Top AI Agents for Natural Language Targeting in B2B Sales

Deep Research Answer for the Top AI Agents for Natural Language Targeting in B2B Sales

AI is revolutionizing how B2B sales teams find and engage their best prospects. In the past, building a target list meant digging through static databases, applying endless filters, and manually verifying contacts – a slow and error-prone process. Data quality was a constant battle, with studies estimating that up to 30% of B2B contact data becomes outdated each year(3). Today, a new class of AI agents is changing the game by allowing go-to-market teams to describe their ideal customers in plain English and letting intelligent software do the rest. These AI-driven agents combine vast data sources with natural language processing to automate audience targeting, lead qualification, and even outreach. The result is faster pipeline generation, higher accuracy, and more time for sales reps to focus on selling instead of searching.

In this article, we’ll explore the top AI agents for natural language targeting in B2B sales. Each solution takes a data-driven, informational approach to finding the right buyers. We’ll look at how they work, their key features, and the real-world impact they deliver – with statistics to highlight their value. From a pioneering agentic platform that builds audiences autonomously, to AI copilots augmenting legacy databases, these tools showcase the state of the art in AI-guided B2B prospecting. Let’s dive in.

Landbase — Agentic AI for Autonomous Audience Building

Landbase is the first agentic AI platform purpose-built for fully autonomous B2B audience discovery and qualification. It allows any business user to find their next customer in seconds simply by describing their ideal market in natural language. There’s no need for complex filters or queries – you just tell Landbase who you want to target, and its AI agent (powered by the proprietary GTM-2 Omni model) does the rest. Landbase’s agent doesn’t merely search a static database; it reasons over a live, massive data graph to identify prospects that fit your description, qualify them against hundreds of signals, and return a ready-to-use lead list.

At the core of Landbase is GTM-2 Omni, a second-generation agentic AI model trained on billions of go-to-market data points. This domain-specific model was designed to unify targeting and messaging: it interprets natural language prompts, searches dynamic data, and even suggests outreach strategies(1). “AI should simplify GTM, not make it harder. With Landbase, finding your next customer is as easy as chatting with AI,” says Landbase CEO Daniel Saks(1). The platform was the first to introduce true natural-language targeting in GTM, via a chat-style interface called Vibe that anyone can try for free(1).

Key Features:

• Natural-Language Audience Search: Users can input prompts like “cybersecurity companies in North America hiring for sales leadership after a Series B” and Landbase will instantly return a fully qualified list of companies and contacts matching that description. The AI understands nuanced criteria (industry, location, job roles, funding stage, etc.) without the user explicitly filtering(1). This means zero setup – the agent figures out the targeting logic on its own.
• Live Data & AI Qualification: Landbase’s data engine spans over 600 million contacts across more than 24 million companies, enriched by 1,500+ business signals from firmographics and technographics to intent and hiring trends(1). Rather than relying on static records, the AI checks real-time signals (like recent funding or job postings) to ensure each prospect is relevant and timely. An AI qualification layer evaluates fit and intent for every query. For edge cases where the AI isn’t fully confident, Landbase seamlessly invokes an offline human-in-the-loop research team to verify and enhance the results (a process they call Offline AI Qualification).
• Lookalike & TAM Modeling: Because the platform has such depth of data, it can identify look-alike accounts and map total addressable market (TAM) segments on the fly. For example, you can ask Landbase to “find companies similar to my top 10 customers” and it will correlate patterns across its 1,500 signals to produce a lookalike list. It can also visualize market coverage – e.g. showing you regions or verticals where you have weak presence versus the full market.
• Omnichannel Outreach Integration: Uniquely, Landbase doesn’t stop at giving you a list – it’s built to feed directly into your go-to-market actions. Exports are instant and login-free; you can generate up to 10,000 verified contacts per query and push them straight to your CRM or engagement tools. The GTM-2 Omni model also pairs targeting with messaging suggestions. In fact, Landbase’s origins were in an earlier system that could pick who to target, what to say, and where to reach them – compressing what used to be weeks of work into minutes(2). Today, Landbase focuses on the targeting piece as the entry point, but it still leverages omnichannel intelligence (email, LinkedIn, ads data) when qualifying leads for best results.

