Sales teams spend a significant portion of their day on work that does not directly involve selling, from researching prospects to keeping records and follow-ups up to date.
AI sales assistants have made many of these processes more efficient by reducing the manual work required throughout the sales process.
However, with so many tools now available, the challenge is knowing which one is worth investing in. Each platform is built for a different purpose, so the right choice depends on the part of your sales process you want to improve.
This guide rounds up the top seven AI sales assistant tools based on the sales use case each one serves best, helping you find the platform that fits your team’s workflow and priorities.
These are the seven best AI sales assistant tools:
An AI sales assistant is software that handles a specific task inside the sales process, whether that is drafting outreach, summarizing a meeting, updating the CRM, or analyzing what happened on a call.
Reps spend a large share of the workweek on that supporting work. It is necessary, but it comes out of the time available for actual selling. McKinsey’s research on B2B growth champions found that applying agentic AI to even one part of the sales journey frees up an additional 10% of seller time.
That is the case for choosing based on use case rather than feature count. The tools below each solve a different part of the problem.
AI sales assistants often get grouped together as if they all do the same job. In reality, each one is built to solve a different sales problem. Here’s a quick overview:
Platform | Best For | Key Capabilities | Pricing |
Insight7 | Call intelligence and coaching |
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Clay | Prospecting |
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Qwilr | Proposal creation |
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Lavender | Email outreach |
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Fathom | Meeting notes and follow-ups |
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Scratchpad | CRM automation |
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Spekit | Sales knowledge and enablement |
| Spekit does not publish public plan rates. |
Insight7 is an AI call intelligence and coaching platform for customer-facing teams. It serves sales, customer service, enablement, and quality leaders who need a clearer understanding of what happens during customer conversations.
The platform analyzes calls against custom performance criteria and identifies coaching opportunities. Managers use those findings to guide rep development based on real sales interactions rather than isolated observations or incomplete call samples.
Its approach connects call data analysis with performance improvement. Teams can identify a skill gap, provide focused coaching, and monitor later conversations to determine whether execution has changed.
Image source: clay.com
Clay is a go-to-market platform that helps revenue teams collect data, research accounts, and build prospecting workflows. It is designed for sellers and operations teams that need more complete information about potential customers before outreach begins.
Its prospecting approach combines access to multiple data providers with AI workflows for web research. Teams use the platform to find contacts, enrich account records, monitor buying signals, and qualify prospects based on their own criteria.
Clay is especially relevant when a team’s prospecting problem starts with incomplete data. Its workflows help sellers move from a broad market to a more focused list of accounts and contacts.
Clay separates usage into data credits and actions. Teams may purchase additional data credits or move to a higher action level when their usage increases.
Image source: qwilr.com
Qwilr is sales proposal software for teams that want to create and send web-based proposals. It replaces traditional document attachments with interactive pages that buyers open through a link.
Its proposal workflow covers content creation, pricing, buyer engagement, e-signatures, and payments. Sales teams use templates and CRM data to create personalized documents while keeping presentation and branding consistent.
Qwilr fits teams that have reached a point where manual proposals are slowing sellers down or producing inconsistent buyer experiences. It also gives managers information about how prospects engage with each proposal after it is sent.
Image source: lavender.ai
Lavender is an AI email coach built for sellers and revenue teams. Its main purpose is to help reps write clearer sales emails and improve the likelihood of receiving a reply.
The assistant uses AI technology to evaluate messages and generate insights while a rep is writing. It provides guidance on factors such as personalization, length, tone, and readability.
Lavender also uses historical email data to help teams understand which writing practices are producing better results.
Lavender is suited to teams that already have prospects but need to improve the quality of their outreach. It focuses on the content of each message rather than running the entire sales engagement process.
Image source: fathom.ai
Fathom is an AI meeting assistant that records, transcribes, and summarizes video meetings. It serves individuals and teams that need an easier way to document conversations and share what happened afterward.
For sales teams, Fathom uses AI models to turn meetings into searchable records with summaries and action items. It also supports CRM updates and follow-up workflows, helping reps preserve important deal information without relying on handwritten notes.
Fathom is a practical choice when sellers spend too much time documenting calls or struggle to keep clear records of commitments. It keeps the focus on meeting capture rather than broader call coaching.
