AI-Informed Test Teams
Are you wondering how to effectively integrate AI into your test team’s workflow or your real-world testing services business? Curious about how AI can improve software quality, increase testing efficiency, accelerate sales, and reduce delivery times from days to minutes? Let’s explore how Checkie.AI and XBOSoft are collaborating to create an AI-Informed approach to software testing that can inspire improvements in your own testing environment. Whether you need to leverage testing services that are faster, smarter thanks to AI, or making your own team AI-Informed, the following should help in that transformation.
The AI-Informed Approach
“AI-Informed” is the term coined by Phillp Lew (CEO of XBOSoft) and myself when establishing this mission and partnership. While many new terms are emerging in this space, AI-Informed perfectly captures our vision: using artificial intelligence to accelerate team performance and company operations. Not just marketing claims or demos.
AI offers vast repositories of testing information accessible through thoughtful prompting. It’s remarkably cost-effective for most basic testing tasks — often just pennies per operation — and can scale to handle hundreds of thousands of testing activities simultaneously. The key question becomes: how can we harness AI’s capabilities to enhance testing processes and the business operations surrounding them?
The XBOSoft and Checkie.AI Partnership
XBOSoft and Checkie.AI have joined forces to identify effective AI integration strategies for software testing. We share our current thinking on how to create AI-Informed versions of traditional testing roles and business processes, and even some of the things that didn’t work well.
AI-Informed Manual and Exploratory Testers
Manual testers traditionally spend significant time searching for bugs and developing new test cases while working in web browsers. In the past year, many testers have adopted the practice of copying website text into ChatGPT to identify potential bugs or to generate fresh testing ideas, including variations of existing tests through different data inputs and parameters.
AI-informed manual and exploratory testing simplifies this workflow through the coTestPilot.ai browser plugins. Rather than the inefficient copy-paste approach, testers can activate the tool with a single click, accessing various testing personalities and specialized experts to:
- Identify bugs on the current page
- Generate innovative test case ideas in the categories of : ‘core’, ‘interesting inputs’, ‘edge/boundary tessting, etc.
- And have AI testing agents automatically discover issues and bugs in areas of : Accessibility, security, usability, design, etc.
This enhancement effectively provides every manual tester with a team of AI testing assistants that improve bug detection efficiency and expand test coverage.
For specific projects, AI-informed testers can create custom AI testing agent profiles using simple prompts. When managers or clients emphasize particular testing priorities, testers can incorporate these requirements into prompt-based profiles and instantly receive targeted feedback and bug reports from those specialized perspectives.
Download the free beta version of coTestPilot for Manual Testers @ Chrome Webstore
AI-Informed Test Automation Engineers
The Reality of Test Automation Today
While many imagine test automation engineers spending their days writing new test scripts, the reality is quite different. Most of their time is consumed by maintaining existing test code that breaks due to website changes, and worring about all the new untested features or backlog of tests yet to be automated.
Even more concerning, traditional test automation often takes longer than a typical sprint cycle to implement. This timing gap means new features frequently ship before automated tests are ready, leaving critical functionality to be verified only through manual and infrequent testing.
Traditional automation scripts also have significant blind spots. They typically follow hardcoded sequences — finding elements, clicking them, entering form values, and verifying specific strings or states. These scripts navigate through pages that might have serious accessibility issues, performance problems, or usability flaws, yet detect none of these issues.
The AI-Informed Approach to Test Automation
AI-informed test automation engineers transform this landscape in two significant ways:
1. Enhanced Checking with Existing Automation
Automation engineers need only add a simple ai_check() method to their automation scripts, called at strategic points in their test flows. This addition enables automatic quality checks across nine different dimensions, identifying bugs that traditional automation would miss. This represents a dramatic shift on coveage and value from automated test scripts—when was the last time your test automation actually found a bug?
