<h2>How Coffee Drives Pipeline and Revenue Lift<\/h2>\n<p>Coffee&#8217;s agent compounds returns across all four levers at once. Automatic contact creation and activity logging from Google Workspace or Microsoft 365 keep records complete and current, which stabilizes pipeline forecasts. <a href=\"https:\/\/digitalapplied.com\/blog\/crm-data-hygiene-2026-contact-management-guide\" target=\"_blank\" rel=\"noindex nofollow\">B2B contact data decays rapidly without active management<\/a>, and Coffee&#8217;s continuous enrichment prevents that decay from eroding coverage. Meeting orchestration features, including pre-call briefings, AI bot transcription, automated summaries, and follow-up drafts, ensure every interaction is captured in structured form such as BANT, MEDDIC, or SPICED, then written back to the CRM record immediately.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678186019-5cc1a76ac78e.gif\" alt=\"Build people lists automatically with Coffee AI CRM Agent\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Build people lists automatically with Coffee AI CRM Agent<\/em><\/figcaption><\/figure>\n<p><a href=\"https:\/\/r-sun.ai\/insights\/ai-driven-b2b-sales-2026\" target=\"_blank\" rel=\"noindex nofollow\">Top-performing AI-enabled teams generate substantially more pipeline volume than average teams<\/a>, largely because of better data completeness and follow-up consistency. <a href=\"https:\/\/clicktoclose.ai\/blog\/sales-automation-tools-save-time-2026-analysis\" target=\"_blank\" rel=\"noindex nofollow\">Automated follow-up sequences achieve higher consistency compared to manual follow-up processes<\/a>, and <a href=\"https:\/\/logiclot.io\/docs\/sales-automation\" target=\"_blank\" rel=\"noindex nofollow\">Brevet Group research shows 80% of sales require 5+ follow-ups, yet 44% of reps stop after one<\/a>. Coffee&#8217;s agent closes that gap by default, which directly supports the win-rate and cycle-time levers in the ROI model.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678549697-4e8d65abe17d.gif\" alt=\"GIF of Coffee platform where user is using AI to prep for a meeting with Coffee AI\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Automated meeting prep with Coffee AI CRM Agent<\/em><\/figcaption><\/figure>\n<h2>Implementation Factors That Shape Realized ROI<\/h2>\n<p>Data quality acts as the primary determinant of realized ROI. Gartner identified seven root-cause data blockers that cause CRM and agentic AI projects to fail, with inaccurate or incomplete CRM data ranking first. <a href=\"https:\/\/salesmotion.io\/blog\/ai-sales-tools-buyers-guide-2026\" target=\"_blank\" rel=\"noindex nofollow\">Realistic timelines for ROI are 3\u20136 months with clean CRM data and established processes, or 6\u20139 months if building processes from scratch<\/a>. Integration depth forms the second major factor. <a href=\"https:\/\/digitalapplied.com\/blog\/marketing-automation-statistics-2026-data-points\" target=\"_blank\" rel=\"noindex nofollow\">The gap between automation leaders and laggards is now largely explained by CRM integration depth, lead scoring maturity, and the speed at which teams adopt agentic AI inside existing workflows<\/a>.<\/p>\n<p>Point-solution stacks that use separate tools for enrichment, recording, forecasting, and outreach increase integration surface area and operational drag. <a href=\"https:\/\/factors.ai\/blog\/ai-marketing-automation-pricing-comparison\" target=\"_blank\" rel=\"noindex nofollow\">A $49 per month automation tool that requires manual CSV exports and constant lead cleanup can cost more overall than a $1,000 per month platform that consolidates three workflows<\/a>. Coffee&#8217;s agent consolidates CRM, enrichment, recording, and pipeline intelligence into a single system, which removes that hidden cost and simplifies adoption.<\/p>\n<h2>Common Pitfalls That Undercut Automation ROI<\/h2>\n<p>Shadow systems emerge when reps distrust CRM data and maintain parallel spreadsheets or Notion databases. Teams lose time chasing data across disconnected systems, and the average B2B team relies on multiple spreadsheets that fail as a single source of truth. Over-reliance on manual entry represents a structural pitfall, not a behavioral one, because reps already spend a substantial amount of time on manual data entry and still report CRM data as incomplete or inaccurate.<\/p>\n<p>Unclear ownership of automation outcomes creates a third failure mode when neither RevOps nor Sales claims accountability for adoption. These three issues reinforce each other, producing low trust, low usage, and low ROI. UK mid-market businesses succeed with vendor-based AI solutions approximately 67% of the time, compared with just 33% for purely internal builds, according to <a href=\"https:\/\/helium42.com\/blog\/build-vs-buy-ai\" target=\"_blank\" rel=\"noindex nofollow\">MIT research published in 2025<\/a>, which highlights the value of proven implementation patterns.<\/p>\n<h2>Readiness Checklist for Coffee Deployment<\/h2>\n<p><strong>Team size:<\/strong> Coffee&#8217;s Companion App targets teams of 10\u201350 reps on Salesforce or HubSpot, and the Standalone CRM is optimized for teams of 1\u201320. Both models work well for mid-market RevOps directors evaluating this investment. <strong>CRM stack:<\/strong> Teams on Salesforce or HubSpot can deploy Coffee as a Companion App through simple authentication, with no migration required. Teams ready to consolidate can adopt Coffee&#8217;s Standalone CRM.<\/p>\n<p><strong>Change-management capacity:<\/strong> Most businesses see measurable ROI within 3\u20136 months through faster lead response times, reduced manual work, and higher conversion rates. Coffee&#8217;s seat-based pricing, with no metering on LLM usage or processes, keeps budget forecasting straightforward. <strong>Data baseline:<\/strong> Leaders should establish current manual costs and pipeline metrics before deployment to shorten time-to-value and to prove impact.