AI is transforming the world of procurement – but it’s no magic wand. We spoke to leading CPOs to find out how they’ve harnessed the full potential of AI within their procurement organisations.
As you might expect, AI was one of the most discussed topics at World Procurement Congress 2024. Excitement around the technology and its potential is high, but many speakers also urged caution and explained why it’s essential that procurement leaders approach and implement AI strategically.
To learn more about what CPOs are achieving with AI, and how they’re implementing it successfully, WNS Procurement hosted a roundtable at the event. Contributors from leading organisations across the globe shared a huge range of valuable perspectives, advice, and insights into how to harness the power and potential of AI in procurement.
To help you learn from their experiences, we’ve condensed the top takeaways into five considerations for every CPO as you navigate the journey of implementing AI across your procurement organisation.
1) Which AI use cases are right for you?
AI has an almost limitless number of potential applications across the procurement function. During our roundtable, we heard from leaders using AI in procurement to pre-populate and validate RFPs, classify and track spend, and even continuously monitor Scope 3 emissions across complex supply chains. These examples and use cases highlight how artificial intelligence in procurement is already reshaping day-to-day operations.
But AI isn’t a magic wand that’s going to solve all your biggest challenges instantly. So, it’s important to consider what it really is you’d like to improve or streamline with the help of AI procurement capabilities and select the most relevant and impactful use cases for your organisation.
Whatever use cases you adopt, it’s also important to ensure that you apply artificial intelligence and procurement strategies in ways that augment and extend the capabilities of your human experts within those areas. At the roundtable, we heard from numerous leaders who use AI co-pilot tools – like WNS’ own PIA+ – to help procurement professionals accelerate and automate numerous routine tasks and spend more time focusing on creating value for the business.
The ideal AI use case should:
-
Help solve at least one specific challenge you’re facing within procurement
-
Support your people in ways that they welcome
-
Drive you towards achieving one (or more) of your top procurement and business goals
2) Is your data foundation AI-ready?
With AI, the quality of what you get out depends on the quality of what you put in. So, it’s essential to have the right data foundation in place before you get started with AI in procurement initiatives.
Contributors at our roundtable discussed how artificial intelligence in procurement demands significant volumes of data that many teams may not have access to today. While AI models can be trained using only your own data, it’s typically worth supplementing that data with external sets to bring additional context and perspective to your AI-generated outputs.
But data quality is an even bigger concern than quantity. If you can’t trust your data, then you won’t be able to trust your AI outputs. And when AI-generated insights and recommendations are informing high-value procurement decisions, that can undermine the effectiveness of any AI procurement effort.
CPOs at the discussion remarked on the need for a transparent AI core – one that helps you understand the quality and trustworthiness of your data and clearly see how it’s being processed. This is critical when aligning artificial intelligence and procurement strategies, as it makes the AI process traceable and transparent, so that if something goes wrong, you can quickly pinpoint exactly how and why it happened and take actions to fix the process going forward.
3) How will you validate the outputs from your AI models?
Today’s leading AI algorithms have taken years to develop and train. If you start training your own model today, don’t expect perfect results right away. And no AI model is 100% reliable, even after many years of training. Organisations adopting AI in procurement must account for this early on.
So, it’s essential that human teams validate everything you use AI to generate. Our CPOs discussed how, in the rush to rapidly scale artificial intelligence in procurement, some of them hadn’t properly scaled their validation efforts – leading to poor decision-making or detrimental stakeholder conversations.
In addition to clear explainability and transparency of AI models, teams also need defined processes and accountability for validating AI outputs before they’re translated into actions.
4) How will you get your people on board and secure stakeholder buy-in?
Generative AI has dominated this year’s headlines – but it isn’t always painted in a positive light. CPOs at the roundtable remarked on rising concerns around security, privacy, and how public generative AI models like ChatGPT use the data provided to them as prompts.
All of these concerns are legitimate – especially for procurement teams that handle sensitive commercial and operational data. The last thing you want is for someone to leak sensitive contract or proposal details accidentally by asking a public AI model to rewrite an RFP, for example.
