Brijesh Deb

Background

About Brijesh Deb

I have spent 25+ years helping organisations close one stubborn gap: the distance between delivery ambition and confidence they can actually defend.Most teams can ship. Far fewer can explain, with credibility, why what they are shipping is fit for purpose, resilient under pressure, commercially sound, and ready for real users. That is where I usually come in.My work sits at the intersection of quality engineering, consulting, and solution design. I help clients turn complex delivery problems into practical, investable solutions across onshore, offshore, and hybrid models. That includes assessments, maturity reviews, transformation roadmaps, bid support, operating model design, and transition-ready approaches that stand up beyond the proposal stage.I have worked across large enterprise transformation, multi-vendor delivery environments, and quality engineering programmes where the challenge is not just better testing, but better decisions. That means shaping repeatable regression, pragmatic automation, stronger non-functional testing, clearer governance, and evidence leaders can use when risk, cost, speed, and confidence pull in different directions.I am hands on by instinct and consultative by experience. I can work through stakeholder trade offs in one conversation and get into the realities of automation, performance, API, UI, and delivery constraints in the next. Good solutioning fails when it becomes too abstract. Good engineering fails when it ignores commercial and organisational reality.AI is now part of that picture. I bring a practical perspective on AI in testing and responsible AI adoption, helping clients apply emerging capabilities in ways that improve quality engineering without losing control. For me, AI only matters when it produces usable value with clear guardrails around risk, governance, explainability, privacy, and accountability.Focus areas:• Quality engineering solution design across complex enterprise programmes• Assessments, maturity reviews, and transformation roadmaps• Pre-sales, bids, solution shaping, and transition into delivery• Automation, regression strategy, and non-functional testing• Quality governance, risk signals, and executive decision support• AI in testing and responsible AI adoption across the SDLC• Multi-vendor delivery, operating models, and scalable QE capability buildingI also lead The Test Chat, a global testing community of professionals, and contribute through podcasts, AMAs, articles, webinars, and conference speaking.