This is a human tendency.

Software developers are no different. You see this in the initial phases of a new technology roll-out.

Consider for example the early days of search online.

Companies like Yahoo!, AltaVista, and the like merely digitized the analog way we would find things. Before the Internet, if you were looking for a plumber to fix a leaky faucet, you would pull out the Yellow Pages directory and look under the letter “P.” There was a listing with a name and a telephone number and an address. Some plumbers would pay for an ad in the form of a box that might show their logo with some slogan. You had no way of distinguishing one plumber from the other based on the information in the book.

As I recall,  Yahoo! had listings of links in alphabetical order. So did everyone else. You used Yahoo! because it was there and everyone else used it. I believe it even started out as something like “Jerry’s List” named after founder Jerry Yang. Advertisers would pay Yahoo! for the equivalent of the boxes in their listings. Instead of phone numbers, clicking on the name of Plumber X would take you to his website. The listings were links.

The experience had the merit of being familiar, at least.

That is until Google came along and did two things that were interesting.

One, the user interface was entirely different. Instead of a home page in which one could see listings of categories, clicking on which links would take you to another page for a specific set of goods or services or interests like Plumbing, there was just a single box under the now-familiar Google logo. It was clean. Yahoo! and the others were cluttered. With Google, you typed in the name of the thing you were searching to find in Google’s box.

Two, the results, presented on a separate page, were a listing of links, including a brief description. However, the links were in the form of on an ordered list. Google determined the ranking of each individual listing based on the number of citations for that link in other sites or the frequency with which people clicked on it. It was like the way in which academic papers are ranked based upon the number of citations. Now, there was a way to distinguish based on relevance or quality that the Yellow Pages methodology did not contemplate.

Yahoo! was a directory. Google was a search engine.

Simplicity and rank ordering won the day.

Rank ordering was so important that an entire cottage industry sprouted up of people who told you they could game the system and trick it into giving you a higher ranking than you would have obtained without their help.

The company didn’t even advertise itself in the old days. It spread by word-of-mouth. I remember when a colleague told me about Google and my shock and awe when I pulled up the site at work for the first time.

Similarly, ERP systems digitized the analog version of bookkeeping and procurement systems and HR systems and payroll systems. Where the accounting had been done manually with clerks updating paper spreadsheets based upon specific functional activities and those spreadsheets rolled up into the general ledger, now those functional activities took place in a digital interface that updated databases. The databases then provided the information for other systems to automate their aggregation into the general ledger. Productivity increased in the sense that there were fewer accounting clerks, even as there was greater IT expense for software licenses and computers and networking equipment and IT professionals. As they say, the productivity was everywhere to see but the financial statements.

Digitization meant taking a pencil-and-paper task and making it screen-based. That’s it. That’s typically all it meant. The business process didn’t change meaningfully.

Even today, there is little distinction in the way in which a Request for Proposals auction takes place between a paper process, an email-and-spreadsheet process, and a contemporary procurement technology approach. The tools may be different but the business process is the same. Perhaps there is some automation on the margin that makes the digital process easier, but maybe it’s offset by a reduction in the quality of the bid solicitation document. Maybe people spend more time focusing on the quality of the process when it’s manual. Maybe.

In anticipation of the introduction of artificial intelligence and leveraging lessons from financial markets where millions of auctions take place daily, EdgeworthBox took the first steps to change the business process.

• We added structured data regarding previous RFPs and contracts and made the data shareable across all the stakeholders in the RFP process, not just people in procurement (for the buyer) or sales operations (for the seller).
• We built a social network with messaging and profiles to help users from both sides and across organizations collaborate.
• We added rapid onboarding tools so that buyers could cast a wider net beyond the narrow ken of suppliers with which they had existing relationships.
• We enhanced the group dynamic at the level of the value chain and the internal stakeholder group.
• We enabled joint RFPs issued by multiple organizations.
• We added scoring tools to speed up and improve the quality of vendor selection.
• We wrapped everything in a cleaner user experience to simplify the process on both sides.

All of these features were designed to deliver specific functional improvements: faster RFPs, better-written RFPs, and greater competition in terms of price and solution that would lead to better problem-solution fit.

Just as Google cleaned up the user experience and added utility in terms of ranking links by quality and utility, we simplified the RFP process to remove the bureaucracy while making it much easier to surface actual intelligence.

We did it with a view to developing troves of the kind of data that we anticipated AI solutions would need. (With our background in matching buyers and sellers in financial markets, we like to think of our approach as closer to a Bloomberg terminal than Google.)

From what we can tell, many incumbent procurement technology providers have rushed their AI products to market with another layer digitizing the analog process. At least that’s my working hypothesis. The AI tools in the market have more in common with the pen-and-paper RFP auction than they do with what AI should be able to do.

Digitizing a process means it goes from pen-and-paper to a screen.

For many solution providers, converting the process to AI so far seems to be just an extension of this digitization. There’s nothing new when it comes to improving the process. At best, AI moves the process from a screen to a software robot. Maybe.

In any case, it’s still the same process.

The only thing we can say for sure is that AI is a way to generate fear-of-missing-out for some solution providers to exploit.

This is why there must be the inevitable adoption blowback, the dip in adoption. People don’t want to overpay for something that doesn’t move the needle.

What AI needs to do is to fix the RFP business process because it is broken. It is bureaucratic. It is inefficient. It is centralized. It is the opposite of nimble.

It is risk averse. It is afraid.

Here are several predictions about how AI agents and more sophisticated AI tools can change the RFP business process.

First, AI will lead to a decentralization of the process. Instead of having procurement officers supervise the process with massive bureaucratic oversight, business units will execute their own RFPs. For example, the CTO will execute his own reverse auction to decide which cloud services to purchase. Procurement will have full transparency into the process and will trust the agents to execute the workflow the organization is comfortable letting the agents use. But the business users will run the process, not procurement. This means it needs to be simple and it needs to be functionally efficient.

