{"id":48987,"date":"2026-06-09T17:32:13","date_gmt":"2026-06-09T15:32:13","guid":{"rendered":"https:\/\/deveho.com\/?p=48987"},"modified":"2026-06-09T17:32:27","modified_gmt":"2026-06-09T15:32:27","slug":"ai-at-the-heart-of-corporate-performance","status":"publish","type":"post","link":"https:\/\/deveho.com\/pl\/aktualnosci\/ai-at-the-heart-of-corporate-performance\/","title":{"rendered":"#AtoZ &#8211; Artificial Intelligence &#8211; From Data to Action: AI at the Heart of Corporate Performance"},"content":{"rendered":"<br \/>\n<h2>Artificial Intelligence, predictive analytics, and intelligent agents serving businesses<\/h2>\n<p>Artificial intelligence (AI) is currently at the very core of corporate digital transformations. But in practical terms, what does it actually change for management systems?<\/p>\n<h3><red>A profound transformation of management systems<\/red><\/h3>\n<p><p data-path-to-node=\"9\">Long confined to data centralisation and operational management, ERP systems are progressively evolving into genuine <b data-path-to-node=\"9\" data-index-in-node=\"117\">decision-making assistants<\/b>. Today, they no longer simply record information: they analyse workflows in real time, detect anomalies, anticipate trends, and actively support users in their day-to-day activities.<\/p>\n<p data-path-to-node=\"10\">In this context, management systems are becoming smarter. They are now capable of understanding the business context, formulating relevant recommendations, and, increasingly, preparing specific operational actions under human supervision.<\/p>\n<p data-path-to-node=\"11\">Driven by predictive analytics and the rise of intelligent agents, this new generation of AI marks a major milestone in the evolution of ERP and financial tools.<\/p>\n<\/p>\n<h3><red>A technology already delivering economic value<\/red><\/h3>\n<p><p data-path-to-node=\"13\">This transformation is no longer just theoretical.<\/p>\n<p data-path-to-node=\"14\">Indeed, according to McKinsey, generative AI could add between $2,600 to $4,400 billion of incremental economic value annually on a global scale.<\/p>\n<p data-path-to-node=\"15\">Furthermore, initial field feedback already confirms tangible gains. A study conducted among more than 5,000 customer service agents demonstrated an average <b data-path-to-node=\"15\" data-index-in-node=\"157\">14% increase in the number of cases resolved per hour<\/b>, thanks to AI assistance.<\/p>\n<p data-path-to-node=\"16\">These results illustrate a clear trend: AI does not just automate; it directly enhances productivity and the quality of decision-making.<\/p>\n<\/p>\n<h3><red>What is Artificial Intelligence?<\/red><\/h3>\n<p><p data-path-to-node=\"19\">Artificial intelligence encompasses a suite of technologies capable of simulating certain human capabilities, such as learning, analysis, reasoning, and decision-making.<\/p>\n<p data-path-to-node=\"20\">In practice, it primarily relies on:<\/p>\n<ul>\n<li data-path-to-node=\"21,0,0\">Machine learning<\/li>\n<li data-path-to-node=\"21,1,0\">Deep learning<\/li>\n<li data-path-to-node=\"21,2,0\">Natural Language Processing (NLP)<\/li>\n<li data-path-to-node=\"21,3,0\">Advanced data analytics<\/li>\n<\/ul>\n<p data-path-to-node=\"22\">Within enterprises, these technologies make it possible to automate repetitive tasks, extract insights from massive volumes of data, and significantly improve the quality of decisions.<\/p>\n<p><em><b data-path-to-node=\"23,0\" data-index-in-node=\"0\">Disclaimer:<\/b> This article is based on original content published by Sage, entitled <i data-path-to-node=\"23,0\" data-index-in-node=\"82\">&#8220;Predictive analytics and AI to boost your cash flow&#8221; (<a href=\"https:\/\/www.sage.com\/fr-fr\/blog\/analyse-predictive-pour-augmenter-vos-flux-de-tresorerie\/\" target=\"_blank\" rel=\"noopener\">read here<\/a>)<\/i>, which we have supplemented with concrete examples and specific perspectives tailored to our solutions, Sage X3 and Sage Intacct.