Milena Traikovich is a seasoned expert in marketing operations and demand generation, specializing in the intersection of performance optimization and technological infrastructure. With years of experience helping brands navigate complex campaign cycles, she has become a leading voice on how artificial intelligence is fundamentally altering the value proposition of the marketing technology stack. Her insights focus on the critical distinction between tools that offer simple coordination and systems that manage high-stakes operational liability.
In this discussion, Milena explores the shifting landscape of MarTech pricing, the hidden risks of internal tool development, and the strategic framework for balancing purchased backbones with custom-built workflows. She highlights why the “SaaS-pocalypse” is less about the disappearance of tools and more about a massive repricing of the “surface layers” of marketing.
Marketing tools often function as coordination wrappers rather than core infrastructure. How do you identify vendors charging premium prices for simple visibility, and what practical steps should operations leaders take to re-evaluate these costs? Please walk us through the metrics or specific scenarios that highlight this pricing shift.
To identify these “coordination wrappers,” look for tools whose primary value proposition is routing tasks, intake forms, or presenting status dashboards. If a vendor is charging infrastructure-level prices for a platform that essentially just reduces friction in communication, they are likely overvalued in the current AI climate. For example, if you are paying a high per-seat license for a tool that just enforces mandatory fields in a briefing form, that is a prime candidate for re-evaluation. Marketing operations leaders should audit their stack by asking if a lightweight internal prototype could achieve 80% of that tool’s functionality. We are seeing a shift where substitution has become credible; when teams realize they can build a functional asset browser over cloud storage in a matter of days, the pricing power of the vendor collapses. The key metric here is the “liability-to-cost ratio”—if the tool doesn’t absorb significant legal or regulatory risk but carries a heavy price tag, you are likely paying a premium for a convenience layer that AI can now replicate for a fraction of the cost.
AI now allows teams to build internal intake forms and approval flows quickly. However, these prototypes often lack production-grade audit trails. What specific risks arise when internal tools bypass official systems of record, and what step-by-step measures ensure teams maintain version control during high-volume production cycles?
The most immediate risk is the lack of “evidentiary traceability,” which becomes a nightmare during regulatory requests or when claims are challenged. When you bypass a system of record, you lose the defensible history of who approved a specific asset and under what specific conditions it was greenlit. In high-volume cycles, you face “collisions” where multiple teams might modify an asset simultaneously, leading to regional adaptations overlapping with global masters in a chaotic way. To prevent this, teams must treat internal tools with product-grade discipline rather than as temporary experiments. First, you must establish a strict version control protocol that mirrors professional engineering standards. Second, ensure that even “thin” internal tools have a dedicated owner and a support model to handle API changes and dependency drifts. Finally, you must mandate that any internal workflow tool automatically syncs its final output to a central repository to maintain a single, authoritative audit trail.
Structural systems manage liability, while surface layers handle coordination. In a hybrid architecture, how do you define the boundary between a purchased backbone and an internally built workflow tool? Please provide an example of how to ensure compliance data remains synchronized across both environments to prevent manual reconciliation.
The boundary is defined by the concentration of risk; you should purchase your “backbone” where failure creates regulatory, contractual, or reputational exposure. For instance, an enterprise Digital Asset Management (DAM) system is a backbone because it manages rights enforcement, expiry rules, and identity integrity across 40,000+ potential users or touchpoints. Conversely, an internal team might build a “thin” intake layer that enforces specific metadata before an asset ever enters that DAM. To keep these synchronized, you need automated triggers rather than manual entry. Imagine a global brand where work is requested and modified in a custom AI-built tool, but the final approved asset is pushed via API to the DAM. If the internal tool captures the metadata and the DAM handles the archival requirements, you avoid the scenario where a regional compliance issue surfaces and the records fail to align. Without this automated bridge, leadership eventually discovers two parallel systems of record, leading to stressful, manual reconciliation.
Deciding to build software internally means assuming long-term responsibility for maintenance and security. What specific indicators suggest an organization has the engineering maturity to support an AI-driven tool, and how do you weigh these capabilities against the risk of regional teams creating fragmented, disconnected workflows?
Engineering maturity is indicated by the presence of a dedicated product owner, a clear governance process, and a long-term support structure that can survive the departure of individual developers. If a tool relies purely on “institutional memory” to function, the organization isn’t ready. You also have to look at whether the team can handle “concurrency”—managing simultaneous users without the system breaking. The risk of fragmentation is very high because AI makes it “locally rational” for a regional team to solve their own bottleneck with a quick tool. However, these solutions are often “globally disconnected,” leading to inconsistent status definitions and private tracking systems. To weigh this, you must apply the “differentiation test”: if the workflow provides a genuine competitive advantage that a vendor cannot match, the build is justified. If it’s just a workaround for a minor inconvenience, you are likely just creating a future “cleanup project” that will eventually require expensive integration or decommissioning.
What is your forecast for the marketing technology stack?
I believe we are entering an era of radical repricing rather than a total collapse of the SaaS ecosystem. We will see a “hollowing out” of the middle-tier vendors—those who sell coordination and visibility wrappers at premium prices—as internal AI builds become the standard for custom workflows. The successful vendors of the future will be those who lean into “structural depth,” offering undeniable value in areas like governance, rights management, and complex activation integrations that are too risky for internal teams to build from scratch. For marketing leaders, this means the stack will become more modular, with a purchased, high-security core surrounded by a constellation of thin, internally-owned AI surfaces. The “SaaS-pocalypse” is really a shift in leverage; the power is moving away from generic interface providers and toward those who can manage the heavy lifting of operational liability.
