Can AI Sell? I Tested 8 Best AI Sales Assistant Software

I went down another rabbit hole of AI tools a few months ago. But it wasn’t for writing, design, or analytics this time. It was for something outside my expertise: sales.

I’ve never had to hit a sales quota, but I’ve seen the pressure of chasing deals and keeping up with follow-ups. Sales is a constant balancing act of research, outreach, relationship-building, and administrative work that consumes more hours than most people realize. That’s what got me curious about the best AI sales assistant software.

I’d heard claims that these AI sales tools can handle prospecting, draft personalized emails, remind reps to follow up at the perfect time and predict which deals will likely close. It sounded like the kind of magic sales teams could use, but I wanted to see if the reality lived up to the hype. 

Recently, I tested these AI sales assistant software solutions. Some were frustratingly rigid, others made big promises but delivered generic automation that no real salesperson would trust. 

If you’ve ever lost a deal because a lead went cold while you were buried in admin work, or if you’ve wished for a way to spend more time selling and less time organizing, these AI sales assistants might be exactly what you need. 

My Experience with the best AI sales assistant software

AI sales assistant software is like having a smart, always-on sales partner that helps manage the busy work so sales teams can focus on closing deals. While I don’t work in sales, I’ve tested various AI-driven tools designed to automate follow-ups, prioritize leads, generate personalized outreach, and analyze deal trends. These tools enhance productivity by handling repetitive tasks, surfacing insights, and ensuring you’re ready to increase sales.

From what I’ve seen, the best AI sales assistant software pinpoints high-priority leads based on engagement data, suggests personalized follow-ups, and seamlessly integrates with CRMs to organize everything. Some tools analyze past deals to predict which prospects will most likely convert. In short, the best AI sales assistants act like intelligent co-pilots, giving sales teams the insights and automation they need to work more efficiently, close more deals, and stay ahead.

How I tested the best AI sales assistant software solutions

Below are the factors I considered while evaluating the best AI sales assistants. 

• Core functionality and automation capabilities: When evaluating AI sales assistant software, I first consider its ability to automate and streamline essential sales tasks. The tool should handle lead qualification, follow-ups, meeting scheduling, CRM updates, and sales forecasting. The best AI assistants use natural language processing (NLP) to engage with prospects intelligently, adapting their responses based on previous interactions. If the AI lacks genuine decision-making capabilities and simply automates repetitive tasks without adding strategic value, it’s not worth investing in.
• Integration with existing tech stack: A great AI sales assistant tool integrates seamlessly with your current sales tools. I don’t want a system that forces me to restructure my workflow or creates data silos. I check if it integrates smoothly with major CRM platforms like Salesforce, HubSpot, or Zoho and communication tools like Slack, Microsoft Teams, and email clients. Real-time bidirectional data syncing is crucial for accessing updated information without manual data entry. 
• Personalization and engagement quality: Personalization is one of the most significant differentiators in sales. I need AI beyond generic responses and tailoring interactions based on customer behavior, preferences, and previous conversations. The ideal AI sales assistant software analyzes past engagements and adjusts its messaging to improve conversion rates. If the AI sends standardized, robotic messages, it risks alienating potential customers instead of nurturing them. I also check whether the AI supports multi-channel outreach, including email, chat, and even voice, so you can connect with prospects in their preferred communication style.
• Scalability and performance under load: A sales assistant software should grow with your business. Scalability is crucial because an AI that works well for a small team might struggle under increased demand. I assess how many leads, conversations, and interactions the AI can handle simultaneously without affecting performance. Machine learning capabilities are another consideration because I want AI that improves over time rather than remains static. If the software starts lagging or failing to keep up with an expanding sales pipeline, it’s not a viable long-term solution.
• Analytics and data-driven insights: Beyond automation, I need AI that provides actionable insights. The software should track and analyze sales performance, lead engagement, and customer behavior, helping refine your sales strategy. AI-powered reporting should exceed basic metrics, offering predictive analytics on deal closures and sales trends. If the reporting tools are too basic or lack customization, you might still need to rely on external analytics platforms, which defeats the purpose of having an AI sales assistant.