\u00a0<\/em><\/p>\n<\/p>\n<h3><red>Understanding the different forms of AI<\/red><\/h3>\n<div class=\"TyagGW_tableContainer\">\n<div class=\"group TyagGW_tableWrapper flex flex-col-reverse w-fit\" tabindex=\"-1\">\n<p data-path-to-node=\"25\">Before going any further, it is essential to distinguish between the main types of artificial intelligence used in business today.<\/p>\n<table data-path-to-node=\"26\">\n<thead>\n<tr>\n<td><strong>Type of AI<\/strong><\/td>\n<td><strong>Objective<\/strong><\/td>\n<td><strong>Example<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><span data-path-to-node=\"26,1,0,0\"><b data-path-to-node=\"26,1,0,0\" data-index-in-node=\"0\">Generative AI<\/b><\/span><\/td>\n<td><span data-path-to-node=\"26,1,1,0\">Produce content or answer queries<\/span><\/td>\n<td><span data-path-to-node=\"26,1,2,0\">Generating a report, summarising a meeting<\/span><\/td>\n<\/tr>\n<tr>\n<td><span data-path-to-node=\"26,2,0,0\"><b data-path-to-node=\"26,2,0,0\" data-index-in-node=\"0\">Predictive AI<\/b><\/span><\/td>\n<td><span data-path-to-node=\"26,2,1,0\">Anticipate future events<\/span><\/td>\n<td><span data-path-to-node=\"26,2,2,0\">Forecasting cash flow, detecting a late payment<\/span><\/td>\n<\/tr>\n<tr>\n<td><span data-path-to-node=\"26,3,0,0\"><b data-path-to-node=\"26,3,0,0\" data-index-in-node=\"0\">Agentic AI<\/b><\/span><\/td>\n<td><span data-path-to-node=\"26,3,1,0\">Execute or prepare business actions<\/span><\/td>\n<td><span data-path-to-node=\"26,3,2,0\">Launching a customer chase-up, triggering a workflow<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p data-path-to-node=\"27\">These approaches are entirely complementary. Generative AI facilitates the production and understanding of information. Predictive AI enables businesses to anticipate. Meanwhile, Agentic AI acts directly within business processes, under human oversight.<\/p>\n<p>&nbsp;<\/p>\n<\/div>\n<\/div>\n<h3><red>The major generations of AI in business<\/red><\/h3>\n<p><p data-path-to-node=\"29\">The use of artificial intelligence within organisations has evolved progressively.<\/p>\n<p data-path-to-node=\"30\">First, <b data-path-to-node=\"30\" data-index-in-node=\"7\">descriptive AI<\/b> made it possible to understand what happened. Next, <b data-path-to-node=\"30\" data-index-in-node=\"74\">diagnostic AI<\/b> helped explain the causes. Then, <b data-path-to-node=\"30\" data-index-in-node=\"121\">predictive AI<\/b> paved the way for anticipating future events.<\/p>\n<p data-path-to-node=\"31\">Subsequently, <b data-path-to-node=\"31\" data-index-in-node=\"14\">prescriptive AI<\/b> introduced action recommendations. Finally, <b data-path-to-node=\"31\" data-index-in-node=\"74\">agentic AI<\/b> represents the latest milestone, capable of preparing or executing specific business tasks under human supervision.<\/p>\n<p data-path-to-node=\"32\">This progression illustrates a profound transformation of information systems, which no longer merely inform, but now actively participate in decision and action.<\/p>\n<\/p>\n<h3><red>Predictive analytics: anticipating rather than reacting<\/red><\/h3>\n<p><h4 data-path-to-node=\"35\">A new way of reading data<\/h4>\n<p data-path-to-node=\"36\">Among the most structural applications of AI in business, predictive analytics occupies a central position.<\/p>\n<p data-path-to-node=\"37\">It relies on exploiting historical and real-time data to identify trends, correlations, and probable scenarios. Thanks to machine learning, it models future behaviours based on past data.<\/p>\n<p data-path-to-node=\"38\">Unlike traditional reporting, which merely describes the past, predictive analytics aims to project what might happen next.