Landbase reports dramatic efficiency gains for users. In initial deployments, companies achieved 4–7× faster audience creation compared to traditional methods, and cut manual list-building costs by up to 80%. Thanks to the human-augmented verification loop, contact data accuracy exceeds 90%, addressing the data decay problem that plagues legacy databases. Perhaps most impressively, pilot campaigns have seen 2–4× increases in lead conversion when using Landbase-qualified targets, versus control groups(1). By compressing the entire go-to-market targeting process into a single AI-driven interaction, Landbase essentially eliminates weeks of tedious research and data crunching. Users report that Landbase has moved GTM from a “tool” to an autonomous system of action – they can generate a precise, AI-qualified prospect list and hit the ground running with outreach, all in one sitting.

Landbase’s disruptive approach is also about accessibility. The platform offers a free audience builder with no login required and no credit card hurdles. Anyone can go to the website, enter a prompt, and get a downloadable list (capped at 10k records for the free tier). This “product-led” growth loop not only provides value upfront but also feeds Landbase’s AI with new prompts and outcomes, continuously improving the model. For advanced needs, Landbase provides an enterprise tier with custom signal integrations and even forward-deployed AI engineers – specialists who work with your team to craft bespoke data signals or CRM enrichments. It’s a flexible model: you can self-serve with the AI agent or bring in human expertise as needed.

Persana AI — Multi-Source Sales Prospecting Copilot

Persana AI emerged in 2023 (through Y Combinator) as a state-of-the-art sales intelligence platform that acts as an AI copilot for prospecting. Persana’s approach is to unify over 75 different data providers and live signals into a single system, then layer on AI-driven automation to find and engage leads. In practice, Persana allows sales teams to generate lead lists and outreach sequences with minimal effort – it automates the heavy lifting of data aggregation, lead qualification, and even some initial engagement. The interface is designed for natural language inputs and simple workflow setups, aligning with the trend of “describe what you need and let the AI do it.”

Key Features:

• Massive Multi-Source Data Integration: One of Persana’s biggest advantages is its data breadth. The platform taps into 75+ data sources – including B2B contact databases, third-party intent feeds, job listing sites, and more – aggregating them behind the scenes(4). It then enriches leads with over 30 data points per contact (like firmographics, technographics, emails, phone numbers, social profiles) to build a comprehensive profile(4). This multi-source “fusion” means that when you ask Persana for prospects, it isn’t limited to a single repository; it’s simultaneously searching dozens of places to maximize coverage and accuracy. In fact, Persana uses a waterfall enrichment approach (sequentially trying different sources) to achieve a higher match rate than any one database alone. According to the company, this yields a better match rate than Apollo and ZoomInfo combined, especially for certain niches or international data(4).
• Natural Language Lead Generation (SalesGPT): Persana offers an AI assistant (often referred to as SalesGPT) that lets you create lead lists by simply describing your ICP or criteria. For example, a user could input a prompt about targeting “VC-backed fintech companies in Europe hiring for data science” and Persana will interpret it and return a list of companies and contacts. Under the hood, Persana’s AI is mapping that request to queries across its data network, but to the user it feels like asking a colleague to do research. This saves significant time compared to manually applying dozens of filters in separate tools. Persana’s website literally poses the question: “What if you could describe your ICP in natural language and AI generates a targeted lead list for you?” – highlighting this capability(4).
• AI Agents for Qualification and Outreach: Beyond just list building, Persana employs 24/7 autonomous AI agents to handle repetitive sales tasks(4). These agents can qualify inbound leads through chatbot-like conversations, follow up with prospects via email, or update the CRM with new intel – all without human intervention. Persana’s agents are programmed to ask the kind of questions an SDR would (e.g. BANT criteria) and route responses appropriately. The platform also tracks intent signals such as job changes, funding announcements, or website visits and can trigger personalized outreach when a prospect shows buying intent(4). In essence, Persana can function as an “AI SDR” alongside your human team: it watches for signals, engages leads with initial messages, and flags the hottest opportunities.
• CRM Integration and Customization: Persana doesn’t operate in a vacuum – it integrates with popular CRMs and sales tools, syncing data and activity. It can combine your internal CRM data with external signals (for example, to prioritize prospects similar to your best customers). This ensures that the leads it surfaces are not only net-new but also contextually relevant to your business. Users can also save prompt “templates” and set up recurring jobs (e.g., refresh a target list every month with new companies that fit the criteria). The platform emphasizes ease of collaboration too, offering unlimited team seats on all plans so that sales, marketing, and ops can work from the same AI-driven insights(4).

