Software testing—long a bottleneck in digital transformation—faces its most significant disruption since the dawn of computing. The catalyst: artificial intelligence. And we at Appvance are leading this change.
Software now runs everything from banking to healthcare to transport. Yet the methods to ensure it works properly remain stuck in the past. Most firms still rely on armies of manual testers clicking through applications, catching only the most obvious flaws.
The numbers tell a stark story. Companies spend hundreds of billions on software testing that checks less than 10% of possible user paths. Manual testing makes up 70% of end-to-end testing. Script writing and maintenance consume 85% of QA automation resources. The result: buggy software, delayed releases, and massive costs.
“The cost of poor software quality in the US has risen to at least $2.41 trillion,” reports the Consortium for Information and Software Quality (CISQ) in their 2022 study. This staggering figure underscores the urgency of our mission.
“We need a new way, a better way,” as we state in our manifesto. Our vision: AI-first quality assurance and testing (QAT) that will eliminate most manual testing within five years.
Our AI-First Approach
Our new AI platform, GENI, converts English test cases to scripts at 100 per hour—400 times faster than human testers. It aims to make manual testing extinct.
“This allows manual tests to be fully automated by AI in minutes,” we proudly note. “Human-led scripting would have taken weeks to complete a similar task and then forever to maintain the scripts.”
We project that by 2030, over 80% of QA testing will be AI-driven. This shift promises to slash costs while expanding test coverage from the current 10% to nearly 100% of application functions.
The implications stretch beyond mere efficiency. Total application coverage means catching bugs before they reach customers. Release cycles accelerate as testing bottlenecks vanish. Apps become more stable, secure, and user-friendly.
Central to the premise of AI-first QAT isn’t the evolution of the practice, but it’s reinvention.
As Jack Azagury, Group Chief Executive of Consulting at Accenture, observes: “Companies that embrace reinvention as a strategy—where they drive a step change in performance through the power of technology, data and AI and new ways of working—are outperforming the competition.”
Our View: From Human-First to AI-First
For decades, software testing followed a simple model: humans writing test cases, humans executing them, and humans checking results. When automation came, it still meant humans writing scripts to mimic human actions.
Our AI-first approach flips this model. AI generates and executes tests without human scripting. It learns from an application Digital Twin and user behaviors to test all possible paths—not just the ones humans think to check.
“We are at the start of a fundamental shift to human-assisted AI,” we state in our manifesto. “The cost of software development and time to users will decrease exponentially.”
This approach aligns with broader trends. McKinsey & Company predicts that by 2028, AI will generate a large portion of code rather than humans. Andrej Karpathy, a former research scientist at OpenAI and director of AI at Tesla, suggests that most software jobs won’t involve programming as we know it within ten years.
Our Business Case: The Economics of AI Testing
The business case for our AI-first testing looks compelling. A typical enterprise might spend $500,000 to $2 million yearly on QA. For large companies with many apps, this jumps to tens of millions.
Banks, insurance firms, and retailers spend over $100 million yearly on testing. Yet they still suffer from escaped bugs that damage their brands and bottom lines.
McKinsey research reveals that “large IT projects typically run 45% over budget and 7% over time, often due to uncontrolled changes.” This pattern of cost overruns is directly tied to testing limitations.
We’ve seen our early adopters achieve dramatic results. One of our banking clients, who has a QA team of 32 people managing 8,500 test scripts, spends about $4 million yearly on testing. After six months with our AI-first approach:
- Their QA team shrunk to 8 people
- Test coverage went from 22% to 94%
- Production bugs dropped by 83%
- Release cycles sped up by 61%
- Total savings hit $3.2 million yearly
Our retail client with 43 apps found they could replace 90% of their testing work with our AI, projecting yearly savings of $8.4 million.
Fixing a bug after release costs 5-15 times more than fixing it during development. McKinsey cites even more dramatic figures: “IBM Systems Sciences Institute reported that fixing a bug after release can cost up to 100 times more than fixing it during the design phase.”
Central to AI-first QAT is the emergence of autonomous and continuous testing.
Our Technology: How GENI Works
Our GENI system uses our patented AIQ Digital Twin (including AI script generation) with multiple transformer models to understand application structures.
The system analyzes page layouts, natural language test cases, and visual cues, matching these to the application architecture. It handles hundreds of test cases at once, dramatically outpacing human testers or recorders.
We stress that all data remains private—not shared between clients or used to train models. This addresses a key concern for enterprises wary of exposing sensitive business logic to external AI systems.
This security focus matters more than ever. According to Orca Security’s State of Cloud Security Report 2024, “62% of organizations have severe vulnerabilities in code repositories.” Our approach helps identify these issues before deployment.
Our Challenge: AI-First or AI-Last?
Our manifesto poses a stark question to business leaders: “Are you going to be AI-first or AI-Last?“
This frames software testing as a strategic choice, not just an operational one. Early adopters gain a competitive advantage through faster releases, better quality, and lower costs. Laggards risk falling behind as their manual processes can’t match the speed and coverage of AI-powered rivals.
