The article analyzes how the Robotic Process Automation (RPA)<\/strong>, integrated with Artificial Intelligence, is becoming a strategic lever for the Operations of Italian manufacturing companies, which are still lagging behind on the digitization front. RPA enables automation of repetitive high-volume processes-from order management to reporting-by connecting 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.<\/p>\n\n\n\n Artificial Intelligence and Generative AI are redefining the Robotic Process Automation,<\/a> 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.<\/p>\n\n\n\n Italian manufacturing companies boast a solid operational maturity in Operations and a well-structured Supply Chain, but highlight delays in overall digitization<\/strong>. According to our\u00a0Benchmarking Study Edition 2025 | What's next in Operations?<\/strong><\/em><\/a>, conducted on a large sample of enterprises from different sectors, many still operate with manual and reactive processes<\/span>vi, rather than proactive.<\/p>\n\n\n\n Only a minority have achieved Smart Factory status, while most are establishing a roadmap for digital transformation. AI and GenAI are recognized as.\u00a0priority<\/strong>, with projects initiated mainly for strategic analysis, productivity enhancement and<\/span>\u2018process automation. This picture underscores the opportunity for Made in Italy to accelerate the adoption To compete in terms of innovation and value<\/strong>.<\/p>\n\n\n\n Process Automation with AI goes beyond traditional automation: it leverages intelligent algorithms to manage routine tasks, predict anomalies and optimize flows in real time. L\u2019Generative AI<\/a> adds an advanced layer, capable of generating complex content, code, or predictions from structured and unstructured data, dramatically reducing human intervention.<\/p>\n\n\n\n This approach with<\/strong><\/span>feels to free up resources for high-value tasks, such as innovation and strategic problem-solving<\/a>.<\/strong> In the industrial context, it transforms episodic operations into predictive ecosystems, integrating data from across the value chain to support data-driven decisions.<\/p>\n\n\n\n <\/p>\n\n\n\n Applications of Process Automation with AI range from transactional processes to manufacturing, offering scalable solutions for Italian manufacturing.<\/p>\n\n\n\n Transactional automations handle billing, order processing and data entry, eliminating manual errors and speeding up cycles. In customer service, AI-powered chatbots and intelligent routing systems filter emails, categorize tickets and suggest personalized responses, improving the customer experience.<\/p>\n\n\n\n In the\u00a0Supply Chain<\/a>, dynamic inventory optimization and demand forecasting reduce<\/span>are 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.<\/p>\n\n\n\n These examples illustrate how AI fits the dominant B2B contexts in the Italian sample, enhancing critical areas such as quality and logistics.<\/p>\n\n\n <\/p>\n\n\n\n The adoption of RPA (Robotic Process Automation)<\/strong>, especially when combined with AI, enables immediate time reduction on repetitive, high-volume tasks (data collection, updates across multiple systems, audits, reporting, <\/span>ticket management, master records), directly impacting operational costs, quality and service levels. <\/p>\n\n\n\n In Operations, RPA becomes the \u201cexecutive engine\u201d <\/strong>which often connects fragmented tools (ERP, MES, CRM, portals, email, Excel), automating end-to-end workflows<\/strong> such as<\/span> opening and updating work orders, balancing and administrative 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: increase the share of touchless processes, reduce<\/span>and bottlenecks and \u201cchase\u201d activities and improves the timeliness of information flows.<\/p>\n\n\n\n Integration with Lean paradigms<\/a> Amplify the benefits<\/strong>: RPA eliminates waste associated with unnecessary information movements, waiting, rework, and redundant controls, making processes more stable and \u201cstandard work.\u201d 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 updates, targeted communications), reducing response time and dispersion.<\/p>\n\n\n\n ROI tends to emerge rapidly 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 \u201cquick win\u201d processes and scaling by end-to-end streams. In addition to efficiency, automation also contributes to the operational sustainability<\/strong>: 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).