2026

Oct 14 2026

Business Process Assurance: Why Passing Tests Isn’t the Same as a Working Business

Business process assurance (BPA) validates whether an entire cross-system business workflow, such as order to cash or procure to pay, still works end to end after a change, rather than whether individual test scripts passed. It matters most in ERP environments like Dynamics 365, SAP, and Oracle Fusion, where upgrades, transports, and quarterly patches introduce risk on a fixed schedule.

Every business runs on processes that cross multiple systems: order to cash, procure to pay, hire to retire, record to report. When a system changes, whether it’s an ERP release wave, a cloud patch, or a new integration, the question that matters isn’t whether the individual test scripts passed. It’s whether the business process still works end to end, in production, under real conditions.

Business process assurance exists to answer that question. This guide covers what business process assurance (BPA) is, how it differs from testing individual applications, where manual approaches break down, and what a no-code, AI-assisted approach to business process assurance looks like for Dynamics 365, SAP, and Oracle Fusion environments in 2026.

Key Takeaways

Business process assurance validates whether an entire cross-system workflow still works after a change, not just whether individual scripts passed. Passing tests and a working business process are not the same thing.
Manual BPA does not scale against today’s release cadence. Dynamics 365 release upgrades, SAP transport changes, and Oracle Fusion quarterly patches all introduce risk on a schedule that manual regression cannot keep up with.
Prioritize test cases that are high-frequency, business-critical, cross-system, and reasonably stable. Automating everything equally is not a strategy.
No-code business process assurance lets IT directors, ERP project managers, functional consultants, and business analysts build and maintain automation directly, without depending on developers for every change.
Business-Aware, Accountable AI extends BPA beyond execution speed. It understands process dependencies and business risk, and keeps every recommendation explainable and subject to human approval, so testing outcomes tie back to release confidence rather than pass or fail counts.

What Is Business Process Assurance?

Business process assurance validates that integrated systems and workflows function correctly together, not just individually. A typical enterprise business process depends on several components working in concert:

Applications: The ERP, CRM, and enterprise systems that run the process, such as Dynamics 365, SAP, or Oracle Fusion.
Databases: The systems storing the transactional and master data the process depends on.
User interfaces: The screens through which business users and functional teams interact with the process.
Integration points: The connections between systems, such as an ERP passing data to a CRM or a payroll provider.
Workflows: The sequence of steps, approvals, and handoffs that make up the end-to-end process.

In a software context, business process assurance looks at the same problem from inside the application layer: validating that modules, APIs, user interfaces, and databases interact correctly to support the business outcome the process was built for. The goal in both cases is the same. BPA confirms that a business-critical workflow, such as order to cash or procure to pay, still holds up end to end after a change, by simulating real operating conditions rather than testing each screen or module in isolation.

Why Business Process Assurance Is Different in ERP Environments

Generic business process assurance guidance treats every application the same way. In practice, the applications that carry the most business risk today are ERP systems, and each one introduces risk on its own schedule:

Microsoft Dynamics 365: Twice-yearly upgrades plus continuous updates mean Finance, Supply Chain, and HR processes are exposed to change constantly. Microsoft has confirmed that the Regression Suite Automation Tool (RSAT) reaches end of support on May 15, 2027, after which there will be no further bug fixes or maintenance. Teams still relying on RSAT for D365 regression coverage need a migration plan well before that date.
SAP ECC and S/4HANA: SAP’s mainstream maintenance for ECC 6.0 (Enhancement Packages 6 through 8) ends December 31, 2027; support for earlier enhancement packages (0 through 5) already ended in December 2025. Either deadline pushes organizations through S/4HANA migrations at the same time they need to keep Order-to-Cash and Procure-to-Pay processes running without disruption.
Oracle Fusion: Quarterly patch cycles mean Finance, HCM, and Supply Chain configurations are retested four times a year at minimum, with FlexField and configuration changes carrying real risk of silently corrupting financial reporting if they go unvalidated.

None of these deadlines are optional or negotiable on the vendor’s side. They are the reason business process assurance has moved from a periodic IT activity to a continuous, business-critical discipline.

