Maqbool Khan

If you have multiple teams reporting to you, all of whom are having team gatherings, you often see 2 directions: 1. Mandate an agenda 2. Let every team’s manager run the offsite however they choose to This is basically looking at either rigidity or chaos, but you don’t have to choose between only those options. The move most executive leaders miss: define overarching outcomes, not agendas. Not "everyone must do a vision exercise." More like: every team leaves with clarity on priorities, documented decisions, and at least a few concrete improvements to how they work together. Teams can then get there however works for them, walking away having achieved what actually matters. Consistency doesn't mean uniformity. It means every team gets the support they need to have an experience that actually moves their work forward. Here’s a link to the blog post to read more: https://lnkd.in/eaD5hK5Y #EngineeringLeadership #TeamEffectiveness

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Over the past 15+ years, I’ve helped engineering organizations scale with clarity, discipline, and measurable execution. Many companies are navigating increasing complexity — modernizing legacy systems, improving delivery predictability, and building strong, accountable teams capable of leading in an AI-driven and rapidly evolving technology landscape. Operational rigor and leadership alignment are no longer optional; they are foundational to sustainable growth.

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Here's what I get from it: - big tech companies are literally burning billions on "A.I." without any clear ROI plan - organic "A.I." adoption is abysmally low by their own employees because it simply doesn't work as promised, which is hypocritically embarassing for them, so now they're desperately trying to mandate its usage to prop up their reputational bubble a bit longer - CEOs still don't understand how their own sausage gets made and still don't care about producing quality products that will delight the true customers (the end users) - we have passed "peak A.I." (as all the big tech companies are starting to panic at the realization they have vastly overextended themselves by vastly overinvesting in "A.I.") and are now beginning to coast down the other side of the roller coaster track

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Lately, I have been focusing a lot on how AI will reshape the way we work and build teams. While doing some reading on this, I came across McKinsey’s article on the “agentic organization”, and it really matches what we are starting to see in business, engineering and product teams. AI is evolving from a mere tool to an active participant in workflows. As agents take on planning and execution tasks, the primary challenge now lies in the operating model, ownership, and decision boundaries. This shift emphasizes the importance of T-shaped roles, deep expertise in one area while having a broad understanding across product, data, and AI. (This is the direction I believe engineering roles will evolve toward.) Such roles enable teams to effectively design and manage human plus AI workflows from start to finish. The bottleneck has transitioned from model capability to how we can redesign teams and establish accountability. Human accountability and oversight remain. I recommend taking a look at the article for further insights: https://lnkd.in/dk_jfrrB #GenAI #EngineeringLeadership #AITransformation

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How to approach a system design 1. Start with Purpose, Not Just Features -What user problem are we solving? -What impact will this have on the business? 2. Design for Change, Not Just Scale -Modular, metadata-driven systems age gracefully. -Anticipate change; don't resist it. 3. Make Simplicity a Strategic Advantage -The best systems are often the ones that are easy to reason about. -Complexity isn’t a sign of brilliance- clarity is. 4. Think in Systems end to end , Not Silos -A service is more than code it's how it logs, scales, fails, recovers and integrate with each components -Observability, security, and resilience are part of the first draft. 5. Enable Teams, Not Just Features -Great architecture unlocks velocity for the entire team -Every design decision should help engineers move faster with confidence. 6. Innovate with Tools, Not Rules -Leverage AI tools like Cursor AI, ChatGPT, and Copilot. -These aren’t shortcuts they’re amplifiers of thought and speed.

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From Mid-Scale Hustle to Big-Scale Systems — My Leadership Journey When I worked at a mid-sized company — McAfee (formerly Intel Security), life as an engineering leader looked very different. I owned the entire tech stack end-to-end for the Real Protect SDK, which powered both enterprise and consumer security products. Decisions were quick. If I had an idea, we could experiment and roll it out within days. The visibility was immediate — the team saw the impact directly, and customers felt it instantly. But there were trade-offs: scale was limited, resources were stretched, and reliability sometimes gave way to speed. I wore multiple hats — tech lead, architect, and often the “on-call fireman.” Fast forward to today at Amazon. The canvas is massive — whether rolling out Prime membership features or building a multi-tenant portal to enable supply chain services for 1P & 3P sellers. Systems serve millions of users globally, reliability is non-negotiable, and playbooks ensure consistency across teams. I learn from some of the brightest minds, and every design decision forces me to think about scale, latency, and resilience. But here too, there are trade-offs: processes are heavier, decisions take longer, and ownership is often of just one slice of a much bigger puzzle. ⚡ What helped me move faster in this environment: Clarity of priorities → tie every discussion back to business + customer outcomes Cross-team relationships → build trust before you need alignment Document → Decide → Drive → cut endless meetings with written context & trade-offs Bias for small wins → ship incremental value instead of waiting for “big bang” launches Empowering the team → unblock quickly and let them own execution 💡 My takeaway: Mid-scale sharpened my ownership, versatility, and bias for action. Big-scale honed my systems thinking, discipline, and focus on reliability. The best leaders blend both — carrying startup agility into enterprise scale. 👉 If you’ve made the shift between mid and big companies, what was your biggest “surprise learning”? #EngineeringLeadership #TechLeadership #CareerGrowth #EngineeringManagement #ScalingTech #BigTech #StartUpCulture #LeadershipJourney

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