Why running fast on cloud projects doesn’t always mean getting anywhere — and what closes that gap.
Think about a treadmill for a second. You can run on it for an hour, sweat through your shirt, feel your legs burn — and still be standing in the exact same spot you started. That’s not a flaw. That’s just how a treadmill works. It measures effort, not distance.
A lot of cloud engineering teams are running on something a lot like that treadmill, and most don’t notice until someone outside the team asks a simple, slightly awkward question.
Picture a quarterly review. The team walks in with a strong list to show: system uptime looks healthy, cloud costs are a little lower, every scheduled job ran on time. That’s real work, and it took real effort. Then a business leader in the room asks one thing: “What changed for our customers because of this?” There’s a pause. Someone mentions a small speed improvement, which is true, and still isn’t really an answer.
“We hit every sprint goal this quarter. I still can’t tell the leadership what changed because of it.” — a familiar line from engineering leads after a strong-looking sprint
This scene repeats far more often than most leadership teams would like to admit. And it isn’t because engineers are lazy — usually it’s the opposite. They’re some of the hardest-working people in the building. They’re just running very fast on a treadmill nobody pointed toward a destination. A recent PwC survey of operations and supply chain leaders found something similar at a much bigger scale: 89% said their tech investments hadn’t fully delivered the results they expected, even with spending still climbing.
1. Activity vs. Impact in Cloud Engineering Teams
“Activity” is everything a team does: tickets closed, servers patched, pipelines built, alerts handled. “Impact” is whether any of that changed something the business or a customer actually feels — faster service, lower cost, a feature that works better than it did last month.
Cloud tools are extremely good at showing activity. Dashboards, ticket counts, uptime percentages — all visible, all real-time. Impact is much harder to see, because it usually shows up weeks or months later, as a business number, not an engineering one. So teams end up managing what they can see, which is activity, and quietly lose track of the thing that was supposed to matter in the first place.
Most teams aren’t idle — they’re stuck in the high-activity, low-impact corner without realizing it.
Plot any real organization on a chart like that and the pattern holds. Teams are almost never idle. They’re rarely in the top-right either, no matter how clean the internal metrics look. Most sit in the bottom-right — high motion, low leverage — and the dashboards make that quadrant feel like success, because everything on it is, technically, working.
2. Why Execution Often Drifts From Business Priorities
The drift is rarely one bad decision. It’s usually a hundred small ones.
A team picks up whatever’s loudest that week — a failing job, an urgent request, a security patch. Each choice makes sense on its own. But business priorities can shift every quarter, sometimes faster, and if nobody’s actively re-checking the map, engineering keeps walking in a direction that made sense six months ago and quietly stopped making sense since.
It’s a bit like two people walking together through a city, both looking at their phones instead of each other. Neither one feels lost. Check back in an hour, though, and they’re on completely different streets.
3. The Cost of Constant Building Without Clear Direction
Building feels productive. It genuinely is, in the moment — shipping something new brings a real sense of progress, and there’s nothing wrong with that feeling on its own.
The problem shows up later. Every pipeline, every environment, every small tool built “just in case” needs to be maintained, secured, and paid for, indefinitely, whether or not anyone still uses it. A few of these are fine. Dozens of them, built without a clear line back to a business goal, quietly turn into a tax the team keeps paying long after anyone remembers why it exists.
This is usually where cloud costs balloon. It’s rarely one bad call — it’s a pile of small, well-meaning ones that never get revisited. Flexera’s 2026 State of the Cloud Report puts a number on it: an estimated 29% of cloud spend now goes to waste, the first increase in five years, driven largely by how hard AI workloads are to predict and size correctly.
Three numbers, one pattern: spending keeps climbing, waste climbs with it, and alignment isn’t keeping pace.
4. Misalignment Between CXOs and Engineering Leads
CXOs — the CEOs, CFOs, and other senior leaders steering the business — think in terms of revenue, growth, and risk. Engineering leads think in terms of uptime, latency, and technical debt. Both views are completely valid. The trouble is, they’re often expressed in two different languages, in the same meeting, and everyone nods along without quite agreeing on what “success” means this quarter.
“I don’t need the technical details. I need to know if this makes us money or protects us from risk.” — the kind of blunt note engineering teams get used to hearing from the boardroom side
Left alone, that gap doesn’t hold steady. It grows, because each side keeps making decisions using its own definition of progress. A 2025 executive survey by Grant Thornton found a version of this at scale: 93% of organizations said they were increasing technology investment that year, but only 27% described their technology as fully aligned with business goals.
Same company, two different dashboards — until something forces them to merge.
5. What Outcome-Driven Cloud Engineering Actually Looks Like
It looks smaller than most people expect, honestly. Fewer projects, not more — each one clearly tied to something the business can point to and say, “yes, that mattered.” It looks like a leader and an engineer looking at the same dashboard, instead of two different ones, and agreeing on what “done” means before the work even starts.
It also means being willing to say no. Turning down a request that feels urgent but isn’t actually important is one of the more underrated engineering skills, and one of the hardest to practice under pressure from above.
6. Where External Expertise Accelerates Clarity
Sometimes a team is simply too close to its own systems to see the treadmill for what it is. Everyone inside the building agrees the pace feels right, mostly because it’s the only pace they’ve ever known.
“You’re not idle. You’re just running fast in the wrong direction.” — the kind of observation an outside review usually surfaces within the first few weeks
An outside perspective — someone who’s watched this exact pattern play out across several companies — can usually spot the gap faster than anyone internal, and say it out loud without the internal politics that make it hard to raise from the inside. That’s not a criticism of the team. It’s just easier to notice a treadmill when you’re not the one out of breath on it.
7. How Atgeir Aligns Cloud Engineering With Business Goals
This is the exact gap Atgeir spends its time closing. Atgeir is a Pune-based data, cloud, and AI engineering team working across AWS, GCP, and Azure, building cloud-native data platforms meant to hold up for years, not just for one quarter’s roadmap.
The starting point usually isn’t “what should we build.” It’s “what result are we actually trying to move, and does the current cloud and data setup support that.” Some engagements start narrow — an AWS consultant or Azure consultant spending a couple of weeks inside one specific environment ahead of a renewal or migration deadline. Others start broader: a full cloud migration as a service engagement for a company moving off legacy infrastructure, or ongoing cloud modernization services for teams rebuilding around today’s priorities instead of the ones from a few years back. For organizations planning large scale-ups and transformations, cloud migration as a service also provides a structured approach to reduce risk while accelerating modernization. As a GCP partner, Atgeir also brings direct access to Google Cloud’s own tooling and support, alongside the AWS and Azure work.
Either way, the work turns into a smaller, clearer roadmap — one that engineering and leadership can both look at and agree on, instead of quietly managing two separate versions of “progress.”
8. Turning the Treadmill Into a Road
None of this is really about working harder. Most cloud engineering teams already are.
It’s about occasionally stepping off the treadmill, checking where the road actually leads, and pointing effort somewhere specific — instead of just making sure it stays visible on a dashboard.
That one shift, made consistently, is usually the difference between a team that’s busy and a team that’s actually moving.