21st Century Marketplace Vision - Our User Community - Part XIII
hebia.ai Seven Pillars of Institutional Intelligence (Part I)
As Artificial Intelligence moves from individual productivity tool to institutional operating force, the central issue is no longer whether it can improve output. It can. The more consequential issue is whether firms possess the structure, discipline, and governing architecture necessary to coordinate that output into coherent institutional performance. This is the issue addressed by hebia.ai founder and Chief Executive Officer George Sivulka in a speech describing the firm’s offering and approach. It is also an issue directly relevant to Synallagi, whose architecture is concerned not with isolated gains in productivity, but with the coordination of work, authority, accountability, and value creation across oil and gas. The academic research in this paper's Appendix III is retrospective and hebia.ai analysis is prospective.
The following observations arise from notes and text taken from that speech. Quotations are presented in indented italics.
One, Coordination
Individual Artificial Intelligence creates chaos.
Institutional Artificial Intelligence creates coordination.
hebia.ai describes this framework as its Seven Pillars of Institutional Intelligence. The point is particularly relevant in the context of Synallagi, which defines oil and gas culture through its Seven Organizational Constructs. Among those seven are Information Technology and Intellectual Property, each of which stands as a distinct construct in its own right. Artificial Intelligence is properly understood as a subset of Information Technology. In that respect, Mr. Sivulka’s remarks are useful because they sharpen our understanding of how institutional coordination is either strengthened or undermined through the design and use of technology.
What he describes is directly relevant to those working in industry today. It is also consistent with what we see on the ground. Artificial Intelligence is increasing the productivity of individuals, while at the same time chipping away at the coherence of firms by bypassing established process discipline and organizational structure. As Mr. Sivulka states:
Individual Artificial Intelligence output circumvents the formal organizational chart and thereby compromises its efficiency.
That observation is of considerable importance. Coordination is critical not only for human participants, but also for Artificial Intelligence agents. Institutional intelligence, if it is to become operationally meaningful, will give rise to what may be described as an agentic management industry focused on defining roles, responsibilities, authorities, and methods of communication for both people and agents alike.
Unfortunately, oil and gas is not yet in a position where institutional coordination through Artificial Intelligence can achieve what is required. First, it cannot yet reliably resist the circumvention caused by individual use of Artificial Intelligence. Second, producers do not possess an Enterprise Resource Planning foundation that offers a coherent alternative. As we have noted repeatedly, officers and directors have deliberately built an unaccountable accounting, Enterprise Resource Planning, and reporting foundation across the industry. They did so by allocating only a fraction of the minimum resources required to develop the capacities and capabilities necessary to account properly for oil and gas activity. The likely result is an industry populated by individuals who are increasingly productive in isolation, yet organizationally uncoordinated and operationally inefficient.
More and more, it is becoming accepted that internal remedial efforts aimed at correcting these cultural and organizational failures are the wrong approach. People, Ideas & Objects has therefore been structured on the basis of a wholesale, industry-wide rip-and-replace implementation methodology. In our view, that is by far the most efficient and effective path available.
We continue to see the industry’s leadership as embodied in its officers and directors. It is that leadership group which has caused the broad damage that has destroyed the industry’s value. We now see signs not of correction, but of capitulation: responsibilities are being abandoned, core issues remain unresolved, and attention appears to be shifting away from North American shale toward the bright lights of Argentina, Libya, and Iraq. That pattern reflects the same familiar herd mentality, or institutional imperative that has repeatedly displaced disciplined judgment.
Synallagi addresses this issue through a different institutional design. It uses Intellectual Property as a coordinating mechanism within the Targeting Framework and within the transaction management architecture of Autonomous Asynchronous Transaction Orchestration. This paper, which focuses on our user community, together with our next paper, which will address their service provider organizations, deals with the implementation of that Targeting Framework. It is within that framework that Artificial Intelligence is used to administer the Autonomous Asynchronous Transaction Orchestration of the marketplace.
The significance of this point should not be understated. The challenge facing oil and gas is not whether individuals can become more productive through Artificial Intelligence. That is already occurring. The challenge is whether that productivity can be institutionally coordinated into a disciplined system of execution. Without that coordination, increased individual capability will simply intensify fragmentation, reduce accountability, and widen the gap between effort and performance. With it, however, Artificial Intelligence can become part of a governing structure that strengthens profitability, control, trust and execution across the industry.
