This will enable us to refine our understanding of the customer workflows and to identify the different players on both sides of the table.

This is not our ultimate ambition.

We are developing Agentic AI models.

Before we can talk about what an Agentic AI approach entails, let’s start by talking about what some have called agent-based models. This is key to understanding both the status quo and the truism of the failure of almost every digital transformation project.

According to its most voluble proponent, agent-based modeling is a human-led approach to solving problems that stands in contrast to what he calls “equation-based modeling.” In the quote below, agents are individuals. They are human beings.

“Agent-based modeling (ABM) simulates the interactions of individual agents within a system, allowing for the study of emergent behavior that arises from these interactions. ABM is particularly valuable for capturing complex, system-wide effects that result from the interactions of autonomous agents.”

Strip out the jargon and the SAT words and the logic here is straightforward. The reason why so many software solutions and digital transformations fail is because they are inflexible. They do not account for the complex interaction of multiple agents using data and intelligence from a wide variety of sources. Every procurement team is different. Every project exists in an idiosyncratic context with unique institutional conditions.

Yet, traditional software approaches attempt to force people into a standard, one-size-fits-all approach to problem solving. This is good for the software vendor. He makes a fixed cost investment. Build a tool and sell it to millions of people. In forcing every different type of imaginable peg into their single round hole, the software vendor generates stratospheric gross margins for themselves. Operating leverage is a beautiful thing. For them.

You can have whatever car you want, as long as it is a Lada.

The end result is that large incumbent procurement systems cost a bucket load to implement and they still don’t work. Their vaunted inadequacies make everyone puck shy when it comes to technology.

The essential complaint that the agent-based modeling proponent appears to make is that procurement is a services problem because of its complexity. Software vendors solve for a software problem.

If a fixed income trading desk needs a machine to calculate the yield-to-maturity or duration of a bond, then you can build software for that. This is an example of a software problem.

If another company needs help understanding the risk of self-insuring the healthcare needs of its workforce, giving them an Excel spreadsheet isn’t going to be as helpful as hiring a team of actuaries to model out the risk based on situation-specific data, using their experience of other such problems and their special tools. This is an example of a services problem.

(As an aside, we built EdgeworthBox to be more flexible and to take into account explicitly the need for teams from differing backgrounds to interact around data in a contained space. We come from a financial markets background. We wanted to build a Bloomberg terminal for the real economy. We either work as a standalone solution or as a complement to the existing procurement technology. Ideally, we help people impose an idiosyncratic interaction on software designed for a standard use case.)

Procurement is a complex beast. The combination of simple building blocks and the peoplewho work these problems introduces complexity. This produces emergent behavior as people and data collide. No two snowflakes are identical.

What is Agentic AI then? There is plenty of confusion about this in the procurement literature, so I will refer to the people who are on the cutting edge of investing in Generative AI. Here’s an outstanding note from Sequoia, the venture capital firm based in Silicon Valley. I’ll try to explain the technology in plain English.

If you take away one conclusion from this note, it should be this key message. Agentic AI isn’t software-as-a-service. It is service-as-software. It is a fundamental disruption of the markets for consulting and outsourcing. Some misunderstand it. They descry it as a self-learning robotic version of the standard one-size-fits-all software approach, peddled by hustlers and snake oil merchants. Their skepticism stems in part from the novelty of Agentic AI and Generative AI and in part from the dismal history of digital transformation. The critics of Agentic AI see it as the same old priest preaching a new religion. This view is naive and it is wrong.

When most people think of Generative AI, they think of chatbots like ChatGPT or Claude. You ask the bot questions and it gives you answers. You might ask it to draft a cover letter for a job application or to edit some marketing copy. Perhaps you ask it to generate a PowerPoint slide deck.

All of this relies on the first wave of Generative AI: the foundation models such as OpenAI’s GPT-4o. These are pre-trained models. The AI companies pour as much data as they can find into as many GPUs as they can align to teach these models how to identify patterns. They are essentially built to predict the next word in a sequence, over and over and over again. They are not infallible. They “hallucinate” or make things up. They organize data into galaxies of multi-dimensional vectors. Think of this as space but with, say, 548 dimensions. It’s impossible to envision, I know.  When we ask for things, the models compose paths between these virtual, conceptual asteroids and planets and stars as they combine words and phrases to make sentences, paragraphs, and documents. They are better with some types of data, like software code, than others. Some of these navigational tracks turn out to be roads to nowhere. But that’s not to dismiss these magnificent machines. The model vendors are focused on identifying and weeding these rogue routes. They will succeed over time.

Sequoia makes the analogy to System 1 thinking in human brains. Foundation model behavior is nothing more than knee-jerk pattern recognition, not the in-depth reasoning of System 2.

Agentic AI is System 2. It breaks projects down into tasks. Think of the agents as people working on your team. Each one has a role to play. In a procurement, one team member may be tasked with developing a list of suppliers relevant to the problem. Another team member may be in charge of finding all the documents from previous related projects. A third team member may be responsible for collecting documents related to similar purchases, say from government databases disclosing contracts and bid solicitations from the public sector. We give them tools. We give them access to databases and the Internet.

The key to a successful Agentic AI implementation will be the way we coach these new artificial team mates to do their jobs. We will give them instructions in the form of natural language prompts that include prior examples of work that was good and work that was bad. We will expose the chain-of-thought that each agent needs to undertake. The agents will have pre-trained DNA from multiple models. They may have chromosomes from OpenAI and also from Anthropic. But in addition to this nature, we will nurture them. We will educate them as we would a child through school. They are inference compute, performing in-the-moment calculations that leverages their education to take advantage of the gifts of the dull calculus pre-trained compute necessary to birth them.

When they graduate from our school, when we determine that they have learned how to apply our various courses in working different kinds of problems, we will unleash them on the real world.

I can hear the Luddites now. This is nothing more than a jumped-up version of the Generative AI foundation models. They will not be any different. This is just marketing.

Wrong.

When I say “we” will nurture them, I mean human beings. This is called Reinforcement Learning with Humans in the Loop. A human expert (not a machine) will now build Agentic AI teams of agents designed for the idiosyncratic circumstances of the individual client. It is a human being who will coach them, iterating through different versions of a task-specific way of thinking about a particular problem: procurement within the context of an individual enterprise buying in a specific category. No two contexts are the same.

The Reinforcement Learning combined with human re-engineering of the application that sits on top of the foundation model will make for customer-specific AI. This isn’t the machine teaching itself based upon some incorrect reward model. This is human-in-the-loop optimization of the AI architecture for applications built for individual customers. There will be thousands of these apps. They may appear similar, but they will reflect the unique context of each customer.

