All of these questions (and the philosophy they represent) revolve around the transaction of acquiring the technology and the solution These questions mainly address the requirements of the procurement department. Indirectly, they address the question of how the procurement department fits into the organization chart. How will the C-Suite see procurement?

They implicitly ignore the end users, the ones who actually translate “strategic goals” into tangible action.

This is entirely the wrong way around. In discounting the end users, the ones who actually translate “strategic goals” into tangible action, the buying organization sets itself up to fail. It is a well-established fact that user-adoption is one of the primary reasons for the failure of technology-centered transformations.

Procurement’s customers are the people who are the future end users of the acquired solution. These are the people procurement should serve in the same way that the company focuses on its own customers.

If we are buying a solution that we intend to use for the next five years, the primary consideration should be the value created over the next five years. The bulk of the return on investment (ROI) comes from this, not from negotiating the price down with the vendor by a couple of percentage points.

The real ROI is going to be determined by those who use the solution, the intensity of their engagement with it, and the outcomes it generates that the organization would not obtain otherwise.

If procurement buys a tool that nobody uses, it’s a failed outcome. It is bad for the organization because they have wasted money. It is bad for the end users because they have to revert back to their old ways or produce kludgy ways to work around the newly acquired good or service. It is bad for suppliers who had hoped to build a lasting relationship.

It is a massive misallocation of resources no matter how compliant the process is.

The ROI of a product nobody uses is -100%.

Early-stage companies focus on building and determining what works and what does not. The nascent entity strives to improve features in their own product offering that the customers like and kill those that the market does not. This is the explore phase. The firm concentrates on determining what the customer needs the most and then works on making the customer successful in the most-efficient manner. When it comes to buying goods and services, procurement talks to internal end users and invests the time and effort to determine their exact requirements. While the procurement approach may be transactionally inefficient, using pen-paper, spreadsheets, and emails, the emphasis is on getting what the users need rapidly. Diligence may be incomplete. The firm may not get the lowest price or generate sufficient options from which to choose or buy the first-best solution, but rapid early-stage revenue growth can make up for these deficits. At least they prioritize end-user needs.

A company enters the exploit phase when it switches focus to managing growth and fulfilling demand for the product they have built during the explore phase. It implements processes designed for rapid completion and execution of tasks rather than on the accretion of value. In procurement, the objectives (and the KPIs) shift to narrow in on compliance, consistency, and efficiency. However, there are very few later stage organizations that incorporate feedback loops to assess how well the end user’s needs have been addressed and to measure the return on investment.

Currently, the entire global economy is undergoing massive change. End user requirements are changing as are the options available. It is imperative today to retain the user-focus of the explore phase. Central to this is collaboration and communication between the various stakeholders of the procurement process. At the same time, efficient processes and thorough risk management are imperative given the intensity of global competition.

In the explore phase, firms should implement a process much earlier than they do in practice. In the exploit phase, procurement should not forget that their primary allegiance is to their internal customers who are responsible for generating real economic value.

Traditionally, the buyers of procurement technology and services have been later stage companies because of the explore-exploit dynamic, so incumbent solutions tend to be over-priced and over-featured. They are not designed for companies in the explore phase. Incumbent solutions do not promote collaboration at all. They are built to support established practices with little to no capability to incorporate the needs of end users and various stakeholders

That’s why we built EdgeworthBox. We have built an easy-to-use platform for procurement that enables firms to establish robust processes when they are in the explore phase and allows firms to retain the collaboration and user-focus when transitioning to the exploit phase. We would love to talk to you about your procurement challenges. Please reach out to us at chand.sooran@edgeworthbox.com or naresh.rao@edgeworthbox.com.

Here’s MIT Nanda talking about the current state of affairs:

“Despite $30–40 billion in enterprise investment into GenAI, this report uncovers a surprising result in that 95% of organizations are getting zero return. The outcomes are so starkly divided across both buyers (enterprises, mid-market, SMBs) and builders (startups, vendors, consultancies) that we call it the GenAI Divide. Just 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact. This divide does not seem to be driven by model quality or regulation, but seems to be determined by approach”

The study also states that the key features of the successful 5% are a focus on a few specific business functions viz. BPO/ Back-office operations and customization of the tools to focus on business outcomes. Vendors who succeeded in these areas are reportedly securing multi-million-dollar engagements rapidly.

The unsuccessful majority leverage tools like ChatGPT but fail to handle integration complexity, fail to align with daily operations and existing workflows, and are slow at contextual learning.

We have seen this movie before with earlier avatars of digital transformations.  However, we firmly believe that the Procurement Technology space is ideal for getting the sought-after success with Enterprise GenAI.

Jon Hansen, the procurement guru – a prolific commentator on the state of procurement technology – summarized what was promised with ERP. It seems eternally relevant:

“Enterprise-wide integration

Automated workflows

Significant cost savings

Process standardization”

Here is what he identifies as the fatal conceit of procurement technology:

“At the heart of this change is a growing realization of a fundamental truth that process and not technology is the driving force behind a successful e-procurement initiative.”

