What “Good” Looks Like Now: A 2027 Playbook for High‑Performing Engineering Teams

By 2027, most engineering leaders are in the same squeeze. They are told to ship more, with tighter budgets, under heavier security expectations, while somehow “getting leverage from AI” and keeping distributed teams aligned. The scoreboard has not changed. You are still judged on reliable, predictable outcomes. The playing field has.

Inside that squeeze, the teams that actually perform well do not look like the hero cultures of 2017. At BetterEngineer, we see them up close in startups, scale ups and enterprise product groups. They look more like calm systems. Fewer emergencies. A small set of outcomes that matter. Five or so metrics everyone understands. Steady improvement instead of spikes of heroics.

Key takeaways

• AI use is near-universal (90% of tech professionals, per DORA 2025). The real gap is between teams with a deliberate AI operating model and teams that improvise.
• High performers anchor work to one to three outcomes, run blameless incident reviews that change the system, and track a handful of DORA metrics.
• Psychological safety and a high technical bar have to coexist.
• A 90-day reset works in three phases: understand, experiment, codify.

This playbook draws on BetterEngineer’s client work and on published insights from Boris Portman, founder and CEO, and Marc Boudria, Chief Innovation Officer, who has over 25 years in digital strategy, innovation and AI.

Four Forces That Changed the Definition of Performance

Four forces now shape what good performance looks like: AI-assisted coding is normal, security is always on, distributed work is the default, and cost discipline is permanent.

Four forces now shape what good performance looks like: AI-assisted coding is normal, security is always on, distributed work is the default, and cost discipline is permanent.

1. AI amplifies whatever system it lands in.

Google’s 2025 DORA report found that around 90% of nearly 5,000 technology professionals already use AI at work, yet roughly a third have little or no trust in the code it produces. That tells you AI is everywhere, and trust is not.

Most teams we work with assume some share of their code is AI assisted through completions, refactoring suggestions or agents inside the IDE. The gap is no longer between teams that use AI and teams that do not. It is between teams with a deliberate AI operating model and teams where every engineer improvises with their own tools and habits.

For Marc Boudria, “AI is a game‑changer, and you cannot simply add it to your existing work methods. You need to rethink how you do things.” (More in AI Made Your Engineers Faster. So Why Are You Not Shipping?)

So what? If AI use is inevitable, your differentiation is not “do we use AI” but “do we have an AI operating model or just AI improvisation?”

2. AI made attackers faster, so security has to start at design.

Verizon’s 2026 Data Breach Investigations Report found that vulnerability exploitation is now the top breach entry point for the first time in 19 years, with attackers shrinking time‑to‑exploit from months to hours on known flaws. At the same time, Veracode’s 2026 GenAI Code Security Report showed that AI‑generated code passes security tests only a little over half the time.

Regulation is catching up as well. The EU AI Act’s first transparency rules began applying in 2026, with high‑risk obligations coming into force over the next few years. That is time to prepare, not a pass.

High performing teams treat AI generated code like unreviewed third party code. They put access controls on AI tools and agents and ask what a safe version of a feature looks like at the design stage, not after launch.

Security and compliance now have to be designed into features from day one, especially where AI is in the loop. You cannot afford to bolt them on at the end.

3. If a decision isn’t written down, it didn’t happen.

Gallup’s 2025 report on remote capable employees found that only a small minority work fully on site. The rest are split between fully remote and hybrid, which in practice means most engineering teams are distributed across locations, time zones and employment models.

In that world, the risk is not just “too many meetings.” It is decisions buried in chat threads, context that never reaches the people who need it, and ownership that is never made explicit. A choice made in Slack on Tuesday becomes a mystery by the next sprint, and new team members have no way to learn why the system looks the way it does.

Strong teams treat design docs, ADRs, runbooks and decision logs as first class work, not chores. They agree on working norms together: when people overlap, how fast questions get answered, which conversations need to happen live, and where decisions are recorded. (Find more insights on Building Teams Across Borders).

4. AI has to earn its budget.

Cost discipline is no longer a temporary reaction to a downturn. Boards and CFOs want to know what each unit of engineering spend returns, and AI is under particular scrutiny. Gartner expects a significant share of agentic AI projects to be canceled by 2027 due to escalating costs, unclear business value or weak risk controls. Many of those projects look impressive in a demo and stall when they hit real data, real users and real integration work.

Marc has warned that an AI prototype “is merely the first step; transforming it into a robust system capable of serving thousands requires considerable additional effort.” The teams that keep their AI budgets are the ones that can show, in plain numbers and a clear narrative, where AI and engineering investment are actually creating leverage in revenue, retention, unit costs or risk reduction. (see also: context engineering).

The Fix Is Rarely More Meetings or More Heroes

Three anonymized composites from recent BetterEngineer engagements show how these forces play out.

