Marché mondial de l’automatisation intelligente des processus par apprentissage automatique (ML) – Tendances et prévisions du secteur jusqu’en 2030

Analyse et taille du marché de l'automatisation intelligente des processus par apprentissage automatique (ML)

La nécessité d'accroître la productivité des entreprises et l'adoption croissante des technologies dans divers secteurs industriels devraient stimuler la croissance du marché de l'automatisation intelligente des processus par apprentissage automatique (ML). Les solutions d'automatisation intelligente des processus offrent aux utilisateurs des outils de pointe et des flux de travail adaptables pour leur permettre de prendre des décisions plus rapidement et avec de meilleures connaissances. Ces solutions gèrent les interfaces et éliminent les goulots d'étranglement dans les systèmes de flux de travail. On prévoit que ce facteur donnera une impulsion au marché dans les années à venir.

Français Data Bridge Market Research analyse que le marché de l’automatisation intelligente des processus d’apprentissage automatique (ML), évalué à 13,6 milliards USD en 2022, atteindra 41,03 milliards USD d’ici 2030, avec un TCAC de 14,80 % au cours de la période de prévision de 2023 à 2030. En plus des informations sur le marché telles que la valeur du marché, le taux de croissance, les segments de marché, la couverture géographique, les acteurs du marché et le scénario du marché, le rapport de marché organisé par l’équipe de recherche sur le marché Data Bridge comprend une analyse approfondie des experts, une analyse des importations/exportations, une analyse des prix, une analyse de la consommation de production et une analyse du pilon.

Portée et segmentation du marché de l'automatisation intelligente des processus par apprentissage automatique (ML)  

Market Definition

Software applications are now able to make predictions more accurately through ML, a subfield of AI. Machine learning algorithms forecast new output values using historical data as input.  Artificial intelligence (AI) technologies are used in cognitive process automation to speed up cognitive processes such as reasoning, machine learning, and natural language processing. Insight to cognitive process automation, these tasks will be finished more quickly and easily by both humans and machines.

Machine Learning (ML) Intelligent Process Automation Market Dynamics

Drivers

• Growing adoption of RPA drives the market

Businesses use RPA technology to automate manual data entry tasks, doing away with the need for human labor. The IPA workflow combines cognitive learning, RPA, ML, and AI. As a result, as RPA gains popularity, IPA demand increases as well. RPA offers effectiveness and quickness. Artificial intelligence (AI) is added to automation to analyze data in a way that a human could not, recognize patterns in data, and learn from previous decisions to make increasingly wise decisions. IPA reduces the time needed for tasks by doing away with the need for human data entry, information validation, and document sorting thus driving the market growth.

• Increase in demand for implementation and training services drives the market

A significant segment that shows growth is design and implementation. Therefore, the growth is attributed to a better understanding of the use of automation solutions to reduce manual labor. As intelligent process automation solutions are more widely used, there is an increase in demand for implementation and training services. The providers focus on providing a customized solution that satisfies business requirements. As a result, it is anticipated that demand for intelligent process automation services will rise over the forecast period.

• High acceptance ratio drives the market

Machine learning (ML) intelligence sees a rapid expansion of the scope and use of IT and automation globally with a high acceptance rate, minimizing human work and error with optimal resource utilization to earn greater business enterprise efficiency. Automation with artificial intelligence helps in better customer experience and faster decision-making across the organization, which drives market growth.

Opportunities

• Growing investment in the intelligent process automation market

Businesses are switching towards work-from-home policies, which has a significant impact on the amount invested in operational process automation. Through increased investment in application areas such as telemedicine, predictive maintenance, and virtual healthcare management, the market is growing. The adoption of IPA solutions has increased across most non-IT verticals, thus the market sees an expansion in the machine learning (ML) intelligent process automation market during the aforementioned projected timeframe.