One consultant will do the job of five. Perhaps company A’s Agentic AI setup can be recycled for subsequent RFx events. Maybe it will need to be tweaked for a different category. The human will take the initial feedback from the agents and grade their work, like a teacher in high school. The agents will learn. They will develop their own voice.

In practice, the agents will do the heavy lifting at the beginning, but it will be a human who oversees them, who coaches them, and who completes the execution.

The agents will use tools, in many cases SaaS solutions designed for specific tasks, e.g., making a payment to a vendor. SaaS vendors will have their own agents, riding astride their software.

Every company will have its own agents who have grown up inside the context of the individual firm. No two groups of agents will be the same.

The business model will be different, too.

Back in the day, software vendors charged for enterprise licenses and maintenance. Then cloud computing came along and vendors switched to charging for seat licenses. Now, Agentic AI will charge for outcomes. This is like paying a consultant on contingency. Imagine a company that comes to you and says, I have knowledge of the sales tax in the Province of Ontario. Let me go through your historical filings. If I find anything, I’ll take half of what I find. It’s a windfall for you, after all. If I find nothing, I will not charge you a penny. In this case, companies will pay Agentic AI for each procurement event that they prosecute to completion under human supervision.

Read the Sequoia paper. Discount it as Silicon Valley claptrap at your own peril. Ignore it and you risk ending up as a quaint telegraph salesman lamenting the use of fax machines. It does a wonderful job of explaining how Agentic AI will teach agents to have task-specific “cognitive architectures” that build upon the core pattern recognition of their foundation model ancestors.

This is why EdgeworthBox is starting with Generative-Procurement-as-a-Service. We want to map out as many different procurement approaches as possible with a view to this idiosyncratic Agentic AI customization in the future. We envision armies of enterprise-specific Agents executing in EdgeworthBox to exploit our repositories of structured data, communicating with their human-in-the-loop overlords using our messaging tools.

We’re open to talking to anyone about this. Give us a shout. Check out EdgeworthBox for more information.

April 2024 | New York, NY – EdgeworthBox, a procurement technology company, today announced it joined the Amazon Web Services (AWS) Partner Network (APN) as an AWS Public Sector Partner. In addition, EdgeworthBox announced that the EdgeworthBox platform passed the AWS Foundational Technical Review (FTR). Passing the AWS FTR validates that EdgeworthBox meets the best practices for security, reliability, and operational excellence.

As an APN member, EdgeworthBox is positioned to benefit customers with all that AWS has to offer. EdgeworthBox can now leverage their relationship with AWS to accelerate customer adoption of cloud-based procurement technology. For some, this means leapfrogging the use of email and spreadsheets to a dedicated contemporary framework for the management of the reverse auction process involving RFPs, RFQs, and RFIs. For others, this means augmenting existing technology approaches with a secure, cloud-based functional procurement technology solution. Also, this will spur social procurement: engaging and developing suppliers from historically disadvantaged communities with a simple, accessible platform. EdgeworthBox has both a global presence and a separate, dedicated Canadian instance on AWS.

“We’re thrilled to be part of a program in which AWS invests in the growth of its APN members, enabling us to reach a broader audience, and benefiting from the tremendous reach of the AWS Marketplace for cloud-based solutions. EdgeworthBox seeks to help public sector organizations at every level of government, in addition to companies in the private sector, simplify and improve the way they purchase and finance goods and services from third parties. Working with AWS extends the EdgeworthBox promise of the right solution, from the right provider, at the right price to a wider audience with an accessible channel,” said Chand Sooran, Founder & CEO, EdgeworthBox.

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About EdgeworthBox: EdgeworthBox is a set of tools, structured data, community, and market intelligence that streamlines the way businesses purchase goods and services. It makes it easy to execute RFPs, RFQs, RFIs, or any other kind of reverse auction, while promoting collaboration, both internally and externally. Companies use EdgeworthBox to replace the chaos and complexity of email and spreadsheets to manage reverse auctions or to insert it as a complementary layer that improves an existing procurement technology installation.

EdgeworthBox                                                                                  CONTACT DETAILS

370 Jay Street, 7th Floor                                                                      Sales Department

Brooklyn, NY 11201                                                                sales@edgeworthbox.com

It’s a simple problem. Jill’s company buys copper all the time. It has half a dozen suppliers on call. She is allowed to go only to the suppliers that her company has onboarded.

She can take the Request for Quotes form from prior purchasing events, change the dates and the amounts, and send it to each of the suppliers. The RFQ contains instructions on when and how to respond. In this case, Jill requires a quote no later than the close of business two weeks after hitting send. Perhaps she has a procurement tech solution. More likely the company just uses email and spreadsheets to manage this process.

The suppliers wait until the last minute to email their quotes. The quotes are good for a forty-eight-hour period (given the volatility in commodity prices).

Jill enters the quotes into a spreadsheet and picks the one with the lowest price. All six suppliers replied. It turns out that the vendor with the lowest bid was long inventory and had a greater sense of urgency when it came to accepting a lower profit if it meant reducing overall risk.

Pretty straightforward, right?

Under the right conditions, procurement isn’t difficult. Most of the time, procurement is a complicated business process.

Condition #1: you are a frequent buyer of the item in question. You know exactly what you’re buying. There is no uncertainty.

Condition #2: you have established relationships with a stable of reliable suppliers.

Condition #3: there is no differentiation between the product any individual vendor offers.

Putting this together, we realize that Jill is purchasing a commodity. It is a simple optimization in one dimension: price. Everything else is exactly the same.

Jill did a good job on her first task.

Now, she gets a more difficult assignment. She is put on a committee that includes the head of a couple of business units. Also present in the group are people from different functional areas including finance, product, and marketing. This set of stakeholders is in charge of purchasing a cloud migration strategy, starting with the selection of a cloud services provider and a consulting firm to manage the transition.

This project is almost the precise opposite of a commodity.

In the first meeting, the CFO sets the tone by pointing out that going with the lowest price is going to be the wrong strategy. They cannot afford to repeat what they have seen in prior “low bid” reverse auctions. A supplier with a spotty track record low balls on price to win the contract, coming in far below the clustered bids from more experienced vendors. They then proceed to fail to deliver and beg to renegotiate the price higher, claiming unforeseen and undisclosed conditions. The ultimate price ends up being much greater than what the company would have paid one of the other seasoned players. The project was delayed. The company is still working out problems, months after acceptance (and final payment).

Sometimes, picking a supplier solely on the lowest price turns out to be very expensive.

