For years, the common refrain in adopting new technology in the enterprise was skepticism around “Garbage In, Garbage Out.” (GIGO)

This mentality is no different in GTM. In some organizations, it's common to hear from RevOps teams that AI can only be effective once Salesforce is clean, every enrichment source is perfectly synced, and every rep follows the sales process as designed. 

Under that model, AI becomes something you adopt after the data is pristine, which usually means never.

In the era of modern LLMs and agent systems, that belief no longer matches reality.

Sophisticated enterprises are already seeing major productivity gains from AI across every business function, even though their data is just as inconsistent as everyone else’s. Years of acquisitions, growth, shifting priorities, and human error are universal. The difference is that AI today can handle it.

What changed is not the cleanliness of data. It is the maturity of the systems interpreting it.

LLMs and agents can read context across calls, emails, product usage, internal docs, and public information with a deep understanding of your business and GTM motions. They can reason about imperfect inputs, fill in gaps on their own, and update themselves as more information appears. This shift redefines what is possible, even in chaotic environments.

Here is why the change is happening and why the most cutting-edge GTM organizations have rethought their approach. 

The Status Quo: Messy Data Is the Norm

Every revenue organization operates with messy data. Inconsistent enrichment, partial notes, stale contacts, forgotten details, and critical information scattered across Slack, email, call transcripts, and internal docs are all standard. This is simply how modern GTM works.

And despite this, some sellers still manage to hit and crush quota.

They don’t blame the data foundations.

They replay the last call when CRM notes fall short.
They search LinkedIn, read 10Ks, and piece together public context.
They validate assumptions live when talking to prospects.
They continually refine their mental map of the account.

It is tedious, but it works. Humans thrive in messy environments because they can reason their way through ambiguity. With the right architecture, AI can do the same at enterprise scale.

Why Garbage In Becomes Intelligence Out

LLMs and agent systems do not behave like old scoring engines or rigid workflow tools. They interpret information. They fill in gaps. They cross check conflicting signals. They maintain memory. They update their understanding every time something new happens.

If you design them with the expectation that data is messy and imperfect, they become incredibly powerful.

Below are the shifts that make the old GIGO mentality obsolete and actually convert disparate data (“garbage”) into real intelligence.

AI goes straight to the actual source of truth

When a contact field is wrong or a stage date is stale, humans turn to the real sources. AI systems do too. 

If the CRM says a champion is unresponsive but the email thread shows they replied yesterday, an LLM can catch that. If product logs show activity that contradicts a field, the agent can understand the real engagement level.

A single bad field is no longer the foundation of a system’s understanding.

AI fills in missing information through autonomous research and real time updates

If the technical buyer is missing, the agent can find one.
If a title is outdated, it can correct it.
If a meeting was never logged, it can pull the transcript and extract the insights.
If a stakeholder switches companies, the agent can spot it.

This constant background work keeps the system aligned with reality.

The CRM does not need to start perfect because the agent is continuously stitching together the truth, the same way a strong seller does.

AI learns from feedback and adjusts its understanding

When a rep adds nuance or corrects something, the agent incorporates it.
When a field is consistently unreliable, it learns to ignore it and can be directly encoded by an admin to not consider it.
When something shifts in the account, it revises its POV immediately.

The agent maintains multiple layers of memory. Semantic memory stores account facts. Procedural memory captures tactics like “when a new stakeholder appears, draft a tailored intro.” Episodic memory stores the sequence of interactions and outcomes.

Older systems held a static view. Modern agents update their understanding constantly.

AI improves hygiene through behavior change

One of the clearest shifts we see in teams using valuable agents is simple: people contribute more context when they get value back.

Today, sellers have almost no incentive to update the CRM. It gives nothing back.

When a persistent per-account agent is helping you drive deals forward and accelerating a lot of critical work in the background for you, we find this changes behaviors.

You’ll want to update notes from an in-person meeting if you know the agent will automatically help you refine the business case and the org chart based off of those learnings.

This flips the old pattern. Instead of clean data being required before AI, AI becomes what makes the data cleaner.

Over time, agent memory becomes the most complete and reliable view of the account. Not because of enforcement, but because of usefulness.

AI provides clear reasoning and explainability

Older GTM systems were black boxes. They scored or recommended with little clarity.

Modern, stateful agents can show their work. They can highlight the email they used, the call excerpt that mattered, the timeline they reconstructed, and the signals they interpreted. 

This makes the system debuggable and trustworthy. It also enables fast iteration because both reps and leaders can easily understand where reasoning needs refinement.

Transparency drives alignment, not blind trust.

Why This Matters

We are in an AI arms race. Every company is moving on two fronts:

1. Adding meaningful AI into their products

2. Transforming internal functions with AI to increase productivity and output

GTM is the highest leverage place to transform because:

1. Companies spend almost 35-50% of every dollar they earn back into GTM

2. Every improvement shows up directly in top-line impact

3. There is a massive gap between the current workflow reality and what is possible with wall-to-wall AI impact

The “wait until the data is clean” mindset may have made sense a decade ago. Today, it is the mentality that will let AI forward competitors gain ground faster than you can react.

The gap is widening. And once it does, it compounds.

Where Actively Fits In

Just because agents and LLMs give you the power to work through messy, inconsistent, real world data does not mean every AI tool delivers on that promise. Most AI products in GTM still rely heavily on rigid “if, then” logic and don’t learn or improve over time. If those inputs are stale or incomplete, the system breaks. You end up with the same GIGO problem, only wrapped in an AI wrapper.

Actively’s enterprise scale system of intelligence for GTM is built for this environment. Our per account agents assume data is messy and incomplete, and they help multi-segment, multi-geography, multi-product teams across 1000s of sellers.

They read and synthesize raw context across calls, emails, product usage, and internal business context. They run continuously and update their own understanding in real time. They maintain layered memory across the lifecycle of an account – helping drive next best actions from top-of-funnel through deal execution and through expansion. All the while, they are providing clear explanations for their thinking, and learn from feedback at every step. 

This is why companies with complex CRMs, multiple products, and fast moving GTM motions such as Ramp, Samsara, Greenhouse, and Verkada are already seeing meaningful productivity gains across the revenue funnel by adopting Actively’s approach.

Actively agents are not fragile systems waiting for perfect inputs. They are collaborators built for the real world of GTM work, where the truth lives across many systems and changes daily.

If you want to see how it works, contact our team of AI GTM experts to schedule a custom demo.