RPA is a simpler type of software that imitates a task-related activity of a human within a process (e.g. log-in, copy\/paste, field entry etc.). NLP represent algorithms that can interpret, transform and generate human language. Machine Learning (ML) is more complex and its goal is to acquire knowledge (e.g. image\/speech recognition, medical diagnosis etc.). On the other hand, A.I. is driven by data, and it actually aims to become more intelligent, to simulate intelligence (good examples are Siri, Tesla, even Netflix).<\/p>\n\n \u00a0Reasons for A.I. in procurement?<\/em><\/strong><\/p>\n\n Procurement itself is at the center of cost, quality, sustainability and risk, as well as the customer-employee-supplier loop. Therefore, it has a unique advantage and a huge reason for embracing upcoming transformations.<\/p>\n\n \u00a0The most beneficial reasons include:<\/p>\n\n –\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0better decision making<\/p>\n\n –\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0cost reduction<\/p>\n\n –\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0greater speed<\/p>\n\n –\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0time & operation management<\/p>\n\n –\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0task automation<\/p>\n\n –\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0better reach & visibility<\/p>\n\n –\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0improved relationship management<\/p>\n\n –\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0new opportunities<\/p>\n\n –\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0higher levels of analysis that raise strategic contribution<\/p>\n\n There are 7 cognitive capabilities that can provide high-end competitiveness to a company – spend management,\u00a0fraud\/audit management, supplier intel, supply risks and alert management, contract administration\/compliance, price benchmarking\/cost prediction, and buying assistance.<\/p>\n\n \u00a0Still, A.I. is not a magic pill that can solve our procurement problems, as it requires active managerial inputs, expertise, guidance and oversight.<\/p>\n\n \u00a0A.I. in strategic sourcing<\/em><\/p>\n\n Cognitive models can be helpful at\u00a0identifying sourcing\u00a0trends,\u00a0based on the spend data development, procurement category, and early procurement supplier schemes in negotiations. For example, if a supplier is involved in more concepts and early start agreements, they will be able to anticipate the spend growth based on these trigger points. A.I. will help to identify these schemes.<\/p>\n\n \u00a0A.I. in tactical sourcing<\/em><\/p>\n\n Cognitive models can help with identifying saving\u00a0opportunities, where you can analyze your existing contracts and commercial data based on decisions that are made, and challenge it earlier in the supplier universe (like via Titan SIM), outlining possible alternative suppliers with the same expertise.<\/p>\n\n A.I. in procurement<\/em><\/p>\n\n Where cognitive models can be leveraged to automatically\u00a0review and approve\u00a0purchase orders\u00a0up to certain thresholds. For example, from a preferred supplier only or a supplier with low-risk indications in certain spend categories.<\/p>\n\n \u00a0At what point is it worthwhile for a company to adopt A.I. in procurement?<\/em><\/strong><\/p>\n\n There are few aspects to consider before your company decides to embrace A.I.<\/p>\n\n Many experts agree that the main one is the gap between your ambition and the actual execution. Generating value from AI is way more than just buying AI for a specific process. Its development involves a crucial understanding of algorithms, collecting and integrating company-specific data, and the overall supervision (which implies further management\/leadership challenges and analysis).<\/p>\n\n Others include effort-return ratio in procuring processes, as well as time management-savings ratio.<\/p>\n\n Either way, A.I. will surely lift your company in many levels, while revealing more clean opportunities, as well as improving your overall brand.<\/p>\n\n Global spending on A.I. is projected to be more than $110 billion in 2024, with an annual growth of roughly 20%. According to the International Data Corporation (IDC<\/a>), organizations will deploy A.I. as part of their efforts to remain competitive in the digital economy.<\/p>\n\n \u201cCompanies will adopt AI \u2014 not just because they can, but because they must,\u201d said Ritu Jyoti, program vice president, Artificial Intelligence at IDC. He added, \u201cAI is the technology that will help businesses to be agile, innovate, and scale.\u201d<\/p>\n\n Do you agree that the A.I. can be decisive in developing a highly successful organization? Let me know your thoughts.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":" In 2021, high-end organizations are experiencing monumental changes. While balancing between daily challenges, such as exchange rates or regulations, it seems that in the long-term they are exploring new models in accepting the \u201cinevitable\u201d transformation \u2013 the Artificial Intelligence (A.I.). A well-known phenomenon that is hard to ignore, and that often includes terms such as […]<\/p>\n![\"Oliver](\"https:\/\/www.titan-solutions.ch\/wp-content\/uploads\/2024\/04\/image-68.png\")
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<\/figure>\n\n“Companies will adopt A.I. – not just because they can, but because they must.”<\/em><\/strong><\/h3>\n\n