Jill learns that a complex strategic sourcing event where the Company is spending enough to move the needle on the financial statements in this period and future ones brings with it complexity. It is multi-dimensional.

What are some of the potential dimensions to consider in a strategic sourcing event?

• Competition: are we seeing enough supplier proposals for this reverse auction to be labeled a success or are we receiving only two or three substantive responses?
• Supplier reliability: can we count on them to deliver what they say, at the cost they promise?
• Problem-solution fit: is this the best possible solution for the problem we seek to fix with the procurement or will we be stuck with higher COGs or higher operating costs or lower revenue because we picked a second-best solution?
• Collaboration: is this a supplier with whom we want to build a longer-term, deeper collaborative relationship, potentially including joint innovation?
• Diversity: how much of our spending with this third-party will end up helping disadvantaged suppliers grow their capacity and capabilities?
• Price: are we seeing true competition on price or are they padding their bid?

Buyers who run multi-dimensional, complex strategic sourcing events as “low bid” auctions will get a low price, even as they bear additional risk in multiple other dimensions. The low price they pay means they are underwriting risks that their flawed approach magnifies.

We built EdgeworthBox to help companies run reverse auctions that attract real competition by making it easier for suppliers to engage with a simple user experience and a faster sales cycle. A balanced assessment of risks in an auction that discusses the full gamut of risks, not just price, will lead to better outcomes. Give us a shout. We’d love to talk to you.

There is a way to turn these situations around, though.

What Is Digital Transformation?

Here is CIO:

“Additionally, companies often forget that a digital transformation is doing two things at once: digitizing your backbone as well as automating and simplifying by ideally, reusing components or processes.”

Pulling this apart, there is an underlying core technology. There may be, for example, a database that is the single source of truth. Think of it as a general ledger for enterprise applications. A digital backbone may include an inter-application messaging system. This is a technical resource.

Sitting on top of it are the applications themselves. In the most primitive incarnation, we can imagine a combination of email and spreadsheets used to manage a workflow. Or it might include some legacy applications, either built in-house or potentially the long-deprecated modules for an enterprise resource planning system. This is the business toolkit.

Why transform digitally?

Companies put into place new systems for two reasons: to improve operational efficiency, or to give staff the ability to do new things. We want novelty because it can open up opportunities for new products or new revenue streams or new combinations of things and people that will create value.

Why has there been so much pressure to transform digitally?

We are emerging from a fifteen-year period of extraordinary monetary policy that fostered an environment of experimentation by making it easier to take risk financially, leading to a self-reinforcing cycle of trying new approaches. When your competitors are putting in place new ways of doing things, the pressure is difficult to withstand. When everyone around you is pushing the boundary, it is insurmountable.

The accelerating pace of change forces companies to improve business outcomes.

Almost All Digital Transformation Projects Fail

According to Forbes:

“In 2016, Forbes assessed the risk of failure in digital transformation to be 84%. According to McKinsey, BCG, KPMG and Bain & Company, the risk of failure falls somewhere between 70% and 95%.”

Consulting firms are predisposed to tell you how to avoid such an outcome. But there are two other questions we could ask.

Why do transformations fail?

Assuming that the project has run off the rails, how can we fix it?

Being able to rescue the ship (and ensuring that it can sail off without incident) requires understanding why and how it has hit the shoals. We want to design a solution that addresses the core problem.

Digital Transformations Fail Because They Optimize the Wrong Objective Function

There are two dimensions to a digital transformation: the technology implementation and the cultural change.

Technology implementation is a fixed, finite engineering problem. The company’s IT department must optimize the installation of the designated toolset subject to the constraints of time, budget, and existing infrastructure. The team must make decisions about how to plug it into the current fabric of software and hardware. All of this factors into the decision about what system to buy.

Forbes talks about the “shiny toy problem”. Are you purchasing the system that is the right solution for your problem, or are you buying something because it is marketed well and it seems to be gaining traction in the marketplace? CIO refers to “delighting” the customer. Here the customer is the internal users you want to embrace the new approach. Does the user experience excite them?

Who is running the project? If IT dominates the conversation, does it devolve into an exercise in integrating the new tool with the current architecture as elegantly as possible? Are they taking into consideration what the business user wants? Do they even understand the business problem?

It is easy to understand why IT might take over. They can measure success. Did the team select and integrate a system that didn’t blow up the company’s existing infrastructure? Did they do so on-time and under budget? Did they get the system to function the way the vendor designed it to work?

These are necessary conditions for success, but they are far from sufficient for getting value-for-money.

A successful project means users embrace the new technology’s features to obtain the best business result. They do not deem a project successful because they can use the features the way the vendor intended, necessarily. Users exist in a context and success is judged within it.

Cultural change represents an indefinite business problem requiring strategic insight into the business and soft skills. Enterprise leadership must solve for the business outcomes they hope to achieve subject to the constraints of overall strategy and the people on the team. Ideally, this is seen to be critical in the decision about what system to purchase.

When we see the lists of problems as described in Forbes, CIO, Kissflow, and Archdesk, this is where the meat of the matter resides.

We can boil it down to several categories of failure: misalignment, poor leadership, and fear.

Forbes addresses the question of alignment by which we refer to the degree to which the solution solves the business problem. IT solves a technology problem (i.e., upgrading the digital backbone), but if the digital transformation will work as change, then the project must address the needs of the end users.

The CFO wants to know if this project has a positive net present value. Do the economics of using the toolset lead to lower costs or higher revenue that will generate increased margins over time? Does the project avoid a degradation in margins in the future the company might expect if they were to stand still? Is the company optimizing profitability or is this a question of politics?

In a world dominated by managers, it is no wonder that leadership capable of handling complex projects such as these is in short supply. Leadership here means getting people to do what they may not want to do, given the stickiness of legacy systems and current business processes. This involves frequent and consistent conversations with every stakeholder as to why this project is vital for the company’s future success and how it will extend their capabilities and capacities in a meaningful and fulfilling manner.

Engineers can handle the minutiae; these are important, no doubt. Make no mistake about it. It is also true that nobody buys into change because an engineer prattled on about overcoming the challenges of some abstruse coding problem or the elegance with which they set up the database. Often, engineers gain personal meaning from solving difficult technical puzzles; this may coincide with the business case, or it may not.

Leaders need to tell stories about what the user experience will feel like in the future universe once this system is in place, tales of how much easier life will be for businesspeople to do their jobs, and of how much more they can accomplish. People want to be effective. They also dislike friction. The best kind of digital transformation removes obstacles. Successful projects help line up what the individual sees as progress with what the enterprise needs. It is a fundamentally emotional exercise.