However, not all AI is generative. Our AI-enhanced Smart Risk platform, for example, currently doesn’t have a generative element, it simply makes it easier for teams to monitor and assess supplier risks, by combining human intelligence and artificial intelligence to help companies make sense of large volumes of data.
When you’re discussing AI with other senior stakeholders, draw the distinction between AI and generative AI to ensure that everybody understands the true level of risk involved with the use cases you propose.
5) How will you measure and control AI’s TCO?
Many AI models and capabilities are charged at a by-user or by-search price. That’s great when you’re just getting started. However, several of our contributors remarked on how they’ve seen AI costs rapidly spiral.
If your entire team starts using an AI model to perform a task like category monitoring – something each person does numerous times every day – or if you want to roll out an AI-powered buying desk across a large organisation, it’s easy to see how costs can get out of control.
As you start forecasting the TCO of your AI tools, it’s essential to look at how costs could potentially scale in the future. Sometimes, that will mean pay-per-search solutions aren’t viable. In areas like that, it may well be worth working with a partner to develop your own bespoke solutions that can scale more cost-effectively.
See how WNS helps procurement teams apply AI safely and effectively.
Accelerate your journey with our latest AI insights
AI will undoubtedly play a massive role in the procurement organisations of tomorrow. And the decisions you make today will have a big impact on what you ultimately get out of the technology.
Leverage AI-driven Procurement Advisory, Supplier Management Services, and Digital Procurement Solutions to enhance decision-making, optimize category management, and mitigate supplier risks. Explore how WNS Procurement can help you integrate AI strategically—follow our latest insights today!
FAQs
1. What are the most impactful AI use cases in procurement?
The most impactful AI use cases in procurement include autonomous sourcing, predictive spend analytics, and automated contract extraction. By leveraging Large Language Models (LLMs), teams can now conduct real-time supplier risk monitoring and complex tail spend classification, effectively transforming massive amounts of unstructured data into actionable insights for better negotiation and category management.
2. How can procurement teams ensure their data is AI-ready?
To achieve AI-ready data for procurement, teams must prioritize data hygiene by centralizing fragmented silos and standardizing taxonomy across the Sourcing-to-Pay (S2P) lifecycle. Implementing robust data governance ensures that the underlying information is accurate, consistent, and labeled correctly, which is essential for training reliable machine learning models and preventing "garbage in, garbage out" scenarios.
3. Why is validating AI outputs important for procurement decisions?
Rigorous AI output validation is critical to mitigate risks like "hallucinations" or biased supplier recommendations. Since procurement decisions involve significant financial commitments and regulatory compliance, human-in-the-loop verification ensures that AI-generated insights—such as contract risk scores or market forecasts—are accurate and align with the organization’s actual risk tolerance and ethical sourcing standards.
4. How can organizations secure stakeholder buy-in for AI adoption in procurement?
Securing AI stakeholder buy-in in procurement requires demonstrating tangible "quick wins" through small-scale pilots. By focusing on how AI reduces administrative friction for end-users and provides superior visibility for executives, leaders can build trust. Clear communication regarding how AI augments human roles, rather than replacing them, is vital for long-term cultural adoption and cross-functional support.
5. How should procurement teams measure and control the total cost of ownership (TCO) of AI solutions?
Managing AI TCO in procurement involves looking beyond initial license fees to include long-term data maintenance, integration, and training costs. Teams should measure ROI by tracking productivity gains and realized savings. Regular audits of token usage and API costs are essential to ensure the AI solution remains cost-effective as it scales across the enterprise.
6. How does WNS help organizations bridge the gap between AI potential and actual ROI?
WNS AI procurement solutions utilize a proprietary "AI + HI" (Human Intelligence) framework to ensure digital tools deliver measurable business results. By combining cutting-edge GenAI platforms with deep category expertise, WNS helps firms clean their data, validate AI-driven insights, and implement scalable operating models that turn experimental AI projects into permanent, value-generating procurement assets.