Ever get that sinking feeling when someone asks you to take on one more responsibility? That was me when I was nominated to lead formal QA for technology delivery at West Monroe. Between multiple projects, client needs, mentoring colleagues, account management, hitting revenue targets, and my NYC technology team leadership responsibilities, I wasn’t sure if I could balance yet another role. But after mulling it over, I decided to give it a shot—and I’m so glad I did.

To get ready for my QA responsibilities, I went through formal QA training to refresh my understanding of how to ensure our engagements not only meet but exceed client expectations. It was a solid reminder of best practices and also got me thinking about how we could improve the QA process with data and AI (more to come).

By the end of the training, I realized just how much potential this opportunity holds. As a QA lead, I get to meet new clients, check in on teams I don’t always work with, and see alternative approaches to problem-solving. It’s a chance to broaden my perspective and help ensure we deliver top-notch outcomes.

Quality Assurance is all about validating that an engagement is on the right track. It checks:

• Deliverables: Are we providing what we promised (or more)?

• Client Expectations: Are we meeting or exceeding what clients really want?

• Process Compliance: Are we following our established guidelines for finance, change orders, and so on?

Ultimately, QA is a guardrail to ensure we’re doing right by our clients. As Peter Drucker said, “Quality in a service or product is not what you put into it. It is what the client or customer gets out of it.”

Stepping into the QA reviewer role at our firm has been both strategic and hands-on. Here’s what it looks like in practice:

Setting the Foundation: Once assigned as the QA lead for a project, I work with Engagement Leads (ELs) and Project Managers (PMs) to establish a clear QA cadence. We align on what “success” looks like and confirm everyone’s on the same page.

Monitoring Progress: Throughout the engagement, I conduct periodic reviews to assess health and progress. This helps surface potential issues early. By collaborating with delivery teams and client stakeholders, we can fix minor hiccups before they become major headaches.

Aligning Expectations: I constantly track whether deliverables match (or surpass) the client’s stated objectives. Keeping that alignment front and center makes sure there are no surprises down the road.

Enhancing Collaboration: Sometimes, the biggest risk to a project is poor communication. Part of my job is to spot communication gaps and help teams close them. This fosters trust and clarity for everyone involved.

Driving Continuous Improvement: QA isn’t just about checking boxes. I gather feedback from teams and clients to identify areas for future growth. Translating that into actionable recommendations leads to real, lasting improvements.

Escalating Issues: When critical concerns arise, I flag them with the right people. That might mean working with our teams directly or escalating to leadership. A key part of QA is creating accountability and ensuring swift resolution.

In practice, these responsibilities revolve around seven core categories of engagement health: Client Expectations, Scope/Product, Client Relationships and Collaboration, Communications, Resources, Financials, and Timeline.

The training I completed was a solid refresher on QA basics—nothing revolutionary, but a great reminder of the fundamentals. As a data and AI practitioner, though, I spotted some interesting opportunities to modernize and level-up the process:

Digital Feedback Mechanisms: Right now, our feedback-gathering is fairly manual. What if we introduced digital surveys or AI-powered assistants to document findings in real time? This could reduce overhead and create better proof points for our QA insights.

Predictive Analytics for QA: Once we have that data, imagine storing it and applying predictive models. We could forecast potential risks—timeline delays, scope creep, client dissatisfaction—before they happen.

Real-Time Dashboards: Combine QA data and model outputs into live dashboards that leadership can check any time. Seeing project health and financial performance at a glance makes it easier to intervene early.

Cross-Engagement Learning: A centralized repository of QA insights, especially for data projects, could be gold. Using models to cluster and highlight common issues might surface best practices for handling sticky problems. You could also leverage an LLM with a UI to ask interesting questions about the overall QA at the firm to identify patterns in the unstructured data.

Client-Focused Enhancements: If we integrated client data—like legal agreements, stakeholder backgrounds, account plans, or previous engagement history—QA reviews could become even more targeted. Each client has unique needs, and a data-driven approach would let us focus on the most critical areas for each engagement.

Let’s be honest: these ideas won’t all happen tomorrow. They’d require significant investment in time and budget—and let’s not forget data. Ideally, there is a platform or tool we could buy that does all of this (if you build this tool give me credit 😄). We’d need a clear cost-benefit analysis to justify any large-scale upgrades. Frankly, I’m not pulling those numbers right now—I’ve got enough on my plate! But the exercise of imagining new possibilities was super valuable. It also sparked some ideas I can bring to clients who face similar manual process challenges (though not necessarily QA-related).

Quality is everyone’s responsibility. (Thanks, W. Edwards Deming!) By weaving QA into every step of our delivery process, we set our projects—and our clients—up for success. As I dive deeper into this expanded QA role, I’m excited to bring fresh ideas into our data and AI projects. There’s an opportunity to double down on the role QA plays in how we deliver impact and ensure our clients walk away feeling they got more than they expected.

Now that I’m getting comfortable in my QA role, I’m eager to see how we can continue refining our approach. Whether we implement predictive analytics tomorrow or just continue nailing the fundamentals, every step forward in QA means more trust, better collaboration, and happier clients. And isn’t that the goal?

P.S.

The picture at the top of my post is the view from our apartment - pretty unreal huh?