To be included in the AI sales assistant software category, a product must:

• Support sales teams by automating routine tasks and delivering predictive insights
• Use built-in artificial intelligence as a core functionality

*This data was pulled from G2 in 2025. Some reviews may have been edited for clarity.  

I find it impressive how Salesforce Sales Cloud can predict sales trends with AI-driven forecasting. Instead of just generating static reports, it identifies patterns and gives a clearer picture of what might happen next. I can see how this would be useful for sales teams trying to make data-backed decisions instead of relying on instinct. The ability to anticipate changes in the pipeline could help businesses prepare rather than react.

One thing I appreciate about AI-driven sales tools is how they simplify lead management. I’ve seen that Salesforce Sales Cloud assigns priority levels to leads based on their engagement and historical data, which seems like a smart way to focus efforts on the right prospects. I imagine this would help sales teams avoid wasting time on leads that are unlikely to convert. 

I like having AI suggest the best next steps for a deal. I appreciate that Salesforce Sales Cloud analyzes ongoing sales activities and offers data-driven recommendations to keep things moving. If a deal is stalling, it can flag potential issues and provide actionable advice.

I’ve noticed how AI in sales software is becoming more personalized, and Salesforce Sales Cloud seems to lean into that. Based on previous interactions, it suggests the best way to engage with each prospect. I can see how this would help sales reps avoid sounding robotic or scripted. 

I can see how the AI features in Salesforce Sales Cloud might take some time to master. While they offer powerful insights, they also seem complex, especially for sales teams that aren’t used to working with AI-driven tools. I imagine that without the right training, many users might not take full advantage of everything the software can do.

One thing that stands out about AI-driven sales tools is that they’re only as good as the data they work with. The AI’s recommendations could be misleading if the information entering Salesforce Sales Cloud is incomplete or outdated. This would require businesses to be extra diligent about keeping their data up-to-date.

I’ve seen how AI-powered systems can sometimes be rigid regarding customization, and Salesforce Sales Cloud seems to have similar limitations. While AI offers useful recommendations, adjusting them to fit a company’s sales strategy might be difficult. Making deeper changes requires technical expertise or additional paid customizations.

While the basic features provide some automation, the more powerful AI-driven insights often require an upgrade to premium tiers or additional purchases. I imagine this could be a barrier for smaller businesses that want the benefits of AI but can’t justify the extra expense.

What I dislike about Salesforce Sales Cloud:

• I can see how the AI features in Salesforce Sales Cloud might take some time to master. While the insights are powerful, they also seem complex, especially for teams that aren’t used to working with AI-driven tools.
• The AI’s recommendations could be misleading if the information going into Salesforce Sales Cloud is outdated or incomplete. This would require businesses to be extra diligent about keeping their data current.

What G2 users dislike about Salesforce Sales Cloud:

“I’ve found its complexity and steep learning curve challenging, requiring significant training and time. Slow speeds and occasional downtime have disrupted our operations, especially with large datasets. The user interface feels somewhat overwhelming, making navigating and finding what I need difficult. Sometimes, the standard reports and dashboards lack the customization needed for more advanced reporting.”

– Salesforce Sales Cloud Review, Patricio G.

I like how HubSpot Sales Hub offers flexibility in adapting to different sales processes. It doesn’t look like a platform that forces a rigid workflow, which is important for sales strategies that evolve. I would want a system that lets me create pipelines and automation in a way that makes sense for my sales style rather than being stuck with a predefined setup.

I see the appeal of automation for repetitive sales tasks. Things like follow-ups, email sequences, and deal tracking can be time-consuming, so having a system that ensures no lead slips through the cracks sounds valuable. I would want AI-driven insights that optimize outreach without manually checking in on every prospect. 