Second, procurement staff will focus more on more strategic functions like contract enforcement and vendor performance evaluations, helping the firm ensure that they get the value they paid to obtain.

Third, suppliers will have their own AI agents. We may see a fully decentralized process in which buyer agents deal with supplier agents.

Fourth, the process will be much faster and much less expensive.

Fifth, it may be the case that firms replace the use of catalogs managed by expensive middlemen with hasty RFP auctions.

Sixth, the combination of collaborative transparency and AI agents may make procurement-as-a-service a much more attractive option, especially for smaller organizations. It will be cheaper to execute. It will be more efficient.

These are the things we’re working on for the next iteration of EdgeworthBox. We love to talk. Give us a shout.

Imagine the early days of the automobile industry. Cars were made by hand. Skilled individuals used tools (screwdrivers? wrenches? hammers?). The quality of the cars was variable. It was difficult to produce in high volumes.

Then, Henry Ford applied principles developed in other industries (like meat packing) to automobile manufacturing. He standardized components, broke down the process into steps, and set up a production line in which workers (specializing in individual stages) sequentially performed all of the tasks necessary to make a car. Production engineers studied the line constantly, using the data they gathered from time-and-motion studies to tweak the work at every point to make output more efficient on the margin.

“By 1914, Ford was producing more cars than all other automakers combined, with a Model T rolling off the line every three minutes.”

Automation of the Ford line brought prices down, making cars more affordable. He also took care of his workers, raising wages and implementing work rules that improved their lives and enabled them to afford the cars they manufactured. The impact this industrialization had on the broader face of America was even more significant. Other industries adopted his production techniques and his labor policies. Urban geography changed as automobile adoption became widespread with the rise of the suburbs; Ford had compressed distance. Other countries followed suit.

Car companies started to outsource some of their production lines, such as the assembly of parts (or, in some cases, whole cars), sometimes sending work outside of the country.

Fast forward to today and many automobile manufacturing plants employ robots instead of humans. The robots work tirelessly. They are less expensive. They are more reliable. They use more specialized tools. They are custom-designed; a robot on the automotive line is different from a robot in a shoe factory.

The modern company is full of so-called knowledge workers who work with data, using their judgment to make decisions. They have specific expertise in one or more functional areas, including procurement. Often, they must collaborate with other knowledge workers within the firm who have access to different data and look at it through the lens of different knowledge.

To quote Daniel Kahneman, Olivier Saboy, and Cass R. Sunstein from their book Noise, “Judgment can therefore be described as measurement in which the instrument is a human mind. Implicit in the notion of measurement is the goal of accuracy – to approach truth and minimize error.”

There is a parallel here to the evolution of manufacturing.

In the beginning, there was pen and paper. Procurement staff did everything manually. At the RFP end of the spectrum (EdgeworthBox’s area of focus), this meant coordinating internal stakeholder committees, developing requirements, writing bid solicitation documents, and mailing potential suppliers. It involved tracking everything in spreadsheets kept up by hand. Suppliers wrote out their responses and buyers evaluated them.

The advent of email and spreadsheet software made this easier to track and coordinate. Instead of corresponding with suppliers using letters, buyers could do so with electronic forms of communication. Spreadsheets helped procurement staff plan and organize reverse auction RFPs and RFQs. Theoretically at least, it was easier to manage and retain data for subsequent use.

In practice, the judgment of the staff derived from their experiences. Procurement events were noisy and biased in that there was tremendous variability around the outcomes. Even for one-off decisions, the outcome could be very different depending on who was running the process. Purchases of goods and services did not always obtain the best value-for-money in terms of the problem-solution fit the firm sought or obtaining the right price from the right supplier. Procurement judgment was prone to missing the truth.

ERP solutions and other sourcing software tools emerged over the past thirty years. These sought to take the knowledge worker’s process (for various functions) and to translate them into code, replacing the use of email and spreadsheets. The underlying (unstated) assumption behind these systems is that all companies execute these functions using the same process. Developers held this assumption even more firmly when building software for use in a specific industry, say manufacturing. All manufacturing companies were deemed to use roughly the same accounting, for example. And they were thought to approach RFPs the same way.

It makes sense to do so from the developer’s perspective. There are fixed costs to writing code. To the extent that you can sell the same code to more people, you amortize these over a larger base, improving your profitability. However, you avoid contextualizing the solution for each company.

You can have any color suit you like, sir, as long as it’s grey.

This lack of contextualization is a problem. Consider system A. It was originally written for manufacturers to help them execute RFPs. The vendor obtained tremendous success and sold themselves to a large ERP company. The ERP company then integrated the acquired solution into their offering as their procurement module, something its customers could elect to bolt on as an option. The ERP company wants to have a comprehensive suite of solutions so that they become the single source of back-office truth for their customers. Software is about scale, after all.

What happens if your company is a medical products company that is using system A. You have to jam your square peg into their round hole. What’s even more difficult is that the ERP system sells expensive seat licenses. Your CFO wants to control how many people get these, so it’s typically only people from the procurement department who warrant access. The other stakeholders are left on the outside.

In the end, many people end up using email and spreadsheets to overcome the limitations of the ERP module. Combine this reduced business-software fit between system A and the user firm’s context with the fact that the data in the system is restricted in terms of who can access it (and who practically does look at it), judgment gets noisy and biased.

Companies don’t buy the right products. They don’t find the right suppliers. They don’t pay the right prices.

Companies don’t get value-for-money.

AI takes two forms: tools and agents.