Persana AI claims impressive results for teams adopting its platform. According to Persana, sales teams have seen their sales cycle time drop by 65% and conversion rates jump by 30% after implementing its multi-source AI prospecting workflow(4). By automating data gathering and first-touch outreach, reps can spend more time on high-value conversations. Another stat Persana highlights: companies using the platform achieved a 95% increase in qualified leads in their pipeline(4). These gains speak to the power of casting a wider net (through many data sources) while also zeroing in on the right prospects (through AI signal analysis). Persana’s customers also report saving significant hours – one of their case studies notes that the average sales professional saves 8–10 hours per week that would have been spent on manual research(4).

It’s worth noting that Persana, being a newer entrant, is iterating rapidly. Users have praised its richness of data and automation, but also note a learning curve to fully leverage all features(4). The platform’s flexibility (like custom prompt workflows and varied data sources) means initial setup can be complex. Persana has addressed some of this by publishing guides and even introducing an “Autopilot 2.0” feature to streamline usage(4). For small and mid-sized businesses, Persana’s transparent pricing and unlimited user model can be very attractive – it allows entire teams to leverage AI without per-seat costs. Overall, Persana AI stands out as a comprehensive AI-driven prospecting agent that blends data from everywhere and activates it through natural language and automation. It effectively gives you an army of AI “research assistants” that never sleep, ensuring you don’t miss opportunities in your TAM.

Apollo AI — Generative Prospecting on a Vast B2B Database

Apollo is a well-established name in B2B sales intelligence, known for its extensive database of business contacts (over 200 million contacts and Companies, by recent counts)(6). In 2024, Apollo introduced Apollo AI, an umbrella of generative AI features layered on its platform, turning what was once a manual prospecting tool into a more automated, smarter system. Apollo AI is not a single agent but rather a suite of AI-driven capabilities – however, it’s very much in line with the natural language targeting trend. Notably, Apollo’s new release lets users pull lead lists with natural language, not filters(5), bringing an agent-like experience to what used to be a GUI of checkboxes.

Key Features:

• Natural Language List Building: Apollo’s interface now includes an AI assistant (akin to a chatbot) where you can type a description of your target and get back a list of prospects. This is a significant shift from the traditional way of using Apollo, which required applying filters for industry, headcount, titles, etc. For example, instead of selecting filters one-by-one, a user can simply ask, “Show me VP-level marketing contacts at fintech startups in Southeast Asia”, and Apollo’s AI will interpret that and generate a list. Behind the scenes, it uses Apollo’s massive contact database and likely semantic search algorithms to fulfill the request. This saves time and lowers the barrier for less-experienced users to get value from the data. Apollo explicitly advertises this as “Pull lead lists with natural language, not filters”(5) – a key selling point of Apollo AI.
• AI-Powered Recommendations: Apollo AI goes beyond on-demand queries by surfacing recommendations on who to contact and when proactively. In other words, it functions as an AI sales assistant that analyzes your target market and pipeline to suggest the best prospects to pursue next. This feature, similar to a “copilot,” uses Apollo’s extensive data plus your engagement history to identify high-fit, high-intent contacts. According to ZoomInfo (one of Apollo’s competitors), applying generative AI in this manner can tell salespeople who to reach out to and when(8) – Apollo AI offers a comparable capability. It might, for instance, alert a rep that “These 50 accounts show surging interest in your space this week” or recommend new contacts at an account that just had a leadership change.
• Integrated Email/Content Generation: Apollo AI also includes generative writing assistance. Sales reps can use it to create personalized outreach emails, call scripts, and social messages in seconds(5). The AI can pull in relevant details (like recent company news or prospect traits) to customize each message, aligning with the trend of hyper-personalization at scale. For example, it can generate an email opener that references a prospect’s company announcement, saving the rep time in crafting a tailored hook. Apollo lets you choose tone and style to match your brand, and can generate entire sequences or just help with snippets. Essentially, it’s like having a copywriter on call, fed by Apollo’s data on the contact. This boosts productivity and consistency in outbound efforts.
• End-to-End Sales Automation: Apollo has combined its data, AI research, writing, and engagement tools to enable a level of automation across the sales cycle. With Apollo AI, you can automatically add leads to multi-channel sequences, trigger follow-ups based on prospect behavior, and analyze the results – all in one platform(5). For instance, Apollo can auto-dial a contact, send an email, and schedule a LinkedIn message, then adjust the cadence if the prospect opens an email or visits your website. It also provides an AI chatbot assistant that can summarize call transcripts or answer questions about a contact by drawing on the data (built with large language models, reportedly Google Gemini in Apollo’s case(5)). This means sales reps can query, “What are the key talking points from my last call with Company X?” and the AI will synthesize the transcript for them. The overarching goal is to let AI handle the tedious tasks – data research, logging, drafting communications, sequencing – so humans can focus on high-level strategy and closing deals.

