The ongoing AI-automation convergence, and what this means for the enterprise

This is a guest blogpost by Matthew Roberts, Ansible Platform Lead, EMEA, Red Hat

Enterprises are being led to believe that their success with AI hinges on whether they use the largest model (or the smallest, based on more recent thinking); have the most skilled AI practitioners; or can afford the most powerful hardware. Furthermore, in the face of economic pressures and geopolitical challenges, European organisations are being compelled to innovate and rethink their technology investments at an unprecedented pace. Achieving all this while managing cost, cybersecurity and regulatory demands requires a delicate balance, with IT automation emerging as a critical enabler.

To maintain a competitive edge, businesses must transition from siloed IT automation efforts towards a comprehensive, purposeful automation strategy that spans the entire enterprise. This transformation must foster collaboration across diverse tools, vendors, and cloud environments, empowering teams to be innovative and respond end-to-end to events across the business. By taking this unified approach, teams can also share best practice and learnings, even rolling out standard blueprints, eliminating duplication and streamlining efficiency.

Unlocking success through AI-enhanced automation

Incorporating AI into unified systems can bring powerful benefits to the enterprise. Businesses can move from static, rule-based processes to dynamic, intelligent workflows by integrating AI into automated systems. AI-infused automation can predict system failures, distribute resources nearly in real-time and even create system specific code. Therefore, AI enabled Ops can support events for which playbooks have yet to be written, reducing manual workloads and accelerating time-to-market for new solutions.

‘AI-infused automation’ can even help address skills shortages by empowering junior developers through natural language-driven code generation. AI tools simplify complex tasks, acting as force multipliers that allow teams to focus on strategic initiatives while routine operations are handled autonomously. Another example is that teams can access powerful scripts written by automation specialists. AI with Model Context Protocol (MCP – an open protocol that standardises how applications provide context to LLMs) gives teams the ability to use natural language to query AAP (Administrative Access Point) and OCP (Open Compute Project) servers, helping them easily understand the status of jobs and errors, or detail configuration using natural language prompts.

As AI models improve and deployment options diversify, AI support for coding and automation scripting will expand, enhancing productivity and fostering the development of new skills within teams.

Enabling regulatory compliance and operational resilience

As AI and automation technologies advance, the regulatory environment is also evolving, adding another compelling reason for change. These regulatory changes, like the EU’s DORA (Digital Operational Resilience Act), are reshaping how organisations manage IT environments, emphasising digital resilience in the face of global instability. At the same time, AI is driving a fundamental shift in what infrastructure and cloud teams must manage—not only more applications interacting with AI models, but also the models themselves, the data used for training, and broader operational complexities.

DORA is just one example of how regulatory change is spurring innovation and operational resilience. It aims to fortify the IT security of European financial institutions, ensuring that they can withstand significant operational interruptions. As financial services more than ever rely on digital platforms and third-party vendors, operational resilience throughout the supply chain is paramount.

This resilience requires businesses to go beyond merely reacting to disruptions. They must incorporate flexibility into their regular operations through agile, adaptable IT systems. Currently, many organisations are using predictive AI in production, but as generative AI evolves, new challenges and ongoing cost pressures will arise. If teams do not automate from the outset, or if they adopt disparate new tools for specific use cases, there’s a risk of repeating past mistakes—creating fragmented, siloed environments reminiscent of the early days of cloud adoption, where different teams favoured their own tools.

CTOs must guard against the risk of increasing technical debt from excessive tooling and instead leverage their existing investments. By doing so, they can reduce silos between teams, ensure greater consistency, and promote a more unified, resilient operational approach.

An automation-first mindset towards enterprise innovation

The dual forces of automation and AI are reshaping business operations, enhancing productivity and scalability. Concurrently, EU regulations necessitate a re-evaluation of operational resilience and security measures. An automation-first mindset effectively addresses these challenges, empowering teams to innovate rapidly while adhering to regulatory requirements. Together, a unified, AI-infused approach to automation, and strategic regulatory management, offer a pathway for businesses to thrive in an increasingly complex and competitive landscape.