AI's $500 Billion Opportunity Begins With Reimagining Procurement

Forbes

a day ago

Leagh Turner is Chief Executive Officer at Coupa.
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We hear it all the time: AI is changing the way we work. Its capabilities are accelerating, transforming roles, redefining career paths and opening new avenues for growth and innovation. McKinsey and others argue that there is up to $500 billion in inefficiency trapped in procurement processes that are waiting to be transformed by automation and AI.
But what now? And where do you get started? Let me make a case for why AI should start with how buyers and sellers interact with one another to purchase goods and services in a more autonomous way.
This isn't just about digitizing or automating a business process. It's about transforming how you better source business relationships for materials, goods and services; redefining your teams' roles, decision-making speed and scale in the process; and revolutionizing business outcomes. When you change how work gets done and how things are procured, you can reshape your P&L—transforming it from a backward-looking ledger into a forward-looking growth engine.
Nowhere is this opportunity more apparent than in the world of procurement and spend management. Companies that embrace advanced AI technologies, such as agentic AI and GenAI, will widen the gap between themselves and their competitors. The ones that hesitate will struggle to play to win.
So, how do you start getting after this $500-billion opportunity?
Traditional procurement and cost management were reactive: tracking costs, managing risk, tackling strategic issues after the fact. We worked with fragmented data. We chased down invoices. We waited weeks to detect and respond to supply chain disruptions. We spent valuable time playing catch-up.
Data is the new flywheel.
AI flips that old model on its head. Today, speed and time are the new currency, with data serving as a flywheel effect. In a volatile economy, whoever can respond the fastest with the best data and intelligence wins.
In spend management, better data means smarter sourcing decisions, improved cash flow management, faster supplier collaboration and greater resilience across the board.
When you look at Gartner's recent CEO survey, 82% of companies expect productivity increases of 5% or more directly tied to AI. But that is just the tip of the iceberg. Efficiency gains of 10-50% are quickly becoming the new norm.
The message is clear: Good data is no longer a nice-to-have. It is a survival skill.
Recently, we hosted our annual customer conference in Las Vegas with over 2,000 of our enterprise customers and partners, and I was shocked by the number of customers that were actively operationalizing AI strategies across our platform or getting started on how to do so.
At this stage, merely experimenting with AI is not enough. Operationalizing AI—embedding it into the core of your enterprise management processes—is what will separate the leaders from everyone else.
The businesses that will thrive in the new normal are the ones where AI is actively driving their sourcing and purchasing decisions, optimizing things like supplier selection and payment timings, proactively identifying potential supplier or compliance risks and freeing up teams from manual tasks to perform higher-order, strategic work that generates revenue as opposed to merely cost-savings.
This is the new reality for CEOs and finance and procurement leaders, including chief supply chain officers. They are responsible now for this AI transformation, shaping how their businesses respond to disruption and capitalize on opportunity. They're no longer managing just the bottom line—they're helping redefine how we all work.
And this is not just theory. AI is taking the administrative burden off 'back office' teams so they can be repurposed and reimagined to perform more front office work. Take, for example, the case of a global bank, where they used AI to empower their former accounts payable team, refashioning their work to serve as customer renewal support teams and enabling them to earn 15% more in wages in the process. This is the true opportunity of today's leading AI platforms: to transform work, people and teams in this way.
They and other leading companies have taken manual tasks like invoice processing, fraud detection and payment reconciliation and fully automated them—giving their own people more time and space to focus on more impactful work.
AI is not just about eliminating jobs. It's about elevating them. It's about giving smart, capable people the best tools to do their best work—faster, with greater data and insight and with more purpose.
There is a common misconception that AI success is just about volume: Collect more data, feed it into models and wait for the magic to happen. The reality is different and far more demanding.
Getting the best data is what matters—data that is unified, trustworthy and purpose-built for the specific challenges you need to solve.
When it comes to spend management, that means ensuring that the platform you choose is built on ethically sourced, normalized and intelligently organized data from actual customer interactions. Without that 'real data' foundation, no amount of AI power will deliver meaningful results by simply scraping the internet.
Leaders need to ask themselves hard questions when it comes to the data feeding their AI strategies:
• What is the quality and source of our data behind this AI?
• How is it protected and governed?
• What outcomes has it proven in real business terms, and what is it telling us?
Speed without trust is reckless. Trust without speed is irrelevant. The reality is, you need both.
Many companies fall into the trap of assuming that any technology with an "AI-powered" label will meet their needs.
But there is a real difference between AI-native platforms—those built natively from the cloud, ground up to embed intelligence and orchestration at every layer—and legacy systems that bolt on AI capabilities after the fact, or AI that's built on nonstructured, unified data. AI models trained on fragmented data sets can miss key context and correlations, leading to slower AI model development, with inconsistent governance and quality control.
An AI-native platform learns and scales faster and adapts automatically as your business needs change. It treats AI not as an add-on but as the engine riding shotgun with your business, enabling real-time, holistic insights that drive better cross-functional outcomes.
In high-stakes areas like cost and spend management, where trillions in working capital, supplier relationships and operational resilience are on the line, this difference is not academic. It is critical.
Scrutinize your technology platforms. Ask yourself whether AI is embedded throughout the platform and is not just a new feature layered on top. Future-proofing your operations means investing in systems built for AI, speed, intelligence and trust from the start.
AI will be a game changer—not just for who survives, but for who leads. And in a world where the best data moat wins, the smartest investment is in systems and teams that can leverage this data to move fast, act strategically and create lasting enterprise value.
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