Apollo’s blend of a huge database with AI has shown promising results. The company cites numerous customer success stories: for example, BDRs at one firm were able to send 10× more personalized emails by leveraging Apollo’s AI features(5). Another Apollo client achieved a 50% higher reply rate on their cold outreach after the platform helped them target and personalize better(5). These are significant lifts in productivity and effectiveness. Apollo’s AI lead scoring and prioritization features ensure reps spend time on the most promising prospects, which can shorten sales cycles and improve pipeline conversion. The platform still requires careful setup (and good data hygiene) to get the best results – some users note that with so many features, Apollo can feel complex or occasionally lag on large data loads(6). Nonetheless, for teams that invest in it, Apollo AI offers an all-in-one prospecting agent that not only finds leads with natural language ease, but also helps engage them at scale. It effectively marries the strengths of a large contact database (breadth of data) with modern AI (depth of insight), making it a powerful tool for data-driven sales organizations.

Clay — AI-Powered Workflow Agent for Prospecting and Enrichment

Clay takes a unique approach among AI targeting tools – it is not just a database or a single agent, but rather an automation platform that lets you build custom prospecting workflows, enhanced by AI at various steps. Think of Clay as an incredibly flexible LEGO set for lead generation: it has integrations with  75+ data sources (from LinkedIn and Crunchbase to niche scrapers and APIs)(6), plus built-in actions for enrichment, email finding, and more. What makes Clay an “AI agent” in this context is its recent introduction of Clay Sculptor, an AI co-pilot that can generate these workflows using natural language, and Claygent, an AI web scraper that autonomously researches prospects. For growth hackers and RevOps teams, Clay can be a goldmine – it can automate complex targeting tasks that would otherwise require multiple tools or engineering support.

Key Features:

• Natural Language Workflow Building (Sculptor): Clay’s Sculptor feature serves as a co-pilot that can build GTM workflows from a simple prompt. For example, you might tell Sculptor, “Find SaaS companies that recently raised Series A, get their CTO’s email, and check if they use AWS,” and Sculptor will actually configure the multi-step workflow in Clay to do just that. It interprets the request, chooses the right data sources, and sets up the sequence of actions in your Clay workbook. According to a review, “Sculptor is Clay’s new AI copilot designed to help users build and analyze tables using natural language”, eliminating dozens of manual clicks. It excels at generating prospect lists via natural language and creating new enrichment steps from scratch. This is a game-changer for non-technical users – you don’t need to be a coding expert to leverage the power of Clay’s integrations; the AI will piece it together for you. Of course, you can edit and refine the workflow after, but Sculptor gives you a head start in seconds.
• Agentic Web Scraping and Research (Claygent): Clay recognizes that not all valuable data is sitting in a neat database – sometimes you need to crawl websites or social media to find intel on prospects. Claygent is an AI agent (based on large language model techniques) that can perform open-ended web research tasks. You can instruct Claygent to, say, “find me the head of marketing at each of these companies and see if they’ve posted about AI on LinkedIn recently,” and it will attempt to navigate the web, parse content, and return the answers. It’s described as “the most eager-to-please employee you’ll ever hire,” ready to tackle unstructured tasks. Claygent essentially automates what a human researcher might do with Google and LinkedIn, but at scale and speed. This is especially useful for that “last mile” of data – insights that aren’t in any database (like a personalized tidbit from a LinkedIn post or a tech stack detail from a company’s website). By having an AI agent to fetch those, Clay enables a level of personalization and targeting that others can’t easily match.
• Data Enrichment and Multi-Step Automation: At its core, Clay is a powerful data enrichment and workflow engine. It can take a list of companies or people and enrich each with dozens of attributes by pinging various services (Clearbit, Hunter, Snov.io, ZoomInfo, social media, etc.). It uses a waterfall enrichment strategy where it tries one source, then another, to maximize fill rates(6). Users report achieving 85–92% accuracy on their lead lists when using Clay’s email finding and verification in a strict ICP workflow(7). Clay also automates routine tasks: you can set it to auto-refresh data, score leads based on signals (e.g., +10 points if a company just hired a VP Sales), and even trigger actions like adding a lead to an email sequence. One guide noted that by enriching data with over 100+ tools and segmenting intelligently, Clay users can significantly increase email engagement rates(7). Essentially, Clay can be the central hub where raw prospect data becomes refined, segmented, and ready-to-engage output – with very little manual effort once set up.
• Integration and Customization: Clay’s philosophy is to integrate with everything. It has native integrations or connectors for 150+ services, including CRM systems (Salesforce, HubSpot), outbound email tools (e.g., Smartlead, Instantly), marketing apps, and more(7). This means the leads and intelligence you gather in Clay can automatically flow out to where your salespeople need them. For example, you can push enriched leads directly to a Google Sheet or your CRM, or trigger a Slack alert when a hot lead is found. Clay also supports custom code and formulas for those who want to get technical, allowing virtually any custom data manipulation. The platform is as open-ended as you want it to be – which is both a strength and a potential challenge, as mastering all its capabilities can take time(6). To help users, Clay provides an online university of lessons and templates (from basic how-tos to advanced use cases like multi-channel sequences and AI image generation for personalized videos).