It is not only the rank-and-file the leaders need to address. Management needs to buy-in at every stage. The C-Suite must see this project as something that advances its strategy. There is the base calculation of net present value, of course. But the best projects have a net perceived value to all stakeholders.

People buy into change because it sets them up for success.

This is why addressing fear is vital.

Here is Archdesk:

“… employees can also fear that new technologies can take over their jobs, making them redundant.”

Leadership here is paramount. The absence of communication creates an environment of uncertainty. Not only do employees fear the consequences personally, there is a natural concern that the new approach will be worse than the old one. Better the devil you know is a powerful sentiment. When there is lack of information, then the anticipation of what the new regime will look like can create its own doubts. Leadership needs to be constant in working with users to identify their worries and in addressing them, incorporating feedback, and highlighting that transformation is a process, not a discrete event.

Relentless focus on the business and the users is a magic salve for transformation projects.

How Do You Fix a Broken Transformation?

If the thesis of this blog post is correct, the digital backbone is in place. The underlying data infrastructure works the way it is supposed to work, but the issues arise at the application layer, either in getting different related software tools to interact efficiently or, more likely, in having the users embrace the new approach in ways that improve business outcomes.

The way to fix a broken transformation may, paradoxically, involves adding new applications.

The new applications can act as a “skin” on top of the functioning infrastructure and as a type of middleware that fixes the connectivity issues.

HBR talks about the three stages of successful digital transformation: “simplifying and digitizing existing processes and functions”; “enterprise-wide transformation” involving “complex cross-value-chain change”; and “new business creation.”

The first job of the skin is to take the pre-existing processes and functions and transition them to the new environment by designing the right user experience and plugging it into the data infrastructure. This may be the core failure of the digital transformation such that success here is a home run.

Get this right and you reduce fear.

The second job of the skin is to make it easier for the new overlay to plug together different systems, breaking down departmental silos and converting value chains into value networks. Not everyone buys in, but many will.

Get these two jobs right and the third stage takes care of itself. As users embrace the new setup, opportunities to create new businesses or cut costs will emerge.

If the transformation failed because it was driven by IT and lacked proper leadership, the kind of fix discussed here can work. The engineers would have ensured that the bones of the data structure were solid. This only makes the introduction of another layer more attractive. It can be economically appealing if the business model for the new layer is the right one.

This is what we have built at EdgeworthBox. It is a complementary application layer that is agnostic to the underlying architecture and one that can engage business users, while integrating with underlying infrastructure layers in the procurement technology stack. Our elastic procurement™ business model makes the economics of transformation attractive.

Give us a shout. We’d love to talk to you.

The arrival of “generative AI” is timely with its suggestion of the possibility of a liberating quantum leap forward after the incremental progress of robotic process automation.

Generative AI has much to offer procurement, but it’s important to understand what it can and cannot do. If we use it properly, cognizant of its strengths and weaknesses, artificial intelligence tools can improve our performance. While they’re at it, generative text applications might be able to improve the quality of our jobs, too.

The best way to think about AI tools such as ChatGPT in a procurement context is as an inexpensive, young associate: naïve, inexperienced, passably good at writing, hardworking, and willing to learn. Use them to make the most of the valuable time (and mental wellbeing) of your senior staff. These experienced hands can focus on developing supplier relationships, evaluating performance, training people, and working strategically with the business. Good AI augments staff capacity; it does not replace staff capacity.

This article dives into what the new technologies do and the conditions under which they can be most useful for procurement staff. We’ll end with a detailed example of how we might use these solutions (in combination with other technology) to write a first draft of a Food Services RFP.

What Is “Generative Text?”

Generative text refers to the technology underpinning artificial intelligence tools that can write blocks of text. Initially, researchers focused on the problem of predicting the next word in a sentence or a fragment. Imagine the word suggestion feature in a messaging app or Gmail. You can see here that the email application predicts I want to type in the words “next week” once I type the letter “n” after having written already “Let’s figure out a time to meet …”

Developers teach these models how to predict the next word most accurately by collecting massive amounts of data, preprocessing them so that the computer can read the text numerically, and training different combinations of AI building blocks to learn relationships between words. These deep learning models are evolving to become increasingly sophisticated by combining the building blocks in different sequences (and adding more of them), even as they train on bigger and bigger data sets. Models can have hundreds of millions or billions of parameters. It can be extraordinarily difficult to explain how they reach their conclusions.

Having created these behemoth machines to work with text generally, developers have introduced new so-called natural language tasks.

Generating text that sounds plausible is one of the most exciting jobs we have built models to execute. The progress we have made is akin to predicting large blocks of text, one next word at a time, iteratively, with an increasing sense of nuance for not only the relationship of words to one another generally, but to one another in a specific sentence.

There are thousands of different models out there. ChatGPT is a portal into only one of them. It is a ready user interface for the underlying GPT-3+ model from OpenAI. All the large technology companies have these kinds of models. We’re on the verge of seeing an explosion of ChatGPT-like applications that are easy for laypeople to use. Up until this point, the models have been usable but accessible only with subsequent coding to extract and manipulate the results.

The promise of ChatGPT and other tools like it is the democratization of this powerful technology.

We are moving from the equivalent of bespoke early automobiles, hand-crafted by specialists and afficionados in their garages, to the rollout of mass-model cars such as the Ford Model-T built for widespread use. Like the transition from the horse-and-buggy, there will be bumps along the way, but the evolution is inevitable.

One of the key advances in recent years has been the evolution of models called “Transformers.” They have progressed to be good at understanding the context of words within a sentence. This improved their performance in next word generation substantially. Yet the quality of their presentation obscures their limitations. Here is the MIT Technology Review:

“They are excellent at predicting the next word in a sentence, but they have no knowledge of what the sentence actually means.”

The world is complicated and messy. Yann LeCun, one of the top AI researchers in the world, says of large language models like GPT3 (the model underlying ChatGPT):

“Why do LLMs appear much better at generating code than generating general text? Because, unlike the real world, the universe that a program manipulates (the state of the variables) is limited, discrete, deterministic, and fully observable. The real world is none of that.”

The output of these models comes across as sensible, but that doesn’t mean what we obtain is reasonable.

“Gary Marcus, author of Rebooting AI, explained on Ezra Klein’s podcast: ‘Everything it produces sounds plausible because it’s all derived from things that humans have said. But it doesn’t always know the connections between the things that it’s putting together.’”

These models may not generate text per se; they may plagiarize text from their training data sets.