I’d want a sales platform that provides meaningful insights beyond basic activity tracking, and HubSpot Sales Hub appears to offer that. Access to data on how prospects engage with emails, links, and other content would make it easier to understand their level of interest. Instead of guessing which leads are worth pursuing, you can rely on engagement metrics to make more informed follow-ups.

Sales software must allow users to access detailed reports. HubSpot Sales Hub offers analytics that break down sales performance by individual reps, pipeline stages, and outreach methods. I want a system that helps identify which strategies work and which need adjustment. 

Customization is important in a sales platform. While HubSpot Sales Hub seems flexible, I can see how predefined workflow structures could be frustrating when tailoring automation to a specific sales process. If you had a particular way you wanted to set up follow-ups or deal tracking, you wouldn’t want to run into restrictions that force you to adjust your strategy to fit the software.

AI-powered sales insights sound great, but predictions aren’t always perfect. I’ve seen that HubSpot Sales Hub’s AI-driven lead scoring can sometimes misjudge a prospect’s quality. I wouldn’t want to rely too heavily on AI only to discover that some flagged leads aren’t interested while others get overlooked.

What I dislike about HubSpot Sales Hub:

• While there’s flexibility, I can see how some workflows might feel restrictive. If you had a specific way of managing follow-ups and deal tracking, you wouldn’t want to be forced into a predefined structure.
• AI-powered lead scoring sounds helpful, but I wouldn’t rely on it completely. If the system misjudges lead quality, it could mean wasted effort on unqualified prospects while missing strong opportunities. 

What G2 users dislike about HubSpot Sales Hub:

“There is some redundancy with adding information into fields, but perhaps AI advancements can minimize its need. Specialized templates for certain industries like biotechnology or pharmaceuticals would be a nice addition and minimize the time involved in customizations.”

– HubSpot Sales Hub Review, Verified User in Pharmaceuticals

I find the idea of AI-powered lead prioritization valuable, especially when working with many prospects. Apollo.io seems to take the guesswork out of deciding who to reach out to by ranking leads based on engagement data. Instead of spending time chasing cold leads, this AI sales assistant helps focus efforts on those most likely to convert.

Email timing and messaging play a huge role in outreach success, and I like how Apollo.io uses AI to optimize this. From what I understand, it analyzes engagement data to predict the best times to reach out and which email styles are most effective. That could mean fewer wasted emails and higher response rates, which every salesperson would appreciate.

Keeping up with leads across multiple platforms can be overwhelming, and I appreciate that Apollo.io handles that with AI-driven automation. From what I’ve seen, it can manage email sequences, call scheduling, and LinkedIn outreach, which would keep sales teams on track without manual follow-ups.

I also appreciate that Apollo.io uses AI to suggest personalized messaging based on a prospect’s background, industry, or past interactions. That sounds useful for keeping emails and messages relevant without spending too much time researching each contact. AI-generated recommendations could help make outreach more effective, especially for teams handling many leads.

While AI can be incredibly powerful, it usually takes some time to understand how to use it effectively. If the system requires a lot of input and adjustments before making accurate recommendations, that could slow down adoption. Sales teams don’t always have time to experiment with AI settings, so an overly complex setup might be frustrating. If the learning curve is steep, I imagine some users might not fully take advantage of what AI offers.

AI-generated lead scoring sounds great in theory, but I know that algorithms don’t always get things right. From what I’ve gathered, Apollo.io ranks leads based on engagement and behavior, but no AI can perfectly predict who’s ready to buy. If the system over-prioritizes certain leads while overlooking others, that could lead to missed opportunities.

AI-driven messaging seems useful, but I wonder if it could create unintended biases. If Apollo.io’s AI bases its suggestions on past successful outreach, it might repeatedly push similar messaging styles. That could lead to repetitive communication that lacks originality or fails to connect with diverse prospects. 