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>Does Coffee integrate with Salesforce and HubSpot, or require a full CRM migration?<\/h3>\n<p>Coffee offers two distinct deployment models. The Companion App connects to an existing Salesforce or HubSpot instance through simple authentication. After connection, Coffee&#8217;s agent handles data entry, enrichment, meeting summaries, and activity logging, then writes that structured data back into the existing system of record. No migration is required. Teams that want to replace their CRM entirely can adopt Coffee&#8217;s Standalone CRM, where the agent powers the full platform. Both models use Coffee&#8217;s seat-based pricing plan.<\/p>\n<h3>How does Coffee handle data security and compliance?<\/h3>\n<p>Coffee is SOC 2 Type 2 and GDPR compliant. Data ingested by the agent, including emails, calendar events, and call transcripts, does not train public AI models. For mid-market teams in regulated-adjacent industries, this approach allows the agent to process sensitive deal communications without exposing proprietary data to third-party model training pipelines.<\/p>\n<h3>What is the realistic payback period for a 10\u201320 rep team deploying Coffee?<\/h3>\n<p>Payback depends on three variables: current manual cost baseline, deal volume, and how quickly saved hours are reinvested into selling activities. For a 10-rep team saving 10 hours per rep per week at a $70 fully loaded hourly rate, annual time-savings value alone exceeds $364,000. At Coffee&#8217;s seat-based pricing, payback typically falls within the 2\u20136 month range, consistent with 2026 industry benchmarks for agent-led automation deployments. Teams with clean CRM data and established sales processes reach payback faster than those building processes from scratch.<\/p>\n<h3>How does Coffee&#8217;s agent improve pipeline forecast accuracy?<\/h3>\n<p>Forecast accuracy degrades when deal records are incomplete or stale. Coffee&#8217;s agent logs every interaction, including emails, calls, and meetings, automatically and tracks pipeline changes week over week through its Pipeline Compare feature. This removes the manual CSV exports and end-of-quarter data scrambles that distort forecasts. Because the agent captures history in a built-in data warehouse, pipeline reviews become a strategic discussion rather than a data-verification exercise. Teams using automated activity logging consistently achieve higher CRM data accuracy, which directly supports more reliable revenue projections.<\/p>\n<h3>Is Coffee suitable for teams that already use point solutions like Gong, ZoomInfo, or SalesLoft?<\/h3>\n<p>Coffee is designed to consolidate those stacks. Its agent handles conversation intelligence, which can replace Gong, contact and company enrichment, which can replace ZoomInfo, and meeting orchestration and follow-up automation, which can replace SalesLoft, within a single platform. For teams paying for multiple disconnected tools, Coffee reduces both subscription cost and the operational drag of keeping those tools synchronized. The agent&#8217;s enrichment data matches dedicated enrichment providers for most mid-market use cases and is included in the seat-based price with no additional per-record fees.<\/p>\n<h2>Summary and Next Steps for Proving ROI<\/h2>\n<p>Sales workflow automation ROI for B2B teams in 2026 is measurable, benchmarked, and achievable within a single fiscal year. The four-lever framework of time savings, pipeline accuracy, win-rate lift, and cycle-time reduction provides a practical structure. <a href=\"https:\/\/builts.ai\/blog\/automation-roi-how-to-calculate\/\" target=\"_blank\" rel=\"noindex nofollow\">Analyses of Forrester TEI studies citing a 200% return in year one<\/a>, <a href=\"https:\/\/strivelabs.ai\/blog\/marketing-automation-strategy-b2b-framework\/\" target=\"_blank\" rel=\"noindex nofollow\">marketing automation programs achieving $8.71 return per dollar for top-quartile programs<\/a>, and many sales teams reporting positive ROI within year one all point to the same conclusion: delaying automation carries more risk than adopting it. The variable that separates high-ROI deployments from failed ones is data quality at the point of entry, which Coffee&#8217;s agent is built to protect. Whether deployed as a Standalone CRM or as a Companion App on Salesforce or HubSpot, Coffee keeps inputs accurate so outputs stay profitable.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">Explore Coffee plans and build a CFO-ready ROI model for your team<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>B2B teams using Coffee hit 200\u2013400% year-one ROI with payback in just 4.2 months. Automate your sales workflow and maximize revenue \u2014 start today!<\/p>\n","protected":false},"author":11,"featured_media":1509,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1642","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/1642","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/comments?post=1642"}],"version-history":[{"count":3,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/1642\/revisions"}],"predecessor-version":[{"id":8057,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/1642\/revisions\/8057"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media\/1509"}],"wp:attachment":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media?parent=1642"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/categories?post=1642"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/tags?post=1642"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}