“Past literature has illustrated that language models do not fully understand the context and sensitivity of text and can sometimes memorize phrases or sentences present in their training sets.”

Another way to think about generative text is that it is like a modern photocopier. Contemporary digital copiers encode an image into a compressed, digital file and then decode the digital file to print it. There is some slippage ‘twixt the cup and the lip if it is not a perfect compression. A lossy compression might make sense if you wanted to make it easier or less expensive.

“Think of ChatGPT as a blurry JPEG of all the text on the Web. It retains much of the information on the Web, in the same way that a JPEG retains much of the information of a higher-resolution image, but if you’re looking for an exact sequence of bits, you won’t find it; all you will ever get is an approximation. But, because the approximation is in the form of grammatical text, which ChatGPT excels at creating, it’s usually acceptable. You’re still looking at a blurry JPEG, but the blurriness occurs in a way that doesn’t make the picture as a whole look less sharp.”

What Does Generative Text Mean for Procurement?

Put this all together and it means that AI doesn’t eliminate work; it changes work.

The machine lays out the building blocks for a project and the human, with the knowledge of context and complexity, ensures that the building blocks fit together as intended. The AI does the brute set-up work; the individual brings the meaning. This is where the value lies.

“What makes businesses highly profitable is using humans to do these higher order things, these creative strategic thinking things that are still a long way from anything I’ve seen the computers able to do.”

AI tools such as ChatGPT (laughingly referred to as “mansplaining as a service”) deliver their output with what appears to be complete certainty wrapped in a reassuring grammatical correctness that together make them seem more credible. But as familiarity with these instruments becomes more widespread, we’re beginning to realize that we can’t trust the answers that they give us blindly. We need to verify, too.

“… it has a tendency to confidently deliver incorrect information. This means that step one in making this technology mainstream is building it, and step two is minimizing the variety and number of mistakes it inevitably makes …

“… Dr. Dai says that one analogy for the future of trust in AI systems could be one of the least algorithmically generated sites on the internet: Wikipedia. While the entirely human-written and human-edited encyclopedia isn’t as trustworthy as primary source material, its users generally know that and find it useful anyway. Wikipedia shows that ‘social solutions’ to problems like trust in the output of an algorithm – or trust in the output of human Wikipedia editors – are possible.”

Over time, armies of laborers may end up tweaking these products to avoid the myriad errors they generate today. For now, we can use generative text to get us started on projects, doing the first cut of the tedious work, freeing us up to work on the meaningful work. Who wouldn’t want that?

AI can make our jobs better by bearing the brunt of those things we don’t like to do, so we can focus on making a real impact.

One other key potential benefit is that tools such as these have the potential to turn anyone into a procurement officer. Instead of being an arcane process that other parts of the organization tolerate, it can give basic purchasing knowledge to people on the front line. This will help everyone understand the important benefits procurement brings.

We can obtain value from generative text tools if we remember that they are not foolproof, despite an appealing presentation. Once we realize their limitations, then we can use them responsibly. They will be the plough horse in the fields of digital transformation, emancipating manual labor to focus on adding greater intellectual and commercial value. It just may be the avenue for the long-delayed, organization-wide recognition of procurement as a strategically vital business process.

It’s already being tested. The US Department of Defense has something they call “Acqbot.” It doesn’t make any contracting decisions, but it does generate text. Ideally, this would make it easier for suppliers to respond, as well.

It makes sense that the largest organizations would move first. They have the largest base over which to spread the costs.

But there is nothing to stop even the smallest organizations from using these tools.

Consider an Example: a RFP for Food Services

Perhaps the best way to understand what is happening here is to look at an example. Consider a company that is looking for a supplier to provide food services.

Here’s what we get when we “prompt” ChatGPT with the following question: “show me a detailed food services statement of work for an rfp.”

Sure, here is an example of a detailed statement of work for a food services RFP:

Overview: The purpose of this RFP is to solicit proposals for food services for a new corporate office building. The food services should provide a high-quality dining experience to employees, guests, and clients.

Scope of Work: The selected vendor will be responsible for providing food services for the following areas of the building:

Cafeteria: The cafeteria should offer a variety of healthy, fresh, and locally-sourced breakfast, lunch, and snack options to employees and visitors. The vendor will be responsible for menu planning, food preparation, service, and cleaning.

Coffee Bar: The coffee bar should offer a variety of hot and cold beverages, including coffee, tea, and specialty drinks, as well as baked goods and light snacks. The vendor will be responsible for menu planning, food preparation, service, and cleaning.

Catering: The vendor will be responsible for providing catering services for meetings, events, and conferences hosted in the building. The catering services should include a variety of menu options, professional service, and on-time delivery.

Vendor Requirements: Vendors interested in submitting a proposal for food services must meet the following requirements:

Experience: The vendor must have at least 5 years of experience in providing food services to large corporate offices.

Quality: The vendor must have a proven track record of providing high-quality food services, including menu planning, food preparation, service, and cleaning.

Sustainability: The vendor must demonstrate a commitment to sustainability, including the use of locally-sourced and organic ingredients, waste reduction, and recycling.

Staffing: The vendor must have a sufficient number of qualified staff, including chefs, servers, and catering personnel, to provide timely and professional service.

Compliance: The vendor must comply with all applicable health and safety regulations, including food handling, storage, and preparation.

Proposal Requirements: Proposals must include the following information:

Executive Summary: A brief summary of the vendor’s experience, capabilities, and approach to food services.

Scope of Work: A detailed description of the vendor’s proposed services, including menu options, staffing, and hours of operation.

Pricing: A complete pricing schedule for all food services, including cafeteria, coffee bar, and catering.

References: A list of at least three references from current or previous clients.

Certifications: Proof of compliance with all applicable health and safety regulations.

Sustainability: A description of the vendor’s commitment to sustainability, including the use of locally-sourced and organic ingredients, waste reduction, and recycling.

Timeline: The following timeline applies to this RFP:

RFP Issued: February 23, 2023

Proposal Deadline: March 23, 2023

Vendor Presentations: April 2-6, 2023

Vendor Selection: April 13, 2023

Contract Start Date: May 1, 2023

Evaluation Criteria: Proposals will be evaluated based on the following criteria:

Experience: 20%

Quality: 30%

Sustainability: 20%

Pricing: 20%

References: 10%

Submission Instructions: Proposals must be submitted by email to the contact person listed below:

Contact Person: John Smith Title: Director of Facilities Management Email: john.smith@company.com

Please include “Food Services RFP” in the subject line of the email.

Thank you for considering this RFP. We look forward to receiving your proposal.

Regenerate response

We can see several things straightaway.

