AI ambitions meet automation reality: The case for a unified automation platform

AI ambitions meet automation reality: The case for a unified automation platform The DIY dilemma: More tools, more problems Automation translates to tangible benefits for AI Additional resources Red Hat Ansible Automation Platform | Product Trial About the author Harper Buete More like this A 5-step playbook for unified automation and AI Solving tool overload, one automation step at a time Technically Speaking | Platform engineering for AI agents Technically Speaking | Driving healthcare discoveries with AI Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share IT teams are stuck between wanting to implement AI solutions across their organizations and dealing with the messy reality of increasingly complex infrastructure. Many are attempting to build their own automation solutions, cobbling together a patchwork of tools that, while well-intentioned, can actually make things worse. Red Hat dug into this with S&P Global Market Intelligence 451 Research, and their findings point to a simpler alternative : use a unified platform instead of patchworking tools together. Most teams are drowning in tools, often ending up with too many dashboards with too little coordination. A remarkable 72% of organizations are using up to 50 different IT tools, while nearly a third (28%) are managing over 50. This fragmented approach creates significant hurdles for effective IT automation, including: Complex implementations : This is a top obstacle for 58% of organizations. We often see this arise when customers deploy too many resources and move components piecemeal, slowing progress and increasing effort. Integration challenges : Integrating existing systems is a challenge for 51% of IT organizations. This isn't just about connecting tools, it's about achieving true interoperability for automation workflows. Skills gaps : 40% of respondents point to specific skills shortages. Maintaining expertise across a vast array of different tools is a constant drain on time and resources. This "do-it-yourself" mentality is particularly detrimental to AI initiatives.