The Empowerment Imperative: What Should Agentic Marketing Optimize?

A marketing system that remembers responses and changes what it does next is doing more than placing an ad. Its objective matters because it shapes which actions the system repeats.

Here, I use agentic marketing to mean a system that can plan marketing actions, carry them out, observe outcomes, and adapt its next action. A tool that only drafts copy does not meet that definition. The distinction matters because an adaptive system can optimize for a business result while leaving a separate question unanswered: did its actions help the person make a choice they would endorse?

This is a design question, not a claim that current marketing agents already learn a person’s deepest goals or control a complete customer journey. The article proposes a way to frame and test a more human-centered system. It does not present a validated objective function, a proven commercial advantage, or a completed implementation.

A procedure can have a purpose without choosing a good one

In work submitted in 1936 and published in 1937, Alan Turing described a formal model for mechanical procedures. The model helps us ask what a system can do, what state it retains, and how rules map inputs to outputs. It does not tell us whether the system’s purpose is worth pursuing.

The same distinction applies to marketing automation. A system may take a signal, select an action, record a response, and update its next choice. Its objective might be clicks, conversions, revenue, or some combination. Those goals can be stated precisely and optimized effectively. Precision does not make the goal complete.

For example, a campaign may increase purchases while also increasing returns, unwanted subscriptions, or decisions made under a misleading impression. Those outcomes are not equivalent, and they may not appear in the same reporting window. A system trained to reward only the first event has no built-in reason to account for the rest.

The objective function does not act alone. Data, available actions, constraints, time horizon, and feedback all shape what the system can learn. But the objective still matters: it determines which outcomes count as success when the system chooses what to repeat.

When influence becomes adaptive

Advertising has always influenced the information people encounter. Digital systems can add rapid feedback: show a message, observe a response, and change what is shown next. If that loop also retains state, coordinates actions across channels, or adapts over time, influence can become less like a single message and more like a changing choice environment.

Legal scholar Karen Yeung uses the term hypernudge for data-driven systems that continually reshape a person’s choice context to steer attention or decisions. Her paper analyzes algorithmic decision-guidance as a form of design-based regulation. It is a useful lens for examining adaptive marketing, but it does not establish that every recommender or marketing agent behaves in the same way. Yeung’s account focuses attention on the system around a choice, not just on the wording of an individual message.

That shift changes the ethical question. We can still ask whether an ad is truthful or a recommendation is relevant. We also need to ask what the system learns from a response, which choices it makes easier to see, what alternatives it stops showing, and whether a person can understand or interrupt the process.

These are risks to investigate, not inevitable outcomes. An optimization system does not automatically produce a compulsion loop or a narrow view of the world. Those outcomes become more plausible when the system repeatedly rewards short-term engagement, has broad access to personal signals, faces few constraints, and receives little feedback about harm or regret.

Conversion is not a complete account of benefit

Conversion is useful. It tells a business that a defined action occurred. It does not, by itself, show whether the purchase fit the person’s needs, whether the offer was understood, or whether the decision had consequences the person later regretted.

The same limits apply to common “trust” or “agency” proxies. Repeat use may indicate satisfaction, but it may also reflect habit or lack of alternatives. A return may signal regret, but it can also be a normal part of trying a product. Positive sentiment can coexist with a lack of meaningful choice. No single behavioral measure can stand in for human flourishing.

I use empowerment here in a narrower, testable sense: a system supports a person’s ability to understand options, make a choice consistent with their own stated aims, and revise or refuse that choice without obstruction. This definition does not assume that people have one stable goal, that they always know what they want, or that a system can infer their interests better than they can.

The positive ambition goes further than making a recommendation easy to accept or reject. A system could help someone become better able to make the next decision: understand which trade-offs matter, question a claim, or compare an unfamiliar alternative without relying entirely on the same assistant. That expansion of capability is worth investigating even when it produces no immediate transaction. It also requires evidence beyond whether someone followed the recommendation.

This ambition does not make a commercial system a neutral adviser. A marketer has a legitimate interest in selling a product. An empowerment-oriented system should make that role visible and keep commercial success distinct from claims about user benefit.

A proposed agency layer

The phrase agency layer names a set of design and evaluation practices that could make human interests harder to overlook. It is a proposal, not an existing standard or a feature set demonstrated in a working product.

Start with a stated task. Define what the person is trying to accomplish and what counts as a useful outcome. Where possible, let people supply or correct that goal. Treat inferred interests as uncertain signals, not declarations of intent. A stated goal can also change, so give people ways to revise it.

Make personalization a choice. My proposed default is that behavioral personalization stays off until the person chooses it. The choice should explain which signals will be used, for what purpose, and for how long, with a working way to stop or revise it. Using a delivery address to fulfil an order is a different purpose from reusing shopping history to shape later persuasion. Keeping those purposes separate can reduce convenience or relevance in some cases; that trade-off should be visible rather than silently decided for the person. This is a design position, not a description of current platform practice or a claim about legal requirements.

