GenAI has immense potential – it can help people in a huge range of roles.
AI in Procurement
How GenAI cuts through the complexity
Generative AI powers a new generation of technology tools that will transform almost every aspect of how today’s procurement departments operate.
Generative artificial intelligence (GenAI) has quickly turned into the hottest business technology topic in the world. Less than three years after the first publicly accessible GenAI tool, ChatGPT appeared, there is an overwhelming appetite among the world’s leading companies to understand and harness the promise of GenAI. Growing interest has been reflected in the share price performance of Nvidia, the leading maker of the chips used to run AI models: its shares rose nearly 240 % during 2023 and by mid-June 2024 had gained another 190 %, making Nvidia briefly the world’s most valuable company.
As these gains suggest, GenAI has immense potential. It can help people in a huge range of roles – from call handlers to biochemists – to do more, go faster and achieve higher quality results. A 2023 joint study between Harvard Business School and Boston Consulting Group (BCG), examined the impact of using Open AI’s GPT4 across 18 knowledge-intensive tasks carried out by consultants, including idea generation, text writing and market analysis.
The study concluded that “consultants using AI were significantly more productive”. It found that they completed 12.2% more tasks on average and completed them 25.1% more quickly. The group using AI also produced results of more than 40 % higher quality than the control group. Gen AI was not suited for every task, but in areas where it was most relevant, it produced striking improvements in productivity as well as significant performance gains among less skilled individuals.
Benefits like these are especially relevant to procurement and supply chain management, where many processes currently involve laborious manual operations and resilience depends on the ability to navigate complexity and manage multiple risks.
GenAI in Procurement & Supply Chain Management
In procurement teams as in many other departments, GenAI can provide a significant productivity gain by enabling people to complete everyday tasks around 25 % faster and more accurately. These might include drafting emails and documents, researching information, summarizing documents, producing translations or answering employees’ work-related questions. However, this layer of “everyday efficiency gains” is only the beginning. Beyond these more general benefits, GenAI has numerous potential specialist applications in procurement. According to BCG research, beyond the 44 % of procurement tasks that are already automated – a further 38 % can be enhanced with new AI solutions including GenAI.
The analysis found particularly large benefits in areas such as using predictive analytics and risk models to enhance supplier selection and generating documents to accelerate RFP and contracting processes. AI tools are particularly well-suited to produce more accurate demand and inventory forecasts based on quantitative input. By understanding the underlying context and providing know-how for decision making, GenAI is suited to propose solutions, for example optimized route planning to improve logistics efficiency, enhancing supply chain transparency and traceability through real-time tracking and managing risks through scenario modeling and analysis of supply chain resilience. In areas such as running negotiations with suppliers or calculating the effects of input cost inflation and finding ways to protect margins, GenAI can also bring major benefits, thanks to its capacity to analyze very large and complex data sets and evaluate the many options available.
BCG’s analysis also suggested that procurement teams could capture significant value in indirect spend from external service providers by using GenAI in areas including software development, creation of marketing content, professional services, recruitment and training, and customer service operations. The greatest gains from using GenAI are likely to be seen in software development spend, where between 23 % and 45 % of current spending could be saved within the next three years, and in marketing creation (22 % – 42 %). In customer services, it estimates potential savings at between 18 % and 38 %. Klarna, the buy-now-pay-later consumer finance company, provides a striking example of the potential benefits. Early 2024, it revealed that its GenAI-powered assistant was handling two-thirds of customer service inquiries in 23 markets. The assistant was doing the work of 700 full-time agents, resolving cases in less than two minutes compared to 11 minutes previously and earning satisfaction ratings on a par with human agents. Klarna estimated that this would add $40m to its profits in 2024.
AI tools are particularly well-suited to produce more accurate demand and inventory forecasts based on quantitative input.
// GenAI is delivering measurable gains, creating a unique opportunity to capture value from suppliers
5
x
Faster content creation of everyday marketing activities
~
20
%
Reduction in total consulting time within call-centers
44
%
Productivity increase for software developers using GenAI enabled coding
~
30
%
Efficiency gains from GenAI-enabled workflows for B2B info service provider
Efficiency gain in tender management
As this analysis suggests, GenAI represents a powerful and versatile set of tools that procurement teams can use to automate and streamline many aspects of the procurement process.