<\/p>\n<h4 data-path-to-node=\"39\">A more complete and connected vision<\/h4>\n<p data-path-to-node=\"40\">In a more advanced setup, it combines internal data (cash flow, sales, customer behaviour) with external data (market trends, competition, industry shifts).<\/p>\n<p data-path-to-node=\"41\">Consequently, it offers a more comprehensive and dynamic view of potential developments.<\/p>\n<h4 data-path-to-node=\"42\">Concrete use cases<\/h4>\n<p data-path-to-node=\"43\">In practical terms, it specifically enables businesses to:<\/p>\n<ul>\n<li data-path-to-node=\"44,0,0\">Anticipate budgetary variances before they occur<\/li>\n<li data-path-to-node=\"44,1,0\">Detect operational or financial risks ahead of time<\/li>\n<li data-path-to-node=\"44,2,0\">Forecast business activity or cash flow trends<\/li>\n<li data-path-to-node=\"44,3,0\">Identify anomalies as soon as they emerge<\/li>\n<\/ul>\n<h4 data-path-to-node=\"45\">From data to decision<\/h4>\n<p data-path-to-node=\"46\">In this context, the objective is not to replace human expertise, but to reinforce it.<\/p>\n<p data-path-to-node=\"47\">Predictive analytics makes it possible to detect weak signals earlier, refine data interpretation, and prioritise actions based on their potential impact.<\/p>\n<p data-path-to-node=\"48\">Progressively, companies are moving away from a reactive mindset and embracing a genuinely proactive approach.<\/p>\n<h4 data-path-to-node=\"49\">A measurable impact on performance<\/h4>\n<p data-path-to-node=\"50\">This evolution is already yielding tangible results. According to PwC, sectors most advanced in AI adoption record a <b data-path-to-node=\"50\" data-index-in-node=\"117\">4.3% productivity growth<\/b>, compared to <b data-path-to-node=\"50\" data-index-in-node=\"155\">0.9%<\/b> in less exposed sectors.<\/p>\n<p data-path-to-node=\"51\">As a result, predictive analytics is becoming a direct lever for optimising performance and competitiveness.<\/p>\n<\/p>\n<h3><red>From znalysis to zction: The rise of agentic AI<\/red><\/h3>\n<p><p data-path-to-node=\"54\">Current developments are no longer just about prediction, but also about action.<\/p>\n<p data-path-to-node=\"55\">Intelligent agents now make it possible to:<\/p>\n<ul>\n<li data-path-to-node=\"56,0,0\">Continuously monitor operational data<\/li>\n<li data-path-to-node=\"56,1,0\">Automatically detect anomalies<\/li>\n<li data-path-to-node=\"56,2,0\">Propose or prepare corrective actions<\/li>\n<li data-path-to-node=\"56,3,0\">Support users in their daily decisions<\/li>\n<\/ul>\n<p data-path-to-node=\"57\">However, these actions remain strictly governed by compliance rules and require human validation in sensitive contexts. Thus, AI becomes a true operational assistant embedded within business processes.<\/p>\n<\/p>\n<h3><red>When AI becomes tangible in management tools<\/red><\/h3>\n<p><p data-path-to-node=\"60\">In financial and decision-making environments, these technologies are already actively deployed.<\/p>\n<p data-path-to-node=\"61\">AI assistants integrated into management solutions allow direct interaction with data using natural language.<\/p>\n<p data-path-to-node=\"62\">They notably offer:<\/p>\n<ul>\n<li data-path-to-node=\"63,0,0\">Intuitive data exploration<\/li>\n<li data-path-to-node=\"63,1,0\">Automatic generation of insights<\/li>\n<li data-path-to-node=\"63,2,0\">Real-time anomaly detection<\/li>\n<li data-path-to-node=\"63,3,0\">Identification of significant trends<\/li>\n<\/ul>\n<p data-path-to-node=\"64\">Users can therefore query their systems directly\u2014for instance, to analyse a drop in turnover, understand cash flow evolution, or identify outstanding invoices impacting liquidity.