An example of an AI tool is chatbot, like ChatGPT. People use ChatGPT to write the emails that they send to suppliers or to other internal stakeholders. They may ask ChatGPT to write the Statement of Work. In this case, the machine is predicting a series (sometimes a long one) of next words. We use judgment to predict. The machine has some judgment that it has distilled from its training on vast amounts of general data. The user provides some context (in what is called the context window) and the machine does the rest. For some tasks, it’s probably fine. For other tasks that require more specific knowledge and greater experience in making decisions, it may not be the best solution.

We can think of AI agents like the robots on the manufacturing line. We give them access to tools, some of which may be AI tools like chatbots and others may be more general like access to Internet search or a calculator for performing mathematical operations.

I suspect that many of the early AI tools and AI agents were nothing more than wrappers for the legacy systems and ERP modules they were meant to augment. The predictions were just as biased and as noisy. If anything, the process became opaquer.

Over time, the agents will be more effective and more transparent. They will use chain-of-thought reasoning to emulate the judgment of their human overseers, showing their work. They will have access to a broader array of tools.

Their judgment will improve.

Most importantly, though, the agents will be customizable for the specific context of each user firm and each user case. This will be made much easier for agents using software tools that have built-in flexibility and collaboration around structured data. The flexibility makes it easier to integrate idiosyncratic conditions; no more trying to fit in questions about penetration testing in an RFP template designed for buying diesel generators. Collaboration is key because the agents will become members of the team; the more information and context they can obtain from the team, the better the outcome will be. Structured data, say from prior RFPs and responses received, will be vital for developing the agents’ judgment.

This kind of thoughtful approach will be far superior to just creating an agent backed only by a general large language model, or one reliant only a limited set of tools.

The efficiency gains of a robotic manufacturing line compared to manual assembly are staggering. The gains from agents will be just as impressive.

Please reach out to us. We love talking about the future of procurement and AI, in particular.

They are very good at steering the buyer to weight certain features heavily and to discount other features entirely in the pre-RFP phase. This is, of course, “shaping” the RFP.

They are very good at writing slick proposals. These make the buyer feel good about the purchase.

They succeed at making the buyer feel good about themselves.

Note that none of what I have described has anything to do with the buyer purchasing the item with the best problem-solution fit or at a reasonable price. Value-for-money is not part of this conversation.

The salesperson’s job is not to solve your problem; it’s to convince you to buy their stuff.

It comes down to getting the buyer to think it was their idea to buy the widget that the polished salesperson is peddling.

Salespeople who are good at responding to bid solicitations are really like jedi knights.

Those aren’t the droids you are looking for; mine are.

Let’s conduct a thought experiment. Consider the following scenario.

Imagine a buyer who does their independent research. They don’t hire a consultant. They think about things from first principles. They develop their own expertise by speaking to their internal customers, the people who will use the good or the service the organization proposes to buy. They focus on what’s important and what’s not. Perhaps they talk to people with the same needs in other organizations to understand how they would approach the problem with a clean sheet of paper.

People can be remarkably candid and helpful if you know how to ask the right questions and you have the courage to do so.

Our intrepid and self-directed buyer team puts together a description of the thing that they believe will most closely address the problem their purchase is meant to resolve.

They do all of this without looking at what’s available in the marketplace and without speaking to any supplier. They don’t want to be infected. They want to approach this situation with a beginner’s mind, free of assumptions and history.

Inevitably, this pleasant reverie will collide with market reality.

At some point, our buyer team must speak with the suppliers. Perhaps it’s a pre-RFP conference call to talk about what they want.

Imagine the fireworks.

Invariably, the buyer will describe something that is innovative or that challenges the status quo approach to this particular problem.

Cue the wailing.

What you’re talking about doesn’t exist, the consultants will say. What you’re asking for will require millions of dollars in development spend, the leading incumbents will say. (This is especially true for SaaS software.) I’ve seen situations in which all of the participants on the call take turns laying into the buyer, gently. It’s a delicate balance. They have to get the buyer team to second guess their internally developed understanding of the problem and what’s feasible, but without individually being the bad guy who makes the buyer upset. It’s amazing how organic this shared responsibility diffuses; every supplier seems to do their part in negging the buyer. It’s tacitly collusive.

If they get this right, one lucky supplier will get the opportunity to educate the buyer on the realities of the marketplace for these type of solutions in a follow-up “consultative” session. If, coincidentally, the emergent understanding happens to align with the individual vendor’s product line, then so be it.

But it’s even worse than that.

Our buyer team, having been scolded into timidity, will write an RFP that now caters obviously to one particular supplier. Perhaps they understand this, so the buyer (sheepishly) waits to release the RFP until the Friday before the July 4^th long weekend at 5 pm, the netherworld for buried press releases and bid solicitations.

Anyone sophisticated reads the RFP and realizes that this deal is “wired” for a particular supplier. Unless they feel obligated by the relationship to write a proposal, suppliers drop out. A competitive auction becomes a failed auction quickly. The buyer scrambles to find respondents. An auction with three proposals is still an auction, right?

Even when the buyer does manage to convince several suppliers to respond, potentially by cajoling them with promises of a fair fight or better access on the next one, the default tendency for the committees that vet the final submissions is to lean towards the slick proposal. You know them: the ones with the great graphics and the jargon and the case studies. If there is an oral presentation, these people deliver beautifully.

Here’s the secret that nobody tells you: there is no correlation between proposal quality and ultimate value-for-money.

The better way to do this is, ironically, simpler.

First, the buyer should describe the outcome they want to obtain. If your problem is that you need to figure out how to handle snow clearance now that your current municipal fleet of snow trucks is getting older, then the outcome might be saying the City of Rimouski is interested in having clear streets within six hours of any snowstorm that deposits more than 2 cm of snow in a three-hour period. This would include a description of the constraints particular to Rimouski in the wintertime. This contrasts with the conventional response: issuing an RFP for snow trucks with an overly prescriptive explanation of what the trucks should look like.