Opensource coTestPilot code for Python/Selenium/Playwright: https://github.com/jarbon/coTestPilot
2. Automated Test Generation
A new automation paradigm is emerging where AI analyzes web pages, automatically generates test cases, and executes them. For many core functional tests, automation engineers no longer need to write these scripts manually.
These AI-generated tests offer several advantages:
- They eliminate the need to create basic, repetitive test cases
- They automatically provide coverage for new features as they appear in builds
- They deliver testing value mid-sprint, even for features not yet documented
- Being dynamically generated rather than hardcoded, they’re more resilient to UI changes
Checkout fully automated AI Testing agents, with a free download @ https://testers.ai
The New Focus for Automation Engineers
The AI-informed test automation engineer can now focus on more complex, domain-specific test cases — particularly for applications like accounting software or retail sites where calculations must be precise and backend algorithms need verification beyond what’s visible in the UI.
For tests the AI doesn’t generate automatically, engineers can craft targeted test cases or use prompts to influence test execution parameters. They maintain control over the AI while making strategic decisions about which tests to code manually versus delegating to AI with appropriate prompting.
Most importantly, AI-informed test automation engineers now focus their time and talents on delivering meaningful business value rather than maintaining brittle test frameworks. They can provide comprehensive test coverage within hours instead of days, weeks, or months — making their managers and clients significantly happier.
AI-Informed Test Managers and Project/Delivery Managers
Test managers and team leads traditionally invest significant time creating comprehensive strategy documents and test plans — often before they truly understand the application’s specific quality challenges. This approach can be inefficient because different applications have distinct quality profiles and requirements.
Some applications struggle with accessibility issues while others excel in this area. Some have performance bottlenecks while others run smoothly. Some feature excellent design while others have significant usability problems. The conventional approach to test planning often fails to account for these variations.
The AI-Informed Approach to Test Management
AI-informed test managers take a more efficient approach. They begin by running Checkie AI against critical websites, which generates comprehensive reports across multiple quality dimensions. These reports provide several immediate benefits:
- Identification of which quality areas require the most attention
- Detailed bug information with reporting guidelines
- Predicted user perceptions and reactions to the website
- Overall quality scores that summarize the application’s current state
This approach eliminates the need to ship a product and then gather expensive, slow user panel feedback. Instead, test managers gain immediate visibility into the application’s quality landscape.
Strategic Resource Allocation
Armed with this intelligence, AI-informed test managers can strategically allocate testing resources where they’ll have the greatest impact:
- If security vulnerabilities dominate the findings, they focus testing efforts on security
- If design issues are prevalent, they emphasize accessibility and usability testing
- Most importantly, they avoid wasting resources hunting for issues in areas where the application is already performing well
The old adage says, “Where there’s smoke, there’s fire” — but it’s equally important to recognize where there is no smoke there migth not be any fire. AI testing agents provide test managers with a comprehensive view of application quality, enabling them to deploy resources efficiently and deliver results quickly and cost-effectively.
Enhanced Credibility and Communication
AI-informed test managers receive actionable feedback from day one, allowing them to articulate the overall quality picture to clients, management, and their teams with confidence and clarity. Their data-driven approach demonstrates expertise and establishes credibility with all stakeholders.
In summary, AI-informed test managers are more effective at understanding quality challenges, allocating resources, and communicating with stakeholders — ultimately delivering better results with less effort and in less time.
AI-Informed Testing Sales
In the competitive landscape of testing services, salespeople are discovering the transformative power of AI testing agents. Unlike traditional approaches, these tools provide concrete, data-driven insights that dramatically enhance the sales process.
Testing service providers no longer need to rely on generic pitches. Instead, they can leverage AI testing agents to gain deep insights into potential clients before their first conversation. Within just an hour, these tools can generate comprehensive test reports that reveal both existing quality issues and gaps in the client’s current testing approach.
This intelligence allows sales teams to:
- Craft highly specialized, custom testing service proposals tailored to the client’s specific needs
- Present evidence-based recommendations backed by real bugs discovered through AI testing
- Demonstrate rapid feedback capabilities that outpace traditional testing methodologies
- Differentiate their services with concrete examples of increased efficiency and broader coverage — on the prospects own app!