“This shift is not a choice but an inevitability,” we argue, “driving businesses to embrace AI-first QAT to stay competitive in the evolving software development landscape.”
The timing looks right. The past year saw generative AI break through to mainstream awareness. Tools like ChatGPT demonstrated AI’s ability to understand and generate human language—skills directly applicable to test automation.
Bain & Company’s 2023 survey highlights the importance of following through on this transformation: “Only 12% of major change programs produce lasting results. Too often, leadership accepts disappointing outcomes and moves on.” We’re committed to being in that successful 12%.
Our Perspective: The Human Element
Despite our focus on automation, we know humans won’t vanish from testing entirely. Instead, their roles will transform.
In our manifesto, we outline five emerging roles for humans in an AI-first QAT organization:
- AI Trainer and Validator: Humans will train AI models, define test cases, and validate AI-generated tests. They’ll ensure AI systems understand complex scenarios and edge cases.
- Ethics and Bias Analyst: As AI takes over testing, humans must check for bias in AI systems and ensure ethical outcomes.
- Strategic Test Planner: Humans will focus on strategic planning and prioritization of testing efforts, analyzing business requirements and market trends.
- User Experience Tester: Human testers will evaluate software from a user-centric perspective, performing usability testing and accessibility assessments.
- Complex Scenario Investigator: Some complex cases will still require human expertise to identify, replicate, and test thoroughly.
“These roles demonstrate how human testers can complement AI automation by providing critical oversight, strategic guidance, ethical assurance, user-centric validation, and expertise in complex testing challenges,” we state in our manifesto.
Our Roadmap: The Timeline for Change
We see the impact of AI on software testing unfolding over different timeframes:
In the short term (5-10 years), AI systems will play an increasingly significant role in assisting developers with code generation, optimization, and bug detection. Human developers will still handle higher-level reasoning and decision-making.
In the medium term (10-20 years), AI could generate up to 50% of code for certain applications, particularly those with well-defined requirements. Human oversight will remain essential for critical applications and system design.
The long term (beyond 20 years) remains more speculative. Some envision AI generating most code for many applications, with humans focusing on high-level architecture and requirements gathering. This assumes breakthroughs in artificial general intelligence that remain uncertain.
Our Context: The Broader AI Landscape
Our vision fits within the more significant trend of AI reshaping software development. GitHub’s Copilot helps programmers write code faster. DeepMind’s AlphaCode solves competitive programming problems. Google’s Gemini creates applications from natural language descriptions.
Testing represents a logical early target for AI disruption. It’s rule-based, repetitive, and data-heavy—ideal conditions for machine learning to thrive.
The economic pressure for change looks overwhelming. The Consortium for Information and Software Quality (CISQ) 2022 report states that “the accumulated technical debt (TD) of software has increased to about $1.52 trillion.” This mounting debt demands a revolutionary approach, not incremental improvements.
When apps fail, companies lose money through direct repair costs, lost work time, missed sales, brand damage, and legal risks.
Our Awareness: Challenges and Limitations
Despite our enthusiasm, we recognize questions remain. Can AI truly understand the nuanced requirements of complex enterprise applications? Can it test for subjective qualities like user experience? Will it recognize subtle business logic errors that human experts might catch?
Security presents another concern. As testing shifts to AI, new attack vectors may emerge. If AI becomes the gatekeeper for software quality, compromising that AI could let vulnerable code reach production.
Trust issues may slow adoption. Many enterprises built testing processes over decades and won’t abandon them overnight. QA leaders who built careers on manual testing may resist a shift that fundamentally changes their role.
Our Recommendations: The Path Forward
For companies considering the move to AI-first testing, we suggest starting with a hybrid approach. Begin by automating the most repetitive testing tasks while retaining human oversight.
As confidence in AI testing grows, gradually expand its scope. Focus human testers on areas where they add the most value—complex edge cases, user experience, and strategic planning.
We position ourselves as a guide for this transition, offering “a secure and confident path to digital transformation” through our AI-first platform.
Our Vision: A Turning Point
The central argument of our manifesto—that AI-first testing isn’t just better but inevitable—presents our compelling vision. The economics alone make a strong case. When companies can test faster, more thoroughly, and at lower cost, competitive pressure will drive adoption.
The benefits extend beyond cost savings. Better testing means better software. In a world where digital experiences define brands, software quality directly impacts business success.
For IT leaders, this represents a strategic choice. Early adopters gain advantages in speed, quality, and cost. Late movers risk falling behind as competitors release software faster with fewer defects.
Our closing vision of “accelerating Enterprise transformation and innovation at a lower cost and risk through AI-first software & application delivery” strikes a chord in boardrooms where digital transformation remains a top priority.
As one of our banking CIO clients put it: “We’re saving $4.2 million yearly while testing more of our app than ever before.”
For a function long seen as a cost center, that’s a revolution we’re proud to lead with you.
The AI-first future is here. Join the waitlist!