That is where Synallagi differs. It does not treat Artificial Intelligence as an isolated tool. It places it within an institutional framework defined by Information Technology, governed through Intellectual Property, and executed through the Targeting Framework and Autonomous Asynchronous Transaction Orchestration. The work of our user community, and of their service provider organizations, is therefore not merely technical. It is organizational, cultural, and economic. Their role is to implement a structure through which Artificial Intelligence becomes a coordinated institutional capability rather than productive disorder.
Two Signal
Individual Artificial Intelligence creates noise.
Institutional Artificial Intelligence finds signal.
If coordination is the first institutional challenge created by Artificial Intelligence, signal is the second. The problem is no longer access to machine-generated output. That is now abundant. The problem is whether institutions possess the structure necessary to distinguish what is useful from what is merely voluminous. In that respect, the issue is not generation, but selection. Not speed, but judgment. Not output, but signal.
There are now many uses of Artificial Intelligence in commerce. Yet none of them are embedded within, and autonomously governed by, an Enterprise Resource Planning system in the manner required for disciplined industrial execution. Synallagi is designed to provide that missing structure. It establishes the automation and autonomous administration of transactions within the marketplace of North American oil and gas. In doing so, it removes much of the human role from routine transaction processing and relocates responsibility to the architecture, design, implementation, and accountability framework governing those transactions and their reporting environment. That environment is what we define as the Targeting Framework.
This point is becoming increasingly important because much of what Artificial Intelligence now produces is not signal, but noise. The volume of artificial output has grown so rapidly, and degraded so visibly in quality, that some organizations are now overcorrecting and seeking to prohibit Artificial Intelligence output altogether. That response is understandable, but wrong. The real issue for any serious institution is not whether to ban Artificial Intelligence, but how to generate, identify, and select the right thing within a controlled framework of execution.
Synallagi addresses that problem through what it defines as a Task and Transfer Network. This is a method of design analysis used to determine how transactions and process management should be organized. Its purpose is to identify the most efficient point at which responsibility for a task should transfer to a new role. That analysis is then used by our user community to define the processes they manage and to guide the design and development of the Synallagi software. The objective is to decompose industry work into the most efficient hyper-specialized division of labor possible. From that point forward, automation, and where appropriate Autonomous Asynchronous Transaction Orchestration, manages the transaction, the related processes within a Synallagi, the full set of transaction components.
This is the practical difference between generic Artificial Intelligence use and Synallagi’s institutional design. Synallagi is Artificial Intelligence-enabled transaction processing grounded in industrial design, architectural analysis and ERP. It is not the casual application of tools to isolated tasks. It is the structured redesign of transaction execution itself.
That distinction matters because people are not naturally motivated to remain involved in transaction processing at the level demanded today. They know there must be a better method. Increasingly, they turn to available Artificial Intelligence tools to improve their own productivity, often at the expense of organizational coherence and control. In other words, the institution loses signal precisely when the individual appears to gain efficiency.
The consequences for oil and gas are clear. Without institutional redesign, the producer firm, the Joint Operating Committee, and Markets will be overtaken by a level of transaction speed, volume, and complexity that exceeds ordinary human comprehension. Under those conditions, effort will continue to rise while control deteriorates. Synallagi is designed to prevent that outcome. Through the Task and Transfer Network, the Targeting Framework, and Autonomous Asynchronous Transaction Orchestration, it provides the means by which signal can be recovered from noise and operational control reclaimed from disorder.
That is the underlying point. The future challenge is not simply that more transactions will occur. It is that they will occur at a granularity, speed, volume, and level of interdependence that cannot be managed through conventional administrative methods. The role of our user community, and of their service provider organizations, is therefore not merely to automate existing work. It is to redesign that work so that Artificial Intelligence operates within a disciplined institutional framework capable of identifying signals, executing transactions, and preserving accountability across the marketplace.
Three, Bias.
Individual Artificial Intelligence feeds bias.
Institutional Artificial Intelligence creates objectivity.