What I dislike about Apollo.io:

• AI tools can be powerful, but I know they often come with a learning curve. If Apollo.io requires too much manual setup before delivering accurate insights, that could slow adoption.
• Lead scoring with AI sounds promising, but I’d be cautious about trusting it completely. If Apollo.io over-prioritizes certain leads while overlooking others, that could mean missed opportunities. 

What G2 users dislike about Apollo.io:

“It takes a very technical person to put together your sequencing, workflows, and search contacts properly to put the system to good use. A typical salesperson isn’t able to dive so deep into all the technical aspects of this tool. In other words, ensure you have a technical expert when onboarding this in your business.”

– Apollo.io Review, Chad B.

ZoomInfo Sales makes cold outreach more efficient by providing verified contact information. Rather than wasting time searching for email addresses or phone numbers, I can quickly access a list of prospects more likely to engage. This allows you to scale outreach efforts while maintaining personalization. Additionally, having direct dial numbers means you don’t always have to go through gatekeepers, increasing the likelihood of connecting with decision-makers.

Not all leads are equal, and ZoomInfo helps prioritize high-value prospects. With firmographic and intent data, I can focus on leads that show signs of being in an active buying cycle. Instead of contacting every contact on a list, you can narrow efforts to those more likely to convert. This means you are increasing volume and improving the quality of your outreach.

I also appreciate how ZoomInfo Sales provides detailed information about a company’s tech stack. Access to a prospect’s tech stack makes it easier to tailor outreach with a more relevant pitch. Instead of sending generic sales messages, you can highlight how your product complements or replaces a competitor’s tool.

While ZoomInfo is valuable for enriching data, the accuracy isn’t always perfect. I may still encounter outdated contact details, incorrect job titles, or missing phone numbers. This means users sometimes need to cross-check information before reaching out, adding an extra step to their workflow.

If I’m looking into Asia-Pacific (APAC) markets, I might find the data coverage less reliable than in North America or Europe. Sometimes, the company size estimates or employee counts are inaccurate, making it harder to assess target companies. This means users must supplement their research with other sources to verify details.

While ZoomInfo provides a vast database, sometimes the contacts are redundant or repeated under different listings. This can lead to wasted effort when contacting the same person under slightly different company names or job titles. If I don’t carefully filter and clean the list before outreach, I might accidentally message the same lead twice, creating a negative impression. 

What I dislike about ZoomInfo Sales:

• While ZoomInfo Sales provides valuable contact information, I’ve noticed that the data isn’t always 100% reliable. Sometimes, phone numbers are outdated, job titles are incorrect, or key details are missing altogether.
• One challenge I’ve encountered is that ZoomInfo Sales sometimes lists the same contact multiple times under different entries. If I accidentally reach out to the same person twice, this can lead to wasted effort.

What G2 users dislike about ZoomInfo Sales:

“Due to the huge volume of data, some percentage is always out of date. For instance, a contact might have moved on to a new company, and ZoomInfo Sales might still show them in their previous role, or ZoomInfo Sales might say that they are using a certain infrastructure. When you reach someone and talk to them, you find out they switched suppliers already.”

– ZoomInfo Sales Review, Danny S.

One of the biggest pain points in sales is drafting personalized follow-ups after meetings. With Gong, I don’t have to manually jot down notes or replay entire calls just to extract key details. It automatically summarizes discussions, highlights action items, and sends recaps within minutes. This means users can quickly send impactful follow-ups without second-guessing what was discussed. 

Sales forecasting is usually a cumbersome, manual process, but Gong’s forecasting tool makes it much easier. Instead of juggling spreadsheets and back-and-forth emails, I can see real-time insights. It gives users a clear picture of where deals stand and helps them prioritize outreach. You can also quickly see the timeline of engagements and interactions without digging through multiple sources. 

Unlike other sales tools that require extensive onboarding, Gong integrates into your workflow almost instantly. I have seen users mentioning that their team didn’t need formal training to use it effectively. Calls are automatically recorded and saved without extra steps, so you can focus on selling instead of dealing with administrative tasks. 

