5 Secrets to Great Tech Team Management

The shift toward AI-first development and remote-native teams has fundamentally changed what great management looks like in tech. With 90% of enterprise software engineers expected to use AI code assistants by 2028 and engineering teams embracing AI-powered workflows while rethinking collaboration, the traditional management playbook no longer applies.

Tightening budgets and the rise of generative AI are impacting how work is prioritized, the shape of engineering teams, and the way folks are rewarded for their work. What we’re seeing across SaaS, AI, and Web3 companies is that successful tech managers aren’t just adapting to these changes—they’re using them to build more resilient, high-performing teams.

Here are five management principles that actually work in today’s tech landscape, based on what we’re observing with engineering teams navigating this transition.

1. Build Systems for Both Technical and Human Scalability

The lines between traditional functions are blurring, demanding a new breed of “multi-hat” employees. Your frontend developers now need to understand prompt engineering. Your DevOps engineers are managing security compliance alongside deployments. This isn’t just about being supportive—it’s about systematically building an environment where people can expand their capabilities.

The reality in today’s tech landscape is that effective managers create scaffolding for growth, not just emotional support. This means establishing clear learning pathways for emerging technologies, creating cross-functional project opportunities, and building knowledge-sharing systems that don’t rely solely on you as the bottleneck.

Given the current market shift toward skills-based hiring, with 81% of leaders agreeing that adopting a skills-based approach drives economic growth by improving productivity, innovation, and organizational agility, your role becomes less about traditional people management and more about capability orchestration.

2. Navigate Technical Debt Decisions with Market Context

Being realistic doesn’t just mean setting clear KPIs anymore, it means making technically informed decisions about when to pay down debt versus when to ship fast. With AI’s role in development introducing complexity rather than simplifying projects, your judgment calls around technical trade-offs have become critical strategic decisions.

What most companies miss about this balance is that “realistic” in 2025 means understanding both your team’s technical constraints and the competitive pressure you’re under. When your competitors are shipping AI features weekly, your decision to spend a quarter refactoring might be strategically sound or commercially naive—depending on your specific context.

Here’s what we’re seeing with similar roles: effective managers are developing frameworks for these technical trade-off decisions rather than going with gut instinct. They’re tracking technical debt as a business metric, not just an engineering concern.

3. Master Asynchronous and AI-Enhanced Communication

Remote work allows hiring from diverse geographic locations, increasing access to top talent, but it demands a complete rethinking of how information flows through your team. Good communication in distributed teams isn’t about more meetings—it’s about building systems that preserve context and enable decision-making without constant synchronization.

The challenge many engineering managers face is that anyone can write a grammatically correct, tone-adjusted, and compelling message today using AI tools, which creates new problems around authentic feedback and genuine relationship building. Your communication strategy needs to account for this reality.

Effective communication now means designing information architecture for your team: decision logs, RFC processes, and documentation systems that work across time zones and skill levels. The traditional approach of managing through informal conversations doesn’t scale when your team spans continents.

4. Use Metrics That Actually Predict Team Performance

While technical analysis—like codebase growth, number of microservices, or test coverage—can give some insight into our tech stack, it usually tells us nothing about actual productivity. The engineering leaders who are succeeding right now have moved beyond vanity metrics to measurements that predict team effectiveness and business impact.

Based on our experience with similar roles, the most useful metrics combine technical output with collaboration quality. This includes cycle time for feature delivery, but also knowledge transfer effectiveness, cross-team dependency resolution speed, and how quickly new team members become productive contributors.

Let’s rethink how you approach measurement: instead of tracking lines of code or story points, focus on how quickly your team can adapt to changing requirements and how effectively they’re building systems that support business goals.

5. Adapt to the Multi-Generational and AI-Enhanced Workforce

As Gen Z continues to enter the workforce, we expect to see a growing divide between generations of developers in 2025, particularly around framework preferences and career expectations. Your adaptability as a manager now includes navigating these generational differences while building coherent team practices.

60% of engineering leaders say AI hasn’t significantly boosted team productivity, which means your role includes helping your team find the right integration points for AI tools rather than assuming they’ll automatically improve efficiency.

The current shortage of senior technical talent means you’re likely managing a mix of experienced engineers who are skeptical of new tools and junior developers who may be over-relying on AI assistance. Adapting means building practices that leverage both perspectives.

What we’re seeing with teams that navigate this successfully: they create explicit guidelines for AI tool usage, establish mentorship structures that work across experience levels, and maintain technical standards that ensure code quality regardless of how it’s generated.

The Reality for Tech Managers in 2025

Managing engineering teams today requires a combination of technical depth and strategic thinking that goes beyond traditional people management. 65% of engineering leaders now cover wider responsibilities, which means your success depends on building systems and processes that scale without your constant intervention.

The companies that are thriving aren’t just hiring for technical skills—they’re building management capabilities that can navigate the intersection of AI adoption, remote collaboration, and rapidly evolving technical requirements.

If you’re scaling your engineering organization or rethinking your management approach, let’s discuss how these principles apply to your specific technical context and team composition. The challenge isn’t just finding great engineers, it’s creating an environment where they can do their best work while adapting to an evolving technical landscape.