<\/p>\n<\/p>\n<h2>Towards ERP systems augmented by Artificial Intelligence<\/h2>\n<\/p>\n<h3><red>Sage Copilot in Sage X3<\/red><\/h3>\n<p><p data-path-to-node=\"68\">Since 2025, Sage has embedded <b data-path-to-node=\"68\" data-index-in-node=\"30\">Sage Copilot<\/b> into <b data-path-to-node=\"68\" data-index-in-node=\"48\">Sage X3<\/b>, marking a major shift towards an &#8220;AI-augmented&#8221; ERP. In 2026, the solution has crossed a new threshold: it is no longer confined to an isolated conversational interface, but has become a genuine operational intelligence engine integrated into the Sage X3 environment and business practices.<\/p>\n<p data-path-to-node=\"69\">Sage Copilot has been available since the <b data-path-to-node=\"69\" data-index-in-node=\"42\">Sage X3 Release 2025 R2 (12.0.38)<\/b>. This version serves as the launchpad for its progressive integration into the ERP ecosystem, featuring a rapid ramp-up of use cases tied to data analysis, user assistance, and navigation across X3 objects.<\/p>\n<h4 data-path-to-node=\"70\">Direct integration into the Sage X3 user experience<\/h4>\n<p data-path-to-node=\"71\">Concr\u00e8tely, Sage Copilot is accessible as a conversational interface fully embedded within Sage X3. Users can open it from their workspace without interrupting their current ERP screens (sales, accounts, purchasing, stock).<\/p>\n<p data-path-to-node=\"72\">Interaction happens in natural language, directly connected to live system data. Users no longer need to navigate menus manually, filter lists, or build technical queries. The goal is not just to &#8220;answer questions&#8221;, but to enable <b data-path-to-node=\"72\" data-index-in-node=\"230\">guided data exploration within the business context<\/b>.<\/p>\n<h4>From chat to exploring data and records in Sage X3<\/h4>\n<p data-path-to-node=\"74\">Sage Copilot does more than just display textual answers; it acts as a business assistant tied directly to X3 objects.<\/p>\n<p>Practical examples of use:<\/p>\n<ul>\n<li>From an accounting screen, a user can ask:<br \/>\n<em>\u201cWhich supplier invoices are overdue for approval?\u201d<\/em><br \/>\n\u2192 Sage Copilot identifies the relevant records and makes it easy to view them.<\/li>\n<li>From the sales module:<br \/>\n<em>\u201cWhich customers have seen a drop in orders over the last three months?\u201d<\/em><br \/>\n\u2192 Sage Copilot identifies the relevant customer accounts and links to the third-party records.<\/li>\n<li>From cash flow management:<br \/>\n<em>\u201cWhich pending payments have the greatest impact on my cash position?\u201d<\/em><br \/>\n\u2192 Sage Copilot highlights critical receivables and the associated amounts.<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"attachment-svg aligncenter wp-image-47507\" role=\"img\" src=\"https:\/\/deveho.com\/wp-content\/uploads\/2026\/03\/Sage-Copilot-LIA-au-service-de-votre-productivite.svg\" alt=\"Sage Copilot L'IA au service de votre productivit\u00e9\" width=\"242\" height=\"282\" \/><\/p>\n<h4 data-path-to-node=\"3\">Capabilities already available<\/h4>\n<p data-path-to-node=\"4\">Thanks to Natural Language Processing (NLP) technologies, Sage Copilot allows direct interaction with Sage X3 data:<\/p>\n<ul>\n<li data-path-to-node=\"5,0,0\">Interrogation of financial, commercial, and logistics data in natural language<\/li>\n<li data-path-to-node=\"5,1,0\">Contextual navigation towards ERP objects (invoices, orders, customers, items)<\/li>\n<li data-path-to-node=\"5,2,0\">Delivery of structured responses directly usable in daily work<\/li>\n<\/ul>\n<p data-path-to-node=\"6\"><b data-path-to-node=\"6\" data-index-in-node=\"0\">Intuitive data exploration<\/b> : Users no longer need to master complex filters or build technical queries. The AI queries data from Sage X3 (finance, sales, supply chain, production) and delivers structured, understandable, and directly actionable responses.