Generative AI in procurement: A fast-moving landscape

With the release of ChatGPT in late 2022, AI's potential to disrupt industries changed overnight. This release wasn't just a major shift for the public but also for industry experts. According to Genpact research carried out in partnership with HFS, respondents expect to realize all the benefits of gen AI within two years, on average. Benefits including enhanced productivity and efficiency, customer satisfaction, and faster decision-making are top-rated quick wins.

This sudden pivot in expectations is challenging all industries and business functions to reassess the opportunities and risks from AI at an unprecedented rate. Some functions have a tremendous opportunity to streamline their work and unlock new sources of creativity. In contrast, companies whose business models are based on prior versions of AI could see their entire business disrupted.

In addition to the typical challenges associated with a new technology, gen AI also poses questions in terms of ethics, accuracy, and transparency. The emergence of gen AI technologies like Gemini and Llama and a host of purpose-specific tools presents both opportunities and challenges, particularly in managing rapid change and decision-making processes.

We can look at the impact of AI on procurement from two perspectives:

1. The impact on how procurement operates. Generative AI is the first technology that could fundamentally disrupt the way upstream procurement is managed. Both request for proposal (RFP) creation and suppliers' responses to these requests are document-related processes, and generative AI offers new possibilities to automate this. Perhaps the most important benefit of gen AI for procurement is the ability to generate insights from past data sources, including old strategy documents and RFPs, to guide future decision-making
2. The impact on the supply base. Procurement teams need to understand and manage the opportunities and risks as suppliers embed AI into their offerings. For instance, if a supplier of application development services uses gen AI to write code, it could be more cost-effective and of better quality. But it could also result in erroneous code if not managed properly. Procurement will be on the front line in managing the opportunities and risks of these developments

In terms of the impact on procurement execution, a few common use cases have already emerged. These use cases can be grouped around common underlying needs:

• Insights. Effective procurement is built on insights. These can be from internally generated data, such as supplier performance, or external data, such as benchmarks, market insights, or supplier discovery. Generative AI can surface these insights and present them in an easy-to-digest form in response to simple user queries. For example, Genpact's sourcing intelligence engine generates category insights for tail spend management, and our supplier selection engine identifies suppliers for particular commodities, evaluates them against relevant parameters, and presents the findings in a structured scorecard to aid in decision-making
• Knowledge management. Effective enterprise knowledge management is essential for getting new employees quickly up to speed and offering good customer service. For procurement teams, gen AI can augment and automate knowledge retrieval from the corpus of procurement documents, including standard operating procedures (SOPs), contracts, and purchase orders (POs). Genpact has developed a virtual knowledge assistant that helps operating teams pick the next best action to convert requisitions into POs
• Document creation. The procurement process itself can be thought of as a series of documents being created, culminating in the ultimate document – the contract. Along the way, procurement teams generate numerous documents: requests for information (RFIs), RFPs, supplier communications, nondisclosure agreements (NDAs), statements of work (SOWs), master service agreements (MSAs), and various types of contracts. Generative AI can assist in drafting RFPs and SOWs, creating a preliminary framework for procurement teams. It's essential to incorporate client-specific clauses with AI-generated content to maintain accuracy and compliance
• Summarizing documents. Another time-consuming activity for procurement is analyzing supplier responses during a competitive bidding process. Analyzing prices is just one part of this, and qualitative analysis can be more problematic. For complex purchases, responses can be dozens of pages long, describing the supplier's qualifications, services, specifications, and how it drives value for your company. Gen AI can digest and summarize this text to create a succinct summary that allows the sourcing team to compare bids and identify areas where it should do a closer analysis of the actual responses
• Interactions. The procurement process necessitates many interactions between the procurement team, internal stakeholders, and suppliers. Generative AI can facilitate more interactive and responsive conversations with stakeholders, enhancing the efficiency of procurement or accounts payable helpdesks by providing tailored responses based on existing policies

These use cases highlight how procurement is ripe for transformation from generative AI. But the speed at which they're adopted will largely depend on a team's ability to tune its AI models using proprietary company data.

In addition to assessing how generative AI will impact their own function, procurement professionals also need to assess how it impacts their supply base and what steps and controls are required to manage the technology. This challenge isn't new, but the rapid proliferation of products and services embedded with gen AI has amplified the problem. There are several factors procurement teams should consider:

• Segmentation of the supply base. The opportunities and risks from generative AI will vary by supplier and the product or service being purchased. For example, software development shows great potential but also some risk. However, if a commodity chemical supplier uses generative AI to optimize its internal processes, the risk and opportunity are lower
• Understand your company's gen AI policy. Generative AI highlights challenges inherent in AI systems, particularly those trained on extensive datasets from the internet, which can introduce additional complexities, including:
  • Bias/accuracy. Generative AI processes data from various sources, which may include existing biases and inaccuracies. Implementing measures to identify and mitigate these biases is crucial to ensure fair and accurate outcomes
  • Transparency. Machine-learning AI systems like ChatGPT operate with complex algorithms that can make it challenging to trace the exact reasoning behind specific outputs. Efforts should be made to enhance transparency and accountability in AI-generated results
  • Confidentiality. Gen AI models may incorporate user inputs into their learning processes. To protect confidentiality, it is advisable to use private cloud environments and implement strict data governance policies
    

These are just some of the risks and challenges that procurement leaders are facing, so it's vital that you understand how your suppliers are managing AI responsibly.

Given the speed of evolution and the significant potential of gen AI, procurement leaders should take the following steps to maximize results and minimize the risk:

• Identify a gen AI guru. Nominate someone to keep track of developments and the implications for procurement. While corporate IT teams provide valuable insights, having a dedicated procurement gen AI expert can enhance understanding
• Establish an AI code of conduct for suppliers. Incorporate AI-related dimensions into supplier governance. These can be RFP requirements or questions to assess suppliers during the sourcing process
• Training and upskilling. Provide ongoing training to ensure the team understands AI tools, their applications, and the associated risks. Use resources like online courses, workshops, and hands-on projects
• Investigate the high-impact use cases. Gen AI applications for procurement will continue to evolve as the technology matures. Planning for the implications and being prepared to act when the time is right will pay ongoing dividends

To successfully get the most out of this technology, we recommend that all business teams:

• Embed AI into workflows and rethink processes
• Ensure strong data management and governance
• Partner with internal IT or external providers to scale the technology and operating models
• Nurture new skills and roles for employees
• Address job displacement fears and resistance to change from the start

Generative AI will have a significant impact on procurement, and the capabilities in the market will continue to evolve quickly. Procurement teams that keep pace with these developments will build more effective procurement capabilities and move from control to enablement, with technology at the core.