The potential use cases for artificial intelligence (AI), especially generative AI (GenAI), in go‑to‑market (GTM) are perhaps too numerous to count. Amid an overly-optimistic marketplace outlook for AI, growth leaders — across marketing, sales and customer success — are increasingly turning to AI and/or AI-enhanced technology tools.
Name a go‑to‑market challenge, and you’ll likely find a growth leader who’s convinced AI is now the solution.
What are they tackling? For many, the obvious contenders are content ideation and development for various marketing and sales use cases, as well as chat and sales enablement for prospect and customer conversations. However, some of the most compelling and strategic use cases fall into the higher-order categories of adaptive personalization, ICP qualification and critical-path optimization.
There is no question that AI can have — and already is having — a measurable impact on go‑to‑market programs. “Our research suggests that a fifth of current sales-team functions could be automated,” claims McKinsey.
AI Requires GTM Strategy
Yet here’s the catch that requires constant reminding: AI isn’t automatic. You can’t just deploy AI within a use case and expect it to ‘figure it out’ — especially if there’s any concern about the integrity of the underlying data being used or critical dependencies on the activity to which AI is being applied.
Over the past few months, the ANNUITAS|research team has been covering and releasing reports on a number of recent AI additions to leading GTM Technology Stack platforms, such as Salesforce Marketing.
Many of these platforms have been quick to announce they have ‘added AI.’ Once you dig into the details, however, you realize these additions often fall into the following two categories:
- On one hand, we see a number of tactical end-user productivity enhancements, powered by AI — all focused around use cases that are certainly beneficial, but ultimately won’t be game-changing for your go‑to‑market. Examples include saving time on administrative activities within your marketing automation platform (MAP) or customer relationship management (CRM) system, or shortening the time it takes for a sales or customer success team member to gather talking points to support an upcoming customer conversation.
- On the other hand, we see AI tackling meatier elements, but in a way that creates more uncertainty and volatility in GTM processes and systems — and often compliance risks — rather than really improving productivity. Are you ready to just let AI loose on your customer targeting and dialogue without significant human intervention?
The greater problem is that many of these AI enhancements represent the reinforcement of tactical, “random acts” of marketing and sales, i.e., supporting tactical campaigns and/or classic ‘cold’ outreach. They repeat practices that show diminishing returns, lack the framework of a larger GTM strategy, and fail to move any closer towards building a true Perpetual Growth Engine for the organization.
“The Biggest Thing Missing from AI? Strategy,” writes Meghan Keaney Anderson, Head of Marketing at AI firm Jasper, in a recent post where she explains:
I don’t just mean that the teams adopting AI are missing a strategy for getting the most out of the technology (although that is true for many of them), I mean that the tools themselves are not rooted in strategy. And without having that foundation and business context, the outputs, though impressive, aren’t reaching their potential.
The key to success with AI lies in #1 having a strategic framework — a GTM operating system, to contextualize AI’s role in your go‑to‑market — and #2 structure and integrity of underlying GTM data. Without these guardrails, you’re not really improving your go‑to‑market — you’re just shifting where you are spending your time and resources as an organization.
Strategic AI = Powering Your Perpetual Growth Engine for Go‑to‑Market
So how can we approach marrying AI and go‑to‑market in a more strategic way? The key is to transcend the ‘tactical’ use cases — the opportunities with strong potential for us to get caught up in random acts with short-term productivity gains (or with the potential to take us down a ‘rabbit hole’) — and instead focus on how AI can change our go-to-market game in a repeatable and sustainable way. That game-changing opportunity is for AI to truly power — and optimize — your Perpetual Growth Engine.
Organizations that move away from random acts to build and embrace a Perpetual Growth Engine model improve their go‑to‑market performance not just incrementally, but substantially. For example, after implementing a Perpetual Growth Engine, ANNUITAS’ clients typically see a 4–10x improvement in end-to-end lead-to-revenue conversion. Similarly, the potential of AI to truly optimize and ‘tune’ the performance of your engine is more than mere proof of concept.
