Misalignment between engineering and business goals
Most companies start each quarter with OKRs, Objectives and Key Results. These are supposed to align the entire organization. But too often, technical teams receive goals without real clarity on how or why they matter to the company’s future. You want engineers pushing toward meaningful results, not just chasing arbitrary deadlines. If they can’t see how their work impacts the business, they start solving the wrong problems, fast.
Let’s be clear: engineers are problem solvers. But they need the right frame. If executives don’t provide it, the technical team ends up setting internal priorities based on what they think is important. This leads to breakdowns in communication. Goals become disconnected. And when KPIs are missed, no one really understands why, because the goals were never based on a shared understanding to begin with.
Misalignment is costly. It burns time, resources, and trust. Often, the issue isn’t that the teams aren’t working hard, they’re just working in different directions. That’s a leadership problem. And it can’t be solved by more meetings. Precise, focused alignment between business goals and engineering constraints is the starting point.
For executives, the takeaway here is straightforward, if you want results, you have to speak in terms that align operational output with company value. Top-tier performance doesn’t come from micromanagement. It comes from engineers knowing exactly what problem they’re solving, and why it moves the business forward. Business and tech teams need to operate with shared logic, not just shared calendars.
SLOs provide a shared language between business and engineering
To fix misalignment, you need a mechanism that keeps technical priorities and business outcomes in sync. That’s where Service-Level Objectives (SLOs) come in. Unlike abstract OKRs, SLOs translate performance into clear, measurable targets that matter to both teams, stuff like page load speed, uptime, and successful transactions. These aren’t just metrics; they’re promises your systems make to your customers.
For example, let’s say your business wants to offer the fastest online experience in your market. That’s high-level. Engineering makes it precise: the homepage must load in under 100ms for 99.9% of visitors in a 30-day rolling period. That’s your SLO. It’s no longer a vague goal. It’s a metric tied to customer impact, and more importantly, everyone knows the number and what it means.
The strength of SLOs is clarity. When engineers work with SLIs, Service-Level Indicators, to track performance, and report consistently on meeting or missing their SLOs, everyone else can understand the context. Executives get visibility. Product teams make smarter decisions. Engineering prioritizes tasks that protect key performance thresholds, not directionless optimization.
For leadership, this means adopting a metrics-first structure where your operations are driven by customer impact, not guesswork. Most companies don’t fail because they lack data, they fail because they don’t agree on what to do with the data. With SLOs, the feedback loop tightens. You prevent breakdowns in reliability before they cascade into customer loss. And when trade-offs must be made, say, between releasing a new feature or improving stability, the decision is made with facts, not intuition.
Rushing SLO implementation leads to arbitrary and ineffective targets
When teams get excited about SLOs, the temptation is to jump in and set targets that sound reasonable. Fast deployment is attractive, but if your numbers aren’t driven by real data, they mean nothing. Arbitrary SLOs don’t create alignment. They create confusion, and they quickly fall apart when systems fail or pressure increases. You’ll spend more time reacting than learning.
There’s a better approach. Take a quarter or two to collect operational data before locking into targets. Observe how your services behave under both normal and unexpected conditions. Identify patterns, how downtime aligns with transaction drops, where latency spikes occur, what causes support tickets. Use this information as input, not assumptions. That’s how you define SLOs that match reality, and actually help drive decision-making.
This slower but deliberate rollout establishes trust between engineering and business teams. Once data supports the SLO baselines, they become part of your business logic. You reduce alert noise, improve productivity, and gain insight into which reliability efforts matter. When an error budget gets used up, you won’t panic, you’ll know exactly what happened and what to prioritize.
For executives, the key is strategic patience. Speed without clarity wastes more time later. Use those early quarters to educate your teams, validate assumptions, and test observability pipelines. You want discipline more than speed, because once SLOs are in place, they become operational policy. If rushed or incomplete, they weaken accountability instead of strengthening it. That undermines confidence at every level.
Iterative improvement is key to effective SLO adoption
You don’t need perfect SLOs on day one. What you need is motion backed by feedback. Start with your most critical customer-facing services. Define SLOs based on the initial data you’ve gathered. Then observe. Track performance. Listen to engineering feedback. Talk to product managers. Adjust where necessary. This is normal. It’s also how strong operational culture is built, from real-world use, not static plans.
The best companies understand that every system changes. Traffic spikes, feature rollouts, infrastructure upgrades, everything impacts reliability. Your SLO targets will evolve. What’s important is how they evolve. When the process is iterative, alignment stays tight. Engineering focuses on high-impact issues. Product teams make roadmap decisions knowing where performance trade-offs exist. And business leaders can tie results directly to customer and revenue outcomes.
The flexibility to adjust SLOs improves operational agility. You’re not locked into targets that no longer make sense. Instead, you build a framework that adapts with the system, and with the market. This creates a shared mindset of improvement, not blame.
