Maturity models in automation programs: the new compass for industry
With automation in full swing, maturity models are becoming established as a guide for scaling up from isolated pilots to autonomous operations. Their adoption is growing, driven by the fact that 98% of manufacturers are exploring automation with AI and only 20% say they are ready on a large scale, highlighting the urgency of a structured roadmap.
From tactical automation to strategic vision
Maturity models in automation programsenable organizations to move from scattered initiatives to a portfolio governed by business priorities. They classify organizations into levels, from incipient automation to intelligent orchestration, helping to align investment, capabilities, and results. This prevents the proliferation of isolated bots and drives sustainable growth.
Key maturity layers for automation programs
The industry is moving toward frameworks that combine governance, technology, and talent. This approach seeks to align strategy, processes, and people to scale automation with control and measurable value.
A mature program balances centralized governance, a process catalog, scalable architecture, and value metrics to prioritize initiatives and measure ROI.
The progression ranges from basic RPA to hyperconnected automation, integrating systems, advanced analytics, and AI agents that orchestrate flows.
Each level specifies which competencies to strengthen (change management, data, development, security) and which risks to mitigate (cybersecurity, technological dependence, governance gaps).
The goal is to consolidate reproducible results: greater speed, fewer errors, and data-driven decisions.
Measurable benefits and next steps for organizations
By applying maturity models, companies prioritize cases with clear returns, reduce friction between IT and business, and reinforce confidence in intelligent automation. This guides resources, stabilizes processes, and creates governance frameworks that enable initiatives to be scaled with less risk.
Improve operational availability: fewer interruptions and greater continuity of critical services.
Shorten cycle times and increase efficiency through standardized processes and ROI-focused automation.
Improve data quality and governance, essential for reliable analysis and predictive models.
It paves the way for generative AI and, by defining the current level, makes it easier to plan investments and accelerate value capture.
Maturity models in automation programshave become central to evolving from isolated projects to orchestrated, AI-ready ecosystems. It is essential to rigorously assess the starting point, set realistic milestones, and accompany them with governance and training. To design or verify this roadmap, it is advisable to contact the specialized team at Digital Robotsand move forward with greater confidence.
Related news
