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Selling Smarter: How Sales AI is Reinventing GTM

AI has undoubtedly taken the B2B world by storm, significantly boosting productivity for developers, customer service agents, marketers and increasingly sellers. As a result, the demand for Sales AI is strong and growing rapidly. According to a recent Gartner survey,  over 75% of sales leaders are either currently using, implementing or actively planning to implement GenAI for sales within the next twelve months. 

Our investments in GTM tech companies like 6sense, Clari, Highspot, LeanData, Outreach and Qualified have given us a front-row seat to the profound impact that technology has had across key sales personas, including SDRs/BDRs, account executives and managers, sales enablement and revenue operations. We highlighted the emerging role of AI across GTM in our 2022 market deep dive, “Demystifying the Modern B2B Go-To-Market Tech Stack,” and since then we’ve witnessed how AI has made a substantially greater impact on the way organizations acquire, retain and grow their customer base. We are now more excited than ever about the potential for the latest advancements in AI to unlock the next meaningful wave of efficiency for sales and operations teams alike.

Today, nearly every software company building for GTM teams – from the pre-revenue Sales AI company to industry giants like Salesforce – is placing AI front and center and at the core of their platform and roadmap. While our primary focus in this article is on emerging Sales AI startups, we’d be remiss not to acknowledge how the most established GTM tech companies are adopting AI and rapidly delivering customer value at scale due to their extensive existing market reach.

How the Most Scaled GTM Software Companies are Adopting GenAI

Salesforce formalized its AI stance in 2014 when CEO Marc Benioff declared they would become an “AI-first company” and transform into an intelligent CRM. Over the ensuing decade, Salesforce executed on this vision and today offers a wide range of highly customizable AI apps as part of their Einstein AI platform. For example, users can ask Einstein AI to generate personalized sales emails incorporating customer data from the CRM, surface relevant customer information during support conversations, build custom marketing outreach and customer journeys and more. Developers can also configure Einstein Copilot to act as a conversational assistant, transforming the way Salesforce users interact with the core CRM and the mission-critical customer data that lies within.

Hubspot has also invested heavily in AI for years, culminating in the launch of “HubSpot AI,” the company’s comprehensive AI strategy announced during its customer conference in September 2023. Through HubSpot AI, customers can access an extensive portfolio of AI products and features, including AI Assistants for content generation, AI Agents for automating customer service for SMBs, AI Insights for enhanced analytics, forecasting and recommendations and ChatSpot, which leverages ChatGPT and HubSpot data to automate tasks through a chat interface.

ZoomInfo recently released the newest version of its AI-powered Copilot. ZoomInfo Copilot applies generative AI on top of its B2B and proprietary customer data to segment prospective buyer groups based on intent, build detailed account summaries, answer specific account-related questions through a conversational chat experience and generate personalized email messaging at scale.

While these examples confirm that AI in sales is not a new phenomenon, there have been a number of drivers underscoring the recent explosion of interest in the category.

Why is Sales AI all the Hype Now?

Technology adoption in sales has always been strong, but AI is breathing new life into the category for several reasons:

  • AI technology is quickly maturing, making it increasingly viable for organizations to adopt and trust AI-powered platforms to autonomously engage prospects, capture critical information from meetings, trigger follow-up workflows, suggest coaching tips for sellers and more.
  • Declining sales productivity due to macro headwinds post-ZIRP era has pushed GTM leaders to turn to AI as a cost-efficient and effective tool to increase high-quality top-of-funnel pipeline and enable sales reps to convert more leads.
  • Need to consolidate software costs and strategically align with fewer vendors, after organizations over-tooled their GTM tech stacks. Many AI platforms automate entire workflows and, as a result, abstract away the need for the underlying point solutions that human sellers previously needed to complete the same tasks.

As a result, AI is gaining strong adoption across the GTM function and ushering in a new era for sales.

4 Ways AI is Transforming the Sales Function

Automating and Augmenting the SDR

The modern SDR model gained widespread adoption after Salesforce popularized it in the mid-2000s and early 2010s. Since then, B2B GTM teams have developed highly structured and repeatable playbooks to ensure predictable sales productivity. For example, you hire a specific number of SDRs, set quotas, send outbound messages, and achieve a certain conversion rate. These SDR playbooks are generally based on similar principles regardless of the company, making the function a prime candidate for automation. 

