Four steps to position your organization for genuine transformation

Generative artificial intelligence (AI) is revolutionizing the nature of work across almost every industry, around the globe. We see it in creative work, where large language and image AI models generate automated content. In manufacturing, generative AI augments human-based product design efforts, optimizing key processes, and improving quality control. For example, it’s changing how healthcare is delivered, improving patient outcomes through better diagnosis and information sharing.

To better understand AI’s role in transforming the procurement landscape, ProcureAbility and our AI partner, dSilo, present our co-authored Insights series, “The Future of AI in Procurement.” In the fifth and final installment of this series, we’re sharing our thoughts on where we see generative AI taking us in the long term and the implications for the procurement function.

Current State

We’ve seen a significant evolution in the thinking around generative AI in a short amount of time. Topics business leaders now regularly debate include how quickly it will be deployed, the expected business impact, the ethical considerations, and the implications for humans and society at large. But few companies report experiencing a bottom-line boost from AI: a recent study found that only 15 percent of respondents were seeing a meaningful impact of Gen AI on their company’s earnings before interest and taxes.¹

As business leaders gain more experience with AI, they’re becoming more realistic and less susceptible to hype. This is not unlike major technological breakthroughs of the past: there’s an inevitable adoption curve that takes time to build awareness of technology’s potential, evaluate different approaches, and start to experiment with select opportunities. This process ultimately leads them to make more informed decisions about where to commit meaningful investment for practical applications.

If history is any precedent, then the adoption curve generative AI should follow is a similar path, albeit on an accelerated timeframe. As you think about your own company’s journey toward leveraging generative AI in procurement, we believe it’s important to keep four rules of thumb in mind as you proceed into the future, so as not to stray too far afield and ensure gen AI is a genuine value-add for your organization:

1. Use Generative AI where it’s differentiated

As you look to develop use cases for deploying gen AI, consider where the technology has capabilities that are differentiated from what we humans can do best. For example, gen AI insights are based on what’s happened in the past, so the technology can recognize patterns and flag insights as it looks at new data sets. At least at this point, humans are still better at dealing with new issues or exceptions that draw on a wider range of experiences than large language models have been trained in. Combining the strengths of humans and gen AI engines will get the best out of all your resources.

2. Transform from the bottom up

Most of our current focus for experimentation with gen AI is on taking specific well-defined tasks and automating them. That’s because this is an area where organizations are immediately able to evaluate technology options and implement practical applications. It’s also a lot more manageable – organizations can address each discrete task in an overall workflow and automate work task by task. This approach also fits well within an agile deployment model where each problem is broken down into small steps that can be solved in turn. Working from the bottom up and stringing together a slew of successes also helps teams build their Gen AI literacy through learning by doing, which can help reduce implementation risks down the road.

3. Build a vision from on high

At the same time as you’re automating and transforming individual tasks and knitting them together into the workflow, it’s important to consider what it’s all adding up to. Ask yourself, “What could the future look like?” In some cases, these kinds of top-down appraisals might point to a different path. Instead of simply improving what already exists, you might be better off reengineering the process altogether. Keep focusing on the ultimate outcomes you desire. You might find, for example, that instead of using gen AI to build category reports, pulling data from multiple sources, the better application might be using gen AI capabilities to structure and execute a direct negotiation with key suppliers, clearly articulating what you want to accomplish on price, service, and so on.  The most important question should be: What steps do we take to fully automate commerce, where, for example, our legacy contracts can become smart contracts because all the transactional information has been extracted and embedded into P2P systems?

4. Keep the data clean

The generative AI models function on data – that’s their oxygen, their fuel. But we know that the data we’re working with in many cases isn’t clean for various reasons – meta data hasn’t been captured; mechanisms for data updates, such as supplier information or lead time information, are ineffective; legacy data isn’t pulled through when systems are upgraded. Using the technology to take the foundational step of cleaning up the data and eliminating errors and gaps is a long-term exercise, but essential to ultimately support the top-down strategy of automating commerce. For example, to update order lead times gen AI could request information from suppliers on a routine basis, and push responses into the P2P system. It’s never too soon to work on data integrity.

Remember, we’ve been evolving in how we use the internet for business for more than two decades, and even software-as-a-service (SaaS) took time to gain traction and acceptance over that span. These experiences with breakthrough technology adoption have allowed us to mature and make better, more informed decisions as we now stand on the cusp of putting gen AI to use for maximum effect.

Looking ahead

Humans should always be at the center to position your organization for genuine transformation. Understand that humans will continue to be required to run the procurement function, to deal with new requirements and exceptions. Focus the gen AI roadmap on parts of the function such that the humans on the team can be more effective and more efficient. For example, enabling transactional execution, in compliance with policies, by guiding stakeholders to the optimal suppliers to meet each requirement, with streamlined requisitioning, ordering, receiving, and payment. Or completing the analysis required to develop a category strategy and build an RFP with the relevant data. Or doing the first pass redlining on a contract document, to support Legal reviews. This way humans can work strategically to drive value.

In this Insights series, we explored the transformative role of generative AI in elevating procurement capabilities, with a focus on how ProcureAbility and dSilo are currently implementing these practices to take their clients’ use of this game-changing technology to the next level. This is truly an exciting, once-in-a-generation opportunity for procurement leaders to dramatically increase their contributions to their organizations’ success. Although gen AI has been a hot topic for a while, we’re still in the first mile or two of what promises to be a marathon. It’s important to be open to taking that first step on the road to the finish line by experimenting, learning, and evolving throughout the process. A third-party service provider can help you achieve your procurement organization’s growth to help drive growth, create value, and strategically contribute to the business’ bottom line.