Top Five Lessons from Practitioners Doing The ABM Work in 2026
A grounded view of where ABM breaks and why most programmes still struggle to produce consistent results.
The gap between how ABM is described and how it actually runs inside organisations has always existed.
But in 2026, that gap is no longer subtle. The tools are more capable, the data is richer, and the pressure to show results is higher and more complex than ever. And yet the same problems keep coming up, whether you work in Marketing, Sales or any other commercial function.
ABM is not failing because of tools or talent. It is failing because most programmes are built on systems such as measurement, incentives, and workflows that were never designed for how ABM should actually work.
This article presents a summary of five patterns that show up repeatedly inside real programmes and highlights the key takeaways from the GTM365 event, ‘Scaling ABM in 2026. From Target Lists to Revenue Engines’.
1. Intent data is useful but it is routinely over-trusted.
Here is something that happens more often than people admit.
A target account shows a strong surge in intent signals. High engagement, multiple touchpoints, everything pointing towards an active opportunity. Someone investigates. The activity traces back to an employee researching a refrigerator. Same organisation, entirely unrelated purchase.
The platform surfaces the signal. It does not explain it - this a structural limitation and comes up in most of the signal platforms.
Intent data has become foundational to ABM programmes, but much of it is still aggregated, third party, and opaque. It produces confidence that the underlying data does not always justify. Teams act on the signal without being able to interrogate its origin. The unfortunate truth is that in most organisations where ABM isn’t an adopted growth strategy, the over-reliance on third party signals is the first flag that things need to change.
The reverse happens too. An account outside the ICP shows weak or inconsistent signals and gets deprioritised. Weeks later, it appears in a live tender process. The signal existed, but the system filtered it out as non-ICP.
Both things are true at once. False positives and missed positives.
The teams getting value from intent data do not treat it as truth, or the only truth, but they use it as a prompt for investigation. Everyone else treats it as a shortcut.
2. The new logo bias is a measurement trap
Most ABM programmes are structured around net new acquisition. Once that framing is in place, measurement follows it.
This is why expansion often gets deprioritised, even when it is more efficient.
In one SaaS organisation, a $500k expansion deal required less than a third of the sales and marketing effort of a $500k net new deal. The cost of acquisition becomes dramatically lower when ABM leads with expansion, with shorter sales cycles and higher close rate.
But expansion-focuseed programs still receive less internal attention.
Why? Because in most organisations only net new revenue counts towards the primary growth metrics. Expansion is reported on separately and carries less weight in performance reviews.
This is not a strategic decision, but very often a measurement design flaw.
Acquisition is easier to attribute, so it gets more visibility, more budget, and more reinforcement. Over time, that compounds into a structural bias towards new logos, even when they are less efficient to win. Meanwhile, two customers contributing the same revenue are treated as equal, even if one cost three times as much to acquire.
In most B2B businesses, a small percentage of accounts generate the majority of revenue, yet many ABM programmes allocate disproportionate effort to accounts that have not yet been through the initial awareness or engagement.
It is the inevitable result of a system that prioritises what is easiest to measure.
3. AI is compressing thinking time, not replacing it
Most conversations about AI in ABM focus on output—more content, faster personalisation, better scoring. That emphasis misses the more meaningful shift. High-quality ABM has always depended on understanding what an account is prioritising, how leadership is thinking, and where the business is heading. That depth is what separates work that resonates from messaging that feels automated. Until recently, building that level of understanding took days per account. Now it can be done in hours.
In one team, pre-call account research that previously required six to eight hours -pulling from earnings calls, leadership interviews, and industry reports—was reduced to under ninety minutes using structured AI workflows. The output wasn’t perfect, but it was strong enough to accelerate decision-making and focus attention where it mattered.
The constraint is no longer access to information; it’s how effectively teams interpret and apply it. AI-generated research surfaces patterns and summaries quickly, but it doesn’t validate them. Without human judgement, teams risk moving faster in the wrong direction.
The teams seeing real gains are the ones that have built repeatable workflows for research, synthesis, and application, and then used AI to remove friction within those steps. In those environments, AI expands capacity for thinking rather than replacing the actual thinking.
Everywhere else, it simply increases the volume of output without improving the quality of understanding within the sales and marketing teams.
4. Underinvestment in existing customers is a behavioural default
Consider how much energy companies put into hiring - and how little into developing the people already there. The same dynamic shows up in customer strategy. In one enterprise business, more than 70% of new marketing budget was allocated to acquisition, despite over 60% of annual revenue coming from existing customers. The imbalance wasn’t the result of a deliberate decision.
Expansion doesn’t carry the same weight unless it’s intentionally built into the system. Without that, it gets crowded out.
This is where ABM is consistently underused and the hardest parts - relationships, context, trust - are already in place, yet programmes default outward because that’s where activity is most visible.
In markets with a finite pool of high-value accounts, this becomes more than a missed opportunity; it becomes a constraint on growth.
The shift isn’t complex, but it does require consistency and intent. One team addressed it by assigning dedicated ABM resources to their top 20 existing customers, focusing on multi-threading and stakeholder expansion rather than net-new outreach. Within 12 months, average deal size across those accounts increased by 35%.
The opportunity was always there. It simply wasn’t being prioritised. Most organisations don’t actively neglect existing customers, they just follow the momentum of acquisition unless something redirects it or they realise that this is probably one of the most expensive motion to run on its own.
5. If you can’t tell the story, ABM won’t survive the cycle
For organisations with 12–24 month sales cycles, ABM is difficult to measure in the short term. Investment happens now and outcomes appear much later.
That gap creates pressure and the tricky part is managing expectations while all of that still needs to happen. Pipeline alone rarely tells a convincing story early enough. So programmes get questioned before they’ve had time to work.
The teams that navigate this well do two things differently.
First, they track leading indicators with discipline: depth of engagement, new stakeholders reached, progression within accounts. Not as substitutes for revenue -but as evidence that buying conditions are developing.
Second, they document specific deal journeys.
In one case, a £17m renewal grew into an £85m multi-year agreement. There was no single campaign responsible. Instead, the deal was built through:
early-stage thought leadership engagement nearly a year before renewal
consistent executive outreach across multiple business units
expansion of the buying group from 3 to 11 stakeholders
targeted content aligned to a strategic shift the customer announced mid-cycle
Because the team mapped that progression, they could show how value accumulated over time.
Without that narrative, the deal would have appeared as a late-stage spike- detached from the work that created it.
ABM doesn’t fail in long cycles because it lacks impact. It fails because the impact isn’t made visible early enough to sustain belief.
A closing thought
Pick a small number of accounts. Focus there for 90 days. Learn something specific. Then decide what to scale.
Most teams do the opposite. They go broad early, spread effort thinly, and expect clarity before the work has had time to compound.
Underneath all five of these patterns is the same issue: most ABM programmes aren’t fully designed but they inherit legacy structures and thinking around the most important components that should be producing predictable outcomes: measurement, incentives, workflows.
The tools have improved. The data is richer. But the pressure and organisational change are more complex.
Positive results still come down to a small number of decisions: where to focus, what to trust, and how deliberately the system behind the work is shaped.
That hasn’t changed.