MarTech Alignment: Why Your Stack Isn't the Problem

The average enterprise marketing team is sitting on a martech stack they’ve activated maybe 40% of. Then they ask me whether they should buy something new.

Three converging signals this week — from MarTech, McKinsey via Optimove, and a sharp piece by Shiv Gupta — are all pointing at the same uncomfortable diagnosis: the tools aren’t broken. The organizations using them are.

Vibe Coding Changes the Buy-or-Build Equation — Carefully

MarTech reports on a genuinely interesting shift: AI-assisted coding tools (the so-called “vibe coding” movement) are enabling marketers and growth operators to close product gaps in their stacks without waiting on vendor roadmaps or engineering queues. Think lightweight custom attribution models, bespoke data connectors, or campaign logic that no off-the-shelf tool handles elegantly.

For SEA teams operating across fragmented ecosystems — Shopee seller APIs, LINE OA webhooks, Grab Ads feeds — this is more than theoretical. The connective tissue between platforms has always been a pain point, and vendors rarely prioritize regional edge cases. If a growth lead with moderate technical fluency can now prompt their way to a working integration in days rather than quarters, that changes the calculus significantly.

But MarTech is right to flag the new responsibilities this creates. Custom-built components need documentation, maintenance, and someone who owns them when the original builder leaves. Vibe coding lowers the entry barrier; it doesn’t eliminate the operational overhead. Before reaching for an AI coding tool to patch a gap, the more honest question is: why does that gap exist? Rushed vendor selection, or a use case the stack was never designed to serve?

McKinsey’s Diagnosis: It Was Never the Technology

An Optimove-commissioned analysis of McKinsey’s “Organize to Value” framework makes a point that any stack auditor will recognize immediately — operational transformations fail because of unclear objectives, uncommitted leadership, and cultural inertia. Not because the CDP was the wrong choice or the DSP contract was poorly negotiated.

This is the conversation I have repeatedly with brands that have over-invested in martech and under-invested in the operating model around it. A positionless marketing structure — where specialists move fluidly across functions based on business need rather than fixed job titles — sounds radical until you realize most high-performing growth teams in SEA’s startup ecosystem already work this way by necessity.

The McKinsey framework essentially asks: are your people organized around delivering value, or around protecting functional fiefdoms? For marketing directors at mid-to-large brands, that question tends to produce an uncomfortable silence. Reorganizing around outcomes rather than channels is hard. But it’s the prerequisite for any technology investment to compound rather than decay.

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AI Accelerates CX — It Doesn’t Fix the Org Chart

Gupta’s piece in MarTech is the most practically grounding of the three. AI tools are genuinely improving the speed and scale of customer experience decisions — faster segmentation, real-time personalization triggers, predictive churn models that used to require a data science team and a three-month backlog. That’s real progress.

But the failure mode Gupta identifies is equally real: when the teams responsible for acting on those AI-generated insights are misaligned — different KPIs, different reporting lines, conflicting campaign calendars — the speed advantage evaporates. You get faster decisions that still go in different directions.

This is particularly acute in SEA markets where customer journeys routinely span five or six touchpoints across platforms that don’t share data natively. A customer might discover a product on TikTok Shop, research it on Lazada, ask a question via LINE, and convert through a brand’s own app. AI can map that journey. But if the team running TikTok campaigns, the team managing Lazada storefronts, and the CRM team are each optimizing independently, the AI’s output is noise that no one coordinates around.

The fix isn’t a better AI tool. It’s a shared definition of what CX success looks like — measured at the customer level, not the channel level.

What This Means for Stack Decisions in 2026

Taken together, these three signals suggest a reorientation that’s overdue. The martech industry’s default mode — evaluate, procure, implement, repeat — has produced stacks that are impressively wide and operationally shallow. The new discipline is depth: fewer tools, higher activation rates, clearer ownership.

Vibe coding is a useful pressure valve, not a strategy. Use it to solve specific, well-defined gaps where a custom solution genuinely outperforms anything procurable — not as a workaround for a procurement process you don’t want to go through again. McKinsey’s organizational lens should precede any technology decision: if leadership isn’t committed to the operating model change the tool requires, the ROI case is fiction. And if your AI-powered CX tools are producing recommendations that cross team boundaries with no one accountable for the handoff, you’re funding a very expensive reporting dashboard.

The question worth sitting with: how much of your current martech spend is solving a technology problem, and how much is quietly subsidizing an alignment problem you haven’t named yet?