Top 10 GTM Stack Frustrations

GTM stack frustrations sourced by internal context, layered with external research.

Liam Barnes

Aug 20, 2026

Every Sales and RevOps team has felt this. You have a big problem, so you review internal process to identify gaps and inefficiencies. A clear gap in capability is identified, so you start looking at technology to solve your problem.

You buy a new point solution that fills 80-90% of your issues, and then spend the next 3+ months building integrations and processes to ensure onboarding and operationalizing this new tool is smooth. As you operationalize these tools, it creates an additional layer of complexity that requires more headcount, more cost, or more frustration that causes usage to drop.

As your environment becomes more complex, frustrations continue to rise and you start the cycle again. So our goal was to identify what those key frustrations were and provide you with options on how to solve them.

Scope and methodology

Our goal of this blog is to identify the top pain points of Sales and RevOps leaders. The internal context used includes anonymized Aurasell customer research, business-value assessments, implementation materials, product documentation, and deal evaluations. It provides direct evidence of recurring GTM-stack problems among organizations evaluating or adopting Aurasell. External research is used only to add broader market context and supporting benchmarks; it does not replace the internal evidence.

The ranking below reflects the frequency, severity, and strategic importance of these themes in the internal context. The exact order is directional rather than a universal industry ranking.

Rank	What Sales & RevOps leaders hate	Evidence from internal context	Broader market context
1	Extreme tool sprawl	Customer 1 used 54 sales tools, many reportedly unused. Customer 2 sought to consolidate 14 tools spanning prospecting through project delivery. Other accounts had fragmented stacks involving CRM, sales engagement, conversation intelligence, enrichment, forecasting, BI, and workflow tools.	External RevOps research identifies tool sprawl and integration complexity as a leading operational challenge; one 2026 survey cited 58% of respondents.
2	Poor data quality and missing revenue context	Customer 3 had documented pipeline, velocity, and data-quality challenges. Internal product and customer context repeatedly emphasizes missing meeting data, inconsistent CRM information, and the need to automatically capture activity, enrich accounts, and populate sales-framework fields.	A 2026 RevOps report identified data quality and governance as the top challenge for 71% of respondents.
3	No reliable single source of truth	Customer 4 had no single source of truth across sales, marketing, and customer success. Aurasell's implementation plan explicitly aims to establish the platform as the system of record, eliminate spreadsheets from forecast calls, and centralize GTM activity.	IBM reported that 97% of Salesforce customers collect diverse data types, but only 24% use that data to transform customer experiences, highlighting the gap between data collection and usable insight.
4	Manual forecasting and weak pipeline visibility	Internal materials cite slow, manual reporting; pipeline and velocity concerns at Customer 3; and the need to replace spreadsheet-based forecasting. Aurasell's forecasting guidance focuses on AI insights, daily snapshots, revenue splits, trend analysis, and reconciling human forecasts with system-generated signals.	Gartner commentary and practitioner research consistently describe forecasting as overly dependent on manual reporting, inconsistent stage definitions, and judgment-based overrides.
5	Low adoption of tools and workflows	Aurasell's implementation playbook treats "Seller Activation & Workflow Adoption" as a dedicated phase. Customer evaluations describe clunky or unintuitive traditional tools, including HubSpot, and emphasize the need for reps to work in one environment rather than across multiple applications.	A State of RevOps survey synthesis found that only about 5% of RevOps professionals believed their stack was fully utilized.
6	High spend with unclear ROI	The Customer 3 assessment estimated $760,000 in annual spend across enrichment, Gong, AI consumption, research, dialing, analytics, revenue intelligence, and automation for a 50-seat deployment. Aurasell's projected annual cost savings was 50%, before additional overhead.	External RevOps research shows that executives increasingly evaluate technology through measurable ROI, utilization, and cost-to-value analysis.
7	Fragmented seller workflows and productivity loss	Internal evaluations describe separate tools for prospecting, intent, enrichment, CRM, engagement, forecasting, CPQ, and feedback loops. Customer 7's evaluation characterized traditional stacks such as HubSpot-centered setups as clunky and unintuitive. Aurasell's value assessment projects substantial time savings for forecast preparation, 1:1s, and deal reviews.	External sales-technology research similarly finds that fragmented systems create context switching, duplicated entry, and less time spent selling.
8	Inconsistent execution of sales methodology	Internal context specifically identifies inconsistent MEDDPICC usage and missing meeting data as pain points. Aurasell addresses this by using conversation data to populate qualification fields, identify decision criteria and next steps, and generate deal-health summaries.	Broader research links inconsistent process adoption and unreliable CRM fields to weak forecasting and poor coaching outcomes.
9	AI promises without the necessary foundation	Aurasell is an AI-native platform designed to centralize activity, automate workflows, improve CRM data quality, and support forecasting. The emphasis on AI Autofill, transcript citations, deal coaching, and workflow triggers reflects a need to make AI operational rather than merely add another assistant.	Salesforce's 2026 research found that 51% of AI-agent users said security concerns delayed AI initiatives. External RevOps research also identifies dirty data, tool sprawl, and unclear ownership as major AI blockers.
10	RevOps governance and administration overload	RevOps must manage CRM mapping, lifecycle stages, integrations, ICP and tiering, deny lists, permissions, workflows, field ownership, forecasting, reporting, and adoption. The need for onboarding support, custom dashboards, domain governance, and integration-user management illustrates the administrative burden.	Gartner research reported that sales and revenue operations teams spend approximately 68% of their time on non-client functions, including analytics and technology management.