Manual vs. Automated Business Process Assurance

Aspect	Manual BPA	Automated BPA
Execution speed	Slow, dependent on human effort	Fast, executes on demand
Accuracy	Prone to human error	Consistent, repeatable execution
Resource requirement	Requires ongoing human resources	Requires upfront setup, less ongoing effort
Cost	Higher ongoing labor cost	Higher initial investment, lower ongoing cost
Scalability	Difficult to scale across company codes or business units	Scales through reusable, parameterized automation
Maintenance	Manual and time-consuming	Self-healing automation adapts to UI and workflow changes
Business risk visibility	Surfaces after the fact, often after go-live	Continuous, flags risk before release

Common Business Process Assurance Challenges

BPA runs into the same obstacles in most enterprise environments:

Complexity of business processes. A single process like Order to Cash can span CRM, ERP, logistics, and finance systems. Every integration point is a place where a change in one system can silently break a connected process.
Resource constraints. Manual regression testing pulls functional consultants, ERP project managers, and business analysts away from higher-value work like exception handling and production readiness, every single release cycle.
Constant change. Upgrades, transport changes, and quarterly patches mean test cases go stale quickly, and testing environments themselves can drift out of sync with production.

How to Build a Business Process Assurance Strategy

A working BPA strategy addresses these challenges directly:

Automate with intent, not everywhere. Prioritize test cases that are high-frequency, business-critical, cross-system, and relatively stable. A process that runs every sprint and touches revenue is worth automating first. A rarely used screen with a cosmetic UI change is not.
Validate end to end, not screen by screen. Cover the full path a transaction takes, from creation through every downstream system it touches, so a defect surfaces before it reaches production rather than after.
Build in regression testing as a standing practice. Every release, transport, or patch should trigger a regression pass against the business-critical processes it could affect, not a one-time test at go-live.
Design for reuse across company codes and business units. Parameterized automation that runs the same validated process against different datasets scales without multiplying the number of automation assets a team has to maintain.
Measure business risk, not just pass and fail counts. A dashboard showing 95% of tests passed doesn’t tell you whether the 5% that failed sits inside a revenue-critical workflow. Tie testing outcomes back to which business processes are actually at risk.

What to Look for in Business Process Assurance Software

Choosing the right platform comes down to a few criteria that matter in ERP environments specifically:

No-code test creation. IT directors, ERP project managers, functional consultants, and business analysts should be able to build and maintain automation directly, without writing code or waiting on a developer for every change.
Business-Aware, Accountable AI. AI that understands business process dependencies and prioritizes validation based on business risk, not just screen coverage, with every recommendation explainable and subject to human approval.
End-to-end test coverage. The ability to validate a process from start to finish across every system it touches, not just the primary application.
Cross-platform compatibility. Testing across web, desktop, ERP, and mainframe environments, spanning a wide range of technologies from a single platform.
Parallel and distributed execution. Running tests concurrently to cut regression cycle time, especially valuable when the same process needs to be validated across multiple company codes or business units.
CI/CD integration. Fitting into existing DevOps pipelines so testing runs continuously as changes are deployed, rather than as a separate manual gate.

How Avo Assure Approaches Business Process Assurance

Avo Assure goes beyond test automation to Enterprise Business Process Assurance. With no-code test creation, Business-Aware, Accountable AI, and end-to-end test coverage, Avo Assure helps teams validate business processes across Dynamics 365, SAP, Oracle, and every connected system, without relying on developers.

In practice, that looks like Synergy Marine Group, one of the world’s largest ship management companies, validating standardized SAP business processes across multiple company codes and connected applications, including Microsoft Dynamics 365 Business Central and SAP Concur. Working with Accenture, the team reduced manual regression effort by 90% and reached approximately 80% automation coverage of planned business scenarios, using reusable automation instead of building separate scripts for every company code.

Read the full Synergy Marine Group and Accenture case study for the complete breakdown of the engagement and its results.

Across enterprise deployments generally, Avo Assure customers report:

10x faster time to market with intelligent, end-to-end test automation
90%+ business process automation coverage
85% less maintenance effort, through self-healing automation
80% reduction in effort required to create and execute tests

The right business process assurance approach determines whether a release goes smoothly or becomes a production incident.

See how Avo Assure helps you move beyond test automation to business process assurance and book a demo to get started.

Frequently Asked Questions:

What is business process assurance?

