The article analyzes how Robotic Process Automation (RPA), integrated with Artificial Intelligence, is becoming a strategic lever for the Operations of Italian manufacturing companies, still lagging behind on the digitization front. RPA enables the automation of high-volume repetitive processes-from order management to reporting-by linking fragmented systems and freeing people for higher-value activities. The benefits are rapid and measurable, but implementation requires prior Lean mapping, robust governance and an appropriate change management program to avoid the main risks: cultural resistance, process fragility and compliance vulnerabilities. The challenge for Lean is not simply adopting AI, but knowing how to put it to work in a scalable and secure way.
Artificial Intelligence and Generative AI are redefining Robotic Process Automation, enabling enterprises to automate repetitive tasks and optimize operations through adaptive intelligence. RPA tools have been on the market for years but with the consolidation of AI technologies they are really entering businesses, even those within the manufacturing sector.
Italian manufacturing companies boast solid operational maturity in Operations and a well-structured Supply Chain, but evidence delays in overall digitalization. According to our Benchmarking Study Edition 2025 | What's next in Operations?, conducted on a large sample of companies from different sectors, many realities still operate with manual and reactive processes, rather than proactive ones.
Only a minority have achieved Smart Factory status, while most are defining a roadmap for digital transformation. AI and GenAI are recognized as priorities, with projects initiated primarily for strategic analytics, productivity enhancement, and process automation. This picture underscores the opportunity for AI to accelerate adoption to compete in terms of innovation and value.
Process Automation with AI goes beyond traditional automation: it leverages intelligent algorithms to manage routine tasks, predict anomalies, and optimize flows in real time. The AI Generative adds an advanced layer, capable of generating complex content, code, or predictions from structured and unstructured data, dramatically reducing human intervention.
This approach withreleases resources for high-value tasks, such as innovation and the strategic problem-solving.In the industrial context, it transforms episodic operations into predictive ecosystems, integrating data from across the value chain to support data-driven decisions.
Applications of Process Automation with AI range from transactional processes to manufacturing, offering scalable solutions for Italian manufacturing.
Transactional automations handle invoicing, order processing, and data entry, eliminating manual errors and accelerating cycles. In customer service, AI-powered chatbots and intelligent routing systems filter emails, categorize tickets, and suggest personalized responses, enhancing the customer experience.
In the Supply Chain, dynamic inventory optimization and demand forecasting reduce stock-outs and overstocks. Quality control detects defects in production with high accuracy, while automated compliance checks verify regulatory compliance at all stages. In IT systems, network monitoring identifies and resolves problems autonomously.
These examples illustrate how AI fits into the B2B contexts dominant in the Italian sample, enhancing critical areas such as quality and logistics.
Adoption of RPA (Robotic Process Automation), especially when combined with AI, enables immediate time reductions on repetitive and high-volume activities (data collection, updates across multiple systems, audits, reporting, ticket management, master records), with a direct impact on operational costs, quality and service levels.
In Operations, the RPA becomes the "executive engine"that connects often fragmented tools (ERP, MES, CRM, portals, email, Excel), automating end-to-end flows such as opening and updating work orders, administrative summarization and closures, collecting and consolidating production data, document control, managing internal requests, updating master data, and extracting and distributing KPIs. The result is a more scalable and robust operating model: it increases the share of touchless processes, reduces bottlenecks and "chase" activities, and improves the timeliness of information flows.
Integration with Lean paradigms amplifies the benefits: RPA eliminates waste associated with unnecessary information movement, waiting, rework, and redundant controls, making processes more stable and "standard work." When coupled with AI, the organization shifts from reactive to proactive management: AI intercepts anomalies and weak signals (performance drifts, downtime risks, quality deviations), while RPA automatically triggers standard corrective actions (escalation, task creation, system upgrades, targeted communications), reducing response time and dispersion.
The ROI tends to emerge quickly in contexts with high repetitiveness and critical mass (industrial operations, service operations, competence centers, shared services), thanks to modular approaches: starting with high-volume, low-complexity “quick win” processes and scaling by end-to-end streams. In addition to efficiency, automation also contributes to operational sustainability: less rework and information waste, less urgency generated by misalignments, greater process stability, and higher data quality to measure and govern environmental KPIs (energy, waste, rework).
Measuring the impact of RPA is not just an academic exercise: it is a prerequisite for scaling with awareness. In highly repetitive industrial settings, improvements manifest themselves on multiple dimensions simultaneously - speed, quality, reliability - and become visible as early as the first weeks of operation. Key indicators to monitor before and after implementation include:
Despite the benefits, the introduction of RPA (and even more so when enabled by AI) carries organizational and technical risks that must be managed with a structured approach. The first is human and cultural risk: the perception of "substitution" can generate resistance, declining engagement, and defensive behaviors (shadow process, rule bypass). To avoid this, transparent communication, involvement of people in use cases, and reskilling/upskilling programs toward "augmented" roles (exception management, data quality control, process ownership, bot and model oversight) are needed.
On an operational level, RPA can introduce a risk of process fragility if it automates non-standardized or "exception-filled" tasks: a bot replicates what exists, soif the process is unstable, there is a risk of automating waste instead of eliminating it. This is a typical risk when starting out without end-to-end mapping (Lean/process mining) and without clear rules for handling exceptions and responsibilities (who does what when the bot fails).
In industry and regulatory environments, alignment with compliance, security, and auditability is crucial. bots access systems and data: if you don't properly definegitimate identities, segregation of roles (SoD), action traceability, and access control, you open up legal and operational risks (errors on sensitive data, procedural violations, audit difficulties). cybersecurity is also an issue: hardcoded credentials, shared accounts, or unchecked logs can become vectors of risk.
