Agentic AI is turning strategy into a positioning problem again. When machines decide what gets compared, ranked, and executed, familiar advantages stop working and comfortable middle-ground strategies disappear. The agentic positioning matrix is an attempt to map this new terrain ; not to predict winners, but to explain why some positions will remain viable, others will become traps, and most firms will drift unless they choose deliberately where they want to be legible.
The agentic positioning matrix
If agentic AI changes who transacts, it also changes how firms compete and where they can realistically win. As autonomous agents increasingly mediate search, comparison, and execution, markets do not simply become more efficient. They reorganize. Some sources of advantage weaken, others concentrate, and many familiar positions become unstable.
One way to make sense of this reorganization is to stop asking how much AI a firm uses, and start asking who it is optimized for. In agent-mediated markets, firms increasingly face a choice between serving human principals or serving autonomous agents — and between differentiating their offering or competing as a utility.
These two dimensions define a simple but uncomfortable matrix. The point of this matrix is not to label companies, but to surface trade-offs. Very few firms can occupy more than one quadrant sustainably. And some strategies that made sense in human-centered markets become actively dangerous once agents take over selection. Strategy, in this context, is less about ambition than about legibility. The horizontal axis captures who the firm primarily serves: human principals, or autonomous agents acting on their behalf. The vertical axis captures how the firm competes: through differentiation, or through commoditized execution.
This yields four positions:
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Experience-led brands
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Agent-native specialists
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Legacy traps
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Machine utilities
Each represents a coherent strategy, comes with its own economics and implies very different risks once agents mediate demand.
Quadrant 3: Legacy traps
The most fragile position in agentic markets is not low-tech or anti-AI. It is human-first and commoditized. Firms in this quadrant remain organized around human buyers, compete primarily on price or convenience, and lack both strong emotional differentiation and genuine agent readiness. They are good enough for people but increasingly irrelevant to machines. This is the danger zone.
Offers are complex rather than modular. Pricing is opaque rather than machine-readable. Processes rely on manual intervention rather than automation. Interfaces are designed for dashboards and workflows, not for APIs and autonomous selection. These firms may adopt AI, but typically as surface-level assistants layered onto existing products, without rethinking how agents search, compare, and transact.
Traditional CRM and legacy SaaS vendors illustrate the pattern. Many have begun integrating basic AI features — chatbots, copilots, automated suggestions — while keeping their core interaction model human-centric. Tools like Insightly and similar platforms still assume a human user navigating screens, interpreting bundles, and making discretionary choices. As long as agents increasingly mediate software selection and usage, this creates a mismatch: systems that humans tolerate, but agents bypass.
The strategic outcome is brutal. These firms are squeezed from both sides: too undifferentiated to command loyalty from humans, and too opaque or incompatible to be selected by agents. Agentic AI makes this position existential not because it introduces new competitors, but because it changes the selection mechanism itself. The only viable response is either upward toward meaningful differentiation, or rightward toward agent-first design.
Quadrant 2: Agent-native specialists
Agent-native specialists are firms that design their offerings primarily for autonomous systems rather than human users. They do not treat agents as an interface layered on top of existing products, but as the primary customer. Differentiation in this quadrant is not emotional or narrative; it is technical, contractual, and systemic.
What distinguishes these firms is not that they use AI, but how they compete. Advantage comes from superior APIs, contract clarity, reliability under automation, and machine-readable guarantees. These attributes are often invisible to humans, but decisive for agents that evaluate options continuously and at scale. In this context, differentiation does not disappear; it migrates. It moves away from storytelling and toward integration depth, performance consistency, and predictable behavior.
Salesforce’s Agentforce platform illustrates this shift. Agentforce is designed to let autonomous agents transact across enterprise systems — resolving cases, triggering workflows, negotiating actions — without constant human supervision. The product’s value lies less in its interface than in its ability to be trusted by machines operating inside complex organizational constraints. Similarly, Fluency’s agentic advertising platform differentiates by orchestrating autonomous optimization across multiple ad networks, not by offering better dashboards or creative tools.
Agent-native specialists must invest in selectability rather than brand, and in ecosystem fit rather than surface-level features. This is where new moats can still be built — not through customer lock-in, but through deep embedding in automated decision loops. In agent-mediated markets, the firms that endure will be those whose systems are not just usable by machines, but preferred by them.
Quadrant 1: Experience-led brands
Some firms will remain deliberately human-first. Experience-led brands optimize for perception, emotion, and identity rather than for algorithmic selection. In these markets, brand, story, and design still matter because the act of choosing is itself part of the value proposition. Agents may assist (filtering options, providing information, automating logistics) but they do not fully decide. The final judgment remains human.
This position is most viable where symbolic value dominates functional optimization. Luxury goods, high-end travel and hospitality, and certain consumer brands derive their advantage not from being the cheapest or fastest, but from meaning, trust, and status. Here, friction is not a bug; it is part of the experience. The choice is meant to feel deliberate, personal, and sometimes even inefficient.
The strategic risk for experience-led brands is being loved by humans and filtered out by machines. As agents increasingly pre-select options, even differentiated brands risk invisibility if they do not manage how agents access, interpret, or represent their offering. The imperative, then, is not to reject agents, but to constrain them deliberately ; deciding where automation enhances value and where it erodes it.
Quadrant 4: Machine utilities
At the opposite end of the spectrum sit machine utilities: agent-first, commoditized execution layers that prioritize cost, reliability, and scale above all else. These firms are not designed to be chosen by humans, nor to differentiate through experience or narrative. They exist to be embedded inside automated workflows.
The offerings in this quadrant are familiar: generic compute, commodity logistics, basic data services, undifferentiated APIs. What unites them is not what they do, but how they compete. Performance, uptime, latency, and price dominate. Differentiation is minimal by design, because agents do not reward novelty or personality; they reward predictable execution. In agent-mediated markets, these services become standardized layers underpinning entire industries.
AI infrastructure providers illustrate the pattern. Agentic tooling such as OpenAI’s advanced coding and execution models, along with cloud platforms like AWS, Databricks, and Snowflake, are increasingly consumed as utilities rather than products. They are widely embedded, deeply relied upon, and rarely chosen for brand reasons. Their value is real, but it is infrastructural — measured in throughput, reliability, and integration breadth rather than user affection.
The economic logic of this quadrant is unforgiving. Volume matters more than margin. Scale effects dominate. Winner-takes-most dynamics are likely as switching costs and ecosystem lock-in accumulate. The strategic risk is equally clear: brutal price competition and dependence on upstream infrastructure or capital intensity. For firms in this position, there are only two viable strategies — play relentlessly for scale, or find a way to move upward into agent-native differentiation. In agentic markets, utilities endure, but they rarely escape their role.
Choosing where to be legible
The agentic positioning matrix is not a taxonomy of winners and losers. It is a map of trade-offs. Each quadrant can sustain viable businesses, but only if the position is intentional and reinforced. What disappears in agent-mediated markets is the middle ground, the comfort of being vaguely differentiated, moderately automated, and broadly appealing.
Strategy, in this context, becomes a question of legibility. Who are you optimizing for? Humans, agents, or both — and on what terms? Are you building meaning, infrastructure, execution, or integration?
These are not implementation details; they are identity choices. In a world where machines increasingly decide, the firms that endure will be those that decide first where they want to be seen, and by whom.
Photo de Tanusree Mitra sur Unsplash