Business process assurance (BPA) validates that an integrated business workflow, such as order to cash or procure to pay, works correctly end to end across every system it touches, rather than testing each application or screen in isolation.

How is business process assurance different from regular software testing?

Regular software testing typically validates a single application or module. Business process assurance validates the full workflow a business depends on, which often spans multiple systems, such as an ERP, a CRM, and a payroll provider, and confirms the process still functions correctly as a whole after a change.

Why does business process assurance matter more in ERP environments?

ERP systems like Dynamics 365, SAP, and Oracle Fusion change on a fixed vendor schedule, whether through upgrades, transport changes, or quarterly patches. Each change carries a real chance of breaking a business-critical process like financial close or order fulfillment, which makes continuous validation a business necessity rather than a periodic IT task.

Can business processes assurance be automated without a dedicated QA team?

Yes. No-code platforms let functional consultants, business analysts, and ERP project managers build and maintain business process test directly, without relying on developers or ongoing support from implementation partners.

What is the difference between test automation and business process assurance?

Test automation confirms that a script ran and passed. Business process assurance goes further: it confirms that the underlying business process, such as Order to Cash, still works correctly end to end after a change, and ties that outcome to business risk rather than a pass or fail count alone.

How often should business-critical processes be regression tested?

Business-critical processes should be regression tested every release cycle, and again after any configuration, transport, or patch change, since ERP environments on continuous release schedules introduce risk multiple times a year, not just at major upgrades.

Does business process assurance cover processes that span multiple ERP or enterprise systems?

Yes. Processes like Order to Cash or Hire to Retire rarely stay inside a single system. Business process assurance should validate the full cross-system path, including CRM, logistics, finance, and HCM platforms connected to the core ERP.

What should I look for in business process assurance software?

Look for no-code test creation, Business-Aware AI that prioritizes validation by business risk rather than screen count, end-to-end coverage across every system in the process, and the ability to reuse automation across company codes or business units without rebuilding it each time.

Avo Automation are Exhibitors at AutomationSTAR Conference EXPO 2026. Join us in Antwerp 4-5th November 2026.

Oct 09 2026

A little wild, a little wonderful: your team’s Antwerp mini-guide

A railway station that looks like a cathedral, penguins next door, and an old town best explored without too much of a plan. AutomationSTAR comes to Antwerp on 4–5 November with two days of test automation and AI in testing, held a minute’s walk from the train.

Bring your team and you’ll cover more sessions, bring home more ideas, and save up to 25% on tickets. Plus, leave a little space for new experiences.

Here’s how to make the most of the city between talks.

Look up – you’ve arrived

At Antwerp Central, resist the urge to follow the crowd straight to the exit. Often called the ‘railway cathedral’, it lives up to the name. Stop under the great domed hall, take in the marble and gilt, and gather everyone for a team photo. A Room with a ZOO, our conference venue, is just a minute’s walk away. Getting the team from train to conference is the easy part. Persuading everyone to stop taking pictures might take longer.

Meet the neighbours

Not every conference has penguins next door…

Antwerp ZOO is one of the oldest in the world, and its 19th-century gardens and grand animal houses make a fine first afternoon if you arrive a day early. It’s an easy way to stretch your legs and catch up before the sessions start. It’s also a good escape between talks when your brain needs a breather. November days are short, so check closing times and go early.

Split up, swap notes

Wednesday morning is where a team really pays off. At 9am, seven hands-on tutorials start at the same time. Someone builds and breaks an AI testing agent in the AI Testing Dojo, someone else gets to grips with a scalable Playwright framework, and a third person explores agentic mobile automation. On your own you get to choose one. With a team, you can cover them all.

The keynotes give everyone something to discuss over lunch, and deep dives are already starting to book out – so the sooner your team is registered, the more choice you’ll have.

book tickets

Let the old town lead

Make for Grote Markt, where ornate guild houses that lean over the square reward a slow look upwards. The Brabo fountain tells the city’s founding legend: a giant, a severed hand and a hero with a good throwing arm. Then follow the cobbled streets, take a turn that looks interesting and see where you end up.

Let a different colleague choose each stop: a coffee here, a warm waffle there, a table somewhere inviting. Leave enough room for the conversation to wander too.