A crucial driver of top-of-funnel productivity, the SDR function has historically offered GTM teams a fixed ratio of output tied directly to the number of ramped reps on the team. This model worked when software markets were less competitive and macro demand was stronger. However, SDR productivity has been declining in today’s selling environment and organizations are desperately searching for new strategies to drive predictable and efficient growth. 

AI-powered prospecting tools are now automating existing SDR workflows and improving the lead generation function altogether. In addition, AI is helping sales teams identify higher quality leads, generate more personalized and effective outbound messaging, automate follow-ups and more, all at a greater scale than previously possible. As a result, SDR teams continue to shrink and organizations are increasingly adopting a full-cycle model where deal closers also generate their own leads. While the SDR role is unlikely to go extinct given the importance of the human touch during the sales process, we believe AI will handle a larger share of prospecting responsibilities over time, driving greater sales productivity and stronger sales efficiency.

Enabling Personalized Outbound At the Scale of Traditional Inbound

Outbound lead generation channels like email and phone have become increasingly saturated due to the widespread adoption of high-velocity outbound prospecting. Nearly 70% of B2B buyers and consumers surveyed by Gartner have set up “junk” folders to deflect unwanted emails. Buyers are inundated with a flurry of generic emails, which typically end up in spam with limited to no engagement. The ROI on sales and marketing investments continues to deteriorate as a result. By leveraging the corpus of readily available digital buying signals—such as product usage, content engagement, community conversations and trial/demo activity—AI-powered sales prospecting platforms are quickly demonstrating their ability to orchestrate personalized, 1:1 outbound campaigns at the same scale as traditional, 1:many inbound programs, enabling organizations to increasingly “cut through the fray” with more targeted messaging at greater scale.

Empowering Account Executives to Win More Deals

With organizations looking to consolidate their GTM teams to prioritize efficient growth over growth-at-all-costs, account executives (AEs) are being asked to do more with less and are looking for tools to automate non-core selling activities. AI is answering the call by assisting AEs across key stages of the deal lifecycle, including account research and preparation, providing real-time guidance during sales conversations, automating administrative tasks like note-taking, CRM data entry and follow-ups and more. With AI, AEs are empowered to focus on what they do best—talking to prospects and selling—while automating away repetitive tasks and leaning on AI to ensure deep and systematic coverage of accounts.

Enabling Effective, Real-Time Coaching and Analytics

Although the first generation of AI-powered coaching tools gained strong adoption over the years, the new wave of AI-native coaching and analytics platforms bring a step-function improvement with superior transcription technology and a modern suite of advanced analytics tools delivered within a highly intuitive UI/UX. As a result, managers are equipped with deeper insights into sales conversations and the ability to provide more timely, consistent and effective coaching to sellers. With vertical-specific coaching platforms emerging and offering a highly tailored user experience out-of-the-box, lower-tech industries that have been traditionally slower to adopt next-gen sales enablement tooling have also started to implement more rigorous sales coaching across their teams.

The Emerging Sales AI Landscape

So, who are the AI companies driving this transformation in sales? Below, we’ve laid out some of the most exciting Sales AI tools today, categorized under the three core components of the selling workflow they primarily enable: Prospecting, which represents the typical SDR/BDR workflow, Selling, which represents the typical AE workflow and Sales Operations, which represents support functions like sales enablement and RevOps. 

Prospecting: Capturing the Lead

List Building & Enrichment

The sales workflow typically begins with reps scouring the buyer universe to identify prospects that fit their defined Ideal Customer Profile (ICP) and aggregating as much relevant lead data (e.g., contact data, firmographic/technographic data) as possible to aid in outreach. 

Next-gen list-building and enrichment tools like Clay, Closefactor, Jeeva (a Sapphire Ventures portfolio company) and Unify integrate with numerous data sources including socials, CRMs and contact databases to accelerate and automate lead identification, capture verified contact information and enrich the account record to streamline outbound engagement.

Research & Preparation

As reps look for ways to better tailor and contextualize their prospect outreach, they typically sift through publicly available information (e.g., public filings, blogs, websites, etc.) and internal account history (i.e., stored in the CRM, across internal docs, etc.) to understand the full picture of the prospect and develop effective messaging. 

Enterprise knowledge search platforms like Glean help reps quickly find and synthesize relevant internal information from tools like Salesforce, Gong, Slack and Highspot (a Sapphire Ventures portfolio company) to efficiently develop engagement strategies and messaging narratives. Poggio provides enterprise sellers with an AI-powered research workspace unifying enablement content, market context, LinkedIn data and more to ensure reps are prepared for every prospect interaction.