<\/span><\/p>\n\n\n\n <\/p>\n\n\n\n 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:<\/p>\n\n\n\n <\/p>\n\n\n\n Despite the advantages, the introduction of RPA<\/strong> (and even more so when enabled by AI) involves organizational and technical risks that must be managed <\/span>With a structured approach. The first is. The human and cultural risk<\/strong>: the perception of \u201csubstitution\u201d can generate resistance, decreased engagement and defensive behaviors (shadow process, rule bypass). To avoid this, servon<\/span>o transparent communication, involvement of people in use cases, and reskilling\/upskilling programs to \u201caugmented\u201d roles (exception management, data quality control, process ownership, bot and model oversight).<\/p>\n\n\n\n On an operational level, RPA can introduce a risk of fragility of processes<\/strong> whether it automates nonstandardized or \u201cexception-filled\u201d tasks: a bot replicates what exists, quin<\/span>of, if 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).<\/p>\n\n\n\n In industry and regulatory affairs, alignment with the compliance, security, and auditability.<\/strong><\/span> Bots access systems and data: if identities of<\/span>gital, segregation of roles (SoD), traceability of actions and access control, legal and operational risks open up (errors on sensitive data, procedural violations, audit difficulties). Also the cybersecurity<\/strong> It's a topic: hardcoded credentials, shared accounts, or unmonitored logs can become risk vectors.<\/p>\n\n\n\n On the IT side, integration with legacy systems or interfaces inst<\/span>adept can generate technical debt: many \u201cUI-based\u201d automations are sensitive to changes in screens, fields, or permissions, resulting in increased maintenance and bot downtime. Without governance, this can create a \u201cjungle of scripts\u201d that are difficult to maintain, with impacts on business continuity and unexpected costs.<\/p>\n\n\n\n When AI is added (e.g., document classification, data mining, intelligent routing), additional risks emerge: data quality<\/strong>, model drift, degradation d<\/span>elle performance over time and need for continuous monitoring. In these cases, it becomes essential to define confidence thresholds, spot checks and \u201chuman-in-the-loop\u201d for ambiguous cases to avoid systematic errors that propagate quickly.<\/p>\n\n\n\n Finally, there are economic and delivery risks: initial costs<\/strong> (licensing, setup, change management), excessive expectations (\u201cRPA solves everything\u201d), 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<\/strong>, complexity of the in<\/span>tegration, lack of standardization, lack of ownership and governance.<\/p>\n\n\n\n <\/p>\n\n\n\n AI-based Process Automation implementation strategies are based on structured, phased approaches. The mapping of high-volume, low-value-added processes-the so-called \u201clow-hanging fruits\u201d 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.<\/p>\n\n\n\n Scaling is done through integration with ERP and MES systems, with constant monitoring of KPIs such as cycle times, error rates and overall ROI. The Change Management <\/a>takes a central role<\/strong>: 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.<\/p>\n\n\n\n Vendor management is based on Service Level Agreements (SLA)<\/strong> ch<\/span>iaries, on strategies to avoid lock-in, and on 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.<\/p>\n\n\n\n The results of the Benchmarking Study 2025 | What's next in Operations edition show that the potential is still untapped, with the level of implementation around 10% of the sample of more than 100 cross-industry companies. The areas most affected are manufacturing, quality and customer experience<\/strong>. Based on our experience, cybersecurity, which is often reactive, must integrate by design into IoT and AI to protect connected operations.<\/p>\n\n\n\n <\/p>\n\n\n\n Generative AI<\/a> is transitioning from a \u201csupport tool\u201d to an active component of processes: it not only produces content, but enable agents<\/strong> capable of orchestrating tasks across multiple systems, handling exceptions, learning from feedback, and reprising<\/span>tinate flows when they break down (self-healing). For Made in Italy, this means a paradigm shift: less time spent chasing information, aligning data and coordinating micro-activities; more time dedicated to decisions, quality, service, and innovation<\/strong>. Value arises not from AI \u201cin itself,\u201d but from its integration into an operational architecture composed of standard processes, reliable data, clear rules and defined responsibilities.