The market is evolving rapidly, with CTOs, CFOs, and CEOs increasingly expecting their testing partners to effectively implement AI strategies. Many buyers now specifically look for evidence that testing services incorporate legitimate AI solutions — not just as a buzzword, but as a tangible value-add that delivers measurable results.
For internal test managers and directors, this technology also strengthens their position within the organization. They can leverage AI testing outputs to justify resources, gain control of strategic projects (and corresponding job security/promo), and demonstrate their team’s value beyond simply identifying bugs.
Tips: All testing servivces vendors have the deals that got away. A quick AI-Informed quality analysis of their app can do wonders with an educated excuse re-engage, and also help with retention on existing non-AI-Informed deals!
Sales teams at Testing vendors can also directly resell the managment of the AI checking and testing Agents.
AI-Informed Test Marketing
Traditional marketing in testing services often relies on generic messaging that fails to resonate with specific prospect needs. AI testing agents are changing this paradigm by enabling highly targeted, personalized outreach.
Rather than broadcasting general capabilities, marketing teams can now:
- Conduct targeted analyses of potential clients’ production websites
- Identify actual issues specific to each prospect
- Share relevant bug findings as part of personalized outreach campaigns
- Make prospects feel understood by demonstrating knowledge of their specific challenges
This approach makes prospects feel that you truly understand their unique situation. It positions your team as exceptionally informed and capable — not just selling generic testing services, but offering insights directly relevant to their business.
For testing directors looking to increase their organizational influence, this capability provides unprecedented leverage. The personalized, data-driven approach to marketing and sales represents a fundamental shift in how testing services are positioned and sold, creating differentiation in a market where standing out has traditionally been challenging.
In the longer term, we are looking to integrate upsells to testing serivices offering into large scale pre-tested apps.
Summary: The AI-Informed Testing Revolution
The partnership between XBOSoft and Checkie.AI represents a pioneering approach to integrating artificial intelligence into software testing workflows. This “AI-Informed” methodology transforms traditional testing roles and processes to deliver faster, more comprehensive, and more cost-effective quality assurance.
Key innovations introduced in this article include:
- AI-Informed Manual and Exploratory Testing: Testers now work with AI assistants through browser plugins like CoTestPilot.AI, eliminating inefficient copy-paste workflows and gaining instant access to specialized testing expertise across multiple quality dimensions.
- AI-Informed Test Automation: Engineers enhance existing automation with AI capabilities that can identify bugs across nine different dimensions, while also leveraging fully automated test generation for basic scenarios. This allows automation engineers to focus on complex, high-value test cases instead of maintaining brittle test frameworks.
- AI-Informed Test Management: Managers use AI-generated comprehensive quality reports to strategically allocate resources where they’ll have the greatest impact, rather than creating generic test plans before understanding application-specific quality challenges.
- AI-Informed Sales and Marketing: Testing service providers use AI testing outputs to create data-driven sales proposals tailored to client-specific needs and conduct highly personalized marketing outreach based on actual issues discovered in prospects’ websites.
This AI-Informed approach delivers tangible benefits throughout the testing lifecycle:
- Dramatically reduced delivery times (from days to minutes)
- Increased testing efficiency and coverage
- Enhanced ability to find real bugs that matter
- Accelerated sales cycles with evidence-based proposals
- Improved competitive differentiation in the testing services market
By embracing these AI-Informed testing methodologies, organizations can transform their testing practices while meeting the growing expectation from executive leadership for legitimate AI integration in quality assurance processes.
How to get Started:
- Schedule a demo of XBOSofts AI-Informed testing services.
- Access Checkie.AI to apply to your internal testing teams: https://checkie.ai
— Jason Arbon CEO https://checkie.ai and https://testers.ai and Phillip Lew, CEO https://xbosoft.com
