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72\u002F100",[8],{"items":485,"count":54},[486,488,490,492],{"value":487,"label":487},"EMEA",{"value":489,"label":489},"APAC",{"value":491,"label":491},"Americas",{"value":493,"label":493},"Africa",[495],{"id":496,"date":497,"date_gmt":497,"guid":498,"modified":499,"modified_gmt":499,"slug":500,"status":10,"type":501,"link":502,"title":503,"content":505,"excerpt":508,"author":510,"featured_media":511,"comment_status":11,"ping_status":11,"sticky":507,"template":8,"format":512,"meta":513,"categories":514,"tags":516,"cbtw_industries":517,"cbtw_services":518,"class_list":519,"acf":528,"yoast_head":558,"yoast_head_json":559,"lang":653,"translations":654,"cbtw_post_categories":655,"header":658,"layout_builder":659,"featured_image":669,"industries":675,"services":676,"cbtw_post_author":580,"breadcrumb":677,"primary_category_name":207,"post_region":533,"featured_image_url":578,"primary_industry_name":8,"primary_service_name":8,"pll_sync_post":683,"_links":684,"_embedded":733},15979,"2026-07-31T06:44:44",{"rendered":189},"2026-07-31T07:20:50","the-qa-teams-winning-with-ai-arent-the-ones-spending-the-most","post","https:\u002F\u002Fcbtw.tech\u002Finsights\u002Fthe-qa-teams-winning-with-ai-arent-the-ones-spending-the-most",{"rendered":504},"The QA Teams Winning with AI Aren’t the Ones Spending the Most ",{"rendered":506,"protected":507},"\n\u003Cdiv class=\"wp-block-group is-vertical is-layout-flex wp-container-core-group-is-layout-1 wp-block-group-is-layout-flex\">\n\u003Cp>BrowserStack’s \u003Ca href=\"https:\u002F\u002Fwww.browserstack.com\u002Fblog\u002Finside-the-state-of-ai-in-software-testing-2026\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">2026 survey\u003C\u002Fa> of more than 250 CTOs, VPs of Engineering, and QA leaders found that 61 percent of organizations already use AI across most of their testing workflows, and 18 percent see returns over 100 percent. Higher spending alone did not predict which teams landed in that group. The strongest returns went to teams that prioritized integrating AI into existing workflows over adopting more sophisticated tools, since disconnected AI tools were the top obstacle teams reported, ahead of budget. \u003C\u002Fp>\n\n\n\n\u003Cp>The gap, then, sits in the structure around AI rather than the tooling itself. AI needs a Quality Assurance function with clear release criteria, scalable automation, and a governance model defining where AI can move fast and where a person signs off first. CBTW’s quality engineering practice builds that structure, then places AI at the points where it adds clarity rather than noise.\u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-the-four-pillars-that-shape-qa-performance\">\u003Cstrong>The four pillars that shape QA performance\u003C\u002Fstrong> \u003C\u002Fh2>\n\n\n\u003Cfigure class=\"wp-block-table\">\u003Ctable class=\"has-fixed-layout\">\u003Cthead>\u003Ctr>\u003Cth>\u003Cstrong>Pillar\u003C\u002Fstrong> \u003C\u002Fth>\u003Cth>\u003Cstrong>What we apply\u003C\u002Fstrong> \u003C\u002Fth>\u003Cth>\u003Cstrong>What you get\u003C\u002Fstrong> \u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Release Confidence and Risk Management \u003C\u002Ftd>\u003Ctd>AI-assisted defect analysis, risk detection, and log insights, release readiness scoring, performance and stability analysis \u003C\u002Ftd>\u003Ctd>Fewer production incidents, clearer go-live readiness, earlier detection of critical risks \u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Automation and Continuous Testing \u003C\u002Ftd>\u003Ctd>Scalable web, API, and mobile automation frameworks, AI-assisted test design and scripting, CI\u002FCD-integrated testing \u003C\u002Ftd>\u003Ctd>Faster release cycles, less manual regression effort, continuous feedback loops \u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>AI-Driven QA and Platform Enablement \u003C\u002Ftd>\u003Ctd>AI-assisted test generation, defect analysis, and reporting, AI workflows and agents, QA platform setup with dashboards and governance \u003C\u002Ftd>\u003Ctd>Higher QA productivity, better visibility into quality data, practical AI adoption across the QA lifecycle \u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Quality Engineering Capability and Transformation \u003C\u002Ftd>\u003Ctd>QA operating model design, team build and coaching, governance and scalable QA processes \u003C\u002Ftd>\u003Ctd>Lower cost of quality over time, a QA capability the client owns, sustainable quality engineering practice \u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Ffigure>\n\n\n\n\u003Cp>Across these four pillars, the pattern holds consistently. \u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Release cycles move 20 to 40 percent faster \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Production incidents drop 30 to 50 percent \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>QA productivity improves 30 to 60 percent \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Visibility and decision speed improve 50 to 80 percent \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Cost of quality falls 20 to 40 percent \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>These outcomes come from the same source. Release confidence, automation coverage, AI enablement, and Quality Assurance capability are treated as one connected system, not four separate initiatives.