Most procurement technology is built for the large enterprise. It is neither suitable nor economic for small and medium-sized enterprises (including semi-autonomous divisions within larger organizations, such as IT). People, including Hansen, have made the analogy that many of the leading systems are like a Ferrari. One wouldn’t buy a Ferrari SF90 Spider to haul rocks or take the kids to school.  A more suitable vehicle might be a Ford F150 or a Hyundai Santa Cruz which is not only cheaper but may more appropriately serve the need of a family or small business.

Sometimes, it pays to start small and simple. This, in turn, provides a flexibility that is necessary to function as a business. Yet, the existing suite of procurement technology takes a one-size-fits-all approach. They are built for the large enterprise with plenty of large enterprise features and a large enterprise price tag. Many of them are older. They lack a contemporary user experience. They bolt on AI as an afterthought, not as a central part of the functionality.

There are so many players in the procurement technology space because there are so many different types of buyers.

In procurement technology, there need to be tools targeted for small to medium-businesses that seek to transition from running RFPs with emails and spreadsheets to running a lean operation leveraging the most appropriate AI-enabled capabilities.

EdgeworthBox is a flexible platform of tools, structured data, and community focused on executing RFPs. Our AI suite integrates and adapts to existing customer workflows, improving rapidly with usage by propagating best practices, replicating successful approaches, and significantly enhancing collaboration across business functions

We’d love to hear from you. Please reach out. And watch this space.

While this blog focuses primarily on topics related to Procurement & Contracts, I am taking the liberty of discussing some principles that apply to other areas as well.

I read the MIT article stating that over 95% of GenAI initiatives have failed with mixed emotions. Finally, a prestigious institution stated a fact known to those who of us who have worked in this area. Having driven significant digital transformations successfully, I can confidently assert that there really is no excuse for these failures. With the possible exception of integration, the primary technical & technological limitations – processing power, computational speed, storage, network connectivity and speed, analytical libraries – have been largely addressed. IMHO, there are two main reasons – other informed sources will give you between 5 and 7 – viz. data quality and user education/adoption. Address those early and the scales tilt dramatically in your favor.

With regard to GenAI, let us state some basic facts

• Agentic AI / GenAI work on relationships primarily within a textual content. Simply put, it identifies combinations of words to make sentences, paragraphs, posts & presentations. Thanks to the incredible underlying technologies, it builds layers upon layers of relationships and amazingly produces high quality content which appears to make sense. Note however that it is not trying to do a traditional optimization nor make a true business decision i.e. caveat lector applies to all recommendations.
• The content that GenAI generates is dependent upon the information that it is fed. As was seen with Grok, it generated some extremely antisemitic content since the corpus of information inadvertently included some very inappropriate content. On the flip side, the higher the quality of the corpus content, the better the recommendation.
• GenAI is not free. The more extensive the search, the more expensive it is. As information proliferates, the cost of tokenizing the content – a geeky way of saying searching for and incorporating content – will at least partially offset the efficiencies due to improvement in technologies.

Having said that, I am extremely optimistic of the value that can be realized through GenAI and Agentic AI. Both are extremely well suited for text heavy business functions such as Procurement & Contracts. Using a very rudimentary version of the AI agents (think primordial Agentic AI), my team was able to save over $1.5 Million per quarter and it took only 3 months to set up and to start generating the value. Just FYI, this number was validated by the CFO. I am certain that today’s initiatives will realize significantly larger benefits.

A couple of quick points to consider a possible approach for your GenAI / Agentic AI initiative:

• Focus on the problem not the technology – (This is surely a trope by now). Most initiatives focus solely on leveraging a single capability or technology. That is similar building a house with only vertical pillars. A holistic or complete solution may also incorporate ML/AI (the quantitative part of AI), visibility, optimization, etc.
• Address the aforementioned bottlenecks by:
  • Educating the end-users at the onset of the project … or earlier. Increasing the user involvement significantly improves user-adoption, a key factor for success.
  • Limit the data scope and focus on high quality data. As much as possible, focus on data relevant to your business objective. Adding additional data has diminishing returns and usually brings additional issues such as data quality.

While developing and deploying such solutions, I had some very pleasant surprises.

• Value realization is actually quite rapid. Bottom-line benefits show up in as soon a month. Set up KPIs to track the realized value.
• Data quality improves quickly. A virtuous cycle is created where data is used more when the data quality is high and the increased usage improves data quality.
• Business silos start to break and cross-functional collaboration increases significantly. A second virtuous cycle occurs when the high(er) quality data and information is used by other business functions which in turn drives improved analytics and data quality.
• The users directly improve the solution quality. An educated user has an intuitive idea of what “good” looks like and where the value lies. They take it upon themselves to suggest modifications and actively participate in issue resolutions.

Regardless of whether you are planning to start a GenAI / Agentic AI project or planning to pause one, I recommend an excellent (free) book about building AI agents by Sam Bhagwat. It gives you several ideas to incorporate into your own efforts. If you need any assistance, please do not hesitate to reach us – Naresh Rao or Chand Sooran. We would love to connect.