Make influence legible and controllable. Explain when a message or recommendation is sponsored and what signals are being used where that can be disclosed. An opt-in does not authorize every later use. Consent is not a substitute for good design, and influence that is hidden or hard to reverse makes meaningful choice harder.

Set limits before the system acts. Define prohibited uses, such as deceptive scarcity or targeting based on sensitive inferences. Set rules for frequency, eligibility, budget, and escalation. Require a human review when a proposed action exceeds those limits. A model’s confidence or an attractive performance forecast should not override a policy constraint.

Evaluate both the business result and the person’s experience. Keep conversion, revenue, and cost measures. Alongside them, test whether people understood the offer, saw relevant alternatives, could explain the recommendation, and felt able to decline. Use interviews or surveys where behavior alone cannot answer the question. Treat returns, complaints, blocks, and rapid cancellations as prompts for investigation, not as a ready-made agency score.

These practices are not a recipe for measuring a person’s agency with one number. They create questions a team can examine before and after deployment, and make it possible to detect when an apparent performance gain depends on an experience the organization would not defend.

The difference appears over time. For someone comparing a purchase against a stated budget, hesitation could trigger repeated urgency messages and progressively narrower offers. Another design could explain the cost and trade-offs, show a lower-cost alternative, and allow the person to pause or decline without renewed pressure. Neither path follows inevitably from using an agent. The available actions, memory, constraints, and feedback determine which response can become the next one.

Turn the proposal into a test

The argument becomes useful when it leads to a falsifiable question. For a specific product and task, a team might compare two recommendation designs: one optimized only for immediate conversion, and one that also explains trade-offs and provides a clear way to reject or change the recommendation.

Both designs must obey the same minimum constraints; an experiment does not justify exposing people to prohibited manipulation. Keep two views of the results rather than blending them into one reward:

View	Evidence to examine	Limit on interpretation
Commercial results	Conversion, cost, revenue, margin, and later returns or cancellations	A sale does not establish that the person understood or benefited from the choice
Person’s experience and capability	Understanding of trade-offs, ability to compare another option, ability to revise or decline, and reported fit with the stated task	Surveys and behavior can each mislead; repeat use or positive sentiment alone does not establish agency

Check constraint adherence alongside both views. A gain in revenue must not compensate for a prohibited action in an aggregate score. Disagreement between the views is a finding to investigate, not a reason to hide one of them.

Before running such a comparison, define the audience, task, time window, and safety boundaries. Measure the business outcome, but also ask whether participants understood the options, whether the selected product met the stated need, and whether they felt able to choose differently. Follow later outcomes such as cancellation, return, or support contact where appropriate. Record what the study cannot establish, including whether results apply to other products, people, or contexts.

This would test a design choice. It would not prove that an abstract system “maximizes empowerment,” or that a particular measure captures flourishing. A useful next step is to validate measures with the people whose choices they are meant to describe, then repeat the evaluation as the system and its context change.

The commercial case should be tested separately. It is plausible that an experience people find more useful could support lasting customer relationships. It is not established here that empowerment always increases profit, or that long-term trust will compensate for every short-term cost. Ethical constraints should not depend on that business hypothesis being true. Distribution Alone Is Not a Moat examines the commercial test of retained value and defensibility; that test remains separate from whether people understood and endorsed their choices.

Commerce makes the boundary visible

When a system helps someone compare products and then complete a purchase, recommendation and transaction may sit close together. That does not mean every marketing system inevitably becomes an agentic-commerce system. It does mean that the distinction between helping someone decide and steering them toward a sale deserves attention wherever the same system can do both.

In that setting, useful questions include: Can the person see when a recommendation is paid? Can they compare alternatives? Does the system disclose relevant trade-offs? Can they decline, wait, or change course? Does the business measure what happened after the transaction, including returns or cancellation?

These questions do not remove the commercial purpose. They make its relationship to the person’s purpose visible enough to evaluate.

Build evidence before making the claim

I want to test the agency layer through working artifacts and reported evaluations. This article supplies a design proposal and research questions; it does not yet supply the implementation or results needed to claim that the proposal works.

The research direction remains worth pursuing. The related essays on search representations, memory and agency, and intention explore connected questions. They are conceptual work, not proof that a marketing system can reliably infer a person’s goals or optimize for human flourishing.

The distinction I want readers to keep is simple: a system can be effective at optimizing the outcome it was given and still leave us unsure whether that outcome served the person. “Human-centered” should therefore be more than a stated intention. It should name a design choice, a constraint, and an evaluation that people affected by the system can question.

References

• Turing, A. M. (1937). “On Computable Numbers, with an Application to the Entscheidungsproblem.” Proceedings of the London Mathematical Society, 42(1), 230-265.
• Yeung, K. (2017). “‘Hypernudge’: Big Data as a mode of regulation by design.” Information, Communication & Society, 20(1), 118–136.