Concepts of our own: the vocabulary of the bridge

These are the terms with which the paradigm thinks and writes. They are not textbook definitions: they are operational senses, sharpened for the work. When a term has an academic owner, it is noted and referred to Authors and currents.

Naming well is not an academic luxury. On ground where business and technique misunderstand each other all the time —the same words for different things—, a precise vocabulary is already half the translation done. Each term below is a tool: it names something that, without the word, was overlooked.

Core terms

Sociotechnical bridge

The paradigm —and the practice that embodies it— that integrates the competence to read the human fabric of an organization with that of building its technical system, without delegating either of the two. It is not coordinating two specialists (that is project management): it is a single competence that embodies the translation instead of outsourcing it. → The thesis of the bridge

Translation in flesh and blood

The operational version of translation from actor-network theory.¹ In the theory, to translate is to align the interests of the actors so that an artifact stabilizes. “In flesh and blood” means that it is not done by a process nor by a requirements document passed from hand to hand, but by an integrated practice that preserves the meaning of the human problem all the way to the code. It stops being a step —which degrades at every handover— to become a continuous practice.

Translation gap

The structural hollow in the market between those who understand people but do not build, and those who build but do not read people. There the digital transformation projects die; that is the space the bridge occupies. → The thesis of the bridge

The loop of the four links

The method: sociology of organizations → software engineering → data engineering → AI architecture and governance → and back to the person. What is distinctive is not the four disciplines (they exist loose in the market) but that they form a closed cycle without handovers, so that the sociological hypothesis is not lost at the edges. → The four links

Sociotechnical diagnosis

The deliverable of the first link and the condition of everything else: a reading of the organization before touching the technology, which separates the declared problem from the real one, maps power, incentives and resistances, and rules on where AI pays off and where it only destroys value. → The diagnostic method

Sociotechnical maturity

The degree to which an organization is ready for AI to add to it instead of subtracting from it. It is not measured only by infrastructure: it is multidimensional —strategy, culture, talent, governance and data quality—, an idea anchored in the research on AI readiness,² whose categories the IMIA model adapts and expands to make them measurable —its definition lives in IMIA, the maturity instrument—. Measuring maturity first is rule number one of the method.

From silo to architecture

Most organizations do not reach AI from an excess of failed technology, but from scarcity: management in spreadsheets, data in silos with no interconnection, manual processes, decisions without evidence. The real and today attainable opportunity is a constructive arc: connect the silos, raise an information architecture, govern the data, and only then add agents and AI where the organization is ready. The counterintuitive nuance: starting from little or no digitization does not reduce the risk, it concentrates it, because it forces building the technical subsystem and the human one at the same time —exactly what the bridge knows how to do—. → The bridge applied to small businesses

The adoption ladder

If from silo to architecture says where an organization starts from, the ladder says toward where it climbs, and that one climbs one step at a time: from the everyday office work to the systems that order the operation, from there to the AI that anticipates instead of merely recording and, on today’s step, to the agents that no longer suggest but execute end to end. What matters is not the inventory of steps, but its slope: the higher one climbs, the dearer the cost of having skipped the human reading. At the bottom, a badly-understood system is avoided —people go back to their spreadsheet—; at the top, a process no one understood is not executed more slowly, it is executed on its own, at scale and with no witness. That is why the paradigm does not age with the technology: each new step makes it more necessary, not less. → IMIA, the maturity instrument

Warning terms (what the thesis combats)

Automating the error faster

What happens when technology is mounted on a broken process or dirty data: the problem is not fixed, it is executed at greater speed and scale. It sums up why order matters: first understand, then automate.

The data that lies

A technically “correct” piece of data (well typed, no nulls) that nevertheless does not say what people believe it says, because its meaning depends on a human context that was lost —the “status” field that for one team means collections and for another, shipping—. It operationalizes Wang & Strong’s idea³: data quality is contextual and fitness-for-use, not an isolated technical attribute. Almost always, a data that lies is the mark of a badly-incentivized human process. → Authors and currents

Sociotechnical debt

By analogy with technical debt: the liability that accumulates each time technology is installed without resolving the social subsystem (resistances not worked through, processes not understood, users not involved). It does not appear in the code, but it is collected in null adoption, sabotage and rejection. It is invisible until the “perfect” system goes unused by everyone. And like all debt, it accrues interest: the later it is recognized, the more expensive it is to settle the human subsystem that had been skipped.

Technological solutionism

The belief that every social or organizational problem has a buyable solution in the form of a tool. The direct antithesis of the bridge: it confuses having the technology with solving the problem. (A term in wide use, adopted as a recurring target, not as an invention of our own.)

Vanity metric vs. value metric

The vanity metric measures what is easy to measure and what looks good on a dashboard (number of models, registered users, “AI usage”). The value metric measures what changes the day of the person in link 1: an hour freed, a better decision, a gap that closes. The bridge measures value.

Value terms (the promise)

Value in the person, not in the model

The measurement principle of the fourth link: the deliverable is never a model, it is a better human decision. An impeccable model that changes no decision is worth zero; a modest system that returns an hour a day to a team is worth a great deal.

AI as an extension of judgment (not as a substitute)

The position on the role of AI, above all in the State: it does not replace the worker (the public servant, the small business), it amplifies their judgment. Good AI returns decision-making power to the person; bad AI takes it away and leaves them out. Basis: human-centered AI.⁴ → The bridge applied to the public sector

Judgment in the design, not in every transaction

How AI as an extension of judgment survives when the software stops suggesting and moves to executing on its own. If an agent approves a refund, routes a complaint or prioritizes a case file without anyone looking at each case, the old “human in the loop” —a person reviewing one decision at a time— stops being possible, and at scale stops being desirable. Judgment does not disappear: it moves elsewhere. It goes from filtering decisions to defining, auditing and being able to revoke the rules with which the agent decides —what it does on its own, where it has to escalate to a person, what it leaves on the record for review—. The extension of judgment becomes the governance of judgment. It is the answer to whoever fears that agentic AI will make the human reading obsolete: it makes it more necessary, because without auditable rules autonomy is not extended judgment, it is delegation in the dark.

Closing the gap (not just delivering profit)

The definition of economic value used for small businesses and the State: the result is not only one client’s gain, it is shortening the distance between the few who already use AI and the rest of the productive fabric. In Latin America, with AI penetration below 4%,⁵ that gap is the opportunity and the mission at once. → The bridge applied to small businesses

AI as a multiplier with a sign

The synthesis that reconciles two assertions that seem to clash: the mission is to close the gap, but the evidence shows that badly-applied AI widens it. They do not contradict each other. AI is a multiplier of what it finds: on order and judgment, it multiplies value; on disorder and without a judgment to filter it, it multiplies the harm —in the Kenya experiment it helped those already doing well and sank those doing badly—.⁶ What decides the sign is not the power of the model, but the human reading that orients it. That is why the bridge is not an ethical add-on: it is, literally, what sets the sign.

None of these terms is a theoretical ornament. They are the way not to lose sight, amid the jargon, of what is being done and for whom. They all return, sooner or later, to the same question: does this serve the person on the other side? A vocabulary that did not answer that would be, itself, another way of automating the error faster.

See also: The thesis of the bridge · The four links · The diagnostic method · Bibliography