• Advancement in technology creates a lucrative opportunity to grow

Emerging intelligent automation technologies such as virtual agents and natural language processing, among others, are bringing in opportunities to improve the customer experience, and machine learning solutions are significantly improving efficiency. Systems can automatically learn from experience and get better with machine learning, which eliminates the need for explicit programming, thus advancement in technology can create lucrative opportunities in the market.

Restraints/Challenges

• Lack of highly qualified and sophisticated labor restricts growth

Skilled labor is required to run a newly automated operational model, but it's crucial to find individuals with RPA and AI expertise. Technical proficiency, an understanding of the business procedures of the organization, and the ability to adapt management techniques are all part of machine learning intelligence. Assigning people to ongoing maintenance, support, and troubleshooting is equally important to develop automation, and the absence of such skills can limit growth.

• Increased cybersecurity threats restrain market expansion

Cybersecurity is one of the most significant concerns in the digital age. Malware and ransomware attacks are becoming increasingly organized forms of cybercrime. Every day, businesses receive an increasing number of security notifications. According to CERT-In, more than 6.07 lakh cybersecurity incidents were reported in the first half of 2021. As a result, using cybersecurity for IPA is necessary to create an efficient security architecture that shields the organization from rising risks. 77% of businesses intend to increase automation in their security ecosystems in the ensuing years, according to a Cisco survey, which restricts the market.

This machine learning (ML) intelligent process automation market report provides details of new recent developments, trade regulations, import-export analysis, production analysis, value chain optimization, market share, impact of domestic and localized market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on the machine learning (ML) intelligent process automation market contact Data Bridge Market Research for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

Recent Developments

• In 2021, Cisco and IBM worked together to coordinate and manage 5G networks.
• In 2021, Google Cloud Marketplace users can now purchase DRYiCE iAutomate, according to HCL Technologies.
• In 2021, the debut of IBM Cloud Pak for Network Automation was announced by IBM.
• In 2021, to continue working together, Atos and du extended the contract for an additional five years. The modernization of applications and the digital transformation will be aided by this collaboration for du.
• In 2020, The Pega Platform has received a new improvement, according to Pegasystems. Pega Process AI now has a new feature to assist businesses in real-time business and customer operations optimization.

Global Machine Learning (ML) Intelligent Process Automation Market Scope

Le marché de l'automatisation intelligente des processus d'apprentissage automatique (ML) est segmenté en fonction des composants, du type, de la technologie, de la taille de l'organisation, de l'application, de la fonction commerciale, du mode de déploiement et de l'utilisateur final. La croissance parmi ces segments vous aidera à analyser les segments de croissance faibles dans les industries et à fournir aux utilisateurs un aperçu précieux du marché et des informations sur le marché pour les aider à prendre des décisions stratégiques pour identifier les principales applications du marché.

Composant

• Solutions
• Outils logiciels
• Plateformes
• Services
• Services professionnels
• Conseil/Consultation
• Conception et mise en œuvre
• Entraînement
• Assistance et maintenance
• Services gérés

Taper

• Structuré
• Non structuré

Technologie

• Traitement du langage naturel
• Machine learning et apprentissage profond
• Réseaux neuronaux
• Agents virtuels
• Mini-bots
• Vision par ordinateur
• Autres

Taille de l'organisation

• Grandes entreprises
• PME

Application

• Opérations informatiques
• Gestion du centre de contact
• Automatisation des processus d'entreprise
• Gestion des applications
• Gestion de contenu
• Gestion de la sécurité
• Autres

Voyages et hospitalité

Fonction commerciale

• Informatique
• Finances et comptabilité
• Ressources humaines
• Opérations et chaîne d'approvisionnement

Mode de déploiement

• Sur site
• Nuage

Utilisateur final

• Banque, services financiers, assurances (BFSI)
• Télécommunications et informatique
• Transport et logistique
• Médias et divertissement
• Commerce de détail et commerce électronique
• Fabrication
• Santé et sciences de la vie
• Autres
• Gestion des ressources humaines
• Résolution des incidents
• Orchestration des services
• Éducation
• Gouvernement et secteur public
• Utilitaires

Analyse/perspectives régionales du marché de l'automatisation intelligente des processus par apprentissage automatique (ML)

Le marché de l’automatisation intelligente des processus d’apprentissage automatique (ML) est analysé et des informations et tendances sur la taille du marché sont fournies par composants, type, technologie, taille de l’organisation, application, fonction commerciale, mode de déploiement et utilisateur final comme référencé ci-dessus.

