Orchestrating the Portfolio: The SIs and Agencies the Next Martech Era Will Demand
For fifteen years, enterprise platform services providers have had a reliable playbook. A client picks a suite — Salesforce, Adobe, Oracle, more recently HubSpot — and a small army of certified consultants spends eighteen months standing it up, migrating data, building integrations, and training the business. Rinse, repeat, renew.
Note: This post references: Brinker’s ChiefMartech newsletter article: Build vs. buy is the wrong question for martech AI
That playbook is quietly expiring.
Scott Brinker has described where the market is heading with a phrase worth holding onto: clients are increasingly being asked to orchestrate a portfolio of capabilities across stratified layers. That framing captures something important about how enterprise marketing stacks are evolving — but it also describes, almost perfectly, the shape of the services firm that will be needed to help clients pull it off. And that firm looks meaningfully different from the one most agencies and SIs have spent the last decade becoming.
From implementer to orchestrator
Think about what a typical enterprise marketing team now manages. A core platform they've invested in for years. A growing collection of AI-native point tools their functional leads have adopted, often without central coordination. Internal experiments with custom agents or retrieval pipelines built by a data science team. A warehouse that's quietly become the real center of gravity for customer data. And a vendor landscape churning fast enough that any given tool could be acquired, pivoted, or deprecated within the fiscal year.
No single-platform implementation team is built to navigate that. The core competency that matters has shifted from "deep expertise in one stack" to "architectural judgment across many." Which capabilities belong in the system of record? Which are better served by specialist tools? Where does a custom build actually earn its keep, and where is it a vanity project that someone will regret maintaining in eighteen months? These are the questions clients increasingly need help answering, and the firms that can answer them credibly will own the highest-leverage seat at the table.
The uncomfortable implication for SIs whose revenue is concentrated in a single vendor partnership: it's difficult to sell neutral architectural advice when your bonus pool depends on one platform hitting its quota. Clients can feel the conflict, and they're starting to route around it.
Engineering depth is the new minimum bar
A shift I've watched accelerate over the past two years: clients are no longer satisfied with services partners who only configure. They want partners who ship working software — custom integrations, bespoke agents, production-grade data pipelines — on the same timelines that used to cover a requirements workshop.
Some of this is AI compressing development cycles. What took a squad of engineers a quarter can now be prototyped in a week by a small team with strong tooling. That's a threat to services firms whose margins depend on selling labor, and an opportunity for firms whose margins depend on selling outcomes. The operating model that wins in this environment resembles a product engineering shop more than a traditional consultancy: smaller teams, higher per-head capability, a premium on applied AI and data engineering talent, and a delivery culture oriented toward running code rather than polished decks.
Strategy-and-slideware still has a role, but as a front end to real delivery. When there's nothing behind the deck, the engagement thins out fast — especially as clients get better at doing their own strategy work with AI assistance.
The data layer redraws the competitive map
A quieter but equally important shift: the warehouse is increasingly where customer data actually lives, and activation tooling is following it there. Platforms that plug directly into Snowflake, Databricks, or BigQuery and run their activation logic against the customer's own data — rather than requiring a wholesale copy into a vendor's environment — are changing the economics of what belongs where in the stack.
For services providers, the downstream effect is that the competitive field has widened. Data platform partners with no historical presence in martech can now win activation work that used to route exclusively through application-platform SIs. Firms whose benches are heavy on application configuration and light on modern data engineering are finding themselves competing for a narrower and less strategic slice of the work.
The firms best positioned for this shift don't treat "data" and "martech" as separate practices with separate P&Ls. They treat them as one capability, because that's how clients are starting to treat the problem.
Two service models for two buyers
One pattern worth pulling apart: enterprise B2B and enterprise B2C are heading in different directions with AI, and serving them well increasingly requires different operating models.
B2B organizations tend to adopt AI broadly and pragmatically. Content teams are perpetually behind on demand; sales ops needs coverage it doesn't have; the underlying systems are already instrumented to receive AI augmentation. The services need is volume and velocity: help them adopt across many use cases quickly, integrate cleanly with what they already own, and handle the enablement and change management that turns pilot projects into production.
B2C organizations tend to adopt more selectively but build more deeply when they do. When the AI output touches the brand — copy, creative, conversational interfaces, personalization — the cost of getting it wrong is public, and the tolerable margin of error is thin. The services need shifts accordingly: fewer engagements, each one more bespoke, with real investment in brand-specific tuning, guardrails, and proprietary data integration.
The punchline for services leaders: these are different businesses. The talent model, pricing structure, sales motion, and even the right client stakeholder are not the same. Firms that try to run both through a single delivery org usually do one of them well and the other badly.
Governance becomes a discrete practice
As clients accumulate AI capabilities across their stack — some embedded in platforms, some bought as point tools, some built in-house — the gap between what they've adopted and what they can actually govern keeps widening. Model monitoring, brand safety controls, audit trails, data lineage, vendor risk, regulatory alignment: each of these is becoming a real line item rather than a footnote in an implementation SOW.
For services firms, this is some of the stickiest revenue available. It maps neatly onto capabilities that already exist in adjacent practices (information security, data governance, regulatory compliance), and it recurs rather than retiring with a go-live date. Firms that package it as a first-class offering — with a named practice, real methodology, and its own delivery leadership — are finding it anchors longer-term client relationships than implementation work ever did.
Stack advisory gets serious
The rate of change in the vendor ecosystem has turned "which tools should we bet on?" from a pre-sales conversation into a genuine advisory question. Categories are consolidating in some places and fragmenting in others. Products that looked dominant two years ago are being quietly wound down. New agentic entrants are redrawing the boundaries of what counts as a CDP, an MAP, or a CRM.
Clients need help making these calls, and they increasingly need it on a recurring basis — not as a one-time selection exercise but as an ongoing portfolio discipline. Stack rationalization, vendor consolidation reviews, capability gap assessments, and renewal strategy are turning into retained advisory engagements with real scope and real fees. The firms that take this seriously as a practice, rather than treating it as unbilled relationship investment, are building a revenue stream that didn't meaningfully exist five years ago.
The uncomfortable read
The work that defined enterprise martech services for the last decade — long, labor-heavy implementations of a handful of suites — is the work most at risk in the next one. It's not disappearing, but it's compressing, and the margins are softening faster than most firm leaders want to acknowledge publicly.
The work that replaces it rewards a different profile. Architectural judgment over platform certification. Engineering depth over configuration hours. Data-layer fluency over application-layer specialization. Governance rigor. Multi-vendor credibility. A willingness to play orchestrator across a stack the firm doesn't own end-to-end.
Orchestrating a portfolio of capabilities across stratified layers is, first, a description of what clients now need to do. But it's also a fair description of what services firms will need to become if they want to be the ones clients call to do it. The firms that make that transition will look meaningfully different from the ones they are today. The firms that don't will find the work they're built for is still there — just smaller, cheaper, and increasingly commoditized every year.
Want more from The Wolf Tank? Click here to be notified when new posts are published.