Cutting status meetings gave engineers their time back.

A 40-engineer B2B SaaS team had standups running past 30 minutes and a separate sync for nearly every feature area. Engineers had no time to code, and leadership still felt blind. Squads replaced status meetings with one weekly review against one to three outcomes, time-boxed standups to blockers and decisions, and moved updates into short written notes. Within months, engineers reported more uninterrupted maker time and leadership had better visibility, avoiding the cost Paul Graham describes in Maker’s Schedule, Manager’s Schedule.

Senior engineers should own systems, not fight fires.

At a growth-stage company with several hundred engineers, Staff and Principal engineers spent their weeks on incidents and escalations, mostly repeats of the same problems. We mapped a year of incidents into a few recurring patterns, gave those engineers system-ownership charters with protected time, and required every incident above a severity threshold to produce at least one systemic change. Over four to six months, incident rate and severity fell, and morale rose.

An AI policy turned hidden risk into trust.

A mid-sized product org let every engineer choose their own AI assistant, with no visibility into AI use in safety-critical code. Subtle bugs and friction between engineering, QA and security followed. We introduced a simple usage policy (approved tools, allowed tasks, off-limits domains), a PR-template field flagging significant AI contribution, AI-specific review checks, and baselines for PR review time, change failure rate and incident categories. The next quarter brought fewer “mystery bugs” and more trust across teams (AI tools worth using).

What High‑Performing Teams Actually Do Differently (5 Habits)

Across our engagements, five traits consistently separate teams that perform well and stay healthy.

1. Squads work toward narrow product outcomes

High‑performing squads can answer, in a single sentence, what they are trying to change this quarter. It is specific and observable: reduce drop‑off at a particular onboarding step, increase adoption of a feature among a target segment, or bring recovery time for a key service under a defined threshold.

Planning, prioritization and weekly reviews hang off that outcome instead of a generic notion of “velocity.” This mirrors what product thinkers like Marty Cagan call empowered product teams: teams accountable for results, not just output. It also aligns with Google’s Project Aristotle finding that “structure and clarity” of goals is one of the core dynamics of effective teams.

Test yourself: could each of your squads answer in one sentence, “What are we trying to change this quarter?” If not, you do not have outcomes; you have a backlog.

2. Every serious incident must change the system

That means clear severity levels, a real on-call rotation, runbooks people use, and quick blameless postmortems that look for contributing causes instead of culprits. The test is whether each meaningful incident produces a concrete system change: a guardrail, a test, an automation or a clarified process. DORA’s research shows speed and stability are not trade-offs; top performers do well on both.

Test yourself: When you look at your last five significant incidents, can you point to a specific system change that came out of each one? If not, you are firefighting, not learning.

3. AI use is intentional

By 2027, “everyone is playing with AI” is table stakes. It is also a risk. When each engineer picks a different assistant and uses it in their own way, quality becomes uneven, risk is hidden, and reviewers do not know what they are looking at.

High‑performing teams write down a short, clear AI philosophy. It spells out which tools are approved, what classes of tasks AI is expected to accelerate, which parts of the codebase need extra review, and how AI involvement is signaled in pull requests. Clarifying this is one of the first recommendations in DORA’s AI guidance and in most credible AI risk frameworks.

Training shifts too. Engineers learn not just how to get good completions, but when it is unsafe to trust them. As Marc likes to say, “AI is not going to take your job. Somebody using AI is going to take your job.”

Test yourself: If you asked ten engineers on your team, “When are you expected to use AI? When are you not allowed to? How do you signal it?” would you get the same answer ten times?

4. Psychological safety and a high technical bar coexist

Google’s Project Aristotle put psychological safety at the top of the five dynamics of effective teams. Amy Edmondson’s work at Harvard explains why: people need to feel safe to speak up, admit uncertainty and surface problems early.

In high‑performing engineering teams that does not mean “anything goes.” Juniors can say, “I do not understand this,” and seniors can say, “I was wrong about that.” People raise concerns without fear of punishment. At the same time, there are clear expectations for code quality, testing, documentation and ownership, and those expectations are enforced.

Kindness without standards drifts into mediocrity. Standards without safety turn into fear and burnout. The best orgs insist on both.

Test yourself: When was the last time someone on your team openly admitted a mistake or asked a “basic” question in front of others? What happened next?

5. Each squad improves one or two metrics, not a dashboard of dozens

The DORA research program showed that a small set of delivery metrics (such as lead time for changes, deployment frequency, change failure rate and recovery time) says a great deal about system health. The trap is turning dozens of metrics into targets and dashboards that no one really uses.

In the teams we trust, each squad has one or two numbers they are actively working to improve over a defined period. Experiments are designed and evaluated against those numbers. Dashboards are simple and visible, and they show up in weekly conversations. Dozens of charts with no clear focus create the illusion of control without direction.