Raise a bolleke

If your team enjoys a Belgian beer, book the De Koninck brewery experience to learn how Antwerp’s favourite beer is made. Then order a bolleke, as the locals call it after its round glass. The site has food shops and places to eat, so you can easily stay for dinner. Book ahead for groups.

Or keep it simple: after Wednesday’s EXPO party, reserve a long table in the old town and let the day’s talks carry on over dinner. Expect at least one heated debate about flaky tests.

Follow the river to a fortress

A medieval fortress on your evening wander? Antwerp has that covered. Head towards the Scheldt and you’ll find Het Steen, the city’s oldest surviving building. Stop for a photo, take in the river views and give yourselves a little time before dinner. A few centuries of history makes a good change from talking about your next sprint.

Make room for chocolate

For a team outing with broad appeal, put Chocolate Nation on the list. Its interactive museum explores the world of Belgian chocolate, making it a tempting stop for a spare afternoon — especially if November sends you indoors.

Afterwards, seek out a chocolate shop together and compare your choices. Anything that makes it back to the office is a bonus.

Bring something back together

Attending AutomationSTAR as a team means you’ll come home with more sessions covered, more practical takeaways and a shared plan to act on them. Antwerp adds the long dinners, the detours and the conversations you rarely find time for during a normal working week. And it costs less:

Team of 3–4: 15% off
Team of 5–9: 20% off
Team of 10: 25% off, just €1,050 per person

Bring your colleagues, comfortable shoes, and a little curiosity. Antwerp will take care of the detours! You can find out more about hotels and getting here on our Location page.

Your next team trip is less than a month away! Book your tickets now.

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Sep 21 2026

Who Tests the Tests? The Hidden Risk in AI-Generated Test Cases

A test suite goes green across the board. Every check passes, and the release ships to production. Then the support tickets start coming in: a regression got through, something the tests were supposed to catch. Nobody investigates too deeply, since it happened only once and could easily have been an isolated case.

Then it happens again. The same pattern repeats: a green suite, a real bug, and a gap nobody can explain. That is when teams finally start asking the question they should have asked from the beginning: did these tests ever actually work, or did they simply never fail?

That question matters more now than it did two years ago because AI can write test cases in seconds, allowing teams to generate large numbers of tests in a fraction of the time. The problem is that teams can quickly end up with a suite full of tests that look thorough but fail to catch the bugs that matter. A green checkmark tells you that a test passed, but it says nothing about whether that test was ever capable of catching a real bug.

The AI doesn’t know your product, it’s guessing

An AI model writing a test case doesn’t know your product. It knows patterns. It has seen thousands of checkout flows, login forms, and API calls, and when you ask it to test yours, it draws on what those examples have in common. That’s often a reasonable starting point, since the basic structure of a checkout test is fairly predictable across products. But looking like a good test and actually checking the right thing are not the same, and the model has no way to tell the difference on its own.

Left unchecked, the model guesses. It might assert that a page loaded, that a field is not empty, that a button exists, none of which tell you the feature actually works the way your business needs it to. The test runs, returns green, and sits in your suite as false confidence.

Does this test actually earn its green checkmark?

Take a concrete case: an AI tool generates a test for a checkout discount code. The test enters a code, clicks apply, and checks that the page didn’t show an error. It passes. Should you trust it?

Here’s what actually you should do:

Break the code on purpose: this is one of the most reliable checks you can run, so it deserves close attention. Go into the discount logic and deliberately break it: make the discount apply twice, ignore the expiry date, or apply to an already-discounted item. If the test still passes after you’ve broken the logic it is meant to protect, it was never checking the right thing. This is called mutation testing. Instead of asking, “Does the test pass?”, it asks, “Can this test fail when it should?” Gartner recommends this approach for teams using AI to expand test coverage, since AI-generated tests can lack the depth needed to catch real defects on their own. In practice, you create several broken versions of the discount logic and check that the test catches each one. Any failure to detect a mutation tells you exactly what needs to be fixed before you can trust the green result.

Have a second AI model check the assertion: Once a test survives mutation testing, ask a different question: does its assertion actually match the requirement, or does it just confirm that something happened? For the discount code, the requirement isn’t simply that no error appears. It’s that the discount matches what the code entitles the customer to and that the order total reflects it correctly. A second model can compare the assertion against the written requirement and flag any mismatch. The goal is to close the gap between a test that confirms something happened and one that confirms the right thing happened.