Email Enhancement

One of the earliest AI use cases in sales has been email copywriting automation. AI email assistants like Lavender sit alongside sellers as they draft emails and provide real-time suggestions to improve email tone, language efficiency, subject lines and general deliverability. These platforms then leverage sales data integrations, such as Apollo or Clearbit, along with internal CRM context to auto-generate entire email copy once provided a prospect list. This saves significant time for reps and improves the overall quality of their outbound emails.

Voice & Video Outreach

Despite what social media may lead you to believe, cold calling in sales is far from dead. In fact, most C-level and VP-level decision-makers actually prefer speaking with salespeople over the phone. AI-powered parallel dialing platforms like Nooks and Orum are supercharging the outbound voice channel by leveraging AI to accelerate time-to-connect, surface contextualized sales enablement content to reps in real time and structure conversational data to enable coaching and analytics.

As sellers look for differentiated engagement strategies to stand out from the crowd, video has emerged as an effective tool to prospect in a more “humanized” and personalized way. AI video generation platforms like Tavus enable sellers to quickly create account-specific, attention-grabbing video content using simple natural language prompts, which empowers reps to seamlessly integrate video into their workflow without requiring support from video content experts.

Outreach Automation

Once the seller develops their outbound content, AI-powered sales engagement platforms like Outreach (a Sapphire Ventures portfolio company) enable organizations to create automated outreach sequences with conditional follow-up logic, conversational capabilities and scheduling to drive prospects to a meeting or demo. Tofu takes this one step further by leveraging AI to generate hyper-personalized email sequences automatically.

Sales Auto-Pilots & Digital Workers

Generative AI has unlocked virtually unlimited potential for what can be automated, giving rise to the Autonomous Digital Workforce, a new category of AI platforms automating end-to-end workflows and providing organizations with a more scalable approach to increasing productivity vs. proportionally growing headcount. 

For example, Qualified, a Sapphire Ventures portfolio company, recently launched an AI copilot that suggests responses for inbound reps communicating with customers through website chat and auto-engages with specified customer segments within predefined guardrails. Additionally, the company introduced a full inbound AI SDR product that leverages customer data to drive lead capture, meeting scheduling and engagement. 11x and Regie.AI provide outbound-focused autopilot / digital worker platforms that automate the SDR role from lead identification to conversion (i.e., to a meeting booked), reducing the need for GTM teams to scale SDR headcount and, in some cases, consolidating their prospecting tech stacks (i.e., by eliminating the need for bespoke sales data, engagement, research, etc. tools). 

End-to-End Prospecting

End-to-end prospecting platforms offer a single, data-complete interface through which SDRs can build and enrich lead lists, research accounts, develop contextualized and personalized messaging and automate outreach. For instance, 6sense (a Sapphire Ventures portfolio company), offers a multi-persona revenue platform that unifies publicly-available prospect data (i.e., firmographic, technographic, psychographic, etc.), 6sense intent data and proprietary customer CRM data to support reps with buyer discovery, prospect list management and prospecting workflow automation. Amplemarket unifies prospect list building and prioritization, email copywriting, outreach sequencing, a dialer and more to provide an all-in-one platform for sales teams to engage prospects. 

Selling: Winning the Deal

Presentation Building & Meeting Preparation

Throughout the entire sales process, from initial discovery meetings to deal close, sales reps must diligently prepare for each prospect interaction by deeply researching the account and developing thoughtful sales collateral and messaging. 

AI-powered presentation-building platforms like Gamma and Tome empower reps to generate beautiful and engaging pitch decks, project proposals and more using natural language prompts, all while minimizing the time reps spend manually building outputs and standardizing the overall document quality. Databook applies AI to translate millions of market data points into actionable insights and strategic narratives, enabling sellers to craft and deliver the right message at the right time and to the right person.

Sales Meetings

During sales meetings, sellers are often forced to multitask by taking notes to capture key requirements and painpoints disclosed by prospects and other insights that could help drive the sales process. Manually transcribing conversations and then updating the CRM record is incredibly time-consuming and pulls the rep away from what they do best—engaging prospects and closing deals. 

Platforms like Fireflies, and join virtual sales meetings and automatically capture meeting notes, update the CRM record and enable managers to provide lightweight coaching after the fact.

Live Demo

Live product demos, where users can interact with a fully functional, data-complete and personalized version of a SaaS product, enable organizations to demonstrate the value of their product to customers early and in an impactful way, and ultimately improve both win rates and sales velocity. 