Français Les pays couverts dans le rapport sur le marché de l'automatisation intelligente des processus d'apprentissage automatique (ML) sont les États-Unis, le Canada et le Mexique en Amérique du Nord, l'Allemagne, la France, le Royaume-Uni, les Pays-Bas, la Suisse, la Belgique, la Russie, l'Italie, l'Espagne, la Turquie, le reste de l'Europe en Europe, la Chine, le Japon, l'Inde, la Corée du Sud, Singapour, la Malaisie, l'Australie, la Thaïlande, l'Indonésie, les Philippines, le reste de l'Asie-Pacifique (APAC) en Asie-Pacifique (APAC), l'Arabie saoudite, les Émirats arabes unis, l'Afrique du Sud, l'Égypte, Israël, le reste du Moyen-Orient et de l'Afrique (MEA) en tant que partie du Moyen-Orient et de l'Afrique (MEA), le Brésil, l'Argentine et le reste de l'Amérique du Sud en tant que partie de l'Amérique du Sud.

North America dominates the market and will continue to flourish its trend of dominance during the forecast period. The major factors attributable to the region’s dominance are Along with the prevalence of various market players in the area, process management and automation solutions are becoming more widely adopted across the enterprise. Additionally, regional growth is being fueled by the growing enterprise adoption of process management and automation solutions in the United States. The primary drivers of the segmental growth are increased spending on business operations optimization and widespread adoption of cutting-edge technologies such as AI, machine learning, and RPA.

Asia-Pacific will undergo the highest growth rate during the forecast period owing to the region's adoption of cloud-based technologies and growing awareness of automation, machine learning, and artificial intelligence. The demand for intelligent process automation solutions and services is also being further boosted by growing awareness of automation, machine learning, and artificial intelligence. The major drivers of the regional market growth are globalization, economic development, digitalization, and increased adoption of cloud-based technologies.

The country section of the report also provides individual market impacting factors and changes in regulation in the market domestically that impacts the current and future trends of the market. Data points such as down-stream and upstream value chain analysis, technical trends and porter's five forces analysis, case studies are some of the pointers used to forecast the market scenario for individual countries. Also, the presence and availability of global brands and their challenges faced due to large or scarce competition from local and domestic brands, impact of domestic tariffs and trade routes are considered while providing forecast analysis of the country data.

Competitive Landscape and Machine Learning (ML) Intelligent Process Automation Market Share Analysis

The machine learning (ML) intelligent process automation market competitive landscape provides details by competitor. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, global presence, production sites and facilities, production capacities, company strengths and weaknesses, product launch, product width and breadth, application dominance. The above data points provided are only related to the companies' focus related to machine learning (ML) intelligent process automation market.

Some of the major players operating in the machine learning (ML) intelligent process automation market are:

• Automation Anywhere, Inc. (U.S.)
• UiPath (U.S.)
• Blue Prism Limited (U.K.)
• Pegasystems Inc. (U.S.)
• AntWorks (Singapore)
• NICE (Israel)
• Kofax Inc. (U.S.)
• SAP SE (Germany)
• AutomationEdge (U.S.)
• Larc AI (Pty) Ltd. (South Africa)
• Autologyx (U.K.)
• Sanbot Innovation Technology., Ltd (Chine)
• Cinnamon Inc. (Japon)
• Wipro (Inde)
• Xerox Corporation (États-Unis)
• TATA Consultancy Services Limited. (Inde)
• IBM (États-Unis)
• Atos SE (France)
• Capgemini (France)
• Accenture (Irlande)

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