From the IT side, integration with legacy systems or instable interfaces can generate technical debt: many "UI-based" automations are sensitive to changes in screens, fields, or permissions, resulting in increased maintenance and bot downtime. Without governance, this can create a “jungle of scripts” that are difficult to maintain, with impacts on business continuity and unexpected costs.
When AI is added (e.g., document classification, data mining, intelligent routing), additional risks emerge: data quality, model drift, performance degradation dover time, and the need for continuous monitoring. In these cases, it becomes essential to define confidence thresholds, spot checks and "human-in-the-loop" for ambiguous cases to avoid systematic errors that propagate quickly.
Finally, there are economic and delivery risks: initial costs (licensing, setup, change management), excessive expectations (“RPA solves everything”), wrong choice of priorities, and time-to-value that gets longer if you start with overly complex processes. The most common barriers remain: poor data quality, complexity of integration, lack of standardization, lack of ownership and governance.
AI-based Process Automation implementation strategies are based on structured and phased approaches. Mapping high-volume, low-value-added processes-the so-called "low-hanging fruits" such as reporting or billing management-is the starting point. Rigorous evaluation of data quality and selection of reliable vendors precede the launch of pilot projects on one or two specific use cases.
The scaling phase takes place through integration with ERP and MES systems, with constant monitoring of KPIs such as cycle times, error rates and overall ROI. The Change Management assumes a central role: ongoing training programs on the potential and limitations of AI help reduce internal resistance, while the involvement of corporate leadership fosters the development of a data-driven culture.
Vendor management is based on Service Level Agreements (SLAs) ch, strategies to avoid lock-in, and customized solutions. Ethics and governance-through dedicated AI committees, periodic bias audits, and transparency protocols-ensure responsible use, particularly crucial for black-box generative models.
Results from the Benchmarking Study 2025 | What's next in Operations edition show that the potential is still untapped, with implementation levels around 10% of the sample of more than 100 cross-industry companies. The most affected areas are manufacturing, quality, and customer experience. Based on our experience, cybersecurity, which is often reactive, needs to integrate by design into IoT and AI to protect connected operations.
Generative AI is moving from being a "support tool" to an active component of processes: it not only produces content, but enables agents capable of orchestrating tasks across multiple systems, managing exceptions, learning from feedback, and reprising flows when they break down (self-healing). For AI, this means a paradigm shift: less time spent chasing information, aligning data and coordinating micro-tasks; more time spent on decision-making, quality, service and innovation. Value does not come from AI “in itself,” but from its integration into an operational architecture composed of standard processes, reliable data, clear rules and defined responsibilities.
This transition is not automatic: it requires targeted investment in training, Knowledge Management and new roles (Process Owner, Automation Lead, Bot/Agent Supervisor) to avoid the “eternal pilot” effect and transform technology into industrial capability. In practice, you need: a Lean foundation to reduce variability and waste before automating; governance to ensure quality, safety, and auditability; and a wealth of corporate knowledge (standards, procedures, lessons learned) that is also "readable" by machines, so that agents operate with consistency and continuity.
Bonfiglioli Consulting, thanks to the Knowledge Office and the Lean Factory School®, accompanies companies in combining Operations Excellence and AI: from process and operating model design, to the selection of high-impact use cases, to the building of internal competencies and continuous improvement mechanisms.
Process Automation is no longer just an efficiency upgrade: it is the lever to make Operations more resilient, faster and more governable. In 2026 and beyond, the competitive difference will not be "having AI," but knowing how to implement it in a scalable, measurable, and secure way.
RPA is a software technology that automates repetitive, rule-based tasks--such as data entry, order updating, or report generation--by replicating the actions that a human operator would normally perform on enterprise information systems (ERP, MES, CRM, email). Unlike traditional AI, RPA executes predefined instructions without "learning." When integrated with AI, the system becomes adaptive: it not only executes, but also interprets unstructured data, detects anomalies and handles exceptions autonomously, enabling hyperautomation.
In the manufacturing sector, the processes most suitable for RPA are those with high volume and low variability: opening and updating work orders, administrative balances and closures, collecting and consolidating production data, document control, managing internal requests, updating master data and extracting KPIs. The supply chain also benefits significantly: demand forecasting, inventory optimization, and supplier monitoring are areas where bots get quick and measurable results. The guiding principle is simple: the more repetitive and standardized a process is, the more of a candidate for automation.
Return on investment tends to emerge quickly in the highly repetitive, critical-mass environments typical of industrial operations and skill centers. The most effective approach is modular: starting with high-volume, low-complexity quick-win processes-such as reporting or invoice management-and progressively scaling to end-to-end flows. In these scenarios, the reduction in lead time of administrative activities can go from hours to minutes, directly impacting operational costs, data quality, and service levels as early as the first weeks of operation.
The main risks are of three types. The first is organizational and cultural: the perception of "replacement" can generate resistance and defensive behavior; it is essential to accompany change with transparent communication and reskilling programs. The second is technical: automating unstable or exception-rich processes means “automating waste”-that is why preliminary Lean mapping is essential. The third is compliance and security: bots access sensitive systems and data, so digital identities, role segregation, action tracking and cybersecurity must be defined by design, not added after the fact.
The starting point is to map the highest volume, lowest value-added processes--so-called low-hanging fruits--by first checking the quality of available data. One or two pilot use cases are selected, reliable vendors with clear SLAs are chosen, and the project is started in a controlled manner. The scaling phase is done through integration with existing systems (ERP, MES) and constant monitoring of KPIs such as cycle times, error rates and touchless rate. According to Bonfiglioli Consulting's Benchmarking Study 2025, only 10% of the companies in the sample have already implemented structured solutions: the untapped potential is huge, especially in the areas of manufacturing, quality and customer experience.
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