Second, instead of being swayed by a glossy proposal, do real reference checks and speak with as many people as possible to understand the actual performance of different suppliers under real-world conditions. If you had to do it all over again, would you still select vendor A?

Very few people check references properly. They may ask the suppliers for the names of some referees, but these are cherry-picked for positive reviews. If you’re interviewing someone for a job, would you rather speak with their best friend or someone who was on the other side of a deal table from them? It’s difficult to find the right people, but it’s not impossible. Even then, you have to know the right questions to ask. Nobody is going to tell you outright that company XYZ is terrible. People are nice. People are afraid of litigation. Buyers need to be able to ask questions in such a way that their interviewees reveal their true preferences. This is an artform.

If the buyer can get this right, they obtain value-for-money nirvana.

You need the right tools, though.

This is what we have built at EdgeworthBox: a flexible RFP platform that enables outcome-oriented sourcing and real communication (not stilted pro forma FAQs) using our built-in messaging functionality. We can facilitate sophisticated vendor reference checks, too. We would love to hear from you. Please reach out.

How do sophisticated investors behave in financial markets?

The professional acts on her assessment of the tradeoff between risk and reward.

She acts knowing that she won’t be right every time. Her edge, if she has one, comes from, at least, two things.

One, she needs to have a good method for estimating the risk of permanent loss and the potential to lock in gain in an individual situation.

Two, she must have the discipline to invest only when the tradeoff skews predominantly in her favor, i.e., when the potential gain far exceeds the permanent loss she may incur if things don’t work out.

We could add other factors such as her assessment of the probability of getting a favorable outcome vs. an unfavorable one. She needs to be disciplined about selling when the valuation hits her target or when counterfactual evidence emerges to invalidate her original view. Or we could consider her willingness to withstand the vicissitudes of the market’s ups-and-downs so that she doesn’t get forced into selling into weakness.

These and other considerations only act to modify the core two dimensions of edge. Her evaluation of the risk/reward in any individual situation and for the portfolio as a whole is a continuous process. Her buying and selling is an act of constant, conscious discipline.

It sounds easy, but it’s difficult.

For example, some people may have a good ability to estimate risk and reward, even as they lack the patience to act only on the right kind of setup.

Warren Buffett compares investing to a game of baseball in which there are no called strikes. You can only strike yourself out by swinging at the wrong pitch. In Buffett’s game, you wouldn’t swing without a strong belief that you would hit a homer. You would wait for the fat pitch.

It’s a Law of Large Numbers exercise. This means investors have to be confident enough in their edge to be able to stick with their system even when they endure a losing stretch. Their edge will work over a long enough period of time, if it’s real. Of course, you won’t know if you have a genuine edge until you have been in the game for a while. If you have a bad run at the beginning, you might abandon a winning approach before it starts to pay off.

It’s incredibly difficult.

People are weak and impatient and lack faith in themselves. It’s rare to have someone with an authentic, disciplined edge.

There is an edge to taking risk in an organization just as there is in investing in capital markets. The objective is different, though. The risk/reward tradeoff isn’t about maximizing profit; it’s about generating political capital.

Large organizations make decisions every day. They have problems to solve. Some of these choices are big and some of them are small. Some of them work out and some of them fail. For example, a large organization may elect to implement an Enterprise Resource Planning system. ERPs (even just individual modules) are notoriously difficult to implement, not to mention extraordinarily expensive. Or organizations may try to replace a system that works because, as the kids say, “reasons.” The current failed FAFSA rollout is a good example of what can go wrong.

In large organizations, people do things to advance their own interests. If Alice can lead the successful implementation of a system that improves the output of the firm or makes her boss look good or that replies to a demand from the Board of Directors, then Alice increases the size of her political capital account. This puts her in a better position to be promoted or to see her compensation increase. Maybe she can use her win to get a better job elsewhere. That’s the reward.

Alice is like an investor who plunges into the stock market with borrowed money. If she wins, her bank balance goes up. If she loses, the institution that lent her the money loses. Alice moves on to the next trade. If she loses a lot of money, nobody will lend to her anymore. Or it might cost her more to finance the next trade.

Except in the large organization context, it’s about Alice’s political capital. These are made up of her power and her compensation.

The risk is that the project doesn’t go well and the firm suffers increased costs or foregone revenue. These may or may not translate to a depletion of Alice’s political capital account. Perhaps she’ll be lucky and it blows up after she moves to her next role (or, ideally, to another company). But if she’s still in the seat when it fails, she will suffer. She may even lose her job over it.

In both cases, the risk/reward calculus focuses on Alice. Alice can decide to sit on the opportunity or she can foist it on someone else, say a rival peer.

Alice, if she is a canny operator, will take on only projects with a high likelihood of success, in which the consequences of failure are small (or deniable) and the upside from success are personally large and tangible. People have made their careers from leading these kinds of large projects.

Naturally, this risk/reward calculus is a function of the organization.

Culturally, if there have been a number of heralded previous successes, Alice’s evaluation of the risk/reward tradeoff may be biased in favor of overestimating the reward and underestimating the risk. There are many examples of people who have benefited personally from leading a successful project while there is scant evidence of people who suffered from project failure. Alice swings at every pitch. This is like the momentum investor who chases the stock market higher.

On the other side, if the corporate history is littered with high-profile project failures that have killed or maimed individual career trajectories, Alice’s sense for the risk/reward will tend towards the pessimistic, overemphasizing the risk while underestimating the reward. She won’t swing at any pitches, fat or skinny. She just hopes that there is some miracle way for her to walk to first base. If there is an uncertain benefit from propelling change, but the consequence of failure is perceived as career death, no one will do anything requiring initiative. This is a bear market in initiative.