The core design principle for creating a Perpetual Growth Engine is to design GTM programmatic interactions that are two-way and adaptive. I first covered this concept a decade ago in my book, Balancing the Demand Equation, and it is a concept that ANNUITAS continues to deploy with clients today.
Perpetual design requires creating a system that can both talk and listen — constantly processing what is ‘heard’ to sort out what to send next. As I note in Balancing, it is “a holistic, two-way content-based ‘dialogue’ between buyer and the [engine] — a perpetual interaction between delivering content offers and profiling the buyer.”
It also requires tight integration between Demand Process layers. Content personalization works closely with ongoing qualification. The systems, data and GTM organization work in harmony around the GTM motion — pre- and post-sale.
Evaluating the opportunity to leverage AI in go‑to‑market through the lens of how it can enable your Perpetual Growth Engine leads us to focus on two leading use cases that are truly strategic:
- #1 Customer Journey-Adaptive Personalization
- #2 Real-Time, Intent-Based Qualification of Prospects
GTM Strategic Use Case #1: Customer Journey-Adaptive Personalization
Our first strategic use case is leveraging AI to power the dynamic process of adapting and delivering personalized content to current and prospective customers — through multiple channels — as they proceed through their buying journey. In other words, using AI to coordinate true multi-channel customer engagement that centers around the customer lifecycle.
This is not ‘drip nurture,’ nor is it simply the CMS teeing up ‘if you were interested in this, you may like this.’ This is truly understanding where your buyer is in their journey, and delivering relevant information via the right channel, at the right moment in time. It is scaling this interaction across all customers and journeys.
“Personalization is key,” comments McKinsey in a recent research report on AI in go‑to‑market. “Winning B2B companies go beyond account-based marketing and disproportionately use hyper-personalization in their outreach.”
The “Growth Engine Marketing Era” is one where “personalization based on data is mainstream, no longer ‘innovative’,” argues marketing consultancy Prophet.
This means orchestrating engagement, both inbound and outbound, and keeping the logic and interactions in sync so that if, for example, someone downloads content from an email link, then that interaction can inform their next on-site web visit. It means that before someone interacts with a chat engine, you know where they are in their journey and whether they need more nurturing or should engage in a live interaction with a rep.
It also includes the adoption of the tactical improvements we cited earlier — Enhanced Personalization and Critical Path Analysis — at the same time.
There is no question as to whether AI can serve as the core ‘decision’ engine to drive this process. However, the success of the AI engine and the optimized customer interaction must be based on a contextual framework to scale the Engine’s interactions — which requires a comprehensive strategy.
Three key frameworks to deliver this strategy are:
- Conversation Track Architecture: A Conversation Track Architecture (CTA) is the foundational framework for operationalizing go‑to‑market around customer journey — providing a central organizing structure for orchestrating people, process, content, technology and data interactions with customers throughout their entire lifecycle. An effective CTA serves as both the starting point to ensure all customer journey stages and customer segments are covered, as well as the basis for defining programmatic alignment of marketing, sales and service programs and teams with the customer journey. This is what makes the CTA the bedrock of a Perpetual Growth Engine AND the critical context AI needs to power personalization in your engine.
- Content Marketing Model: A Content Marketing Model follows and pairs with the logic of the Conversation Track Architecture, assigning content by stage and by track to each customer journey. It is essentially the matrix of potential interactions with a buyer, which can adapt as buyers jump around. The model is not linear; instead, it creates options and paths per customer journey stage — a level of sophistication that enables the Perpetual Growth Engine to adapt content pieces to the specific needs of the customer as they proceed through a buying journey. Setting the stage for AI to serve up the right content, to the right buyer, at the right stage of a considered purchase path requires a Content Marketing Model as a critical framework.