When working with multiple stakeholders, details can easily slip through the cracks. With Gong, I can quickly check whether a specific topic was discussed in a previous call. This is particularly useful when aligning with account managers, customer success teams, or other sales reps. You don’t have to rely on secondhand information if a client claims something was promised. You can pull up the exact call and verify what was said.

While Gong captures conversations well, I sometimes struggle to locate specific information across calls. For example, the search function doesn’t always make it easy if you want to see all discussions related to a particular topic from a specific customer. You must sift through multiple results instead of getting a consolidated view of relevant moments. A more refined search capability would make it easier to pinpoint exactly when and where a topic was discussed.

Right now, all my recorded meetings exist in one central location, which can make finding specific calls tedious. It would be useful to have customizable folders or categories for meetings like discovery calls, negotiations, or internal discussions. Instead, I must rely on naming conventions or timestamps to sort through my calls.

When working with multiple stakeholders from a single company, I’d like an easier way to see all past interactions grouped. You must manually piece together different conversations, which can be inefficient. It would simplify my workflow if I could pull up a single timeline that consolidates all calls, emails, and key points from one customer in one place.

What I dislike about Gong:

• If I need to track a particular discussion across different meetings, the search function doesn’t always make it easy. I often have to sift through multiple results instead of getting a consolidated view.
• All my calls exist in one central location, making it hard to find the ones I need quickly. Customizable folders or categories for different types of meetings would help streamline my workflow. 

What G2 users dislike about Gong:

“The search function isn’t exactly top-notch. I don’t usually get the results I am looking for, although some are pretty close. It would be good if I could search based on the types of calls, e.g., cadence, success review, onboarding, etc.”

– Gong Review, Michael O.

The AI in Fathom captures details effectively, even in fast-paced conversations. It ensures that nothing important is missed during sales calls. I can rely on it to extract meaningful insights without reviewing every call. This level of accuracy makes it a valuable tool for staying on top of sales conversations.

The AI significantly reduces the time I spend manually capturing and reviewing notes. It allows users to focus more on the sales process than administrative tasks. This efficiency ensures you can move quickly from one prospect to another without losing track of key details. The time that would have been spent on documentation is now redirected toward engaging with potential clients.

With Fathom, I don’t have to worry about manually initiating the note-taking process on every call. The AI captures and summarizes conversations automatically. This means you can step into meetings without extra preparation related to note-taking.

Fathom does its job without interfering with the actual conversation. It works in the background, ensuring that interactions feel natural and not overly monitored. This makes it easier to build rapport with clients without them feeling like AI is analyzing them. I don’t have to worry about the software interrupting the conversation or creating an awkward dynamic. 

After a call, I can quickly refer to the AI-generated notes rather than going through an entire recording. This makes follow-ups more precise since you can address the exact points discussed. You don’t have to rely on memory or hastily written notes, which can sometimes miss critical details. 

If I’m using Asana or similar project management tools, I must manually transfer notes or summaries. This creates an extra step that reduces efficiency, contradicting the time-saving benefit of AI. It would be more convenient to link Fathom directly with my workflow rather than working around its limitations.

While it excels at transcribing calls, it lacks the flexibility to handle handwritten notes or spontaneous ideas outside of meetings. Sometimes, I want to add quick thoughts or insights that don’t come directly from a call. Having a tool that strictly works within the call context limits how I can use it. 

What I dislike about Fathom:

• Fathom sometimes joins calls when I don’t want it to. I’d prefer more control over when it activates to avoid unnecessary recordings.
• I have to manually transfer notes to Asana, which adds an extra step and reduces efficiency.

What G2 users dislike about Fathom:

“I would love it if Fathom integrated with Asana to create tasks from the action items.”

– Fathom Review, Lorilee P.

I like how Instantly handles follow-ups automatically. You don’t have to manually track when to send a second or third message, saving you much time. The system ensures your outreach stays consistent without you remembering every interaction.