In areas such as preparing tender documents, a GenAI-powered automated assistant can help teams collaborate on the drafting and reviewing of tender documents, including checking the functional and technical specifications. Because the model is pre-trained on tender documents that exemplify best practice, it can aid users by suggesting improvements to the documents. The ability of GenAI assistants to respond to “natural language” chat-based prompts makes these tools easy and intuitive to use.
In trials, GenAI-powered assistants have halved the time taken to prepare tender documents, resulting in a 10-hour efficiency gain per tender. Users also found the assistant improved the uniformity and quality of documents, enhancing the organization’s negotiating position with suppliers and meeting employee demands for smart digital tools to improve work processes.
Similarly, a GenAI assistant can be used to compare and evaluate offers received from different suppliers. The offers are uploaded to the GenAI model, enabling users to ask the assistant to compare them to the original RFP document, summarize the various bids and answer questions on any aspect of the offers.
GenAI represents a powerful and versatile set of tools that procurement teams can use to automate and streamline many aspects of the procurement process.
Data analysis and opportunity identification
GenAI is also well-suited for demanding tasks that unlock the value of a company’s data assets, enabling them to generate insights from the data and identify opportunities. However, to access this value, the data held in companies´ ERP systems must first be cleaned and harmonized.
Companies frequently hold data across multiple ERP systems, each of which may structure its information differently. Using existing manual methods, it can take hundreds of hours to clean and categorize the data for detailed analysis – a task that an AI model can accomplish in a fraction of the time. GenAI’s ability to interpret natural language and understand context makes it adept at cleaning and harmonizing data, for example identifying typos and abbreviations for a single supplier such as Microsoft (Microsoft, Microsoft, MSFT,…) with one standard way of referring to Microsoft or replacing several names for the same material with a single label. It can also transform data from the disparate structures followed by different systems into a standard format for analysis.
Having cleaned and transformed the data into a standard format, GenAI can categorize the data into product and spend types, which it can analyze in order to identify potential alternative sources of supply and provide insights about the client’s spend with each supplier. It can also identify potential savings and efficiency gains and suggest possible courses of action such as consolidating the buying of a product in a smaller group of suppliers, identifying possible alternative products or suppliers, or leasing equipment rather than owning it, quantifying the savings and benefits each step would achieve.
PROCUREMENT: LET’S GET REAL!
GenAI and Advanced Analytics
Keeping confidential data secure
Even among many of the companies that can see the potential of GenAI in their own business, questions remain to data security. There have been many warnings that data uploaded to public LLMs, such as the free versions of ChatGPT, is no longer confidential and will be incorporated into the training data that the model learns from. However, confidentiality concerns can be overcome.
Companies can use solutions provided by third parties, including for example Microsoft Co-Pilot or Adobe’s AI Assistant, which run on private servers in the cloud to ensure that confidential information is protected. Those who want to implement GenAI tools they have built internally or commissioned from a supplier, have several choices of how to create a secure, private LLM. They can license ChatGPT Enterprise directly from OpenAI or use one of the leading cloud providers such as Microsoft Azure, which has close ties with OpenAI, or Amazon’s AWS unit. With this in place, they can license an LLM from one of the major providers and train it using their private data without external access.
Are you hallucinating?
The capacity of GenAI models to invent information, events and people that are presented as if they were real is well known and numerous examples of so-called hallucinations generated by public GenAI models such as ChatGPT have been reported. However, it is important to understand the mechanism underlying these AI-generated “flights of fancy”.
The extent to which a GenAI model hallucinates can be controlled by adjusting its temperature parameter. Public versions of LLMs such as ChatGPT have a fixed temperature that users cannot alter, which means they cannot influence how much the GenAI will hallucinate. However, for companies that want to implement and train their own LLMs, there are several critical factors to consider.
First, all GenAI models hallucinate. This is desirable because one of the reasons to use GenAI is, that it can generate new content and alternative possibilities. When companies implement a private LLM trained on their own data, they can decide how much hallucination the model will be permitted by altering its “temperature” parameter rather than being forced to accept a standard temperature setting.
Proper prompt engineering
Other factors also have a bearing on how much the model will hallucinate. For example, some LLMs are better suited to certain tasks than others, so choosing the right model is vital to avoid unwanted hallucinations. Also, prompt design and prompt engineering require close attention since many of the problems that hallucinations create can be avoided by writing better prompts.