One, at less than two pages, it is much shorter than we would have anticipated.

Two, this is because it lacks specificity. There are very few questions related to the buyer’s requirements. There is no description of the buyer’s situation. In fact, it assumes that this is for an office building.

Really, it is an outline of what a general bid solicitation might look like. It asks the supplier to fill in the blanks. The best projects tell the supplier’s as much as possible about the buyer’s constraints. This is so open-ended as to discourage suppliers from investing in a response.

Of course, we can prompt ChatGPT for more detail. We did that by asking, “Give me more detailed questions.”

Sure, here are some more detailed questions that you can include in an RFP for food services:

Menu Planning: a. How will you develop and revise the menu to ensure it meets the nutritional needs and preferences of our employees, guests, and clients? b. How do you source your ingredients, and what steps do you take to ensure the quality and safety of your ingredients?

Food Preparation: a. What measures do you have in place to ensure food safety during preparation and service, including temperature control, hygiene, and cross-contamination prevention? b. How do you accommodate special dietary requirements, such as allergies, intolerances, and cultural preferences?

Service: a. How do you ensure timely and efficient service, especially during peak hours? b. What steps do you take to ensure that the dining area and service stations are kept clean and tidy throughout the day?

Catering: a. What types of events do you specialize in, and what is your capacity for catering large events? b. How do you handle last-minute changes to catering orders, and what is your policy for cancellations and refunds?

Sustainability: a. How do you source and dispose of food waste, and what steps do you take to minimize food waste? b. How do you incorporate sustainability practices, such as using eco-friendly packaging and reducing energy and water usage, into your operations?

Staffing: a. How do you ensure that your staff are properly trained and qualified to perform their duties, including food preparation, service, and cleaning? b. How do you manage staffing levels to ensure that the dining area is adequately staffed at all times?

Technology: a. What technology solutions do you use to streamline your operations, such as online ordering, inventory management, or kitchen automation systems? b. How do you use data and analytics to optimize your menu, staffing, and operations?

Pricing: a. How do you structure your pricing, and what factors influence the cost of your services? b. What is your policy for pricing adjustments and how frequently do you review your pricing structure?

Contract Terms: a. What is the minimum term for your contract, and what is your policy for early termination? b. What is your policy for price adjustments, and how frequently do you review your pricing structure?

These questions should give you a more comprehensive view of a vendor’s capabilities, experience, and approach to food services.

This is a page in length. Much of it consists of general questions related to contracting with no reference to the specifics of a food services project.

ChatGPT does not have sufficient category-specific knowledge to write a complete first draft.

The ideal application would be to give ChatGPT to someone who knew the category well but didn’t know how to write clearly.

What if we needed category-specific questions, too?

Ideally, we would like to have specific information about multiple sub-topics within the category, such as the following:

Food Supplies

Maintain control receive and store food supplies properly ensuring storage and first in first out basis with minimum wastage

Meat must be from federally inspected source and Canadian Food Inspection Agency CFIA approved or equivalent

Only products that meet the standards of the food industry and of the Canadian General Standards Board may be used

Foodstuffs must not be spoiled dirty or infested at the time of delivery

Frozen food must show no signs of defrosting Perishable food must be delivered in refrigerated vehicles in which the temperature does not exceed 4C

To do something with this level of detail, repeated across various sub-topics, requires access to relevant data.

Either the buyer would have a way of cataloguing and sharing structured data about prior work in the category or they would have access to services and consulting firms that had this kind of intelligence themselves.

Once a buyer had the data, it should be possible to fine tune the same sorts of tools underpinning the ChatGPT interfaces of the world to extract, curate, and present the detailed, myriad questions a buyer would need to ask.

Being more specific actually makes it easier to attract quality supplier proposals. It demonstrates that the buyer understands the problem they seek to solve. The detail limits the problem space to something familiar and straightforward to address.

This second piece is what we have built at EdgeworthBox. You can think of it as a limited procurement-as-a-service functionality. We have hundreds of thousands of statements of work, spanning multiple categories, in our structured data, made accessible by the standardization of its formatting.

Combine this service with the writing of a ChatGPT interface and the procurement cycle becomes much shorter and buyers attract more (and better) supplier responses.

We’d love to talk to you about it. Reach out anytime. EdgeworthBox is a set of tools, data, community, and market intelligence that help B2B buyers purchase the right solution, from the right supplier, at the right price.

Why are opportunity costs more difficult to manage than transactions costs? They’re hidden. There is no line item for opportunity costs. There is only foregone revenue or higher costs than what the company could have realized in the future because of decisions made today.

When a company purchases solution A instead of solution B and solution B had a better problem-solution fit, say in terms of lower total lifetime costs, then solution A carries it with the opportunity costs of these foregone future savings.

We never see what the income statement or the balance sheet or the cashflow would have been if the company had selected B, instead of A.

Now imagine this issue occurring hundreds of times.

Many companies focus on the transparent costs of the procurement today. What are the transactions costs for executing this purchase in terms of internal resources it will expend to go through a compliant process? How much are we paying upfront for solution A vs. what we would pay for solution B?

The CFO might say, why would I use a procurement approach if the transactions costs are higher than executing internally without considering the opportunity costs?

What gets measured, gets valued.

Of course, this is a knife that cuts both ways.

The company’s revenue, cost structure, cashflow, and net assets will be measured in the future. And the market will value them.

Good CFOs optimize future financial statements by making the right decisions today. They set up systems and processes that create the best path in the future. They realize that they need to tradeoff current costs for future opportunities. The cheapest solution or the most easily purchased solution isn’t necessarily the best value-for-money, fully costed for opportunity costs.

How do companies lower opportunity costs? Opportunity costs arise when buyers don’t consider enough solutions in competition or when buyers prioritize more tangible economics such as transactions costs.

The key to driving opportunity costs down (and which also drive down initial purchasing costs) is competition. You can’t buy the better solution B if you never see it. You won’t see competition on price unless suppliers know it’s a real auction with multiple potential solutions.

Good CFOs create procurement processes that manifest the greatest competition on price and solution, subject to the tradeoff of manageable procedural costs.

Key to success here is the notion that procurement is reverse sales in that the buyer is selling the supplier just as much as the supplier is trying to close the deal.

The stereotype of enterprise purchasing is that the buyer is in the catbird’s seat. But the Pandemic has reminded us that it can be a seller’s market just as easily.

In a buyer’s market, there is perfect competition. Vendors trip over one another to capture the business. They bid down the price until there are no profits left for them to capture at the margin.