\u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-what-this-looks-like-in-practice-nbsp-nbsp\">\u003Cstrong>What this looks like in practice \u003C\u002Fstrong> \u003C\u002Fh2>\n\n\n\u003Cp>For a connected wearable technology provider, Quality Assurance was spending 30 to 40 percent of its time writing and maintaining tests, leaving little room for anything else. An AI agent took over first-pass test case generation, cutting review time from 30 to 45 minutes down to 5 to 20 minutes per case, while an agentic IDE generated Playwright automation scripts with self-healing logic built in. Test creation time fell by 30 to 40 percent, maintenance effort dropped by 25 to 35 percent, and the team covered 1.5 times more scenarios than before, without adding headcount. \u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"1001\" height=\"667\" src=\"https:\u002F\u002Fadmin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fsoftware-developer-coding-at-desk-with-multiple-mo-2026-01-06-10-44-40-utc-1.webp\" alt=\"QA Test Automation and Software Quality Assurance\" class=\"wp-image-15981\" srcset=\"https:\u002F\u002Fadmin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fsoftware-developer-coding-at-desk-with-multiple-mo-2026-01-06-10-44-40-utc-1.webp 1001w, https:\u002F\u002Fadmin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fsoftware-developer-coding-at-desk-with-multiple-mo-2026-01-06-10-44-40-utc-1-800x533.webp 800w, https:\u002F\u002Fadmin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fsoftware-developer-coding-at-desk-with-multiple-mo-2026-01-06-10-44-40-utc-1-100x67.webp 100w\" sizes=\"auto, (max-width: 1001px) 100vw, 1001px\" \u002F>\u003Cfigcaption class=\"wp-element-caption\">QA teams improve software quality through automation\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-where-ai-actually-fits-inside-the-qa-lifecycle-nbsp\">\u003Cstrong>Where AI actually fits inside the QA lifecycle\u003C\u002Fstrong> \u003C\u002Fh2>\n\n\n\u003Cp>AI works best in QA when it sits inside the existing lifecycle rather than beside it. It supports every stage: test strategy, requirements analysis, estimation, test environment setup, test design, execution, release testing, UAT, go-production, and test reporting and feedback. The same tools carry across all of them, Claude, Rovo, ChatGPT, GitHub Copilot, MS Copilot, and MCP-connected agents. The output feeds straight into daily standups and sprint backlogs rather than running as a separate process. \u003C\u002Fp>\n\n\n\n\u003Cp>The collaboration model behind this is deliberately split by role. \u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>QA drives intent, validation, and daily use of AI agents \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>AQA governs frameworks, standards, CI\u002FCD, and AI rules \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>AI agents accelerate creation, execution, analysis, and reporting \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Human approval gates protect quality, safety, and governance at every critical step \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>AI is embedded end to end across the lifecycle, but every critical decision still passes through a person accountable for it. \u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-where-to-start-depends-on-which-pillar-hurts-most-right-now-nbsp\">\u003Cstrong>Where to start depends on which pillar hurts most right now\u003C\u002Fstrong> \u003C\u002Fh2>\n\n\n\u003Cp>Most QA functions don’t need all four pillars addressed at once, they need the one causing the most pain today. CBTW’s starter packages are built around exactly that entry point: \u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>Releases are unstable or risk is unclear\u003C\u002Fstrong>, start with a Performance and Stability Analysis, 2 to 4 weeks, aimed directly at release risk and instability. \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>Testing is manual, with no automation in place\u003C\u002Fstrong>, start with an Automation Quick Start, 1 to 4 weeks, built for teams with manual testing and no automation. \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>AI hasn’t been introduced to QA yet\u003C\u002Fstrong>, start with the AI-Powered QA Accelerator, a 5-day engagement to establish first use of AI in QA. \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>There’s no QA function to speak of\u003C\u002Fstrong>, start with QA Capability Setup, 4 weeks, for teams with no QA team. \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>Each starter package sits inside a broader menu. That includes automation acceleration, codeless automation enablement, Quality Assurance training and coaching, AI and GenAI quality assurance, operating model transformation, and platform and governance setup. So the next step is already mapped.  \u003C\u002Fp>\n\n\n\n\u003Cp>From there, the same pillar scales further. Release moves into full risk and governance, Automation into continuous testing setup, AI into AI-driven QA transformation, Capability into full quality engineering transformation. \u003C\u002Fp>\n\n\n\n\u003Cp>Four services extend this beyond QA: release and stability assurance with tech leads and delivery managers, a quality and security audit with security specialists, vendor quality due diligence with procurement and tech, and AI workflow and agent enablement with tech and product. \u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-what-this-means-for-your-organization-nbsp\">\u003Cstrong>What this means for your organization\u003C\u002Fstrong> \u003C\u002Fh2>\n\n\n\u003Cp>Assessed this way, a QA function gives Product, Tech, and Delivery a shared, evidence-based view of where quality risk sits, rather than assumptions about where AI belongs. From there, teams can prioritize which QA activities are ready for AI support, which still need human judgment, and how far to scale automation based on what the operating model can absorb. \u003C\u002Fp>\n\n\n\n\u003Cp>The result is a QA function that moves faster and holds steadier at the same time, because AI sits where the structure can support it. \u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-make-your-next-qa-investment-the-right-one-nbsp\">\u003Cstrong>Make your next QA investment the right one\u003C\u002Fstrong> \u003C\u002Fh2>\n\n\n\u003Cp>A full Quality Assurance transformation is not required to find where AI helps first. A 5-day AI-Powered QA Accelerator shows exactly where the gaps sit, and which pillar deserves attention first. \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Talk to CBTW for a first read-in as little as 5 days, with a path toward 20 to 40 percent faster release cycles\u003C\u002Fstrong>\u003C\u002Fp>\n\u003C\u002Fdiv>\n\n\n\n\u003Cp> \u003C\u002Fp>\n\n\n\n\u003Cp>\u003C\u002Fp>\n",false,{"rendered":509,"protected":507},"\u003Cp>BrowserStack’s 2026 survey of more than 250 CTOs, VPs of Engineering, and QA leaders found that 61 percent of organizations already use AI across most of their testing workflows, and 18 percent see returns over 100 percent. Higher spending alone did not predict which teams landed in that group. The strongest returns went to teams that prioritized […]\u003C\u002Fp>\n",42,15982,"standard",{"_acf_changed":507,"content-type":8,"inline_featured_image":507,"footnotes":8},[43,515],46,[],[],[],[520,501,521,522,523,524,525,526,527],"post-15979","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-case-studies","category-insights",{"authors":8,"article_authors":529,"direct_external_link":8,"post_region":533,"layout_builder":534},[530],{"author_name":531,"author_url":532},"Quy.Ngo Senior Specialist Marketing - CBTW APAC","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fquyngothanh\u002F","global",[535,543],{"acf_fc_layout":536,"tag_name":537,"section_title":538,"description":8,"image":507,"cta_link":539,"hubspot_form_id":542,"align_center":507},"block_lets_connect","AI-Driven QA That Delivers Faster Releases","Discover where AI creates the greatest impact in your QA lifecycle and build a path to 20–40% faster release cycles.",{"title":540,"url":541,"target":248},"Talk to our experts","https:\u002F\u002Fcbtw.tech\u002Fapac\u002Fcontact","2b771724-6b01-4d7b-aeaa-591dfb86a794",{"acf_fc_layout":544,"title":190,"ui_v2":545,"short_description":546,"cbtw_related_group_filters":547,"items":507,"link":554,"hubspot_form_id":8,"mainPostId":496,"region":192},"block_insights",true,"Access related expert insights",{"cbtw_related_category_filter":548,"cbtw_related_tag_filter":549,"cbtw_related_industry_filter":551,"cbtw_related_service_filter":552},[192,379],[550],"ai",[],[553],"ai-data-platforms",{"title":555,"url":556,"target":557},"View all insights","https:\u002F\u002Fcbtw.tech\u002Finsights","_self","\u003C!-- This site is optimized with the Yoast SEO Premium plugin v25.4 (Yoast SEO v25.4) - https:\u002F\u002Fyoast.com\u002Fwordpress\u002Fplugins\u002Fseo\u002F -->\n\u003Ctitle>The QA Teams Winning with AI Aren't the Ones Spending the Most  - CBTW\u003C\u002Ftitle>\n\u003Cmeta name=\"description\" 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