ABM was supposed to bridge the gap and bring sales, marketing, and customer success teams closer to buyers within the accounts that teams want to land and expand. But the desire to scale and automate is actually widening the gap. As platforms like Demandbase and 6sense push forward with AI to stay relevant, GTM teams are letting systems take over, and it’s replacing human judgment and our ability to connect with the right content, messaging, and stories. The “human touch” is being replaced by the desire for more activity, more outreach, more target accounts, and more pipeline.
In a recent article, “ABM’s Silent Killer – When Tech Replaces Thinking,” Eve Chen wrote:
“Intent platforms, predictive models, engagement dashboards, and now AI all claim to be the missing link between marketing precision and revenue growth. And for a while, it feels like progress. Signals become sharper. Workflows become faster. Teams feel empowered by automation and AI. Until one day, someone asks the question: “If we know so much about our buyers, why aren’t we converting more of them?”
This is the question we routinely ask on LinkedIn and in our ABM Done Right Podcasts, as stage progression, win rates, deal sizes, and ARR/GRR/NRR growth should increase as the pipeline grows. But this is not happening as depicted in the image below. Enterprise win rates are dropping!
Every time we see posts from people like Shari Johnston at Winning by Design who share a performance review of their AI SDR, we ask: “Why aren’t the accounts converting?”
Let’s take a closer look at the numbers Shari provided in the LinkedIn post above:
2030 conversations
85 handoffs to human reps at the right time – What happened to all those other conversations? Whether it’s AI or a human, why are there so many conversations with accounts that aren’t predictably going to move forward? Less than 5% of conversations being handed off to a human rep is not good.
40 MQLs – So, less than 5% of conversations were handed off, and then we cut that number in half for MQLs.
Then 1/5 of that number led to pipeline.
We don’t have a revenue number yet – but let’s say 2 close (a little more than 20% of the pipeline),
Now, from this perspective, what do the numbers look like if they are using the AI SDR for outbound?
Relying on 6sense’s 6QAs is a Prime Example of How GTM Teams Are Allowing Systems to Replace Human Judgment.
A 6QA is an account lead that supposedly has been thoroughly vetted and deemed highly qualified by the 6sense platform. Unlike traditional marketing-qualified leads (MQLs) or sales-qualified leads (SQLs), 6QAs supposedly take a more holistic approach to account qualification. They supposedly go beyond basic demographic and behavior data to consider additional factors, using AI and machine learning to:
- Identify buying intent signals
- Understand account and profile fit
- Measure engagement levels
With these insights, 6sense can supposedly more accurately predict which accounts are most likely to convert into customers.
But…
Even though 6sense and other intent data platforms will promote a “predictive” analytics platform, intent itself is not predictive in any way. It’s speculative. Intent data only reflects the current state of mind or interest of potential buyers, indicating what they might be looking for or considering. But it does not necessarily guarantee future actions or buying behavior (which is what “predictive” implies).
- Intent data captures current activity.
- Intent data tracks online behaviors such as website visits, keyword searches, and content downloads, revealing what a person is actively researching at that moment.
- Intent doesn’t show what buyers will most likely do in the future.
- And, most importantly, intent data is not predictive of whether an account will grow with you vs. churn.
You do not want a big, wide funnel filled with intent-qualified leads and opportunities that do not move forward.. You want to focus on accounts with a very low chance of losing and a high chance of winning at a high ACV. You want customers who will stay with you and grow, and become the backbone of your business. But when 6sense automates qualification, it does not look into:
- What shifts are the target accounts making so they can be successful with the vendor’s solution?
- Do the target accounts align operationally, executionally, and culturally with the vendor and its solutions?
- Are the accounts strategically ready for the vendor’s solution?
- Are there current and future growth opportunities so GTM teams can land and expand the account?
- Are target accounts facing a situation where the competitors are irrelevant?
- Can the vendor embed their solutions across the enterprise?
- Do the target accounts match the business problem profile?