*Pulled from internal research at Aurasell and external research

What we have found at Aurasell

1. Tool sprawl is the clearest recurring problem

The strongest internal pattern is not simply that companies have many tools; it is that their tools have become difficult to operate as a coherent GTM process.

Examples include:

Customer 1: 54 sales tools, many unused.
Customer 2: 14 tools across prospecting, sales execution, and project delivery.
Customer 3: Salesforce, Salesloft, Gong, Clari, ZoomInfo, and Highspot, plus complex partner and end-customer activity-routing requirements.
Customer 4: Salesforce, Gong, Clari, Apollo, Clay, People.ai, BI tools, and workflow engines, without a single source of truth.
Customer 5: A 50-seat stack with estimated annual spend of $760,000 across eight major categories.

This indicates that leaders dislike the cumulative operating model: multiple contracts, multiple data models, multiple interfaces, and multiple sources of truth.

2. Leaders want activity and intelligence captured automatically

Internal product and customer context repeatedly points to the same expectation: sellers should not have to reconstruct the deal record manually after every interaction.

Aurasell’s internal capabilities address this through:

Automatic association of emails and meetings with accounts, contacts, and opportunities
Transcript analysis for competition, decision criteria, interest, and next steps
Participant-level summaries identifying roles, priorities, pain points, and stakeholders
AI Autofill for deal-health indicators
Automatic creation of follow-up tasks with owners, deadlines, and transcript citations
AI-generated MEDDPICC and sales-framework fields
Workflow-triggered alerts for buyer intent and deal risk

The underlying complaint is that traditional systems ask sellers to record the information that their daily work already contains.

3. Forecasting frustration is both data and process related

The internal context does not frame forecasting as merely a reporting problem. It identifies several connected issues:

Incomplete meeting and activity data
Inconsistent sales methodology usage
Manual spreadsheet-based forecast calls
Slow analytics and QBR preparation
Difficulty challenging or refining rep forecasts
Need for multi-dimensional reporting by region, motion, product, and line of business
Risk of misinterpreting revenue splits or double-counting

Aurasell’s internal forecasting guidance specifically addresses revenue-split aggregation, daily snapshots, waterfall views, trend analysis, AI forecast insights, and executive overrides.

4. Consolidation is valued because it changes seller behavior

Consolidation is more than a procurement initiative. The goal is to let sellers operate in one environment.

Customer 8, for example, plans to phase in Aurasell over several years, with a near-term goal of replacing Salesloft’s core capabilities and enabling reps to work entirely within Aurasell while initially retaining Salesforce as the CRM.

This reflects a practical concern: replacing a tool is less valuable if sellers still have to maintain the same fragmented workflow elsewhere.

5. AI is valuable only when embedded in revenue workflows

Applied AI rather than generic AI functionality is the focal point. The high-value use cases are tied to specific operating moments:

Capture the next step after a meeting
Identify missing decision-makers
Detect competitive threats
Score engagement
Populate qualification frameworks
Alert managers to deal risks
Create tasks automatically
Generate account-level value hypotheses
Support cross-sell and multi-threading
Compare AI-generated forecasts with human forecasts

Sales and RevOps leaders are not primarily asking for more AI features. They are asking for fewer manual steps and more reliable execution.

How the internal and external evidence reinforce each other

Internal finding	External validation
Customers have accumulated 14–54 tools, many unused.	External RevOps research ranks tool sprawl and integration complexity among the leading challenges.
Aurasell implementation prioritizes CRM mapping, governance, adoption, and a single source of truth.	External research identifies data quality, governance, and stack utilization as major barriers to GTM performance.
Customers struggle with manual forecasting, QBRs, and deal reviews.	Gartner and practitioner research describe reporting and forecasting as major operational burdens.
Internal cases quantify large consolidation savings, including 50% in annual net avoidance.	External research shows increasing executive pressure to prove GTM technology ROI.
Aurasell automates meeting capture, sales-framework fields, tasks, and deal-risk alerts.	External research finds that low adoption and manual entry undermine CRM value and AI performance.
Internal context emphasizes AI-native workflows built on centralized data.	Salesforce and other research identify data quality, security, and governance as prerequisites for AI success.

*Pulled from internal research at Aurasell and external research

The central strategic insight

Sales and RevOps leaders do not hate individual tools in isolation. They hate the fragmented operating model created when too many tools, inconsistent data, and manual processes are stitched together.

The recurring desired state is:

One operational revenue system
Automatic capture of customer and deal activity
Consistent sales methodology and qualification data
Reliable forecasting and pipeline visibility
Fewer tools and lower operating cost
AI embedded directly into seller and manager workflows
Clear governance without excessive RevOps administration

Aurasell’s customer evidence supports this positioning through examples of tool consolidation, improved data capture, forecast automation, workflow adoption, and quantified cost or productivity gains.

*External research confirms that these are not isolated customer complaints but broader GTM technology patterns during 2024–2026.

On this page

Go from human to superhuman operators in under two hours.
Start without risk, scale without limits.

Schedule a Demo