Instantly provides valuable insights into how my emails perform, giving me a clear understanding of what’s working. It helps you track open rates, reply rates, and other key metrics that guide your outreach strategy. Instead of guessing, you can make data-driven decisions to improve your messaging and approach.

One of the standout features is how Instantly uses AI to help personalize emails at scale. Instead of sending generic messages, I can craft outreach that feels more tailored without spending hours on customization. This makes cold emails feel less robotic and increases the likelihood of a positive response.

While AI personalization is helpful, it doesn’t always get the tone right. I don’t like how the suggested messages sometimes feel too templated or lack a natural, human touch. You must still tweak the output to ensure emails sound authentic.

Instantly uses AI to rank or score leads, but no system is perfect. Sometimes, you might prioritize the wrong prospects because the AI misjudges their likelihood of engagement. I dislike that you can miss out on high-value leads that don’t fit the AI’s scoring model. While AI can process data quickly, it still lacks the intuition of a seasoned sales professional.

What I dislike about Instantly:

• While AI personalization is helpful, the messages sometimes feel too templated. I still have to tweak them to make sure they sound natural. Without adjustments, emails may come across as robotic.
• AI sometimes misjudges which leads are worth prioritizing. This means I could miss valuable prospects who don’t fit the scoring model. I still need to double-check rankings instead of relying on AI alone.

What G2 users dislike about Instantly:

“The new lead finder feels like an incomplete idea. Sure, it helps gather leads, but it lacks depth. The inability to craft or automate ice breakers directly within the tool is a huge letdown. It feels like a missed opportunity for personalization, which is key in outreach. Without that feature, it’s just another basic lead scraper.”

–  Instantly Review, Khaled A.

I find it valuable when a tool provides real-time forecasting, making anticipating shifts in the sales pipeline easier. Clari’s forecasting module seems built for that, offering waterfall, funnel, and trend views that help visualize data. Access to these insights allows teams to adjust their strategies quickly instead of reacting too late. I like that it eliminates the guesswork often involved in sales forecasting, which can improve decision-making at all levels.

I appreciate it when a tool removes unnecessary manual work, especially regarding CRM data entry. Clari’s automation helps eliminate the risk of human error while ensuring that sales data remains current. Having accurate records at all times allows managers to make more informed pipeline assessments without wasting time double-checking inputs.

I think having a clear view of at-risk deals is essential before it’s too late to act. Clari’s risk detection system helps highlight weak points in the pipeline, allowing sales teams to prioritize their efforts. Identifying problem areas before they impact revenue makes it easier to avoid potential losses.

I find it frustrating when decision-makers don’t have access to the full picture, which is why visibility across teams is so important. Clari offers leadership teams a way to assess sales performance from multiple angles in real-time. The ability to make adjustments on the fly, rather than waiting for reports, makes a big difference in overall efficiency. 

I think sales teams need flexibility when analyzing data, and Clari’s limitations could be frustrating. Some users wish they had more options for creating custom views and calculated fields. When a tool is too rigid in its reporting structure, extracting the exact insights needed for unique business models can be challenging. 

I think consistency in data reporting is crucial, especially for global businesses. Some users have noted that Clari’s pipeline roll-up process hasn’t worked smoothly in certain regions. If data isn’t consolidated correctly, it can create forecasting issues and confusion for sales teams. 

What I dislike about Clari:

• I find it frustrating when a tool lacks flexibility in creating custom reports. Clari doesn’t offer enough options for personalized views and calculated fields.
• I think consistency in reporting is critical, especially for global teams. Some users have mentioned issues with Clari’s pipeline roll-up not consolidating data correctly.

What G2 users dislike about Clari:

“Some users may find Clari’s analytics and forecasting tools complex, requiring significant onboarding and training. While Clari integrates with many CRM platforms, users occasionally report difficulties syncing data seamlessly, especially with custom CRM setups.”

– Clari Review, Bharat K.

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