- Is there a differentiated, relevant story to tell the target accounts?
- Is there a strategic priority where the vendor can play a strong, differentiated role?
Answers to the questions above are predictive of accounts landing and expanding with you – everything else is speculative. Because teams are relying on 6QAs, even though 6sense proxies only show you the accounts that are supposedly in-market, that you can target. They don’t focus on the accounts you should target, and the ones you should target now, because they match your ICP and they are predictably winnable and expandable.
It’s the same thing with Demandbase.
Demandbase talks about scoring accounts by ICP and AI-powered qualification score. They talk about how their machine learning and AI solutions automate account selection, and they extract predictive insights without the need for manual input or extra costs. They mention…no human intervention needed. They say to define the opportunities that best fit your ideal customer profile and let their machine learning model do the rest, scoring accounts on how well they fit. This sounds a lot like “lookalikes” mixed with intent (which we already said was not predictive). They are not taking into account that companies may look a lot alike from the outside, but once you peel back the layers and look at their culture and how companies operate and execute, you’ll find that one company is from Mars, while another one will be from Venus.
We need human intervention and human judgment to make sure accounts are GTM qualified and that the accounts we’re targeting are strategically ready, and they are a long-term fit for our companies and solutions. While platforms like Rev, Keyplay, and Clay have stronger AI capabilities to uncover ICP accounts and can be added on top of platforms like 6sense and Demandbase, they still require human intervention and human understanding of key customers to uncover the right ICP signals.
6sense, Demandbase, and other ABM Platforms Are Not Only Getting the ICP Wrong, But There’s Also an Interpretation Gap.
In Eve Chen’s article, she also mentioned,
“Between every signal and every sale lies an invisible space: the Interpretation Gap. It is the distance between what your systems record and what your buyers actually mean. When teams rely on automation to define meaning, that gap widens. Signals are captured but not contextualized. Patterns appear, but stories disappear. The result is data that looks intelligent while the go-to-market motion remains blind.”
This is what happened with Uniphore. They believed that, from intent detection to automated plays, Demandbase would orchestrate the entire revenue engine. They believed they’d identify and engage the right buying groups with messages tailored to exactly what they’d need. They stopped translating signals into actionable, human insight. They stopped becoming relevant to key tier 1 banks like Bank of America as they continued to push out industry- and persona-based thought leadership based on signals rather than telling the account story. They failed to match the personalization journey to the buying journey.
Click here to read the case study
More Proof That There’s an Interpretation Gap…
In an article on the Association of National Advertisers website, they discuss how companies like Malbec are using 6sense and its agentic AI to research, create personalized content, and act as SDRs to boost the pipeline by 17X. We see conversations about building efficiency and scaling ABM with AI. As 6sense and others focus on intent-based outbound and on providing email agents that support sales prospecting by multi-threading accounts with coordinated ad campaigns and BDR outreach, the number of AI-written emails jumped by 464% according to Gong. But benchmarks place global reply rates in the 5% range. In other words, companies are automating noise to targeted accounts based on intent vs. responding to why there may be intent in the first place. There’s more activity, but sales and marketing teams are failing to connect with human buyers.
Even though companies can do more outreach, build a bigger pipeline with fewer resources, and increase pipeline capacity, when will they come to their senses and realize that their AI-driven activity is not translating into greater GTM efficiency and revenue returns? GTM efficiency is the combination of improved sales/marketing productivity and effectiveness. As the pipeline increases, so should stage progression, win rates, deal sizes, and revenue growth. You shouldn’t be seeing an AI-driven pipeline that does not land and expand.
The Interpretation Gap Extends to Other Tech Platforms Like Tofu and Mutiny
Ashley Lewin at Aligned recently posted on LinkedIn:
Of course, Ashley mentioned to use Tofu or Mutiny, which can can take information, enrich it, research with AI, and build on-brand campaigns you give guidance on (style and tone, brand guidelines, etc.) across email, landing pages, ads, content, and even decks – so your 1:1 approach actually scales like 1:many, but without the blood, sweat, and tears it would normally take.