Image source: scratchpad.com
Scratchpad is an AI workspace for sales teams using Salesforce. It gives sellers a faster interface for managing deals, taking notes, and keeping CRM information current.
Its automation approach uses AI algorithms to capture information from seller activity and turns it into suggested CRM updates. Sales teams use the platform to reduce repetitive data entry while keeping Salesforce as their main system of record.
Scratchpad is suitable when CRM accuracy depends on reps remembering to update fields after every customer interaction. It brings CRM work closer to the rep’s normal workflow and gives revenue leaders a clearer view of incomplete records.
Image source: spekit.com
Spekit is an AI-native revenue enablement platform for sales teams, enablement leaders, and revenue operations. It centralizes approved knowledge and delivers guidance inside the tools reps use during their work.
Its approach focuses on in-workflow execution. Rather than expecting sellers to search through a separate content portal, Spekit brings product information, messaging, and coaching into systems such as Salesforce, Gmail, and Gong.
Spekit is relevant when sales knowledge exists, but reps struggle to find or apply it during active deals. The platform also helps enablement teams keep content current and understand whether sellers are using it.
Spekit does not disclose its pricing publicly.
Now that you know the available options, the next step is understanding what separates one AI sales assistant from another.
The following criteria will help you evaluate them based on daily operations and long-term value.
Different AI sales assistants are built for different jobs. That means the right choice depends less on the number of AI features a platform offers and more on the problem you want it to solve.
Start by identifying the task that slows your sales team down the most, whether that is prospecting volume, proposal turnaround, or sales coaching that never happens because nobody has time to review calls.
Choosing a tool built for that specific use case makes it easier to measure its impact and avoid paying for capabilities your team is unlikely to use.
An AI sales assistant should work alongside the tools your team already relies on, not force everyone to adopt a completely different workflow.
Check how well it connects with your CRM, email platform, meeting software, or other systems involved in your sales process. The fewer manual steps your team has to take, the more likely the tool is to be used consistently.
The value of an AI sales assistant depends on the quality of the work it produces.
Whether it generates emails, summarizes meetings, recommends coaching opportunities, or updates CRM records, the output should be accurate enough to reduce manual work instead of creating more of it.
Testing the platform with real sales conversations and workflows is often the best way to judge how reliable it will be for your team.
Using AI does not automatically improve sales performance. The real value comes from whether the tool helps your team work more efficiently or make better decisions.
Look for reporting that shows how the assistant is being used and whether it is improving the process it was meant to support. Depending on the use case, that could mean better CRM data, higher email engagement, faster proposal turnaround, or stronger coaching outcomes.
The right AI sales assistant should continue to support your team as your sales organization grows.
Compare pricing alongside factors such as user limits, AI usage, and available plans. Understanding how costs change over time helps you choose a platform that remains practical as your team expands and your requirements evolve.
The right AI sales assistant should solve a defined problem within the way your team already sells. Choosing by use case creates a clearer path from software investment to measurable performance improvement.
For teams focused on customer conversations, Insight7 connects call analysis with coaching. It helps leaders understand which behaviors are affecting performance and gives reps clearer direction for what to improve next.
When call insights and coaching work together, leaders can turn what they learn from customer conversations into clear action for the team. This gives reps a more consistent path to improve and helps your business get more value from every conversation your team makes.
I was able to analyze customer calls I had using Insight7 and I did find it valuable! I loved how it showed the sentiment behind each comment that the client made.
Tobi Oluwole, Co-founder, 3skillz
AI sales assistants reduce repetitive work and give reps more structured support during specific sales activities. They also help managers maintain consistent processes by applying the same guidance or evaluation criteria throughout the team.
Sales automation follows predefined rules to complete repetitive tasks, such as assigning leads or sending scheduled messages. An AI sales assistant, on the other hand, interprets information and produces an output, such as a meeting summary, personalized email draft, or coaching recommendation.
AI sales assistants improve as AI systems learn from new data, user feedback, and changing sales patterns, although results depend on the quality of the information they receive. Advances in generative artificial intelligence and computer systems help these tools deliver more accurate recommendations and automate a wider range of sales tasks.