<\/p>\n<p data-path-to-node=\"7\"><b data-path-to-node=\"7\" data-index-in-node=\"0\">Identification of data variances or anomalies<\/b> : Beyond simple querying, the system continuously monitors operational workflows. It is capable of identifying significant budgetary, financial, or operational variances, and flagging them before they impact company performance.<\/p>\n<p data-path-to-node=\"8\"><b data-path-to-node=\"8\" data-index-in-node=\"0\">Automatic generation of insights<\/b> : Sage Copilot also produces <i data-path-to-node=\"8\" data-index-in-node=\"60\">insight cards<\/i>, highlighting trends or weak signals:<\/p>\n<ul>\n<li data-path-to-node=\"9,0,0\">Evolution of average customer payment terms (debtor days)<\/li>\n<li data-path-to-node=\"9,1,0\">Unusual variations in operating costs<\/li>\n<li data-path-to-node=\"9,2,0\">Behavioural changes within specific customer segments<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-47523\" src=\"https:\/\/deveho.com\/wp-content\/uploads\/2026\/03\/Sage-Copilot-taches-rapides.png\" alt=\"IA - Sage Copilot taches rapides \" width=\"513\" height=\"333\" srcset=\"https:\/\/deveho.com\/wp-content\/uploads\/2026\/03\/Sage-Copilot-taches-rapides.png 1996w, https:\/\/deveho.com\/wp-content\/uploads\/2026\/03\/Sage-Copilot-taches-rapides-300x194.png 300w, https:\/\/deveho.com\/wp-content\/uploads\/2026\/03\/Sage-Copilot-taches-rapides-1024x664.png 1024w, https:\/\/deveho.com\/wp-content\/uploads\/2026\/03\/Sage-Copilot-taches-rapides-150x97.png 150w, https:\/\/deveho.com\/wp-content\/uploads\/2026\/03\/Sage-Copilot-taches-rapides-768x498.png 768w, https:\/\/deveho.com\/wp-content\/uploads\/2026\/03\/Sage-Copilot-taches-rapides-1536x996.png 1536w, https:\/\/deveho.com\/wp-content\/uploads\/2026\/03\/Sage-Copilot-taches-rapides-1920x1245.png 1920w\" sizes=\"(max-width: 513px) 100vw, 513px\" \/><\/p>\n<p data-start=\"2828\" data-end=\"3073\">These features are gradually transforming Sage X3 into a more interactive and intelligent analytical environment, one that is no longer focused solely on generating data, but on its <strong>immediate and operational interpretation.<\/strong><\/p>\n<\/p>\n<h3><red>AI in Sage Intacct<\/red><\/h3>\n<p><p data-path-to-node=\"82\">In modern financial environments, Artificial Intelligence does more than just produce analyses: it has become a <b data-path-to-node=\"82\" data-index-in-node=\"112\">genuine lever for automation and continuous steering<\/b>. <b data-path-to-node=\"82\" data-index-in-node=\"166\">Sage Intacct<\/b> perfectly illustrates this shift with an approach centered on <b data-path-to-node=\"82\" data-index-in-node=\"241\">augmented finance<\/b>, where teams gain speed, reliability, and foresight.<\/p>\n<h4 data-path-to-node=\"83\">Towards a &#8220;continuous accounting&#8221; model<\/h4>\n<p data-path-to-node=\"84\">One of the most significant evolutions in Sage Intacct is the transition towards a <b data-path-to-node=\"84\" data-index-in-node=\"83\">continuous close<\/b>. The objective is no longer to wait for the month-end to analyse results, but to have financial records updated in real time.<\/p>\n<p data-path-to-node=\"85\">Thanks to AI and automation:<\/p>\n<ul>\n<li data-path-to-node=\"86,0,0\">Journal entries are automatically categorised and reconciled<\/li>\n<li data-path-to-node=\"86,1,0\">Anomalies are detected continuously<\/li>\n<li data-path-to-node=\"86,2,0\">Financial workflows are consolidated without heavy manual intervention<\/li>\n<\/ul>\n<p data-path-to-node=\"87\">This approach profoundly reshapes the role of finance teams: they shift from <i data-path-to-node=\"87\" data-index-in-node=\"77\">a posteriori<\/i> control to <b data-path-to-node=\"87\" data-index-in-node=\"101\">continuous piloting<\/b>, seamlessly embedded into the company&#8217;s operations.<\/p>\n<p data-path-to-node=\"88\">According to Deloitte, automating closing processes significantly reduces the workload for finance teams while improving the speed and reliability of financial reporting. The most advanced organisations no longer talk about a monthly close, but rather a continuous accounting process integrated into operational management.