As a result, a number of next-gen AI-powered live demo platforms have emerged to streamline and improve the demo experience for sellers and prospective buyers alike. For example, Reprise augments its demo creation platform with AI that can generate intelligent walkthrough guides and auto-populate demo content. Demostack leverages AI to generate tailored demo data libraries and personalize demo experiences for individual organizations and personas. Walnut applies AI to help generate and enrich written content in a demo, like a sales demo opener. Saleo provides a no-code AI modeling engine to translate existing production/demo environments into realistic, functional demos.

Sales Operations: Enabling The Team

Sales Enablement

Sales coaching is getting a makeover. With new and improved vertically-oriented platforms like Rilla, face-to-face sellers, who account for over 70% of sales teams, are now being equipped with AI-enabled conversational intelligence and coaching platforms for the first time. Other players, like Attention, are going horizontal with a particular emphasis on leveraging next-generation AI to improve conversational data capture and power a uniquely intuitive sales analytics experience.


While we’ve mostly focused on the innovation enabling sellers, the impact of RevOps and sales management on sales productivity cannot be overstated. There has been an explosion in tooling to supercharge the revenue “command center” across critical capabilities like sales planning and forecasting, pipeline management, deal management and commissions. 

For instance, TigerEye leverages AI and advanced statistics to simulate how changes in win rates, renewal rates, average deal size, hiring plans and other key sales drivers impact overall sales productivity over time, enabling teams to build highly accurate sales projections. offers a conversational AI analytics experience allowing managers and operations teams to easily understand rep performance, pipeline status and more. Fullcast applies AI to support dynamic territory, quota and capacity planning, automate lead routing, enable in-depth scenario modeling and provide customized analytics. XFactor aggregates internal GTM best practices and advisory insights from the market to power an “always-on” AI assistant that helps sales teams continuously identify new revenue opportunities, optimize rep hiring and retention, cultivate customer relationships and demonstrate customer value during sales conversations.

Building a Sales AI Company? Get in Touch.

Whether attempting to fully automate sales roles with auto-pilots and digital workers or equipping sellers and supporting operations with intelligent co-pilots, the new wave of Sales AI companies is beginning to drive meaningful ROI for organizations of all sizes and across all verticals. In an economic environment that demands efficient growth, sales teams are being asked to produce more leads and drive more conversions, all while being increasingly resource-constrained. To overcome these challenges, we believe sales organizations will increasingly turn to AI, creating a golden opportunity for the near-term adoption of Sales AI technologies. 

As we continue to dive deeper into the space and refine our thesis, here are a few of our most critical open questions:

  1. Which emerging Sales AI companies will break out and build standalone businesses vs. ultimately be consolidated into existing platforms?
  2. Excitement around AI is driving buyers to make experimental purchases, fueling demand for Sales AI products. Which products will deliver a convincing ROI and earn the right to retain customers in the coming years? 
  3. Who will win the battle between copilots and autopilots? Are we ultimately moving towards full autopilots or will there be a place for both approaches longer term?
  4. Does GenAI shift the “center of gravity” in sales tech away from the CRM?
  5. How does the pricing model for sales tech evolve as the category centers around AI, which is increasingly monetized via consumption?

If you’re building or operating in the space, we’d love to hear your reactions to this piece and any perspectives on the market. Please reach out to Rajeev at [email protected], Demi at [email protected], Adi at [email protected] and Sarah at [email protected]. We look forward to hearing from you!

Legal disclaimer

Nothing presented within this article is intended to constitute investment advice, and under no circumstances should any information provided herein be used or considered as an offer to sell or a solicitation of an offer to buy an interest in any investment fund managed by Sapphire Ventures (“Sapphire”). Information provided reflects Sapphires’ views as of a time, whereby such views are subject to change at any point and Sapphire shall not be obligated to provide notice of any change. Companies mentioned in this article are a representative sample of portfolio companies in which Sapphire has invested in which the author believes such companies fit the objective criteria stated in commentary, which do not reflect all investments made by Sapphire. A complete alphabetical list of Sapphire’s investments made by Its direct growth and sports investing strategies is available here. No assumptions should be made that investments described were or will be profitable. Due to various risks and uncertainties, actual events, results or the actual experience may differ materially from those reflected or contemplated in these statements. Nothing contained in this article may be relied upon as a guarantee or assurance as to the future success of any particular company. Past performance is not indicative of future results.