Check for near-duplicates: AI tools generate tests quickly, but that can also mean generating the same test several times with small variations. If you already have four tests confirming that a discount code works correctly, a fifth that only changes the code string is not adding coverage. It is just adding another test to maintain. The ASTQB calls this “false confidence from volume”: a large test suite can look thorough while still leaving the same risks uncovered. Merge tests that overlap heavily and keep near-duplicates only when they cover a genuinely different path, such as combining a discount code with a gift card.

Save human review for what matters most: Not every test needs the same level of scrutiny. Sonar’s 2026 State of Code Developer Survey found that 96% of developers don’t fully trust AI-generated code to be functionally correct, while only 48% always check it before committing. The same caution should apply to AI-generated tests, particularly in areas where a failure can have a direct impact on customers and the business. Pricing, payments, and authentication deserve closer attention: a discount applied twice, a payment processed more than once, or a login that gives access to the wrong session can all have serious consequences. Reserve manual review for these high-stakes flows, while using mutation testing and AI-assisted review to validate the rest of the suite.

The next question testingON is built to answer

Most AI test-generation tools, including the ones enterprise QA teams are piloting right now, stop at generation. They write the test, hand it back green, and move on. They don’t run the checks described above, they don’t break the code on purpose, they don’t flag near-duplicates, they don’t know which paths are high-stakes enough to need a human. That’s true of most AI test-generation vendors too: speed is the pitch, and validation is left entirely to whoever’s using the tool.

We built testingON’s roadmap around closing that specific gap. Under an initiative we’re calling Agentic AI, we’re building an agent whose job is exactly what this post describes: taking a generated test and verifying it can actually catch a real defect, not just checking that it runs green. It sits alongside a companion agent that generates tests overnight against new user stories, and another that runs tests in the background and reports on execution results and performance, so generation, validation, and execution work as one connected process instead of three separate

tools bolted together. This is a roadmap direction we’re actively building toward, not a shipped feature today, but it’s a direct answer to the exact problem this post lays out.

If you’re piloting AI test generation and want to talk about how to validate what it’s producing before it reaches production, see what testingON is building.

Author

Tiago Honorato, Quality Management DevOps & Automation Associate Director at Noesis

I am a technology leader with more than 18 years of experience in Software Engineering, Quality Assurance, Product Innovation, and Digital Transformation.

I started my career as a Software Developer at Nokia Siemens Networks after completing my Master’s degree in Computer Science in Lisbon, Portugal. Shortly after, I embraced a new challenge at Noesis, where I founded and scaled the company’s Test Automation practice, helping clients accelerate their software delivery lifecycle and significantly improve quality standards.

Although I transitioned from development to Quality Assurance, I have always maintained a strong connection to software engineering — combining deep technical expertise with strategic quality vision. Over time, I led multiple initiatives across test automation, application development, and quality transformation programs for major enterprise clients.

Leveraging this cross-functional experience, I conceived and led the creation of testingON — a test management and automation platform designed to reduce testing time while increasing coverage and enabling non-technical users to actively contribute to quality processes. The platform integrates with leading test management, CI/CD, and project management tools and is currently used by major Telco, Retail, Banking, and Insurance organizations in Portugal and Brazil.

I later accepted a new challenge at NTT DATA, where I served as Senior Manager responsible for the Quality Assurance & Testing offering, leading strategic accounts, strengthening delivery capabilities, and expanding quality services.

Subsequently, I returned to Noesis as Associate Director to lead the strategic evolution of testingON. In this role, I am responsible for product vision, innovation roadmap, and growth strategy — including the integration of Generative AI capabilities and the modernization of the platform’s architecture to ensure scalability, cloud readiness, and long-term competitiveness.

testingON are Platinum Partners at AutomationSTAR Conference EXPO 2026. Join us in Antwerp 4-5th November 2026.

Sep 17 2026

The Skills I Learned and the Ones That Got Away

Ahead of her AutomationSTAR Deep Dive, Close Encounters with Systems, Rachel Kibler reflects on the skills that shaped her testing career—and how systems thinking gave her a name for what she was already doing.

