QA's New Role in Digital Transformation: Part 2

Collaboration with Business Stakeholders

Another critical aspect of QA transforming itself is how QA professionals work with their stakeholders. QA leaders must get used to the idea of being part of decision-making teams rather than solely execution teams. They also need to adapt & adapt to the new titles and roles that agile models and digital transformations bring. It is quite common for many agile models not to recognize specific test titles. For example, a title like 'test lead' in an agile team is no longer recognized as a stand-alone role.

As QA professionals should aim to build 'soft influence,' collaboration becomes a key tool. Let's explore some examples (for inspiration) of how this collaboration can be effective in day-to-day situations. I've used a spectrum of examples to cut across industries and roles within an organization.

Again, do pause and think about your own situation and ways to apply the idea. My idea is not to give you a script, but something to get your creative juices flowing!

• Product Owner Partnerships: You're regularly meeting with the Product Owner to discuss quality metrics and their impact on user experience. "I've noticed our latest feature has increased user engagement by 15%, but it's also introduced some performance issues. Let's discuss how we can balance functionality and performance in our next sprint."
• Product Manager Collaboration: You're working with a Product Manager on feature prioritization. "Based on our quality metrics and user feedback, I recommend prioritizing the checkout optimization over the new social feature. Here's data showing it could reduce cart abandonment by 15%."
• Customer Support Integration: You're setting up regular syncs with the customer support team. "By analyzing common support tickets, we've identified top recurring usability issues. I'd like to propose some changes to our UX testing approach to catch these earlier."
• Finance Team Collaboration: You're working with the finance team to quantify the ROI of quality initiatives. "Our automated regression suite has reduced manual testing time by 40%, translating to a cost saving of $X per release cycle."
• Marketing Team Alignment: You're consulting with the marketing team on feature readiness. "While the new social sharing feature is functional, our usability testing suggests it might not meet user expectations. Here are some recommendations before we proceed with the marketing campaign."
• Legal and Compliance Partnerships: You're collaborating with the legal team on data privacy testing. "I've developed a test suite specifically for GDPR compliance for the 'right to forget'. Let's review it together to ensure we're covering all necessary aspects."
• Agile Coach Synergy: Example: You're partnering with the Agile Coach to improve team processes. "I've noticed our Definition of Done lacks specific quality gates. Let's work together to incorporate these quality checks, which will improve our first-time-right delivery."

Think about all your key stakeholders - at least all the departments & leaders who have a stake in your product flowing into production. Include all the areas involved in the entire process - be it business decision-makers, customer service ops, finance, compliance, sales, etc., They may not appear to have an active role in your day-to-day functioning, but almost all of them have a role in building your influence in the organization.

Think of your world as a spider web. The more branches you build across your organization, the stronger your influence is (and you might catch even more bugs)!

Key Takeaway: QA professionals build influence by taking quality findings directly to product owners, product managers, finance, marketing, legal and customer support, and framing each finding in the outcome that team already tracks.

Building Soft-Influence: more art than science

I hear you - "Yep, it is cool and you just write it on a blog. But how do I (insert your role here___) actually do it? Nobody listens to me". I would recommend a lot of books, but you already know what they are (if not, just Google it). Here are some of my tips:

• Start Small and Build Credibility: Don't aim to change the entire organization overnight. Begin with small, achievable goals that demonstrate your value. For example, if you're a QA engineer, start by improving the efficiency of your team's testing process. Document the time saved and the increase in bug detection. Use this success as a springboard for larger initiatives. Success gets you more attention than you think!
• Speak the Language of Your Audience: Tailor your communication to your audience. When talking to developers, focus on technical details. With product managers, emphasize user experience and feature quality. For C-level executives, translate quality metrics into business impact and ROI.

Some executives need story-telling, and some others might need facts. Understand what works for your audience. Your ideas may be genius, but it doesn't matter if your audience does not understand (nor is interested) in what you say.

• Be a Problem Solver, Not Just a Problem Finder: Instead of just pointing out issues, come prepared with solutions. If you identify a recurring bug, present a root cause analysis along with a proposed fix. This proactive approach positions you as a valuable contributor rather than just a critic. It might not be practical to find a solution to every problem you identify, but learn to communicate the impact & benefits (& read number 2 above).
• Build Strategic Relationships: Identify key influencers in your organization and invest time in building relationships with them. This could be senior developers, product owners, or even executives. Understand their goals and challenges, and look for ways to align your quality initiatives with their objectives.
• Leverage Data to Tell Your Story: Use data to support your arguments. For instance, if you're advocating for more automated testing, present statistics on how it has reduced manual testing time and improved release quality in similar projects or companies.
• Be Persistent, But Patient: Change doesn't happen overnight. If your ideas are met with resistance, don't give up. Instead, try to understand the reasons behind the resistance and address them. Sometimes, you may need to wait for the right moment or refine your approach.
• Continually Educate Yourself and Others: Stay updated with the latest trends in quality assurance and digital transformation. Share your knowledge through informal chats, lunch-and-learn sessions, or internal blogs. Position yourself as a go-to resource for quality-related information. Also, focus on understanding your business. The more you learn about your business & your organization, the better you place yourself to tap the right opportunities.
• Show, Don't Just Tell: Whenever possible, demonstrate the impact of your ideas through pilot projects or proof-of-concepts. For example, if you're proposing testing tools, set up a small-scale implementation to show its benefits in real-time.
• Embrace Failures as Learning Opportunities: Not all your initiatives will succeed, and that's okay. When things don't go as planned, conduct a thorough retrospective, share the lessons learned, and use this experience to refine your approach. Remember, you'll earn respect even if you try & fail but your heart is in the right place.
• Celebrate and Share Successes: When your initiatives lead to positive outcomes, make sure to celebrate and share these successes widely. This not only boosts your credibility but also helps in gaining support for future initiatives. And don't forget to give credit to everyone who might have contributed.

