Durga Krishnamoorthy is a Product Leader focused on agentic GTM AI strategy, and scaling autonomous monetization.
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Your brand is becoming invisible, and it’s not because your marketing is failing. It’s because many of the "eyes" looking at your products are no longer human.
In a world where personal AI agents (not people) are deciding which organic milk is the freshest or which credit card has the best real-time yield, the traditional go-to-market (GTM) playbook is fundamentally changing. We are moving from an era of brand awareness to an era of agentic preference.
For the executive leaders I advise, this transition has exposed a dangerous strategic flaw: the "build versus buy" trap. While organizations spend months debating whether to own their AI code or lease platforms, others are finding market success by orchestrating.
The central question for the C-suite is no longer as simple as cost versus control. Increasingly, it is about which organizations can execute, adapt and scale AI capabilities most effectively in real time.
For many companies, the advantage comes from a strategic hybrid approach: purchasing robust, compliant core infrastructure to provide stability and speed while building the orchestration and decision-making layers that differentiate the business. In this model, competitive value does not necessarily stem from owning every component of the AI stack, but from how effectively organizations integrate systems, data and specialized agents into business operations.
That orchestration layer—the "intelligence glue"—can become a meaningful source of differentiation. It enables organizations to coordinate workflows, optimize decisions in real time and transform otherwise static systems into more adaptive, revenue-generating platforms. It is where I believe many organizations will either win or lose in the agentic era.
For decades, GTM success was defined by search engine optimization. Today, it is defined by machine agent interaction optimization (MAIO).
Think of it this way: You used to optimize for a human clicking a mouse. Now you must optimize for an AI agent parsing your store, catalog or offerings. If your product’s real-time availability, sustainability credentials or pricing data isn’t "agent-parsable," your brand becomes less visible in the machine-led economy.
When a consumer’s agent asks, "Find me the most sustainable snack within a five-mile radius," it will bypass companies whose data is locked in slow legacy systems and favor those whose information is live, vectorized and semantically rich. GTM is no longer about the biggest billboard; it’s about the most legible, trustworthy and actionable data for AI decision-makers.
The rise of agentic AI is also reshaping value capture. Traditional SaaS models built on per-user seats may become less effective in environments where AI agents can automate and scale work that previously required large teams.
Senior leaders should pay close attention to this emerging AI pricing narrative—the idea that AI agents may soon be treated as paid "seats" inside SaaS platforms, with their own identities, logins and licenses.
Forward-thinking leaders are increasingly exploring outcome-based monetization models. Rather than charging solely for software access, organizations may instead price around measurable business results such as successful restocks, optimized baskets or improved operational efficiency. This shift elevates the importance of risk-adjusted orchestration, where the accuracy, reliability and governance of AI systems directly impact financial performance, customer trust and operational risk.
A powerful way to navigate this transition is a future-back approach: Define what a 2030-era autonomous operation or intelligent storefront should look like for your business, then work backward to identify the necessary capabilities, infrastructure and governance.
Within this architecture, some organizations are beginning to implement an AI gateway—a centralized middleware layer that standardizes how different agents and models (OpenAI, Anthropic, specialized, internal, etc.) interact. It helps prevent agentic sprawl, simplify swapping models as capabilities evolve and maintain security, privacy and cost controls.
That said, an AI gateway is one tool among many. Before adopting any specific solution, leaders should evaluate their internal readiness around data maturity, system interoperability, governance policies and organizational culture. It's also worth considering alternatives, from lighter orchestration frameworks to advanced API management platforms, depending on your scale and complexity. The goal is flexible, future-proof integration rather than any single technology.
As you build toward agentic capability, focus on three parallel workstreams you can scale according to your company’s size, technical maturity and business priorities.
Make core product and operational data discoverable and semantically rich using structured formats (such as JSON-LD/Schema.org) and vector databases. This allows agents to understand contextual intent (for example, linking "heart-healthy" queries to relevant products without exact keyword matches).
Build the ability to coordinate multiple agents and models securely. Orchestration frameworks (such as LangChain, AutoGen or an equivalent) let you encode brand-specific rules, margin protection, supplier preferences and compliance guardrails while maintaining modularity and avoiding vendor lock-in.
Connect physical operations with digital intelligence so AI decisions can respond dynamically to inventory changes, demand shifts or competitive moves. Whether through digital twins, sensor integration or event-driven architectures, the principle is closing the loop between observation, decision and action.
The leaders who I believe will thrive in the agentic era will move beyond the build versus buy debate and embrace orchestration as a core competency. By treating your data as a living sales force and building intelligence glue that connects systems, your brand can become the one AI agents see and prefer first.
The future of GTM belongs to those who optimize not just for human attention, but for agentic preference.
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