- Customer Data Value Chain: A Customer Data Value Chain seeks to address the major issues facing customer data in a GTM Technology Stack. First, customer data is often fragmented across siloed systems. Second, the same data is often represented differently across systems — preventing the successful compositing of this fragmented data into a complete, 360-degree view of the customer and/or the ability of customer state to trigger actions in various GTM Technology Stack systems. A Customer Data Value Chain model thus provides federated governance of customer data — standardizing customer data fields across systems, enabling customer data ‘fractions’ to exist across multiple systems in a synchronized way, and integrating customer journey telemetry to provide context to customer demographics and behaviors. A successful model ensures the GTM Technology Stack works together to build a more complete and actionable customer view over time and across systems. A Customer Data Value Chain approach is thus critical for enabling AI’s success with driving personalization that is contextualized against a customer’s journey and is grounded in a complete picture, as opposed to actioning based on a fragmented customer picture.
GTM Strategic Use Case #2: Real-Time, Intent-Based Qualification of Prospects
Our second strategic use case is utilize AI to qualify prospects in real time to ensure focus — whether via marketing, sales, or customer success interactions — on our ideal customer profiles (ICPs) and to receive continually updated insights into ‘where’ customers are in their journey and their overall level of individual and organizational intent.
The ultimate outcome is improving the productivity and net conversions of go‑to‑market programs by dedicating focus to the ‘right’ customers and organizations at the right time, and engaging in the right way, at the right stages of their buying journey.
When should someone call a prospect/customer? When shouldn’t they? Is there a strong fit with their needs? How can we automate this ‘sensing’ of where the ideal customer is in their journey? How can we differentiate between pre- and post-sale journeys — ensuring various customer-organization stakeholders are being engaged in the right way to drive success, development and growth of the customer account? This is the use case we are talking about, and it is also critical to powering the first use case, above.
Successfully delivering on this use case comes down to two factors: effectively tracking telemetry and assessing intent.
- By telemetry, we mean having a structured way to keep track of customer journey progression and translate signals (i.e., implicit behaviors and explicit communication) into meaningful commercial actions. This requires structured data to provides a strong basis for triggering and supporting decisions — critical to AI success. It also means we have a structure for underlying customer data that ‘means something’ in the context of the qualification of commercial prospects.
- By intent, we mean being able to read real-time interactions (especially with content), organizational signals, and demographics/firmographics to decode lower-funnel needs and progression. Intent is the ‘tell’ that accelerates commercial engagement with a prospect.
From our earlier list of tactical AI use cases, this includes both ICP Analysis and Target Account Identification — at the same time.
As before, clearly AI can serve as a thoughtful qualification and triggering engine, but this use case also must be based on a contextual framework to scale the Engine’s interactions.
Two key frameworks to deliver this strategy are:
- Funnel Management Model: A Funnel Management Framework provides a basis for managing and optimizing the overall lead-to-close process. The goal of an effective Funnel Management Framework is to detect when a buyer is in the right place at the right time, through a combination of sustained engagement and journey progression. The buyer is qualified at an individual and organizational level to ensure triage of commercial resources and engagement. This also helps ensure engagement is prioritized for prospects that fit the ideal customer profile (ICP). This is achieved through account and role-based alignment and through evidence of lower-funnel actions. The Funnel Management Framework thus covers funnel stages and progression through Lead and Opportunity stages. This framework should also be fully integrated with the Conversation Track Architecture and leverage progressive qualification elements and a Progressive Profiling Model as critical inputs for qualification and progression.
- Progressive Profiling Model: What is the complete set of criteria required to qualify and segment the prospective customer? How can you collect, store and evaluate that data over a series of content and live-based interactions? Progressive Profiling provides the governance of the qualification logic, data fields and data structure required to drive Funnel progression, qualification and triggering of GTM resources in a Perpetual Growth Engine Model. Effective Progressive Profiling Models also contribute to a Customer Data Value Chain, as covered above. Thus, it is a critical framework to ensure the success of AI in the funnel.
Final Thoughts
AI has tremendous potential to improve GTM sustainability and performance; however, its success requires focus. We need the right context to focus AI use cases on the ‘highest and best’ applications, and we need focus to ensure we have the right frameworks and credible data to ensure AI success within these applications.
The most strategic opportunity is to ensure AI is an integral component of optimizing execution within your Perpetual Growth Engine, but successfully powering AI decision-making at scale will require strategic frameworks underpinned by meaningfully structured GTM data relative to customer journey progression.