Clay is popular among growth hackers, sales ops, and advanced sales teams because it can yield outstanding results if used cleverly. By automating what used to require a small army of researchers and ops people, Clay saves enormous time – one case study mentioned cutting 93% of the time spent on building a highly targeted list, from multiple days to just an hour or two. More concretely, because Clay allows layering so many signals, users can achieve very high precision in targeting (e.g., filtering a list down to exactly the 50 accounts with a specific tech stack and hiring pattern). This precision leads to better conversion rates on outreach. While specific metrics vary, many teams report double-digit improvements: 40% higher reply rates or more, when outreach is powered by the richer personalization that Clay enables (like referencing a prospect’s recent blog post or funding event). Additionally, Clay’s continuous enrichment means data doesn’t go stale – bounce rates stay low (under 5-10% in campaigns) because emails are verified and refreshed, protecting sender reputation(7). The main consideration with Clay is that it’s a toolkit – the ROI comes when you put the pieces together for your specific strategy. For organizations willing to invest some time in building workflows (or now, simply telling Sculptor what they want), Clay can function as a hyper-capable AI agent that automates the entire top-of-funnel process, from lead discovery to CRM entry, with custom logic at every step.

ZoomInfo Copilot — AI Assistance on a Leading B2B Data Platform

No discussion of B2B sales intelligence is complete without ZoomInfo, the industry leader known for its massive database of business contacts and companies. ZoomInfo has traditionally been a go-to solution for sales and marketing teams to find prospects, enriched with org charts, direct dials, and professional insights. In 2024, ZoomInfo introduced its own generative AI assistant called ZoomInfo Copilot to further streamline the targeting and engagement process(8). Rather than a standalone product, Copilot is baked into the ZoomInfo platform as an intelligent layer that surfaces recommendations and automates tasks – effectively acting as an AI agent alongside the user.

Key Features:

