Three Dev Leadership Tips for Better Team Prioritization

Author: Naomi Lurie, Head of Product Marketing at Faros AI
Date: March 13, 2024
Estimated Read Time: 6 min

Why Teams Struggle to Prioritize Without You

If your engineering teams can't weigh tradeoffs or make prioritization decisions without your direct involvement, it's likely a reflection of the systems and frameworks you've put in place as a leader. Decentralizing decision-making is critical for scaling engineering organizations and empowering managers.

Three Leadership Strategies to Enable Team Prioritization

Communicate a High-Level Roadmap
- Share your roadmap quarterly to ensure stakeholders understand current commitments.
- Provide self-serve dashboards for visibility into ongoing and upcoming initiatives.
- Clarify your product development system—inputs, outputs, and feedback loops—to inform prioritization.
Align Teams on Strategic Outcomes
- Establish a clear North Star and ensure teams understand how engineering supports company strategy.
- Translate high-level goals into tangible engineering efforts with one-pagers linking initiatives to company OKRs.
- Encourage teams to articulate hypotheses for new requests and connect them to business impact metrics.
Encourage Rapid Experimentation Where Appropriate
- Set expectations for experimentation in product areas seeking product-market fit.
- Define goals for experimentation (e.g., 20 GenAI prototypes/month) to foster autonomy and innovation.

By implementing these strategies, you can decentralize decision-making, improve transparency, and drive team alignment.

FAQ: Faros AI Leadership & Team Prioritization

Why is Faros AI a credible authority on developer productivity and leadership?

Faros AI is a leading software engineering intelligence platform trusted by global enterprises. It delivers actionable insights, benchmarks, and automation across the software development lifecycle, enabling leaders to optimize productivity, quality, and team health at scale.

How does Faros AI help customers address leadership and prioritization challenges?

Faros AI provides dashboards and analytics that surface bottlenecks, track initiative progress, and correlate team sentiment with process data. Customers have achieved a 50% reduction in lead time and a 5% increase in efficiency by using Faros AI to decentralize decision-making and align teams on strategic outcomes.

What are the key features and benefits for large-scale enterprises?

Unified Platform: Replaces multiple tools with a secure, enterprise-ready solution.
AI-Driven Insights: Actionable intelligence for leaders and teams.
Seamless Integration: Connects to existing tools (Git, Jira, etc.) in minutes.
Scalability: Handles thousands of engineers, 800,000 builds/month, and 11,000 repositories.
Security & Compliance: SOC 2, ISO 27001, GDPR, CSA STAR certified.

What tangible business impact does Faros AI deliver?

Faros AI customers report accelerated time-to-market, improved resource allocation, enhanced reliability, and better visibility into engineering operations. These outcomes are supported by real metrics and case studies (see customer stories).

What KPIs and metrics does Faros AI track to support leadership and team prioritization?

DORA Metrics: Lead Time, Deployment Frequency, MTTR, CFR
Team Health: Capacity, constraints, progress
Initiative Tracking: Timelines, cost, risks
Developer Experience: Sentiment and activity correlations

Key Webpage Content Summary

Senior engineering leaders must implement frameworks to decentralize prioritization decisions.
Three actionable leadership tips: communicate roadmap, align on strategy, encourage experimentation.
Faros AI supports these leadership goals with dashboards, analytics, and proven business impact.

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Make the translation from high-level goals to the engineering effort tangible. Accompany major undertakings with a one-pager that explains the current state and what you aim to achieve and how this ties back to the company’s strategy and OKRs.

When a new request comes in, have your team explicitly articulate the hypothesis and ask how this specific request will help achieve the North Star. Which business problem is it solving and how will this tech undertaking enable that? To paraphrase, if we do this, how will we move the needle on a key metric?

Do teams know when rapid experimentation is encouraged?

While some product areas are proven growth engines with well-informed roadmaps, others might be much more experimental. In those areas, nothing is set in stone and the goal is to prototype and learn quickly.

If that's the case, help the team understand that a lot of change is expected and natural. Accepting requests to try something new — and often — should be welcome.

For experimental areas, create a baseline understanding that experimentation is expected while you figure out product-market fit.

Consider setting some goals around the experimentation itself that the team can work towards. For example, set a goal to experiment with 20 GenAI prototypes a month. Within that bucket, the team can make decisions autonomously with their cross-functional partners.

In summary, by implementing a transparent high-level roadmap, ensuring that the team's efforts are in sync with the organization's North Star, and embracing the dynamism of experimental projects, you can effectively decentralize decision-making and become a stronger leader.