An organization with a recent history of winning takes more risks. One with a recent history of disaster is risk averse. The principals in an organization have a flawed edge because their willingness to swing is either manic in a winning culture or depressed in an unlucky one.

As they say on Wall Street, “when they’re yelling, you should be selling, and when they’re crying, you should be buying.”

These kinds of distortions create opportunity as any value investor will tell you.

Assuming that there is nothing structurally wrong with the organization, making it prone to failure, the valiant Alice should prefer setups where the organization is coming off a run of failed projects. If Alice can succeed where others have failed, she may reap orders of magnitude more plaudits than in an organization in which success is seen as automatic. She can argue credibly that her project succeeded because of her leadership not because of the organization’s culture or resources or history. She will be seen to have made things happen despite these artifacts.

What else can Alice do to tilt the odds in her favor?

She can take a scientific approach, starting with small experiments designed to gather data with which she can build the project from the ground up.

Managing execution risk like this is not easy.

“Acting like a scientist is difficult for leaders because it can challenge their legitimacy. Undoubtedly, that’s because someone’s position in the corporate hierarchy is often assumed to be the result of experience and a track record of successful moves and ideas. Senior executives live in a feedback loop of positive reinforcement that makes them unlikely to question the foundations of their decisions. The scientific method, in contrast, requires intellectual humility in the face of difficult problems and relies on an objective, evidence-based process, rather than predominantly personal insight, to frame and address decisions.”

It means questioning the assumptions that underpin the status quo. Just because everyone else is buying System X doesn’t mean it’s the right choice for Alice’s organization (or any organization, for that matter). It means thinking independently. It means being open to different possibilities.

“When business leaders adopt this mindset, their biases and errors won’t get in the way of finding the truth. They will employ reason, demand evidence, and be open to new ideas. In scientific practice this means seeking independent confirmation of facts, placing more value on expertise than on authority, and examining competing hypotheses. Above all skeptics question assumptions. They ask, ‘Why do we believe this?’ or ‘What is the evidence that this is true?’ History is full of examples where such skepticism helped overturn commonly led ideas and led to important scientific advances.”

Maybe the small vendor that is willing to build a product around Alice’s problem set is a better solution than a large company with a pre-built approach. Maybe it’s sufficient to solve a small set of problems. Maybe Alice’s organization is thinking about the problem the wrong way.

Alice should get information wherever she can.

“In business, ideas for hypotheses can come from multiple sources. A good starting point is customer insights derived from qualitative research (focus groups, usability labs, and the like) or analytics (data collected from calls to customer support, for example). As we have seen, hypotheses can also be inspired by anomalies, which can be found in everything from overheard conversations to successful practices that deviate from the norm at other companies.”

Alice can do three more things to improve her personal risk/reward profile.

One, she can enlist others into her project. Let’s say that her colleagues Bob and Carol each have related projects. If the three of them pool their risk and jointly run the three projects, then they can diffuse the downside should anything happen on any individual project, even as they improve their chances of being part of a winning project. In fact, the lessons they learn from each project can make this portfolio more likely to succeed. Risk pooling is a winning strategy.

Two, Alice should break the project into bite-sized pieces that build on one another. This means that she proceeds only when successful. If any individual step is deemed a failure, then she can abandon the project or she can redo the step until successful. Each individual step is contained and reversible, but the scale of the entire project is large. There’s a reason they call it the Big Bang.

Three, Alice can change the definition of success to make it more likely that the project is deemed a success in the context of the internal competition for political capital among peers within her organization. She can frame the project as an information gathering exercise (at least initially). Combine this with outsourcing execution (and political risk) as much as possible and Alice has transformed the project into a Shark Tank where she is the investor and the risk of failure (at least politically) falls on the heads of the outside contractors.

Alice can increase the likelihood of a fat pitch.

If Alice succeeds, she is seen as an operating hero. If she fails, well, we learned and it was really the outside vendor’s issue. But we now know what approach not to take and we can experiment with another.

The best setup for taking risk in a large organization is in one with a history of loud, public failure because the risk/reward calculus is distorted. People underprice reward and exaggerate risk. There is less competition for the best projects. Even then, the leader can manage her exposure by pooling her risk with others, chunking, outsourcing, and recategorizing execution. Eventually, enough of these experiments strung together will constitute massive progress. Failure at any intermediate sub-step is something to blame on the contractor.

No risk, no reward.

SkyFly Solutions is excited to partner with EdgeworthBox, as an exclusive implementer, to bring a new Social Procurement Platform to the Government of Canada! Canada can now procure the EdgeworthBox Platform directly from the SLSA. As an Aboriginal Business certified by the Canadian Council for Aboriginal Business, SkyFly Solutions applauds the Canadian Federal Government’s efforts to increase and more accurately account for goods and services procurement through Indigenous Business and are eager to help the Government of Canada meet and expand on its social procurement objectives.

– Graham Crate, Director Service Delivery, SkyFly Solutions.

“It’s exciting to work with the Government of Canada and our partner SkyFly Solutions as the federal government leads by example in the promotion of social procurement. This SLSA listing will make it easier for agencies to work with diverse suppliers, while ensuring value-for-money for the taxpayer. Supplier diversity is critical not only as a policy tool, but for procurement risk management and economic development.”

– Chand Sooran, Founder & CEO, EdgeworthBox.

About SkyFly Solutions: SkyFly Solutions is a proud Canadian Indigenous business, committed to delivering leading IT solutions with a focus on transformational and digital projects. With roots in Indigenous heritage, we bring this unique perspective to every endeavour we undertake. We are recognized and certified by the Canadian Federal Government under the Indigenous business directory and by the Canadian Council for Aboriginal Business as a Certified Aboriginal Business.

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 is an AWS Public Sector Partner.