These conditions only apply to commodities. In a commodity market, each vendor is selling precisely the same thing. This is almost always a good. Imagine a pile of copper. Copper is a commodity. People trade it with one another. The terms are standardized.

Most goods that companies purchase are not commodities. They are specialized.

Services are distinct from one another, even when they offer the same type of service, by the people who deliver them. Indeed, most goods that companies, even industrial firms, acquire have services overlaid.

Straightaway, we can see that the vast majority of enterprise purchasing is not commodity-like, with variation from sector to sector.

We’ve written previously about the supply chain risk of poor vendor relations. When things get tight, suppliers put bad customers on allocation.

“Buyers who treat their suppliers as vassals will end up with vassals as suppliers.”

Even more importantly, companies that treat suppliers as objects don’t get real competition on price and solution. Companies that suffer from the delusion that their relationships with suppliers in anything other than a complex ecosystem of relating interests are most likely to have large opportunity costs, leading to underperformance against their potential.

This is really a question of risk management.

We built EdgeworthBox to be a set of tools, data, and community that help B2B buyers and B2B suppliers. It’s built as a way to deal with the supplier ecosystem as it is. Buyers get to buy the right solution, from the right supplier, at the right price. Suppliers see a shorter sales cycle, lower transactions costs, and superior customer fits. You can see more in this short video.

But, having decided to change, we come to the next logical question. What solution should we choose?

The most basic element of this, as it turns out, asks whether it is better to go with a standalone best-of-breed technology solution or to go with the module of a comprehensive single vendor solution like an enterprise resource planning (ERP) system.

We built a best-of-breed solution for strategic sourcing because we think that standalone focused platforms are the right tool for this moment.

Let’s talk about the arguments for and against this choice in greater detail.

The World Is Converging on a Vision of the Future of Supply Chain

There are many factors driving the transformation of procurement in the enterprise.

When decision makers select the new technology stack, they need to do so in the context of a vision for the future.

Lora Cecere, founder of Supply Chain Insights, outlined a view with which we agree:

“There is a shift from ERP-centric architectures to use of ERP as only a system of record for financial transactions and the building of analytics architectures that allow data to stream, pool, and flow drive insights.”

If this premise turns out to be correct, then the ERP will be nothing more than a data warehouse, holding data from multiple, diverse sources.

The evolution of application programming interfaces and potentially tools such as dataware for sharing data among different systems makes this vision feasible.

A Key Element of This Future Vision Is the “Bimodal Supply Chain”

Gartner has a vision that they call the “bimodal supply chain” in which a traditional approach to efficient supply chain execution is married with agile, strategic innovation to manage risk. The way for the enterprise to adapt to the new world in which we find ourselves is to combine cutting-edge digital tools with the backbone of proven analog processes

“The divide between what the supply chain provides and what the enterprise needs is widening. Closing that gap requires a new, agile approach to investment in technology, leadership and talent.”

This gap exists because we live in a risky world in which customer expectations increase monotonically and in which the pressure to extract competitive advantage from the supply chain builds.

Companies need to be efficient and responsive.

The Economics of Technology Are Changing

When evaluating different systems, we should consider the Total Cost of Ownership in a framework of six dimensions.

What will it cost to implement? How much training will it require? What are the ongoing costs of the software? How much will it cost to integrate with other systems? What complexity does it impose on the in-house IT department? How much will it cost to upgrade the system, including with security patches?

This must be weighed against the Total Benefits of Ownership.

How much of a reduction in transactions costs will we see? How much can we quicken the purchasing cycle? How much more data utilization can we expect? How much can we save on market intelligence? How much easier is it for other parts of the enterprise to access a process that traditionally exists in a silo? Does it permit collaboration across the enterprise?

Broadly, there have been three phases of enterprise software: the proliferation of standalone systems; the use of ERP approaches to facilitate integration; and the replacement of ERP systems with cloud-based applications.

Done right, cloud-based standalone systems can lower implementation costs, eliminate virtually the need for training, and convert ongoing costs to a pay-as-you-go utility model, leveraging contemporary integration technologies to render dark data accessible across the enterprise. The best tools will permit collaboration around data across the enterprise (and outside the enterprise) with a platform approach.

What Are the Arguments in Favor of a Single Vendor ERP Approach?

One of the easy, seemingly intuitive arguments people make is that integration is easier with a single vendor approach. There is the perception that modules from ERP systems fit together by design.

One problem with this argument is that data exists in multiple systems regardless of whether the procurement organization chooses an ERP module. Suppliers are unlikely to use the same system. Customers may use different systems. Even people within the enterprise may use different systems. There could be multiple ERPs within one entity. The modules themselves that ERP vendors offer are often nothing more than acquired standalone companies being resold as modules (e.g., Workday’s acquisition of Scout RFP).

Integration is unavoidable.

Modules of ERP systems are not one-size-fits-all; they may require significant customization, giving them little integration advantage, if any.

Integration tools have improved considerably in parallel with the evolution of cloud-based applications.

There is practically little difference between integrating a module and plugging in a standalone.

Single vendors criticize best-of-breed vendors as good at only one thing. Best-of-breed applications companies respond that this is a feature and not a bug. Purchase excellence with the best-of-breed instead of the convenient mediocrity of the single vendor module.

Single vendors argue that a common user interface across modules makes collaboration easier with other functional areas within the enterprise. This may or may not be true, in practice. There is an easy test for this issue. How many people use Excel as a patch for sharing data instead of within the single vendor experience?

One argument in favor of best-of-breed vendors is the flexibility they provide. With single vendors, it is easy to get locked in with all of the implementation expense and required customization work. With best-of-breed, you use it when you need it without having to pay for an expensive suite requiring no implementation and no training.

One Key Question

The defining aspect of the 21st century supply chain will be the need to connect people, data, and processes. Why choose a best-of-breed sourcing vendor? They are more likely to focus on these issues, particularly in terms of the development of a collaborative network.

Ten years ago, one could have argued that there was a tradeoff. Buyers of systems needed to weigh the domain expertise and other advantages of best-of-breed solutions against the cost and difficulty of integration.

Today, integration as a factor confers no significant edge to the single vendors.

We built EdgeworthBox to help make procurement collaborative and effective. It’s an exchange with tools for hosting structured procurement data; standardizing and simplifying onboarding and RFx; and speeding up the sourcing process.

Our approach increases the quantity and the quality of responses buyers receive when they solicit vendors. Sellers like the simplicity and exposure to potential customers with the right product-solution fit.c

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Change is difficult.

Why Now?

The key question confronting anyone in a sales or procurement role is this: why is change necessary now?

