IBM Watson platform<\/a> to analyse procurement data from across its global operations.<\/p>\nThis helped the firm to identify opportunities to negotiate better deals with suppliers and optimise procurement processes, resulting in significant cost savings.<\/p>\n
Further, large language tools can help procurement professionals manage supplier relationships more effectively \u2013 analysing data and providing insights on supplier performance, such as quality of goods, delivery times and responsiveness to customer needs.<\/p>\n
For instance, Amazon uses an NLP model to identify patterns in customer complaints, such as delivery delays or product defects, which alerts Amazon\u2019s procurement team. The team is then able to take action with the supplier.<\/p>\n
There are multiple additional use cases which are increasingly being explored by procurement teams. For instance, these tools are used for streamlining supply chains by leveraging scenario modelling, generating accurate demand forecasts by leveraging historic training data, and optimising inventory levels by feeding training data rich in demand patterns or seasonality.<\/p>\n
Further use cases include performing a holistic risk assessment to aid improved procurement decision making, as well as utilising these tools for a more accurate spend classification and analysis to drive strategic decision making.<\/p>\n
Drivers behind organisations’ use of LLM tools<\/h3>\n One of the key factors behind organisations\u2019 use of LLM tools in procurement is that it improves efficiency. AI tools can help automate manual tasks such as data entry and analysis. This can save procurement professionals a significant amount of time and allow them to focus on more strategic tasks.<\/p>\n
Elsewhere, LLM tools can support decision making. By using AI to analyse large volumes of data, procurement professionals can gain insights into market trends, supplier performance and other key factors that can help inform procurement decisions.<\/p>\n
Additionally, AI tools can help identify potential risks such as supplier fraud, contract non-compliance, and supply chain disruptions, allowing procurement professionals to take proactive measures to mitigate these risks, in addition to enhancing supplier relationships and identifying cost-saving opportunities.<\/p>\n
Further to this, LLM\u2019s can help identify cost-saving opportunities, such as identifying suppliers with lower prices, negotiating better contracts and reducing waste.<\/p>\n
Concerns amongst procurement professionals<\/h3>\n Currently, there are several barriers to the large-scale adoption of large language AI models. This is because AI is still in its development phase.<\/p>\n
One of the major current concerns procurement teams have regarding the adoption of large language AI models is that accuracy is not always guaranteed. They can generate misleading and biased information or make mistakes in contracts, negotiations or other important documents.<\/p>\n
The output of any generative AI model depends on the prompt provided by a user. A misleading prompt could produce inaccurate results. Therefore, extensive testing and training is needed to obtain high-quality outputs.<\/p>\n
Furthermore, procurement professionals also have security concerns regarding large language AI models. AI systems can be vulnerable to cyberattacks, which could put an organisation\u2019s sensitive procurement data at risk of breach.<\/p>\n
Tools like ChatGPT are built without any real corporate privacy governance frameworks, making it challenging for companies to leverage these models in their chatbots.<\/p>\n
Additionally, some procurement professionals are hesitant to adopt large language tools because LLM tools lack the contextual understanding and decision-making abilities that humans possess.<\/p>\n
Procurement professionals need to consider factors such as existing supplier relationships, pricing negotiations, contract terms or any other external forces affecting supply at the time of the negotiation of contracts. These factors require human judgment and expertise, which cannot be fully replicated by an AI system.<\/p>\n
For example, an AI system may reject a supplier that does not meet its stringent criteria in one area, while overlooking the supplier’s reliability overall.<\/p>\n
However, this should not discourage the use of large language AI models within procurement. Instead, procurement teams should consider using AI and human intelligence (HI) in conjunction with one another.<\/p>\n\u00a9 shutterstock\/DRN Studio<\/figcaption><\/figure>\nFor example, a buyer can establish a team of experts to review generative AI outputs or intervene in the generative AI process whenever necessary, adding valuable contextual insights and judgement.<\/p>\n
Combining AI and HI<\/h3>\n When it comes to transactional procurement (P2P) functions like invoicing, contract management, and accounts payable processes, there is significant scope for these activities to be undertaken by AI, as they are repetitive.<\/p>\n
However, in terms of more strategic tasks \u2013 such as category strategy development, business requirement gathering, and supplier management \u2013 AI should be utilised to assist and accelerate human decision making, as opposed to replacing it.<\/p>\n
If AI is harnessed correctly, procurement specialists will be able to spend a greater amount of time focusing on tasks which cannot be automated, and which require some form of HI and emotional intelligence. Individuals will be able to shift their attention towards fostering relationships with stakeholders and suppliers, developing these vital connections.<\/p>\n
Technology isn\u2019t there to take the place of humans \u2013 it is meant to enable more time for us to do our jobs better. As such, the optimum approach towards AI will be about discovering the best use cases for it in tandem with HI \u2013 in the specific contexts of an organisation \u2013 then striking the right balance between the pair so as to best meet the business objectives.<\/p>\n","protected":false},"excerpt":{"rendered":"
Learn about the hesitancy towards adopting large language AI models and the benefits that they can bring to the workplace.<\/p>\n","protected":false},"author":18,"featured_media":33428,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[830],"tags":[570],"acf":[],"yoast_head":"\n
How are large language AI models being used by procurement teams?<\/title>\n \n \n \n \n \n \n \n \n \n \n \n \n \n\t \n\t \n\t \n \n \n \n \n \n\t \n\t \n\t \n