First and foremost, they must obtain value-for-money in purchasing solutions that solve problems their organization faces. This means buying the right solution, from the right supplier, at the right price. This is part of a broader mandate. Private companies exist to generate profitable cash flow for their owners. Government agencies and non-profits must husband their resources in order to deliver the greatest social value with what they have been given.

These B2B buyers also have a duty to the community they serve and in which they operate. They must be conscious of the impact they have on the world and endeavor to make it as positive as possible. The best businesses are conscious of their full set of stakeholders including vendors, employees, customers, and neighbors, at a minimum. This is good corporate citizenship.

When we speak about buying the right solution, from the right supplier, at the right price, it means that we have found the good or service that has the best fit for our problem and we don’t pay more than a fair and reasonable price. But it also means that we want to ensure that, where possible, we are sensitive to who the supplier is. Ideally, our business will help the supplier develop their capacities and their capabilities and to obtain financing, even as we as buyers get what we need.

When we think about social procurement, we talk about what we would need to do to increase our purchasing from businesses owned, operated, and controlled by members of historically disadvantaged groups. Often, buyers interpret this to mean purchasing from them directly, i.e., the diverse businesses are our counterparties.

The majority of diverse suppliers are small and medium-sized businesses. In a country like Canada, 99.7% of businesses were small and medium-sized. They account for roughly half of GDP. If you make it easier for small and medium-sized businesses to sell to you, even indirectly, then the diversity will follow, naturally. So, we should think of the problem as one of engaging small and medium-sized vendors.

These firms may be too small for us to purchase from them directly. We’re more likely to buy from someone who then subcontracts a portion of the business to one of these diverse suppliers, if at all. We would struggle to know how much of our third-party spending trickled down to these suppliers in most (if not all) cases. This makes the prime contractor’s role that much more significant. This gives the large businesses much more sway over the small and medium-sized businesses than is desirable, potentially.

Much has been written about behavioral economics over the past thirty years, looking into the psychological underpinnings of how human beings make decisions. Core to this research is the elucidation of a host  of biases that lead us to make sub-optimal choices. For example, confirmation bias means that we look post facto for evidence that a decision is correct. Or there is loss aversion in which the disutility of losses is greater than the utility of commensurate gains. Or the mere exposure effect that makes us prefer things that we know well to new things. There is in-group bias that leads us to favor people in our in-group over those in our out-group. The list goes on.

Large prime contractors may prefer to stick with vendors they know. The mere exposure effect means that incumbents have an advantage, further distorted by the natural tendency to prefer people who look like us and who speak like us and act like us. The thin margins on many projects, such as construction projects, make primes more sensitive to the potential for loss from including an unknown subcontractor than they are to the gain from increased efficiency across the chain or the gain in capacity that enables scaling project delivery. Large company confirmation bias may make them less likely to see what is wrong with the status quo, preferring an if-it’s-not-broke attitude.

Overcoming these distortions in decision-making as buyers and as contractors means slicing through these behavioral missteps. It’s difficult work, but it makes us all better off.

Instead, let’s reframe the question. Imagine a world in which previously small and medium-sized suppliers (many of which are owned, operated, and controlled by members of historically disadvantaged groups) have grown large enough to compete directly for our business. What would it have taken to get them to that point?

As the ultimate buyer, we would want to make the most of our third-party expenditure to help these small and medium-sized suppliers develop.

Full visibility into the supply chain on any given project helps us understand how our spending flows through the waterfall to the different levels of subcontractors. It would also help us combat the behavioral distortions at every level once the prime contractor knew that we, the buyers, were watching. The knowledge at every level that the ultimate buyer could see with complete transparency the full supply chain would motivate the prime contractor to compete subcontractor selection at every level, ideally with an open, level process that landed the best company for the job at each level.

Ideally, this would attract new SMEs as subcontractors. The potential benefit for the prime contractor extends beyond our project to their subsequent bids, as well, if new SMEs get invited to compete again based upon their now-proven  experience.

Transparency would also improve our understanding of risk in the supply chain. Think of the chain of contractors from the prime contractor down to the lowest level of subcontractors as a game of Jenga. The failure of intermediate pieces may lead to the failure of the project. Being able to identify potential weakness early enables better risk management.

There should be a way for the participants across the chain to interact with one another for mentorship. Larger contractors can help smaller subcontractors develop capacity and capabilities. They could help the smaller subcontractors obtain financing. They could help the smaller companies grow. This makes the SME suppliers more attractive as future partners.

Small and medium-sized businesses are vital to the development of the economy. Buyers should leverage their third-party spending to include development of these smaller subcontractors as part of the value delivered by the prime contractor. The tool to make this happen is visibility which brings its own additional benefits, including enhanced risk management.

We’re all for big business, but small and medium-sized businesses are a vital component of a growing economy.

This is what we have built at EdgeworthBox: a set of tools, data, and community that makes it easy for primes to light up the entire supply chain .Give us a shout. We’d love to hear from you.

Warren Berger is a journalist and speaker who talks about thinking and creativity in a podcast with Farnam Street.

“There’s a great definition that I came across from this group called The Right Question Institute. They’re a non-profit group that studies questioning. They describe questioning as a tool that enables us to organize our thinking around what we don’t know.

“There’s a lot of stuff out there we don’t know. Through questioning, we can attack it, and the different forms of questions you use will allow you to come at this unknown thing from a different angle.”

It is too easy to assume that we know the correct question and to jump into answering it.

Answering may actually be the easy part. The more difficult and more important thing to accomplish is to figure out what the question should be. This is especially true in an Internet age (and now an AI era) where we have what seems like unlimited information (and robotic judgment) at our fingertips.