For senior leaders, the takeaway is operational maturity. Don’t treat SLOs as static performance contracts. Treat them as living agreements that evolve with data, experience, and changing customer expectations. This mindset should be embedded in your leadership style. It signals that your company can adapt fast without sacrificing quality, and that’s a competitive asset.
A structured, multi-step process is key for setting effective SLOs
To get real value from SLOs, you need a structured process. It starts with identifying which parts of your system matter most from a customer and revenue standpoint. If everything matters equally, nothing does. Rank your services by the impact their reliability has on user experience and business outcomes. Don’t delegate this, validate it with your customer-facing teams. They know where reliability issues become business problems.
Once you’ve surfaced meaningful targets, define clear reliability expectations. Speak with product leaders, engineering managers, and business stakeholders. Align on the error budget, the acceptable level of failure before corrective action must take place. Then define what happens when that budget is exceeded. Feature work goes on hold? Infrastructure investments take priority? Make those consequences explicit and documented.
The real payoff comes from clarity. With pre-agreed expectations, no one’s guessing during an incident. Everyone knows the boundaries, which trade-offs are pre-approved, and who owns response decisions. That shortens downtime, supports faster recoveries, and prevents finger-pointing later.
Executive alignment on SLO structure isn’t optional, it’s what ensures the system is enforceable, not just aspirational. Without documented commitments, you end up applying SLOs inconsistently. That weakens their credibility and unscores morale when engineering must justify decisions after the fact. Use the SLO planning process to institutionalize accountability. This makes technical operations transparent, predictable, and better aligned with business targets.
Transparency in reporting error budgets enhances accountability
Once you’ve defined an error budget, you need to track and report on it systematically. The error budget tells you how much reliability risk your service can tolerate within a defined period. When that budget is used up, due to downtime, performance loss, or outlier events, it needs to be flagged clearly. Everyone involved, from engineering leads to product stakeholders, should know what happened and why.
Don’t bury this information in backend-only dashboards. Executive teams need a clear view of how often performance thresholds are missed, how frequently error budget policies are triggered, and what follow-up actions were taken. This isn’t about blame, this is about consistent communication. When an error budget breach happens, the response should not be improvised. It should follow a pre-agreed plan.
Transparent reporting also influences behavior. If a team knows its work is being evaluated on error budget adherence, it tends to make decisions with greater care. Clear visibility into cause and response helps your organization build trust, both internally and with customers.
For leadership, the priority here is systems-level accountability. Not every performance issue will deserve the same attention, but all should be measured against the same standards. When reporting is inconsistent or incomplete, it creates space for misalignment and weakens disciplined execution. Clear reporting reinforces engineering consistency, supports better prioritization, and gives senior leaders the insight they need to make better investment and staffing decisions.
SLOs, when combined with observability, offer competitive advantages
Good SLOs make your performance measurable. Strong observability makes it actionable. When you combine both, you gain a closed feedback loop, engineers can see what’s happening, understand why it’s happening, and take focused action before things break. You find problems early, not when a customer reports them. That’s where operational efficiency turns into strategic advantage.
Observability tools let your teams track key service metrics like latency, availability, saturation, and error rates. But that data only matters if it’s tied to service-level objectives that reflect your business priorities. Without that connection, you’re just collecting telemetry with no guide for action. When observability is structured around SLOs, teams know what’s expected and can prioritize the issues that compromise agreed customer experience targets.
This also reduces alert fatigue. Engineers get fewer false positives because alerts now map to thresholds that actually matter. When alerts do fire, they trigger a defined response based on error budgets and business risk. That makes teams calmer, more focused, and faster at solving problems that affect your users.
SLO-driven observability also helps the business side. Product managers know how reliability efforts impact delivery velocity. Executives get clearer visibility into operational risk tied to growth targets. With every release your ability to scale it consistently.
For C-suite leaders, the key point is leverage. Organizations tend to focus heavily on feature throughput and not enough on system behavior. That’s a mistake. Sustained performance, especially at scale, requires a feedback loop that’s as focused on quality as it is on output. With well-calibrated SLOs and observability, every team in the company improves its signal quality. That makes your operations more predictable, your strategy more efficient, and your user experience more defensible over time.
In conclusion
If your teams are working hard but the results feel scattered or unpredictable, the issue is alignment. Business and engineering are often chasing the same outcomes with different tools, timelines, and assumptions. That slows everything down and raises execution risk.
SLOs give you a framework to fix this, measurable targets tied to what actually matters to customers and revenue. When you build them on real operational data, and back them with solid observability, your teams don’t just work harder, they work smarter, with clarity and urgency. Priorities become tangible. Trade-offs become strategic. And performance becomes something you can trust, not just react to.
If you’re serious about scaling quality, speed, and ownership across your organization, start with alignment. SLOs won’t solve every problem, but they make it obvious what to fix, why it matters, and who should lead it. That’s how great companies build from the inside out.