• Generative Recommendations (Who, When, What): ZoomInfo Copilot applies generative AI to ZoomInfo’s extensive go-to-market data and your CRM data to suggest who to contact, when to reach out, and what to say(8). This is akin to having a virtual sales analyst constantly combing through the data for you. For example, Copilot might analyze intent signals and alert you that a certain account has spiked in web visits or content engagement (indicating now is a good time to reach out). It could recommend specific contacts at that account who match your ICP and even suggest an appropriate talking point based on news or the contact’s role. By prioritizing the day’s outreach list, Copilot helps reps focus on high-return activities instead of guessing or manually researching triggers.
• AI Email Assistant: Within ZoomInfo, the AI doesn’t just tell you whom to contact – it also helps draft the message. The AI Email Assistant can generate tailored email content for prospects(8). It leverages ZoomInfo’s data on that person and company (like their title, pain points common to that persona, recent company news, etc.) to create a rough draft that the rep can then tweak. This can drastically cut down time spent writing cold emails or follow-ups. Given ZoomInfo’s acquisition of Chorus.ai (conversation intelligence) and other tools, it wouldn’t be surprising if Copilot also eventually uses call transcripts or previous email threads to inform its suggestions. The idea is to combine ZoomInfo’s rich data with AI language generation to produce effective, personalized communications at scale.
• Pipeline Intelligence and Updates: ZoomInfo’s strength is data, and Copilot is likely involved in keeping that data fresh and actionable. It can automatically enrich new leads entering your CRM with the latest ZoomInfo information. It might notify you if a key contact changed jobs or if a company in your saved list announced a new round of funding. In other words, Copilot works in the background to ensure you’re aware of notable changes that could impact your targeting. This addresses the data decay issue – instead of you running searches periodically, the AI agent keeps an eye on things. Additionally, Copilot ties into workflow automations: for instance, it could be set to auto-create tasks in your CRM when a high-intent signal is detected for an account, or to add contacts to a sequence if they meet certain AI-scored criteria.
• Conversational Query (Chatbot interface): While primarily focused on recommendations, ZoomInfo has also experimented with conversational interfaces. One could imagine asking the system questions like, “Find me manufacturing companies in Europe that have recently expanded their sales team”, and getting an answer drawing on ZoomInfo’s database. This is essentially natural language search on their data. Though ZoomInfo’s Copilot is more pitched as an assistant that feeds you insights, the underlying capability to answer ad-hoc questions is there. In fact, trends in the industry (and competitor moves) suggest ZoomInfo will ensure its users can interact with the database using natural language queries, similar to how one can with Landbase or Apollo. The difference is ZoomInfo might lean more on delivering insights rather than just raw lists – e.g., “You have 200 accounts in your territory; Copilot found 5 that show intent for cybersecurity solutions this week.”

ZoomInfo’s Copilot is relatively new, but it stands on the shoulders of an extremely robust data platform that many enterprises rely on. The immediate benefit is efficiency – sales reps using Copilot spend less time hunting for information and more time actually selling. By one account, sellers were able to get up to speed faster and “deliver quick impact” by using AI-fueled prospecting insights from ZoomInfo Copilot. The AI prioritization helps teams focus on the most promising leads, which can increase conversion rates and shorten cycles. While we don’t have public hard stats yet on Copilot’s impact (being in early 2024 rollout), we can extrapolate: If Copilot tells a rep exactly who to contact today (out of a huge database) and gives them a tailored pitch, that rep could potentially handle many more accounts effectively. It reduces the analysis paralysis that comes with big data.

One advantage ZoomInfo has is data accuracy and scale – it’s known for a “massive data foundation” that fuels prospecting and pipeline growth(4). That means the AI suggestions are drawing from a very rich well of information. The flipside is cost: ZoomInfo is one of the pricier solutions on the market (with customers reportedly paying around $250/user per month on average for its premium tiers)(4). Copilot is an add-on that makes this investment more worthwhile by boosting each rep’s productivity. For companies that already have ZoomInfo, enabling Copilot is a no-brainer to get more ROI. For those evaluating solutions, the choice might come down to whether you prefer a hands-on agentic tool (like those mentioned above where you “drive” the AI via prompts) versus an assistive intelligence that works in the background like Copilot. ZoomInfo is clearly betting that busy sales teams want the latter – AI that just works, embedded in their daily tools, surfacing insights without even being asked.

Choosing the Right AI Agent in B2B Targeting

Having examined the leading AI agents in B2B targeting – from fully autonomous systems like Landbase to AI-augmented platforms like Persana, Apollo, Clay, and ZoomInfo – it’s clear that sales teams are entering a new era of intelligence and efficiency. These tools highlight a common theme: Natural language targeting and AI-driven automation are compressing the go-to-market process from weeks to minutes. Instead of wrestling with spreadsheets and stale data, GTM professionals can now partner with AI agents that continuously learn and improve with each interaction. The result is not just more leads, but the right leads, reached at the right time with the right message.

For organizations, the takeaway is that embracing these AI-powered solutions can translate to tangible gains – faster pipeline generation, higher conversion rates, and significant time savings. In an environment where B2B data can go bad at a rate of ~30% a year and buyer interest is often fleeting, having an always-on AI assistant can be the definitive competitive advantage. The intent of these platforms is informational and data-driven support for sales: they turn the wealth of information out there into actionable targeting decisions, often in a fully automated fashion.

References

1. businesswire.com
2. thecscafe.com
3. superagi.com
4. persana.ai
5. apollo.io
6. warmly.ai
7. salescaptain.io
8. cdpinstitute.org

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