To get to the right question involves, ironically, asking a whole set of other questions first. We need to fill in the empty areas on our map. We need to add as much dimensionality to our understanding of the problem to be able to say that if we do X, we obtain Y in response, so how do we make X happen? But what we need most is confidence that if we obtain Y, we are acting to improve our situation the way we want it to improve and we realize the underlying goals we set for ourselves.

Ask the wrong question, obtain the wrong outcome.

Let’s consider social procurement.

Here’s the definition from the City of Toronto:

“Social procurement is the achievement of strategic social, economic and workforce development goals using an organization’s process of purchasing goods and services. The City’s Social Procurement Program is comprised of two components: Supply Chain Diversity and Workforce Development.

“What is Supply Chain Diversity?

“Supply Chain Diversity is a business strategy that promotes a diverse supply chain in the procurement of goods and services for any business, not-for-profit, government or private organization. In the City’s Social Procurement Program, Supply Chain Diversity applies to Departmental Purchase Orders from $3000 to $100,000.

“What does Workforce Development mean?

“Workforce development is an interconnected set of solutions to meet employment needs. It prepares workers with needed skills, emphasizes the value of workplace learning and addresses the hiring demands of employers. In the City’s Social Procurement Program, Workforce Development requirements will apply to Request for Proposals and tenders over $5 million.”

What is the problem we are trying to solve by implementing social procurement?

I suspect that a lot of people will wave their hands about the benefits of diversity and the importance of a level playing field in a multi-cultural society. They are correct at a general level. But that doesn’t justify it as a procurement policy, per se. We could address these issues in other ways such as philanthropy, for example.

When it comes to social procurement, the more appropriate question we should be asking is this: how can we reduce risk and increase capacity in our value chain by ensuring that our supplier partners and their contractors have a fair and open process that encourages suppliers from all backgrounds to participate in our third-party spend, directly or indirectly. The ancillary benefits to the disadvantaged communities will come on their own if we get this right. These include economic empowerment, workforce development, and a rise in the general level of welfare. By acting in its economic self-interest to reduce risk and increase capacity, the large purchasing organization will end up doing the right thing.

It’s too easy to rage against the dying of the light and to descry the current inadequate and less-than-proportionate participation of vendors from disadvantaged backgrounds in our direct supplier ken. Buyers need to have visibility into their chains to know how risky they are, in any event. (Something almost none of them has.) With this insight comes the opportunity to de-risk those chains by forcing contractors at every level to compete the business and to develop the capacity, capabilities, and capital of their sub-contractors. Transparency will be the great motivator here. One reason why these supply chains have been slow to diversify is the opacity of the current set-up.

What is the actual problem then?

Buyers don’t have visibility into the supply chains and value ecosystems. What happens if we fix this problem? We can see precisely how diverse our supply chains are. And our contractors can, too. Our contractors will know that we know. So we have an opportunity to de-risk them by fleshing them out, including with diverse suppliers. Our subcontractor will have no excuse. Comply or get off the boat.

This is what we have built with EdgeworthBox. Our set of tools, structured data, and community helps B2B buyers buy the right solution, from the right supplier, at the right price. It also can be used in a way to expose the supply chain on a project or set of procurements with full visibility and interactivity. This leads to lower risk and greater effectiveness when it comes to social procurement by cracking open what are often fixed supply chains to let in suppliers who can develop their capacity and capabilities. Maybe even throw in some mentorship. If you’re interested in learning more or if you just like social procurement, please shoot us an email. We’d love to hear from you.

“Some decisions are consequential and irreversible or nearly irreversible – one-way doors – and these decisions must be made methodically, carefully, slowly, with great deliberation and consultation. If you walk through and don’t like what you see on the other side, you can’t get back to where you were before. We can call these Type 1 decisions. But most decisions aren’t like that – they are changeable, reversible – they’re two-way doors. If you’ve made a suboptimal Type 2 decision, you don’t have to live with the consequences for that long. You can reopen the door and go back through. Type 2 decisions can and should be made quickly by high judgment individuals or small groups.”

For a Type 2 decision in a procurement context, we can imagine executing a pilot of a software program. It will cost much less than what a license or subscription would cost. The buyer can test it out and see how it works, how responsive the support is, how willing the vendor is to make changes based on the experience, etc. If the pilot project doesn’t succeed, then no harm, no foul. Uninstall the software or stop using it and move on. You’ve picked up inexpensively some valuable information that may or may not come in handy in the future.

A Type 1 decision in the Bezos framework involves purchasing something that the buyer is going to live with for a long time, involving significant expense. For example, it could involve purchasing capital equipment for a new semiconductor manufacturing facility. This choice will determine the kinds of chips the fab can produce. Will these be in demand? For how long? Will the facility be able to earn a high enough price with a sufficient volume to generate at least the required rate of return? How vulnerable is this type of chip to the cyclicality that stalks semiconductor markets? This needs to be a thoughtful decision.

In his letter, Bezos goes on to descry the natural tendency to bureaucratize everything by approaching too many things as a Type 1 decision.

“As organizations get larger, there seems to be a tendency to use the heavy-weight Type 1 decision-making process on most decisions, including many Type 2 decisions. The end result of this is slowness, unthoughtful risk aversion, failure to experiment sufficiently, and consequently diminished invention. We’ll have to figure out how to fight that tendency.”

As Farnam Street points out, it’s not a dichotomy between reversible and irreversible; it’s a continuum. You can always get out of a decision. There is a cost to be paid, but you always have an exit. The higher the cost, the more difficult the decision is to reverse, putting it closer to the irreversible end of the spectrum.

So, the rational thing to do is to adapt your process to the type of decision.

“Make reversible decisions as soon as possible and make irreversible decisions as late as possible.

“When decisions are reversible, make them fast. Your biggest risk is dragging your feet and not making a decision. The cost to acquire additional information isn’t worth the effort.