It is because risk is pervasive today in ways that it wasn’t before the Pandemic.

We used to take supply chain stability for granted, enabling us to employ “just-in-time” purchasing. Now, supply chain disruptions persist and we speak increasingly of “just-in-case” acquisition. Some blame extraordinary demand. Others talk of the “ripple effects of disruption.”

The one thing on which everyone seems to agree is that supply chain issues are here to stay.

The simplest answer when speaking to people about the need for immediate change is that companies need to replace an approach built for a low risk world with one designed for managing a high risk one.

Consider this analogy.

Before 2019, people made a living in financial markets by selling options (on stocks, on rates, on commodities, on everything). Effectively these options sellers provided other people with insurance against a change in market conditions from calm to wild. Many options sellers lost tremendous amounts of money in 2020, as they paid out claims to those they had underwritten previously.

Financial markets now exhibit a different “volatility regime.” There are fewer people around to sell options translating into lower competition on price. The sellers who survived remember their losses and charge higher fees. Buyers are willing to pay higher premia because they now know how this kind of insurance pays out in choppy conditions. There are new buyers who only know turbulent markets.

Similarly, markets for real goods and services are in a new phase.

This explains why enterprise buyers and suppliers need to change now. Covid managed to inspire a sense of urgency where previously there was none.

When Discussing Change, Flip the Script

When we make the case for change, we’re talking about the status quo.

The status quo is the default choice.

Agents for change must defeat the status quo first. This note from Seth Godin explains the approach well.

The standard defense case is simple: talk about the benefits of the status quo (while ignoring the costs) and highlight the costs of change (while ignoring the benefits).

Let’s consider the case where the status quo is a buyer using email and spreadsheets to manage reverse auctions in the acquisition of goods and services.

Imagine a buyer defending the use of email and spreadsheets when someone presents an alternative approach.

“We use email and spreadsheets for lots of purposes. So does everyone else. It’s easy and no cost. It’s familiar. It works. Moving to a sourcing platform takes time, incurs huge implementation and training costs, and requires a risky commitment to ongoing subscription fees for a bundle of features of which we will use only a fraction.”

Typically, the person trying to sell the sourcing platform will emphasize its benefits (while ignoring its pitfalls). Given the history of low-risk supply chain environments, the argument was always the same, at least before Covid.

“Our platform enables you to manage reverse auctions in a way that generates an increase in competition, leading to cost savings.”

If you’re unconcerned about risk, then it just comes down to buying at the lowest cost.

Here’s Godin’s key argument:

“And the danger is pretending you’re being fair, when you’re not. In this silly article from the Times, the author (and their editors) are wondering if oat milk and pea milk are a ‘scam.’

“This is a classic case of defending the status quo. Here’s a simple way to tell if that’s what you’re doing: imagine for a second that milk was a new product, designed to take on existing beverages made from hemp, oats, or nuts. Defending oat milk against the incursion of cow milk is pretty easy.

“The author could point out the often horrific conditions used to create cow milk. ‘Wait, you’re going to do what to that cow?’ They could write about the biological difficulty many people have drinking it. Or they could focus on the significant environmental impact, not to mention how easily it spoils, etc.

“Or imagine that solar power was everywhere, and someone invented kerosene, gasoline or whale oil. You get the idea …”

How best to change procurement technology? Proponents of change need to ensure that there is a balanced, complete conversation around costs and benefits by challenging the standalone case for the status quo approach.

What Would This Different Approach Look Like in Terms of Procurement Technology?

In the example of defending the status quo approach to procurement, advocates of a new approach should highlight all the issues that lead to failed outcomes.

“Our traditional approach to purchasing fails on every relevant dimension. We don’t end up buying the right solutions from the right suppliers at the right price.

“The traditional approach is bureaucratic and it takes far too long to execute. Time is value and if we have to wait months for the thing we’re looking to buy as the solution to a problem then that means we have to live with the problem for the additional length of the purchasing cycle.

“The traditional approach is expensive to transact. Specialized procurement staff and senior line managers spend too much time managing these reverse auctions by hand, distracted from other value-additive activities.

“There is no structured data that we can exploit firmwide. Instead of a data lake, we have cesspools of loosely connected spreadsheets. They vary in quality, consistency, and relevance.

“From their perspective, suppliers will tell you that the sales cycle is too long. Six months for technology? That’s even if they see the RFP. Most of the time, we only send RFPs to suppliers we have vetted previously because the process for onboarding a new vendor is itself time-consuming and bureaucratic, taking up to several months to navigate. Do email and spreadsheets make it easier to vet suppliers the way the platform does?

“Often, we send the RFP documents to the wrong suppliers, ignoring the right suppliers. We don’t have good, up-to-date information about who does what. Do email and spreadsheets mitigate this problem the way that the platform does?

“Suppliers make an investment decision in deciding whether to respond to an RFP. They have to weigh the cost of responding to an RFP to the expected benefit of winning. They assess the value should they win: size of the contract, expected margin, etc. They estimate the probability of winning. Do email and spreadsheets give them additional confidence to bid over a platform solution?

“Does using email and spreadsheets lower the supplier’s cost of onboarding and responding? Do suppliers have an easier time on the platform?

“Suppliers also shy away from responding to poorly written RFPs that ask the wrong questions and don’t ask the right ones. Does using email and spreadsheets help us obtain useful market intelligence that leads to better buying decisions? Does the platform have tools for getting smart about a particular category?

“What about opportunity costs for us as buyers? If using email and spreadsheets makes it difficult for the right suppliers to respond to our RFP (or to even know of its existence), how do we know that we’re getting sufficient competition on price and solution? If we don’t get competition on these dimensions, aren’t we overpaying for a second-best solution that we’ll have to live with for years?

“How are we measuring procurement right now? Is that the right way?

“If we had a sourcing platform in place that required no implementation, no training, and no ongoing subscription fees, but permitted us to solicit a wider band of suppliers, with a faster cycle, usage-based pricing, and market intelligence that led us to write more enticing RFPs, would we ever switch to email and spreadsheets?”

We’re Sympathetic

At EdgeworthBox, we feel for buyers who are under pressure as market conditions change across multiple categories simultaneously. Changing the approach to purchasing can feel like replacing the transmission on your car while you’re driving down the freeway.

We built EdgeworthBox to help make procurement collaborative and effective. It’s an exchange with tools for hosting structured procurement data; standardizing and simplifying onboarding and RFx; and speeding up the sourcing process.

Our approach increases the quantity and the quality of responses buyers receive when they solicit vendors. Sellers like the simplicity and exposure to potential customers with the right product-solution fit.

Contact Us