<\/p>\n<h4 data-path-to-node=\"89\">Proactive financial anomaly detection<\/h4>\n<p data-path-to-node=\"90\">The AI embedded within Sage Intacct goes beyond executing rigid accounting rules. It learns from historical patterns to:<\/p>\n<ul>\n<li data-path-to-node=\"91,0,0\">Identify unusual journal entries<\/li>\n<li data-path-to-node=\"91,1,0\">Detect budgetary variances as soon as they arise<\/li>\n<li data-path-to-node=\"91,2,0\">Flag inconsistencies in supplier or customer workflows<\/li>\n<\/ul>\n<p data-path-to-node=\"92\">The benefit is not merely fixing errors faster, but preventing financial slippage before it impacts the bottom line.<\/p>\n<h4 data-path-to-node=\"93\">Intelligent financial process automation<\/h4>\n<p data-path-to-node=\"94\">The latest developments extend far beyond traditional automation:<\/p>\n<ul>\n<li data-path-to-node=\"95,0,0\">Semi-automated bank reconciliations<\/li>\n<li data-path-to-node=\"95,1,0\">Intelligent invoice validation based on dynamic rules<\/li>\n<li data-path-to-node=\"95,2,0\">Nominal ledger coding suggestions based on history<\/li>\n<li data-path-to-node=\"95,3,0\">Automatic prioritisation of month-end closing tasks<\/li>\n<\/ul>\n<p data-path-to-node=\"96\">AI acts here as an operational assistant for the finance function, drastically reducing repetitive tasks whilst increasing data reliability.<\/p>\n<\/p>\n<h3><red>Nectari AI Copilot in Sage Enterprise Intelligence<\/red><\/h3>\n<p><h4 data-path-to-node=\"99\">Nectari AI Copilot in financial BI<\/h4>\n<p data-path-to-node=\"100\">With <b data-path-to-node=\"100\" data-index-in-node=\"5\">Nectari AI Copilot<\/b>, you instantly transform complex data into clear, actionable information within <b data-path-to-node=\"100\" data-index-in-node=\"104\">Sage Enterprise Intelligence<\/b>. The arrival of Nectari AI Copilot marks a major step towards conversational and augmented BI.<\/p>\n<h4 data-path-to-node=\"101\">Decision-oriented conversational BI<\/h4>\n<p data-path-to-node=\"102\">Thanks to generative AI, users can now interact directly with their data:<\/p>\n<ul>\n<li data-path-to-node=\"103,0,0\"><i data-path-to-node=\"103,0,0\" data-index-in-node=\"0\">\u201cShow me sales by region for this quarter.\u201d<\/i><\/li>\n<li data-path-to-node=\"103,1,0\"><i data-path-to-node=\"103,1,0\" data-index-in-node=\"0\">\u201cRecommend ways to make this chart clearer, more precise, and more engaging.\u201d<\/i><\/li>\n<li data-path-to-node=\"103,2,0\"><i data-path-to-node=\"103,2,0\" data-index-in-node=\"0\">\u201cHelp me spot the key highlights in my dashboard for the board meeting.\u201d<\/i><\/li>\n<li data-path-to-node=\"103,3,0\"><i data-path-to-node=\"103,3,0\" data-index-in-node=\"0\">\u201cWhat explains the drop in net margin this month?\u201d<\/i><\/li>\n<\/ul>\n<p data-path-to-node=\"104\">The tool does not just provide an answer: it <b data-path-to-node=\"104\" data-index-in-node=\"45\">explains, contextualises, and highlights key drivers<\/b>.<\/p>\n<h4 data-path-to-node=\"105\">Automated data analysis and storytelling<\/h4>\n<p data-path-to-node=\"106\">Nectari AI Copilot introduces automated data storytelling:<\/p>\n<ul>\n<li data-path-to-node=\"107,0,0\">Synthesis of key performance indicators (KPIs) on a dashboard<\/li>\n<li data-path-to-node=\"107,1,0\">Highlighting of significant trends<\/li>\n<li data-path-to-node=\"107,2,0\">Explanation of performance variances<\/li>\n<li data-path-to-node=\"107,3,0\">Generation of summaries ready for executive committees<\/li>\n<\/ul>\n<p data-path-to-node=\"108\">This reduces dependence on manual analysis and accelerates decision-making.