How Generative AI will transform Sourcing and Procurement Operations
The next disruptor for Source-to-Pay processes is here
Generative AI: A revolutionary disruption
Since ChatGPT went live in November last year, generative AI has been the discussion topic across social media and company boardrooms. With its ability to “create” new content by interpreting and emulating the training dataset, generative AI is expected to disrupt multiple processes, jobs, and industries. The platform is touted to fundamentally alter our perspective on jobs and skills, and tools powered by generative AI are expected to revolutionise current ways of working. Goldman Sachs expects generative AI could expose approximately 300 million jobs to automation and increase global GDP by 7% in 10 years.1
Source-to-pay (S2P) processes have consistently leveraged technological advancements; with such disruptive technology at the door, it is essential to understand what the platform is about, its capabilities, and how it could transform how we procure goods and services.
What is generative AI?
Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio, and synthetic data. The recent buzz around generative AI has been driven by the simplicity of user interfaces and its ability to create high-quality text, imagery, and videos in a short time.
Generative AI is now accessible to all sorts of users thanks to emerging innovations like ChatGPT that can be adapted for use in different applications, including procurement. Walmart has been piloting an AI-based tool “Pactum” for autonomous negotiations with suppliers. While Walmart finds it helpful for landing a good bargain, three out of four suppliers prefer negotiating with AI over a human.2 This strongly indicates that the ecosystem is ready to embrace this disruption.
The sourcing and procurement landscape
Sourcing and procurement operations have historically been at the forefront of technological disruption. From leveraging advanced analytics for spend categorisation to deploying conversational AI for guided buying, source-to-pay tools have continuously innovated to address process challenges. However, many sourcing and procurement functions continue to struggle to optimise efficiency, manage risk, and manage costs (inflationary pressures in recent times).
Deloitte’s 2023 Global Chief Procurement Officer (CPO) Survey sheds light on where procurement leaders across industries are likely to focus in the near term. Procurement leaders have been focusing on improving operational efficiency in their organisations, utilising levers such as hybrid operation models, automation, and centralised processes to gain more control, increase visibility, enforce policy, and reduce process errors. On the talent front, CPOs want to adopt an agile talent development strategy that avoids a one-size-fits-all approach and employs a personalised skill development program for each employee to bridge skill gaps.
According to the survey, 70% of CPOs indicate that procurement-related risk/supply chain disruption has increased in the past 12 months. Risk evaluation tools need capabilities to monitor external risk factors continuously, ingest extensive data, and perform advanced analytics to predict/prescribe risk key performance indicators (KPIs) and preventive management. Though cost management has always been the CPO’s focus, the recent rise in inflation has put additional pressure on procurement organisations to optimise costs further. CPOs have reported high inflation pressures as their organisation’s no. 1 risk.
To address the above challenges, CPOs have continually invested in enhancing digital capabilities. Digital transformation remains the no. 3 priority over the next 12 months, with 80% of CPOs reporting it as their organisation’s top priority.
Generative AI can help address these challenges in procurement by:
- Crunching large sets of data to process scenario-based results—reducing complex manual processes and interventions.
- Leveraging complex automation to increase efficiencies.
- Generating actionable insights based on historic trends, demand profiles, and supplier performance.
- Combining internal data with external data to craft better negotiation strategies.
Generating value in sourcing and procurement with AI
Generative AI’s greatest potential in source-to-pay is likely proactive risk management, process automation, and decision-making. In an increasingly uncertain world, instant access to accurate information is vital for mitigating and managing risk and empowering organisations. Generative AI can help automate “create” processes in source-to-pay, including “creating” documents (request for X, charters, contracts) or “creating” transactions (purchase orders, invoices).
1. Compliance management: Generative AI can be utilised in procurement compliance management to monitor procurement processes and identify potentially fraudulent activities or anomalies. Additionally, the AI system could incorporate insights from historical noncompliance to recognise similar patterns in the future.
Potential benefits: Generative AI can assist organisational governance by analysing policy documents and reports and identifying areas of noncompliance to take corrective action. It can enable organisations to minimise risks, help ensure ethical practices, maintain legal compliance, promote transparency, enhance stakeholder trust, and support sustainable and responsible procurement practices.
2. Strategy development: Procurement strategies involve identifying, evaluating, and selecting the most suitable suppliers in accordance with the organisation’s needs. Generative AI can assist here by analysing vast amounts of structured and unstructured data.
Potential benefits: Generative AI can evaluate the suppliers’ capabilities, performance, and associated risks by analysing past performance data, product specifications, and customer feedback along with external factors such as geopolitical risks, natural disasters, and disruptions in supply chain that may have an impact on supplier performance and availability. Generative AI can assist in the spend cube analysis and identification of opportunities for cost savings. It can also simulate complex negotiation scenarios and predict the outcomes, allowing negotiators to evaluate and identify the most effective tactics.
3. Textual data analysis: Generative AI allows organisations to analyse large amounts of unstructured textual data, such as news articles, social media posts, contracts, and customer feedback.
Potential benefits: Textual data analysis with generative AI can help procurement professionals in vendor evaluation, compliance monitoring, market intelligence, and contractual risk management by deriving valuable insights and knowledge from unstructured text data. Text mining and associated analytics can help generate actionable insights from currently untapped data sources.
4. Predictive modeling: Constructing predictive models can detect potential risks and offer proactive alerts. Predictive modeling may help in effectively integrating procurement into other supply chain processes through forecasting, inventory management, etc.
Potential benefits: Predictive modeling can empower procurement professionals with data-driven insights such as identifying pricing patterns and predicting future price fluctuations, and supplier performance based on various factors such as quality, delivery reliability, pricing, financial stability, etc.
Generative AI and source-to-pay: Art of the possible
A use case of bringing generative AI to real-life source-to-pay scenarios is illustrated below. Right from developing the content for the request for proposal to identifying the prioritised list of vendors for awarding the sourcing bid, generative AI can drive efficiencies.
What’s next for generative AI in the supply chain world?
Many operations in sourcing and procurement still rely on manual activities and swivel chair processes—but generative AI has the potential to transform these day-to-day operations.
Though a definitive tool with the above-discussed capabilities has yet to emerge, generative AI signifies a disruptive change to the evolution of source-to-pay strategy, governance, people, process, and technology.
It is increasingly essential for procurement leaders to recognise the importance of such a change, embrace its capabilities, and incorporate it into their long-term road map. However, embracing the change would require organisations to be prepared to effectively implement the solution. This could include the following steps:
Outline a clear strategy on how generative AI will be integrated into sourcing and procurement operations, including use cases, data requirements, and expected outcomes.
Build the infrastructure required to support the use of generative AI, including data pipelines, computing resources, and analytics tools.
Kick-start initiatives to improve the data quality through data profiling, cleansing, and conversion leveraging rigorous data governance policies.
Develop a robust talent strategy and be prepared to pivot talent to other strategic areas where generative AI changes job definitions significantly.
Prioritise ethics and transparency in using generative AI, including ensuring the responsible use of synthetic data and being transparent about the limitations and potential biases of generative AI models.
The next disrupter for sourcing and procurement operations is definitely here at our doorstep. Stay tuned for our next blog, which focuses on the future of source-to-pay solutions and how generative AI will transform them.
Endnotes:
1 Goldman Sachs, “Generative AI could raise global GDP by 7%,” April 5, 2023.
2 Daniela Sirtori-Cortina and Brendan Case, “Walmart is using AI to negotiate the best price with some vendors,” Bloomberg, April 26, 2023.
Thank you to our contributors: Anantharam B; Rama Krishna N Reddy; Sumit Kumar Singh.