Test yourself: Can each team tell you, without opening a BI tool, “These are the one or two numbers we are trying to move right now, and here is what we are doing about them”?

5 Metrics Reveal What Ticket Counts Hide

Five metrics reveal the shape of an engineering system far better than ticket counts. Most come from DORA’s software delivery research; we go deeper in The 5 Engineering Metrics Every CTO Should Be Looking At.

AI assisted coding makes change failure rate even more important. CircleCI’s 2026 State of Software Delivery found AI tools can raise individual throughput by 59%. More code moving faster means more chances to break things unless review and testing keep pace. DORA’s guidance is consistent: work in small batches if you want to move fast and stay stable.

A 90-Day Roadmap to Better Engineering Performance

You cannot remake an engineering organization in three months, but you can change its trajectory. For a VP of Engineering or CTO, the work in those 90 days boils down to three decisions:

• What is really happening here?
• What one or two experiments are we willing to protect?
• What will we make permanent, and what will we kill on purpose?

Days 1–30: What is really happening here?

The first 30 days are about getting an honest picture of your system rather than a prettier story.

Talk to people at different levels: engineering leaders, managers, senior ICs, product and someone from security or operations. Ask the same simple questions in every conversation: what works, what hurts every week, where do we lose time, where are we taking more risk than we admit?

In parallel, baseline a small set of delivery metrics. Start with deployment frequency, lead time for changes, change failure rate, recovery time, pull request wait time and a rough sense of work in progress. If you are starting from zero, DORA’s free Quick Check is a pragmatic way to get your first cut.

Finally, map your current rituals and decision forums. List recurring meetings, the Slack or Teams channels where work actually moves, and the places where real decisions get made (which is often not where you think).

At the end of this phase you should be able to answer one question clearly: “What is really happening here?” Not what you wish were true, but how work, decisions and risk actually flow through your org today.

Days 31–60: What one or two experiments are we willing to protect?

With that picture in hand, the next 30 days are about choosing, and protecting, a small number of changes that could actually move the system.

Pick one or two leverage points that are directly tied to what you heard and what you measured. For example, you might introduce outcome based weekly reviews and retire a couple of low value status meetings. You might tighten incident reviews so every serious incident must change the system. You might pilot a simple AI usage policy in one part of the codebase, or agree on visible WIP limits for one or two teams.

The key is to frame each change as a 30 day experiment, not a forever policy. Before you start, answer three questions with your leads and senior ICs:

• What exactly are we changing?
• What are we protecting so this experiment has a fair shot?
• How will we know, in 30 days, whether this helped, hurt or did nothing?

Protecting an experiment usually means saying “no” to a few other “priorities” for a month. If everything is equally important, nothing will change.

Days 61–90: What will we make permanent, and what will we kill on purpose?

The last 30 days are about making choices explicit. Bring the numbers and the human feedback back into the room. Did lead times shrink? Did incident reviews feel more useful? Did engineers get more maker time or just more meetings with new names? Were there any unexpected side effects?

On that basis, decide what becomes part of the way you work, what needs adjustment, and what you will stop doing. Codifying what worked means writing it down, updating onboarding materials and runbooks, and changing expectations so new hires encounter the new system rather than recreating the old one by habit.

Killing experiments on purpose is just as important. When you say, “We tried this for 30 days, it did not help, here is what we learned and here is what we are stopping,” you teach the org that trying things is safe. You also avoid accumulating half‑finished “transformations” that no one believes in.

After 90 days you will not have a perfect engineering org, but you should have a clearer picture of reality, one or two proven changes that made life better, and permission from your team to keep improving the system instead of relying on heroics.

AI Raises the Bar for Engineering Talent Instead of Lowering It

The need for that talent is not shrinking. As our CEO Boris emphasized: “We’re not seeing a drop in demand for engineers. If anything, it’s going up. The skills are changing—data, systems thinking, outcomes over years-of-language—but the need for strong technical talent in an AI world is only increasing.” Hear more from Boris and Marc on Off The Algorithm, BetterEngineer’s podcast.

The Right Engineers Turn a Good Team Into a High Performing One

Every pattern in this playbook depends on people: engineers who think in outcomes, review AI-generated code with care, and own systems instead of fighting fires. BetterEngineer can help in two ways.

Build the team. We connect you with vetted senior engineers from Latin America who work in your time zone and are fluent in AI-assisted, distributed delivery. Hire senior engineers

Get AI-ready. Our four-to-six-week AI Readiness Assessment, led by Marc Boudria, maps where AI can create real leverage in your organization, where it adds risk, and the roadmap to get there.

Not sure where to start? Bring a few numbers you already have, like how often you deploy, how long changes take to go live, and how fast you recover from failures, to a 20-minute call with our team. We’ll help you decide whether your next step is people, process or AI.