<\/p>\n\n\n\n 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 \u201ceternal pilot\u201d effect and transform technology into industrial capability. In practice, they serve: A lean base<\/strong> to reduce<\/span>rreduce variability and waste before automating; governance that ensures quality, safety and auditability; and a wealth of corporate knowledge (standards, procedures, lessons learned) that is also \u201creadable\u201d by machines, so that agents operate with consistency and continuity.<\/p>\n\n\n\n Bonfiglioli Consulting, thanks to the Knowledge Office and the Lean Factory School\u00ae<\/a>, accompany businesses in combining Operations Excellence and AI<\/a>: from the design of processes and the operating model,<\/a> to the selection of high-impact use cases to the building of internal expertise and continuous improvement mechanisms. <\/p>\n\n\n\n Process Automation is no longer just an efficiency upgrade: is the lever to make Operations more resilient,<\/strong> faster and more governable. In 2026 and beyond, the competitive difference will not be \u201chaving AI,\u201d but knowing how to implement it in a scalable, measurable, and secure way.<\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n RPA is a software technology that automates repetitive, rule-based tasks-such as data entry, order updating or report generation-by replicating the actions a human operator would normally perform on enterprise information systems (ERP, MES, CRM, email). Unlike traditional AI, RPA executes predefined instructions without \u201clearning.\u201d 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.<\/p>\n\n\n\n <\/p>\n\n\n\n In the manufacturing sector, the processes best suited to RPA are those with high volume and low variability: opening and updating work orders, administrative balancing 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 it is a candidate for automation.<\/p>\n\n\n\n The return on investment tends to emerge quickly in the highly repetitive, critical-mass contexts 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 for 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.<\/p>\n\n\n\n The main risks are of three types. The first is organizational and cultural: the perception of \u201creplacement\u201d 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 \u201cautomating waste\u201d-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.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n <\/p>","protected":false},"excerpt":{"rendered":null,"protected":false},"author":9,"featured_media":73016,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[117],"tags":[],"class_list":["post-73006","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-transformation"],"acf":[],"yoast_head":"\n
\n\n\n\nRobotic Process Automation and generative AI: why act now<\/strong><\/h2>\n\n\n\n
The context of Italian manufacturing: where we are<\/strong><\/h3>\n\n\n\n
Definition and potential of Process Automation<\/strong><\/h3>\n\n\n\n
Main use cases in the industry<\/strong><\/h2>\n\n\n\n
![\"Five](\"https:\/\/www.bonfiglioliconsulting.com\/wp-content\/uploads\/2026\/03\/Aree-applicative-Automazione_RPA.png\")
<\/figure>\n<\/div>\n\n\nTangible benefits for Operations<\/strong><\/h3>\n\n\n\n
Tangible Benefits: KPIs Before and After<\/strong><\/h3>\n\n\n\n
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Risks to be carefully managed<\/strong><\/h2>\n\n\n\n
How to Implement RPA with AI: The Effective Roadmap<\/strong><\/h2>\n\n\n\n
Towards a proactive operating model: the future of Operations<\/strong><\/h2>\n\n\n\n
\n\n\n\nBy Bonfiglioli Consulting Editorial Staff<\/strong>
Each publication stems from industry studies, field research and analysis of global trends integrated with knowledge and expertise gained from transformation projects, with the aim of promoting business culture.<\/em><\/h4>\n\n\n\nPublished on 03\/19\/2026<\/h4>\n\n\n\n
FAQ<\/strong><\/h2>\n\n\n\n
What is Robotic Process Automation (RPA) and how does it differ from AI?<\/strong><\/h3>\n\n\n\n
What business processes can be automated with RPA in the manufacturing sector?<\/strong><\/h3>\n\n\n\n
How long does it take to see the ROI of an RPA project?<\/strong><\/h3>\n\n\n\nWhat are the main risks to be managed in the imple<\/b><\/span>mentation of RPA in the company?<\/strong><\/h3>\n\n\n\n
How to start an RPA project in an Italian manufacturing company?<\/strong><\/h3>\n\n\n\n