AI Adoption in Enterprise Beyond Writing Code
In this episode, we speak with Ivan Bilan, Senior Engineering Manager at Personio, about the transition of AI adoption from experimental phases to routine engineering tasks.
AI can assist with implementation, but software engineering teams still need to handle several critical responsibilities: defining requirements, designing architecture, managing technical debt, testing systems, monitoring production, reviewing changes, planning rollouts, and maintaining their outputs.
We’ll discuss:
How engineering managers can approach AI adoption in enterprise settings.
Where AI can expedite team processes and where it may introduce hidden challenges.
Why companies must be cautious about encouraging everyone to contribute code without investing in processes for releases, review capabilities, observability, and maintenance.
We also explore the wider implications of AI adoption within the software industry, including reduced junior hiring opportunities, evolving expectations for engineers, risks associated with AI-generated systems that may be difficult for humans to comprehend, security concerns, and the shift from “using more tokens” to creating greater value.
About the Speaker
Ivan Bilan is a Senior Engineering Manager at Personio, leading multiple teams in the Identity and Access Management (IAM) domain. Previously, he served as a Data Science & Engineering Manager at TrustYou, where he led NLP and infrastructure groups to optimize massive ETL and ML pipelines. With an MS in Computational Linguistics from LMU Munich and a background at CDTM, Ivan bridges the gap between deep technical NLP research and senior leadership in high-throughput enterprise environments.
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