Remember, building soft influence is a gradual process. It requires consistency, patience, and a genuine commitment to adding value to your organization. As you consistently apply these principles, you'll find that your voice carries more weight, and your influence grows organically.

Key Takeaway: Soft influence in QA is built gradually through small documented wins, communication tailored to each audience, and arriving with a proposed fix instead of only a defect report.

Which Metrics Prove QA's Contribution to Digital Transformation?

Report delivery metrics, not test counts. Defect totals and pass rates describe your test suite. Delivery metrics describe the product, and business stakeholders act on them. DORA now publishes five software delivery metrics: change lead time, deployment frequency, failed deployment recovery time, change fail rate and deployment rework rate. DORA defines change fail rate as the ratio of deployments that need immediate intervention, usually a rollback or a hotfix, and deployment rework rate as the ratio of unplanned deployments caused by a production incident. Both are quality numbers, and both already sit on the engineering dashboard your leadership reads.

These numbers matter more now because AI has changed their shape. Google Cloud reported in its 2025 DORA report that 90% of respondents use AI at work and more than 80% believe it has increased their productivity, while 30% report little or no trust in the code AI generates. The same report observes a positive relationship between AI adoption and software delivery throughput, and a negative relationship with software delivery stability. Teams ship more, and more of what they ship needs fixing. DORA put a cost on that in its ROI of AI-assisted Software Development report, last updated on 22 April 2026, which models change fail rate rising from 5% to 6% after AI adoption and names the extra review effort behind it the verification tax.

That gap is where QA owns the argument. If your organization rolls out coding assistants and the change fail rate climbs, you have evidence for investment in automated testing, version control discipline and fast feedback loops, which the DORA authors name as the control systems that stop extra change volume from turning into instability. Pick two of these metrics, record a baseline before the transformation work starts, and report the same two every month. A trend line a finance lead can read does more for your standing than any coverage percentage.

Key Takeaway: Picking two DORA delivery metrics such as change fail rate and failed deployment recovery time, recording a baseline before transformation work starts and reporting the same two every month proves QA's contribution to business stakeholders.

How Do AI Agents Change QA's Role in Digital Transformation?

AI agents move QA's work from writing tests to reviewing what an agent produced and proving it actually checks something. The tooling is documented, so the shift is not speculative. Microsoft's Playwright MCP server drives a browser on behalf of a language model using Playwright's accessibility tree rather than pixel input, which keeps the agent's steps deterministic and removes any dependency on a vision model. The same README states that Playwright MCP is not a security boundary, so an agent driving a browser needs the isolation you would give any untrusted client.

The authoring side carries equally explicit limits. GitHub documents that its Copilot coding agent runs in a GitHub Actions environment, works on one branch at a time, opens exactly one pull request, and caps each session at 59 minutes. Those limits define where the human work starts. An agent produces a candidate inside a narrow window, and deciding whether the candidate is correct happens outside it.

Three skills gain value as a result:

• Risk-ranked failure analysis: You decide which failure modes carry business consequence, because an agent ranks nothing on its own.
• Assertion review: You judge whether a generated test checks the behavior or only the shape of the code, which is the difference between coverage and a bigger test count.
• Feedback-loop ownership: You keep the review loop fast enough that extra change volume does not turn into delivery instability.

These are the same judgement calls that earn a QA professional a place in decision-making teams, which is why agentic tooling makes the role more valuable rather than less.

Key Takeaway: AI agents such as the GitHub Copilot coding agent and Microsoft's Playwright MCP server produce test candidates inside documented limits, so QA's role becomes ranking failure risk, reviewing generated assertions and owning the feedback loop.

Self-Assessment: Are You Future-Ready?

Let's have some fun!

Grab a pen and rate yourself on a scale of 1 (for "Not at all ready") to 5 (for "Completely Ready") for each statement below. Let's see if you're cruising on the fast lane to future QA mastery or if it's time to refuel!

Scoring Guide:

• 40-50: You're well-positioned for the future of QA. Keep pushing the boundaries!
• 30-39: You're on the right track. Focus on strengthening your weaker areas.
• 20-29: There's room for improvement. Consider creating a personal development plan.
• Below 20: It's time to seriously invest in upskilling. The future is coming fast!

Remember, no matter your score, the key is to cultivate a growth mindset. The QA landscape is constantly evolving, and your willingness to learn and adapt is your most valuable asset. In addition, know more about the top trends in software testing:

Key Takeaway: Rating yourself from 1 to 5 on the ten statements covering automation coding, DevOps, cloud testing, AI knowledge, security and stakeholder communication gives a QA readiness score out of 50, and a total below 20 signals an urgent need to upskill.