Now we love Aligned, the discussions Gal Aga starts, and the team, including Ashley Lewin. We are also proponents for platforms like Tofu.
But in this case, we disagree with Ashley.
You cannot simultaneously do 1: many reach and 1:1 personalization, which needs to be personal to the account, where buyers are in their journey, their mindset, the selling and internal conversations that need to happen, and the buyers’ struggling moments.
By allowing marketing and sales teams to become contextually relevant with the use of AI, Tofu helps marketers scale efficiently and effectively 1: few programs in an effort to drive engagement and pipeline.
But once accounts are in the pipeline, we need to up the game to the next level. Because relevance isn’t increasing as buyers progress, accounts go dark after initially engaging. You cannot automate 1:1 and be personally relevant to specific accounts, specific buyers, and specific stakeholders.
This is something we spoke to Bradley Fehler about on his ABM Done Right Podcast episode. He mentioned that, while you can grow the pipeline by X amount through automated means, accounts are not progressing to revenue. Prolific has seen higher response rates, higher close rates, and higher retention rates with more authentic 1:1 interactions as buyers progress in their journey.
Prolific starts with that human interaction. Now, that doesn’t mean they don’t automate in other areas. They do. But when it comes to 1:1, they insert as much authenticity and as much deep understanding as they can within their content and messaging. When it comes down to 1:1, they get as nuanced, personal, and, again, as authentic as possible.
When you’re doing 1:1 ABM, you are writing content for specific selling conversations with specific buyers. You’re thinking, ‘ How do I need to reframe their specific thoughts? ‘ How do I need to overcome their specific objections? How do I make an emotional connection? How am I managing the struggling moments that sit at the center of every deal? Moments that need to be listened to, understood, cater to… reframed, solved.
When you’re doing 1:1 ABM, you are creating content that enables sales teams to have the right conversations and enables buyers to have the right internal conversations. You are creating a specific buying vision based on the account’s current and future needs so you can land and expand key accounts.
AI can give you all those different dots and an understanding, and they can help you a lot, but they need that human touch to now put that content, that messaging, and the stories together in a way that moves the buyers, so we can have a more intentional, meaningful engagement.
In response to our LinkedIn post, where we challenged Ashley, she wrote:
Unfortunately, many teams are not thinking about post-pipeline. They continue to push out the same content and messaging vs. interpreting the signals and uncovering the account story to deliver greater relevance as buyers move forward and earn the extra attention. Unfortunately, too many teams are going for scale throughout the buying journey, thinking that they can do true 1:1 ABM at scale, and then they wonder why the pipeline is bloated with accounts that do not move forward.
The Interpretation Gap Extends to Platforms like PathFactory
Platforms like PathFactory help GTM teams automatically provide Spotify-like content playlists to buyers that are filled with relevant content based on industry, persona, company and other data. The platform analyzes buyer and content engagement beyond every click, then suggests other content based on that engagement.
But, the delivered content is based on what buyers are engaging with – not based on why they are engaging with the different content. There is no interpretation of what’s happening within those accounts that specific people are reading certain articles, white papers, and case studies. It’s the interpretation data that should guide the content, messaging, and stories that are delivered.
GTM Teams Need More Than What ABM Tech and AI Deliver
Now, don’t get us wrong. Our clients use the different platforms mentioned in this article. They are all very powerful. But the point is…we need more than what AI and ABM automation can deliver at this time. We need to go beyond just orchestrating tech. We need the human element if we want to go beyond just building a pipeline.
This is something that companies like Outreach are beginning to understand. They are looking to companies like Ignitium to help them scale the top part of their account-based GTM with their orchestrated tech stack. But they are looking at Personal ABM to help sales and marketing teams make the personal connection to move accounts to revenue. They want to orchestrate the right interactions and account experiences – and not just orchestrate the tech.