Today we’re excited to announce the general availability of six new generative AI (GenAI) features designed to improve user experiences, accelerate the pace of business, and decrease risk. In this blog post, we’ll discuss ORO’s vision for the use of GenAI in procurement and provide a summary of each of these exciting new capabilities

ORO’s Vision for GenAI-powered Procurement Orchestration

GenAI has the potential to be a game-changer in procurement. And excitement is high. According to Gartner, 73% of procurement leaders plan to start using GenAI by the end of 2024. 

At the same time, however, Gartner warns that GenAI is at the ‘peak of inflated expectations,’ and that it will soon fall into the ‘trough of disillusionment’. In fact, Gartner anticipates that 30% of GenAI projects will be abandoned after proof of concept by the end of 2025. A recent study from Upwork found nearly half (47%) of employees using AI say they have no idea how to achieve the productivity gains their employers expect, and 77% say these tools have actually decreased their productivity and added to their workload.

At ORO, we have been collaborating with some of the biggest and most complex companies in the world to build GenAI features designed to actually improve operational efficiency. We’re not in the business of ‘GenAI-washing’ or adding intelligence simply because we can. Instead, we are building features with three very specific goals in mind: 

1. Dramatically improve business user experiences. Traditional approaches to procurement software development involve fitting features to the needs of procurement professionals. This has led to very poor end-user experiences as occasional users with no procurement background are required to comply with confusing business rules, unintuitive user interfaces, and arcane jargon. The use of GenAI allows us to more easily translate the needs of employees into business requirements and system tasks so they don’t have to learn procurement in order to work with procurement.
2. Accelerate the pace of business. Through the use of historical data, policies, and process documentation, ORO is able to use GenAI to eliminate unnecessary manual steps. Improved end-user experiences also mean more self-service processes.
3. Reduce errors, increase compliance, and improve adoption. ORO uses GenAI to eliminate manual tasks that introduce data entry errors. By increasing self-service experiences (the lack of which is the leading cause of Maverick spend), ORO’s use of GenAI is meant to significantly improve rates of adoption and compliance with procurement policies as a result.

Even as our approach to innovation is laser-focused on addressing business challenges in each of these three areas, we are also committed to 4 key values:

1. Focus on procurement problems. If you try to be everything for everyone you cease to be anything for anyone.  We are laser-focused on procurement use cases like intake management, supplier onboarding and maintenance, and risk process orchestration.
2. Commitment to responsible AI. At ORO, responsible AI means ensuring that the tools we build and use are aligned to organizational goals and standards while also maintaining respect for privacy, security, and ethical considerations. As evidence of our commitment to responsible AI, ORO was the first in the world to earn an accredited ISO 42001 certification for an Artificial Intelligence Management System (AIMS) scope.
3. Flexibility. As models proliferate and security concerns abound, we give our customers the ability to either use the large language models included with the ORO platform and/or bring and maintain their own models without sacrificing core platform functionality.
4. Openness. In order to promote innovation, we encourage our customers and partners to leverage our GenAI framework to expand the core capabilities of ORO to meet their unique needs and business challenges.

With these goals and values in mind, we’re excited to announce the general availability of some cool new features powered by generative AI (GenAI). Let’s dive into how these AI enhancements make procurement processes faster, more accurate, and easier for stakeholders to navigate.

GenAI for Intake Management

1. Intent Detection

Not every procurement request involves a requisition.  A business user may want to onboard a new supplier. Or perhaps they want to create, renew, or cancel a contract.  Or maybe they are looking to create or amend a purchase order. ORO is able to leverage LLM technology to determine a user’s intent and provide the most appropriate response. In some cases, as in status update requests or other help inquiries, ORO will simply provide the answer.  In other cases, it will instantly engage the user in the most relevant workflow. 

What this means is that ORO provides business users not just with a front door for making purchase requests, but rather a universal front door capable of handling any procurement-related inquiry. The result? Business users no longer need to try and learn procurement.  Instead, they can ask questions in their own words and let ORO take care of the translation.

2. Category Recommendations

When it comes to requisitioning, choosing the right category is incredibly important because the category determines the process. Buying IT services is very different from buying a pencil.  Imagine, for example, that you work for a pharmaceutical company and you want to buy a 3D printer. Is this office or lab equipment? It depends. And your choice will make a big difference in terms of who needs to approve the purchase, and the nature of the risk assessment that needs to be performed.

However, business users don’t typically understand the importance of selecting the right category. More than this, they don’t usually know the correct category, and they are prone to frustration when faced with a large category tree. Suffice it to say, that business users are prone to selecting the wrong category. Little do they know that their selection will kick off a series of events defined by error, rework, manual correction, hands-on guidance from procurement, and a lot of wasted time.  

We shouldn’t expect business users to know anything about procurement. That’s why, in addition to determining intent, ORO is able to automatically map a natural language requisition to the correct category so the user doesn’t have to. The result? Reduced cycle times, fewer errors, and happier employees.

3. Buying Channel Guidance

Sorting out the right buying channel can be hard. Determining the correct category is important, but it is not enough, especially for large global organizations.  A further example of our use of GenAI is to consider intent and category alongside things like role, region, and company policies in order to determine the best and most efficient buying channel for a particular request. 

This means users enjoy a streamlined process, better compliance through accurate risk profiles and approval flows, and less manual work since the approach eliminates the need for re-routing and fixing channel selection mistakes.

4. Supplier Recommendation

ORO’s supplier recommendation feature uses an organization’s preferred supplier list, past transactions, and performance data to suggest a shortlist of suppliers for a given request. By looking at a vendor’s profile, relationship status, and past purchase history, ORO tailors recommendations to specific procurement needs. This simplifies the selection process, helps rationalize suppliers, reduces the need to unnecessarily onboard new suppliers, and ensures compliance with purchasing policies while minimizing risk. 

GenAI for Supplier Onboarding & Maintenance

5. Smart PO/PR Creation

Using GenAI, ORO is able to create purchase requisitions and purchase orders by extracting key information from contracts and proposals,  For example, it will extract header-level information like company entity, payment terms, amount, and start/end date.  It can also extract line-level information and map accounting details.  The result is a single or multi-line contract that complies with business rules (i.e. Capex, Opex, Time-based)

The result? A significant reduction in manual effort and improved data quality by eliminating manual transcription errors.

6. Smart Proposal Reviews

Using AI, ORO can extract data from vendor proposals and check to ensure consistency with the buyer’s intent, buying channels, and category. This streamlines the procurement process by auto-extracting data from proposals and ensuring it matches buying intentions and categories, reducing manual work, minimizing errors, and decreasing risk by verifying the right risk profile and approval workflows.

At ORO, we’re driven to solve a well-defined set of business challenges and committed to delivering real value for procurement without sacrificing our values of responsible AI, flexibility, and openness. We’re proud of our collaborative customer relationships and are thrilled to make these features available to all ORO customers.

Want to learn more? Schedule a demo today.