<\/p>\n<h4 data-path-to-node=\"109\">Towards BI augmented by agentic AI<\/h4>\n<p data-path-to-node=\"110\">The next step involves transforming BI into a proactive system:<\/p>\n<ul>\n<li data-path-to-node=\"111,0,0\">Smart alerts on critical thresholds<\/li>\n<li data-path-to-node=\"111,1,0\">Action recommendations based on trends<\/li>\n<li data-path-to-node=\"111,2,0\">Automated preparation of recurring reports<\/li>\n<li data-path-to-node=\"111,3,0\">Suggestions for improving data visualisations<\/li>\n<\/ul>\n<p data-path-to-node=\"112\">BI no longer simply answers questions: it <b data-path-to-node=\"112\" data-index-in-node=\"42\">suggests the right questions to ask<\/b>.<\/p>\n<\/p>\n<h3><red>AI in Yooz<\/red><\/h3>\n<p><p data-path-to-node=\"115\">In the field of invoice scanning and accounts payable (AP) automation, <b data-path-to-node=\"115\" data-index-in-node=\"71\">Yooz<\/b> stands out as a benchmark solution for document AI and automated accounting.<\/p>\n<h4 data-path-to-node=\"116\">An Artificial Intelligence specialised in document understanding<\/h4>\n<p data-path-to-node=\"117\">Yooz\u2019s AI relies on deep learning technologies capable of:<\/p>\n<ul>\n<li data-path-to-node=\"118,0,0\">Automatically recognising invoices, credit notes, and receipts<\/li>\n<li data-path-to-node=\"118,1,0\">Extracting data without requiring predefined templates<\/li>\n<li data-path-to-node=\"118,2,0\">Understanding the structure of non-standardised documents<\/li>\n<\/ul>\n<p data-path-to-node=\"119\">Unlike traditional approaches based on rigid OCR rules, Yooz leverages contextual document understanding.<\/p>\n<ul>\n<li data-path-to-node=\"120,0,0\"><b data-path-to-node=\"120,0,0\" data-index-in-node=\"0\">Zero configuration:<\/b> It automatically detects amounts, VAT, and suppliers without needing manual template creation.<\/li>\n<li data-path-to-node=\"120,1,0\"><b data-path-to-node=\"120,1,0\" data-index-in-node=\"0\">Self-learning:<\/b> The more you use it, the more precise it becomes by learning from your data entry habits.<\/li>\n<li data-path-to-node=\"120,2,0\"><b data-path-to-node=\"120,2,0\" data-index-in-node=\"0\">Reliability:<\/b> Today, the tool achieves automatic recognition rates of over <b data-path-to-node=\"120,2,0\" data-index-in-node=\"74\">80%<\/b> from the very first documents processed.<\/li>\n<\/ul>\n<h4 data-path-to-node=\"121\">Towards &#8220;touchless&#8221; accounts payable automation<\/h4>\n<p data-path-to-node=\"122\">Recent developments are moving towards a virtually autonomous Purchase-to-Pay process:<\/p>\n<ul>\n<li data-path-to-node=\"123,0,0\">Automated multi-channel invoice reception (email, portal, EDI)<\/li>\n<li data-path-to-node=\"123,1,0\">Intelligent data extraction and validation<\/li>\n<li data-path-to-node=\"123,2,0\">Three-way matching with purchase orders and goods received notes (GRNs)<\/li>\n<li data-path-to-node=\"123,3,0\">Automated bookkeeping entry preparation<\/li>\n<li data-path-to-node=\"123,4,0\">Direct integration into the ERP<\/li>\n<\/ul>\n<p data-path-to-node=\"124\">The medium-term objective is clear: driving towards a <b data-path-to-node=\"124\" data-index-in-node=\"54\">\u201czero manual processing\u201d<\/b> model, where human intervention is reserved solely for controlling and validating exceptional cases.<\/p>\n<p data-path-to-node=\"124\"><b data-path-to-node=\"125,0\" data-index-in-node=\"0\">Want to find out more? <\/b><a href=\"https:\/\/deveho.com\/pl\/kontakt\/\" target=\"_blank\" rel=\"noopener\"><u>Contact us<\/u><\/a><\/p>\n<\/p>\n<h3><red>A challenge as much technological as organisational<\/red><\/h3>\n<p><p data-path-to-node=\"128\">Implementing predictive analytics or AI systems is not limited to deploying an extra feature or software tool. It is, above all, a profound transformation that impacts technologies, processes, and working practices alike.