Platform engineering has emerged as one of the most talked-about shifts in modern IT. By packaging infrastructure, workflows and developer tools into consumable platforms, organizations can reduce friction, accelerate delivery and unlock new efficiencies.
However, the secret to success is not technology alone — developing the right cultural climate within the organization has a lot to do with it.
“Developing a platform without putting developers’ needs at the center will not drive natural adoption,” says Henrique Back, head of engineering at Indicium. “If people don’t use it, a platform is just technical debt.”
The Cultural Building Blocks
Back says he believes platform engineering is not about imposing top-down standards but about building a product that developers want to use.
That requires four cultural building blocks: A clear vision, composable tools, continuous education and strong collaboration.
“Enforcing new ways of working without a clear explanation of how they make things better will likely create reluctance,” he explains.
Education is more effective than enforcement, because developers need to understand how the platform improves their day-to-day work.
Collaboration is equally critical. Back argues that platforms should be treated like living systems, shaped by the people who use them.
“Adoption, feedback, and contributions from users are extremely valuable,” he says.
Back compares the dynamic to open-source culture, where contributions, bug reports and pull requests drive constant evolution.
This approach not only improves adoption but ensures the platform evolves in lockstep with developer needs, preventing stagnation and reinforcing trust between platform teams and developers.
Standards That Work for Developers
For many organizations, the promise of platform engineering is standardization. But Back cautions against framing that standardization as a constraint.
“I see platform engineering more as a way to enhance and evolve what developers are already doing — focusing on the company’s most common workflows and making it easier to start new projects, deploy them and monitor them,” Back says.
That means creating a “usable standard,” not a rigid one. Platforms should make 80% of common workflows faster and easier, while allowing space for exceptions.
In practice, this can mean supporting high-flexibility projects outside the platform in the early stages, while providing a well-designed default for most work.
“If the new way of working ends up being more complicated or time-consuming than the old one, people won’t embrace it,” Back says. “You have to prove that the platform makes their life easier.”
AI’s Role in Platform Engineering
AI is beginning to reshape the way organizations think about platform engineering. Back says one of the most important developments is how AI lowers barriers to complex infrastructure tasks.
“AI is lowering the barriers to adopting complex tools for automating infrastructure and workflows,” he notes. “It’s likely that a new breed of platform engineering tools will emerge.”
For example, generative AI can help automate documentation, suggest configuration changes, or generate infrastructure-as-code templates.
When tied into a platform, these capabilities can reduce onboarding time and make previously daunting processes more accessible to less experienced developers.
Back advises organizations to treat AI as an accelerator, not a replacement for strategy.
“Tools alone do not solve problems,” he says. “Good people with good tools do.”
Partnering with early adopters and iterating quickly can help organizations keep pace with AI evolution, but cultural alignment and communication remain the foundation.
Measuring Readiness and Impact
Back stresses that cultural readiness should be measured before platforms are rolled out. That means assessing how developers currently work, identifying bottlenecks, and mapping expectations.
“The first step is to interview teams and survey them to understand where the pain points are,” he explains.
Those insights can guide what the platform should prioritize, whether it’s faster deployments, simplified monitoring, or automated security checks.
Once deployed, organizations should measure impact with both technical and cultural metrics.
Deployment frequency, time to detect failures, and rollback times can track performance improvements, while adoption rates and developer feedback show whether the platform is being embraced.
Common Pitfalls, Culture as Differentiator
Even the best technology can falter when culture is ignored. Back points to several common mistakes: forcing new workflows without explanation, presenting the platform as a “black box,” or layering on bureaucratic standards that slow teams down.
“Communication is critical,” he says. “If people don’t understand why the platform matters or how it actually makes their work easier, they’re just not going to use it.”
Another risk is underestimating the challenge of bridging IT and development culture. Platform teams must earn trust by showing that the platform can deliver the same quality and reliability developers expect.
“Once you’ve built the digital twin and shown that it works with the same quality and performance OT teams expect, you can move toward a more modern architecture within the facilities,” Back says. “That’s when the value of IT and OT working together really becomes clear.”
Ultimately, Back sees platform engineering not as a technology project but as a cultural transformation. Platforms succeed when they align with developer needs, integrate with existing practices and grow through continuous feedback.
“Things may be changing, but the key to a successful platform engineering initiative is a clear strategy, strong communication and education, and the right people driving the project,” he says.