Robotic Process Automation and generative AI: why act now<\/strong><\/h2>\n\n\n\nThe context of Italian manufacturing: where we are<\/strong><\/h3>\n\n\n\nItalian 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 Benchmarking 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\nOnly a minority have achieved Smart Factory status, while most are establishing a roadmap for digital transformation. AI and GenAI are recognized as. priority<\/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\nDefinition and potential of Process Automation<\/strong><\/h3>\n\n\n\nProcess 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\nThis 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
Main use cases in the industry<\/strong><\/h2>\n\n\n\nApplications 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 Supply 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\nThese 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
\n![\"Five](\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\")
<\/figure>\n<\/div>\n\n\n<\/p>\n\n\n\n
Tangible benefits for Operations<\/strong><\/h3>\n\n\n\nThe 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\nIn 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\nIntegration 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\nROI 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
Tangible Benefits: KPIs Before and After<\/strong><\/h3>\n\n\n\nMeasuring 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
\n- Reduction lead time<\/strong> of administrative\/operational activities (hours \u2192 minutes)<\/li>\n\n\n\n
- Increase contactless rate<\/strong> and productivity (transactions\/FTE)<\/li>\n\n\n\n
- Reduction errors<\/strong> (data entry, inconsistencies, rework)<\/li>\n\n\n\n
- Improvement Service Level Agreement<\/strong> and response times to requests (tickets, internal requests)<\/li>\n\n\n\n
- Process stability: fewer repetitive exceptions, better compliance with standards<\/li>\n\n\n\n
- Data quality and reporting timeliness (KPI \u201cnear real-time\u201d).<\/li>\n<\/ul>\n\n\n\n
<\/p>\n\n\n\n
Risks to be carefully managed<\/strong><\/h2>\n\n\n\nDespite 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\nOn 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\nIn 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\nOn 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\nWhen 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\nFinally, 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
How to Implement RPA with AI: The Effective Roadmap<\/strong><\/h2>\n\n\n\nAI-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\nVendor 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\nThe 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
Towards a proactive operating model: the future of Operations<\/strong><\/h2>\n\n\n\nGenerative 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\nThis 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\nBonfiglioli 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
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 Benchmarking 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. priority<\/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 Supply 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\nDefinition 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
\n
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
![\"A](\"https:\/\/www.bonfiglioliconsulting.com\/wp-content\/uploads\/2026\/08\/fonte-preferita-google-1024x179.png\")
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