Coordination is the first institutional challenge and Signal being the second and Bias the third. This issue is central to the work of People, Ideas & Objects. Cognitive bias, motivational bias, audit, and the production of standard and objective reporting are not peripheral concerns. They are among the principal issues that must be addressed if oil & gas is to be rebuilt on a disciplined and profitable basis. Rather than devoting human effort to the repetitive processing of transactions, people need to redirect their time and energy toward the continual improvement of the methods by which these attributes are achieved, measured, and enforced.
What Synallagi must establish is a standard and objective basis of accounting across the industry. The importance of this objective is not always immediately obvious. One benefit is efficiency. Once market participants become familiar with a common standard for industry accounting input and reporting, the quality, speed, and comparability of decision-making will improve. That standard cannot be arbitrary. It must rest on an accurate understanding of industry activity as interpreted and structured by our user community.
The significance of this requirement extends well beyond administrative convenience. Producers will make decisions to produce or to shut-in production on the basis of profitability as determined by this accounting and our decentralized production model. They must therefore know that the accounting has been applied in an objective and standard manner, and that it has been applied consistently. If Synallagi is used consistently across the continent, then producers can rely upon the fact that their profitability is being evaluated on the same objective basis as everyone else.
This is where bias becomes a design issue rather than merely a behavioural one. Cognitive and motivational biases, among others, must be addressed during development and implementation so that undesirable characteristics are not designed into the system, amplified by it, and then normalized through repeated use. The point is straightforward. If this effort were initiated from the inherited structure of a single major producer’s Enterprise Resource Planning implementation, such as Exxon or Chevron, would anyone seriously dispute that a built-in bias would follow from that starting point? The answer is obvious. A system intended to govern an industry cannot begin from a narrow institutional bias and still claim to be objective.
For that reason, we have discussed on several occasions an enhanced role for public auditors in the development of Synallagi. Audit firms will be able to establish their own user community and service provider organizations for the implementation and management of audit controls, or for the creation of autonomous audit processes operating within Synallagi’s Enterprise Resource Planning environment. That is not a peripheral possibility. It is one of the ways objectivity can be institutionalized rather than merely asserted.
It is also here that George Sivulka raises a particularly relevant form of bias created by contemporary Artificial Intelligence tools. He notes that a new problem has emerged in place of the earlier debates over sociopolitical bias. The issue is now one of excessive agreement.
A new problem has taken its place. But this level of agreement, of over alignment, on everything has become comically bad. It's become a meme in its own right. Clauds, reflexive, you're absolutely right. Regardless of whether or not you are, in fact, absolutely right. This sounds harmless. It is not. The loudest AI advocates inside many organizations may soon be the historically worst performing employees. Think about why. Organizations' worst employees, who receive little to no positive reinforcement every day, will soon have AI agreeing with them. They will whisper to themselves, the smartest intelligence that has ever existed agrees with me. My manager is wrong. This is intoxicating. It's also organizationally toxic. This highlights something important.
This highlights the deeper issue. Individual Artificial Intelligence productivity tools tend to reinforce the user. In reality, the most important thing to reinforce is the truth. Over long periods of time, organizations have evolved structures intended to counter exactly this problem. In oil and gas, the Joint Operating Committee has developed as one such structure. Its evolution reflects the ownership requirements of the partnership framework and the need to achieve financial consensus among the partners. People, Ideas & Objects regard that as the dominant culture of oil and gas, and it is one of the reasons the Joint Operating Committee remains the key Organizational Construct of Synallagi’s design.
Organizations rarely fail because people lack confidence. They fail because no one is willing or able to say no. Institutional AI must play that role. Thus, the most important agents inside organizations will not be yes men, but disciplined no men, that interrogate reasoning, surface risks, and enforce standards. Some of the most consequential future applications of AI will be built around institutional constraints. AI board members, AI auditors, AI third party testing, AI compliance, and many more.
That is where Synallagi differs. It does not seek to use Artificial Intelligence to magnify subjective preferences or accelerate institutional drift. It is designed to embed objectivity within the structure of accounting, reporting, control, and transaction execution. The work of our user community, and of their service provider organizations, is therefore not merely to make work faster. It is to ensure that the standards governing work are objective, consistent, auditable, and resistant to the biases that individuals, firms, and software systems otherwise tend to reinforce.