<\/p>\n<p data-path-to-node=\"129\">This transformation rests on several essential pillars:<\/p>\n<ul data-path-to-node=\"130\">\n<li>\n<p data-path-to-node=\"130,0,0\"><b data-path-to-node=\"130,0,0\" data-index-in-node=\"0\">Flawless data quality:<\/b> Reliable, complete, and consistent data is indispensable to guarantee model relevance. Without it, outputs lose value and reliability.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"130,1,0\"><b data-path-to-node=\"130,1,0\" data-index-in-node=\"0\">Clear governance:<\/b> It is necessary to define responsibilities, usage rules, and validation processes to wrap a framework around AI usage.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"130,2,0\"><b data-path-to-node=\"130,2,0\" data-index-in-node=\"0\">Genuine business adoption:<\/b> Results and recommendations must be understood, interpreted, and integrated into daily decision-making to create real value.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"130,3,0\"><b data-path-to-node=\"130,3,0\" data-index-in-node=\"0\">Ethical and operational vigilance:<\/b> Model transparency, decision traceability, bias management, and compliance with regulatory requirements must be integrated right from the design stage.<\/p>\n<\/li>\n<\/ul>\n<p data-path-to-node=\"131\">Without this alignment between technology, data, and organisation, AI risks remaining a powerful tool on paper, but limited in its actual business impact.<\/p>\n<\/p>\n<h3><red>In Summary<\/red><\/h3>\n<p><p data-path-to-node=\"134\">AI is progressively and profoundly transforming corporate management systems and decision-making models.<\/p>\n<p data-path-to-node=\"135\">Initially, it enabled a better understanding of the past through reporting and descriptive analytics. It then paved the way for anticipating future events via predictive analytics. Today, a new chapter is unfolding with the emergence of intelligent agents, capable not only of analysing and recommending, but also of concretely assisting users in executing specific business tasks.<\/p>\n<p data-path-to-node=\"136\">The true value of AI therefore does not reside solely in the technology itself, but in its capacity to integrate effectively into organisations. This requires a balanced combination of reliable data, human expertise, and a robust governance framework.<\/p>\n<p data-path-to-node=\"137\">Early feedback proves that the benefits of AI are real but heavily dependent on implementation quality. According to McKinsey, top-performing companies in AI adoption are starting to observe a return on investment of up to <b data-path-to-node=\"137\" data-index-in-node=\"223\">three pounds generated for every pound invested<\/b>.<\/p>\n<p data-path-to-node=\"138\">Businesses that successfully navigate this transformation will secure a sustainable competitive advantage in an environment where the speed, quality, and relevance of decisions have become critical performance factors.<\/p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Artificial Intelligence, predictive analytics, and intelligent agents serving businesses Artificial intelligence (AI) is currently at the very core of corporate digital transformations. But in practical terms, what does it actually change for management systems? A profound transformation of management systems Long confined to data centralisation and operational management, ERP systems are progressively evolving into genuine [&hellip;]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[149],"tags":[],"class_list":["post-48987","post","type-post","status-publish","format-standard","hentry","category-aktualnosci"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>AI at the Heart of Corporate Performance - Deveho<\/title>\n<meta name=\"description\" content=\"Artificial intelligence (AI) is a major topic of discussion, but what does it actually involve? 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