I started my career by diving headfirst into the context-driven testing community, forming some very strong opinions along the way. I look back at my dogmatism and wonder why I was so convinced that I, a baby tester, was right just because I was parroting something persuasive that resonated with me.

I found my way to conferences, which broadened my horizons and showed me that yes, everyone thinks in terms of context, and the arguments that we have within our testing community can be tiresome, worn-out, and ultimately unhelpful, even harmful when they gatekeep and manufacture heightened emotions around who’s doing testing “the right way”.

Ten years in, I hold a lot of my strong beliefs still, but considerably more loosely, and I’ve become much more understanding of everyone’s journey. Turns out, not everyone needs to do things the way I do them!

In addition to my attitude shift, I found that the skills I learned early on formed a lot of what I care about and still think about. And that these skills that I value turned out to be systems thinking before I had the words for it. Elisabeth Hendrickson’s book, Explore It!, was massively influential in learning how to create test charters and organize my mental models, as was Janet Gregory and Lisa Crispin’s Agile Testing in thinking strategically about how to deliver quickly and not be a bottleneck.

James Whittaker’s How to Break Software was one of the earliest I read, and though he does spend a lot of time on printer testing, the idea of creativity in your testing by shaking up the machine (sometimes literally) was helpful. And doing the Black Box Software Testing foundations course by Cem Kaner and Rebecca Fiedler through the Association for Software Testing played a huge role in shaping my skills too.

Because of those influences, I learned to think about software in creative ways, to not trust what someone says the software does and does not do, and to spend time following my gut instincts about where things could be broken, largely at the boundaries of the software and the boundaries of our understanding of the software.

I learned how to communicate well with my developers and at the right time. I learned how to advocate for my bugs, and hopefully for the user. I learned how to identify risk and to bring people together for conversations about what matters and what quality is for our products, creating feedback loops and shared mental models. These skills felt good to me, they felt right, and they enabled a solid career with good delivery of software.

Some testing skills eluded me though. Writing extensive documentation has never been my strong suit, and though I do strongly believe that test management systems are a kindness and should be a system of record for our software, writing steps down in depth has always felt daunting and overwhelming to start with. But documentation does make our system legible to others, modeling what we understand to be true. I also never really got a handle on what my automation should look like when it’s at its best, when it’s giving the feedback on the system in the right place.

AI changed the game for me, enabling me to pay more attention to the “what” and the “why” of automation rather than the “how” that had been so much of the concern of past me. However, thinking about documentation and automation in terms of systems may be the thing that drives both of these forward for me.

For AutomationSTAR in Antwerp this November, I get to do a deep dive into systems thinking itself. I’ll give a brief overview of the concepts in systems thinking before we dive deeper into time delays and other less intuitive things, pulling heavily from the new work of Elisabeth Hendrickson and Joel Tosi (their book, Signals & Levers, releases on September 22nd, 2026) as well as the work of Donella Meadows (Thinking in Systems: A Primer) and many others. Learning from Elisabeth Hendrickson’s book at the beginning of my testing career and now from her new book with Joel Tosi seems fitting.

I didn’t invent any of this, it’s been around for a long time, but it’s more relevant than ever right now. Systems are becoming more and more complicated as we throw AI into the mix for both the software itself and the processes around it. Feedback loops are getting longer and stranger, and the levers to pull are moving. Testers talk about systems in daily work, but perhaps without the structured framework to make the conversation’s importance clear. Systems thinking gave me language for the things I learned to do by instinct or by mimicry, and I’m excited to pass that on so you can talk about the things you already do and care about in terms of systems.

Rachel Kibler is speaking at AutomationSTAR – check out her Deep Dive and book your tickets.

Sep 14 2026

Meet BQA and Teammaite at AutomationSTAR: from testing expertise to AI-powered quality

Visit BQA and Teammaite at AutomationSTAR and discover how testing expertise, AI and automation come together – with AI as the accelerator and the professional in the driver-seat.

AI-powered development is delivering code faster, sometimes producing an entire feature in a single iteration. Testers therefore need to analyse specifications, risks, test strategy, coverage and automation just as quickly – without sacrificing quality.

That raises a practical question:

How do you stay relevant, valuable and effective as quality becomes more AI-driven?

