The "Dynamics" of Intent: The Era of Composable Journeys
Written by Robin Caller on May 11, 2026
Names in enterprise tech are often dismissed as marketing fluff. For years, Microsoft’s “Dynamics” sat in a category of CRM solutions where the name felt perhaps more aspirational than literal. Compared with Salesforce and Marketo, it was neither sales nor marketing but something otherwise undefined. Perhaps it sat too much in the middle for everyone to understand how futureproof it was. But now, as we watch the convergence of Azure, Databricks, and the composable orchestration of data through solutions like the Leadscale Engine, one has to wonder: Was the name a prophecy?
If we look at the landscape today, we might be seeing the end of the “static” era of marketing. We are entering a phase where the “Dynamics” of a brand isn’t just about a CRM database, but about the fluid, real-time movement of data through an AI-enabled nervous system. It is about leveraging and maximising the value of all your data, to inform the best next action. And often of course, the best next action is to decline/detarget/suppress, or in marketing and sales terms to “stop trying to convert” that person, buying group, or entity because it is no longer economic or valid.
"Engineering" the Journey?
The industry has started to talk about a new champion in the form of the “GTM Engineer.” It’s a title that intends to assign a machine and computational capability, bringing a marketer closer to the marketing operations, and ever closer and more aligned with the sales and revenue operations or even the Chief Revenue Officer. However, the title implies that the laws of physics as they are applied to data and prospect marketing have been settled—that if we know how to connect data X and data Y to platform A via pipe B, the outcome is cosmically guaranteed. As if everyone in digital demand understood data like engineers understand natural science and mathematics and physicists understand forces such as gravity, electromagnetism, and the nuclear force.
The etymology of the word engineer is friendlier to the new “GTM Engineer” label. After all, it emerges from the Latin term ingenium, meaning “cleverness,” “ingenuity,” or “native talent,” and the verb ingeniare, meaning “to contrive, devise, or invent”. It originates from a Medieval Latin root, likely ingeniator, which is quite literally “one who creates/uses an engine”. Given this background, it may seem counterintuitive for Leadscale, which has developed and operates a GTM Engine, to be challenging the adoption of this emerging job title.
But what if we consider demand generation to be less like civil engineering and more like fluid dynamics? What if we accept that the GTM Engineer is responsible for the think and design of composable Journey Designers, and we stop encouraging them to force buyers through a rigid, pre-constructed pipe. Instead, what if we hypothesise a world where the journey is modular? In this model, the “physics” are far from fixed. By using a composable architecture—where data flows freely between the warehouse and the activation layer—we might be able to apply differential activations at the account level. Could the “journey” become something that is designed in real-time, responding to intent signals as they happen, rather than following a map drawn six months ago? There is certainly a body of thought, and one or two heavily backed businesses claiming to deliver full A/B/C creative test-and-learn programmes driven directly by a CDP fed from a Snowflake data lake.
The Hypothesis of the DQL (Dynamically Qualified Lead)
As if the industry needs another acronym, how about we invite the GTM Engineer to curate the DQL. We’ve all lived with the friction of the MQL and the SQL. These are static markers; a lead is “qualified” because it hit a score, and then it sits in a queue. But what if we reimagined this as a DQL: A Dynamically Qualified Lead? (Move aside “demo-qualified”. That is a status, and not a dynamic state.) Imagine a lead that is qualified by an intelligent solution in real-time. In this scenario, qualification isn’t a trophy a lead wins once; it’s a state of being that fluctuates.
- If an account’s intent surges on a Friday, the DQL status rises.
- If the stakeholder’s behaviour shifts on Monday, the system recalibrates the qualification instantly.
By shifting from “Status” to “State,” we suppose a world where sales and marketing no longer argue over lead quality because the quality is being recalculated at the edge of every interaction.
An Outcome Loop of Constant Learning
In this hypothetical “Dynamics” model, the goal isn’t just to “send more emails.” It’s to create an ever-improving composable journey, learning always and iterating the outcome loop. If we treat our GTM stack as a series of composable blocks rather than a monolithic suite, we can test activations with a level of granularity we’ve never had. We might suppose that the true power of AI in demand generation isn’t in writing the copy, but in managing the learning loop—analysing which differential activations moved the needle at the account level and adjusting the “orchestration” accordingly.
A Visionary Shift?
Perhaps the “Dynamics” brand is more visionary than its competitors like Salesforce or Marketo precisely because it isn’t tethered to a specific department. It’s not “Sales-force.” It’s not “Marketo-ing.” It is a descriptor of data in motion. As we move towards a world of composable demand generation, we have to ask: are we ready to stop building static machines and start designing dynamic ecosystems?