“When decisions are irreversible, slow them down. The biggest risk is making the wrong decision. The cost to get the information we need to reduce uncertainty is worth the time and effort.”

To try and map how this logic might work in procurement, let’s put on our game theory hat.

Imagine a game in which we have to guess the picture underlying each of a series of ten puzzles. Every player gets a different set of puzzles, some of which may overlap partially across players.

Let’s assume that in this game we get very few points for guessing the picture for small puzzles correctly and we get a tremendous number of points for identifying the picture of large puzzles.

The more pieces of an individual puzzle we have, the easier it is for us to determine what its picture may be.

According to the rules of the game, we have a budget of time and money. With the budget, we can purchase additional pieces for any puzzle. We don’t have to purchase pieces, but we can.

Players can only make one guess. So they wait until they’re comfortable.

The winner of the game is the player who can identify correctly the greatest number of puzzle pictures in a given period. We’ll break ties by comparing the amount of remaining money each tied player has left, with the individual who finishes the game with the most money winning.

Some players will have an advantage because they may have played one or more of the puzzles previously. They’ll be able to recognize pieces faster and they can skip ahead, moving from one to the next.

What is the optimal strategy in this game?

You would spend most of your time and money on the big puzzles. The small puzzles aren’t worth more than a cursory effort.  For these, you can make an educated guess and move on. With respect to the larger puzzles, she can try and purchase more pieces, conscious of the trade-off. This is where judgment comes into the picture.

Let’s throw four twists into the game.

Twist #1: you can talk to other players and ask them questions. Since everyone is playing different sets of puzzles, it might make sense to collaborate. Player A can help Player B and Player B can reciprocate potentially on a subsequent puzzle. There is no cost to cooperation.

Twist #2: you have a private database of every puzzle you have played in the past. It is easy to search through. The more puzzles you have played in the past, the more likely you are to find one in your history that you are now being asked to solve.

Twist #3: everyone can access a community database of certain puzzles that others before you have seen.

Twist #4: you can pool your efforts with other players if you see that they are working on what appears to be the same puzzle as you.

These twists now change the strategy a rational player should employ.

In addition to the basic strategy of focusing on the larger puzzles, the player should scan the databases for signs of the current puzzle under examination. When it comes to the larger puzzles, if she is making progress, then she should continue. If she hits a roadblock, instead of switching puzzles, she can ask for advice or seek collaboration. These preserve budget and they speed up the time.

Naturally, the more experienced players will have seen more previous puzzles and will be more adept at using the databases. But anyone can employ collaboration, even (and especially) the novices. The collaboration levels the playing field potentially more so than the databases which require familiarity.

How does this game translate to procurement?

Procurement is like trying to solve a puzzle. The buyer wants to have as many pieces as possible to identify the correct picture. However, too often they don’t see enough competition on price and solution and they end up taking a stab in the dark.

It’s best to focus the most procurement department resources on the larger purchases.

Ideally, we’d have structured data of prior procurements: the RFPs the buyer issued, the proposals they received, the vendor they selected, and the contract they signed, in addition to structured data on procurements in the public sector.

It’s best to get information from other buyers. And, if possible, to purchase jointly with other buyers who want to purchase the same products we do. So there should be some sort of networking and collaboration tools built into the process.

This is what we have built at EdgeworthBox. We have a set of tools, structured data (in public and private repositories), and community to help B2B buyers purchase the right solution, from the right supplier, at the right price. We are changing the game to make it easier andRFP, moreRFP, efficient. Give us a shout. We’d love to hear from

As a salesman, Jerry should have hung up on Rod as soon as Rod started with his crazy demands.

How many buyers assume that their salespeople are desperate for the business?

What is a good salesperson anyway?

They’re experts at solving a particular kind of problem using the tool that they represent. The key point of any sales interaction is to determine a few issues.

What is your problem?

Does this tool solve your problem?

What other tools or approaches could put your company on a better path?

Is this the best solution?

Is this the best solution for the money?

Is this the best solution for the money, delivered by a company that will work well with me in the future?

Is this a vendor with which we can collaborate to develop products to solve other problems we might have in the future?

A good salesperson is like a consultant that you can access for free. Yet, this isn’t the framing of too many buyer-salespeople conversations.

Too often, the buyer thinks of it as an adversarial process.

In an adversarial process, the game is zero-sum. The buyer thinks that they have lost if they haven’t taken every single crumb off the table. The buyer can be confrontational. The buyer doesn’t pay promptly. The buyer demands unreasonable things. The buyer doesn’t approach the relationship as something sustainable, even if it’s just for a one-off transaction. They’re aggressive without needing to be.

The Pandemic was great for salespeople because supply chain constraints enabled them to fire (at least for a period of time) their worst customers. It was a luxury to put on allocation the clients who were bullies or who paid late or who demanded perquisites.

The beauty of the Request for Proposal is that it can force the interaction to be more professional. Buyers can focus on getting the information they need. If salespeople are free consultants, why wouldn’t buyers want to generate as much competition as possible on ideas, even as you create the conditions in which suppliers will give you their best price?

Salespeople want opportunities. They want to find companies with problems they can solve. They want a way to engage with them that is simple and efficient. They want to speed the communication to its logical end. They want to find and close customers who will be valuable partners where value means an economic return, but also a productive, long-term relationship of mutual benefit.

This is what we deliver with EdgeworthBox. It is a set of tools, data, and community that simplifies the existing procurement process, sitting as a layer on top of your existing approach be that email and spreadsheets or an ERP module. Leverage the value of sales relationships in a professional way. It is elastic. Buyers only pay for what they use. It is also built for joint procurement. Collaborate internally. Work together with other organizations. Leverage accessible, structured data about the products you are in the market to buy or sell Two heads are better than one. If you’d like to learn more, please email us and we’d be happy to tell you more.