At AutomationSTAR, BQA and Teammaite will explore that question with testers, automation engineers, QA leads and business owners. Our message is simple: AI should not replace testing expertise; it should make that expertise more powerful.

Quality Starts Before Testing Starts

Requirements often look clear until testing reveals the gaps: exception flows are undefined, acceptance criteria are not measurable, risks are unclear or the business expectation does not match what was built.

BQA therefore believes quality must move upstream. Requirements, user stories and acceptance criteria should be analysed before development starts, with risks and test ideas linked to business intent from the beginning.

With AI support, Teammaite can analyse requirements for ambiguity, missing information, inconsistencies and testability, helping teams build a stronger foundation for test design and automation.

The challenge is not simply generating tests faster, but deciding what should be tested:

What does the specification really mean?
Are the requirements complete, unambiguous and testable?
Where are the biggest product and business risks?
Which test techniques and level of coverage are appropriate?
How do we translate those choices into existing processes and tools in a traceable way?

From Dpecification to the Right Test Coverage

At our stand, we will demonstrate one recognisable end-to-end quality flow:

Specifications -> requirements -> quality improvement -> risk analysis -> test strategy -> test design -> execution and management

Teammaite can process documents, models, tables or existing tickets, while a BQA consultant determines the appropriate context and extraction approach.

Teammaite then identifies ambiguity, inconsistencies, missing information and testability issues. AI can propose product risks, impact and priorities, but consultants and stakeholders validate the business context and risk weighting.

Based on clearer requirements and validated risks, teams can define relevant test levels, test types, techniques and desired coverage.

The goal is not automatically more tests, but targeted and explainable coverage of the risks that matter.

From Tester to AI Test Orchestrator

AI accelerates the process, but it does not remove the need for testing expertise. Experienced testers understand context, recognise risk, judge whether test cases are meaningful and decide what should be automated or explored by people.

BQA calls this role the AI test orchestrator: a quality professional who uses AI deliberately, critically and effectively across the software development lifecycle. For test managers and QA leads, governance, prioritisation, traceability and release confidence remain essential.

The key question is no longer only:

“Can AI create tests?”

It is:

“Can we explain why these tests cover the right risks?”

That is the central question behind our stand at AutomationSTAR.

AI in Control: People Make the Decisions

Responsible AI must be visible in the way quality decisions are made: AI makes proposals; people make decisions.

Important steps can include review and approval. Sources, assumptions, modifications and decisions remain traceable, uncertainty is explicit, and the degree of autonomy can be adjusted to the organisation and the risk involved.

Teammaite delivers the intelligent, integrated workflow. BQA provides the human link – from selecting the approach and facilitating stakeholder discussions to risk analysis, test strategy, governance and implementation.

Connect AI to the Tools you Already Use

AI-supported quality should not become an isolated experiment. Approved results need to fit the organisation’s existing way of working.

The approach can connect with tools such as Robot Framework, Testersuite, Jira and Azure DevOps while maintaining traceability from specification through requirements and tests to findings.

Together, BQA and Teammaite support the full quality flow: requirements analysis, test strategy, test cases, automation and release insight.

Teammaite brings the AI capability; BQA brings experienced consultants who understand testing, test management, automation, governance and software delivery in complex organisations.

AI tooling alone does not change an organisation. People, process and implementation determine whether AI becomes a useful colleague or just another experiment.

Bring Us Your Specification

At AutomationSTAR, we want to go beyond a generic AI product presentation.

Bring a specification or sample requirement and take the spec-to-strategy challenge.

In a few minutes, Teammaite can highlight potential improvements, risks and suggested test coverage. A BQA consultant will discuss which proposals make sense, which assumptions need challenging, where human judgement is required and what integration into your quality process could look like.

You leave with a compact AI-in-control quick scan and a clearer view of what AI-supported quality could mean for your organisation.

Come and Challenge Us

We believe in practical quality improvement, not AI hype for its own sake.

Come to the BQA and Teammaite stand with a testing challenge, requirements problem, automation bottleneck, management concern or simply one existing specification.

Ask us what AI can do, where human expertise remains essential and how your team can move from specification to improved requirements, validated risks, an appropriate test strategy and integrated test facts.

And come prepared for one question from us:

Can you explain why your current tests cover the right risks?