Apply to Register Here

Founded in 2015, Activant is a global investment firm with $1.5 B AUM that partners with high-growth founders and teams that are transforming commerce.

Activant

We’re always looking to learn about new perspectives, new research, and new ideas.

Get in touch

Disclaimers & Terms of Use

Privacy Policy

323 Railroad Avenue
Greenwich, CT 06830
USA

Greenwich

New York

Berlin

5th Floor, 17 Jamieson St
Gardens
Cape Town, 8001
South Africa

Cape Town

Harish is providing the Amazon Prime fulfillment experience to everyone else through a combination of AI and an asset-light network of warehouses across the United States.

Alex is automating workflows for the world’s largest enterprises by using process mining to build the connective tissue for businesses.

Mayan is delivering Autonomous IT to the largest companies in the world through systems of intelligence.

Carsten built one of the largest commerce platforms in the world, which powers enterprise merchants and retailers across the globe and now serves as the SAP commerce engine.

Soups is stopping financial crime in its tracks by building the first end-to-end platform for fraud, risk, and compliance.

Adam is enabling restaurant owners to take back their customer relationships by digitizing their entire online and offline operations in a single platform.

Activant is a research-led growth firm that invests in relentless founders

Read past issues

Monthly Newsletter

Browse Research Library

Research

Perspectives

Home

Activant is a research-led global investment firm that partners with high-growth companies.

We partner with founders who have won their initial battles and are ready for the next challenge.

We typically invest when two things come together. First, the company has the potential to achieve escape velocity and transform an entire industry. Second, we believe the founders can lead the company through hyper growth, and our support can significantly increase the probability of this success. 

The critical time window to escape competitors and transform an industry is not open for long.

That’s why after we invest, the real work begins.

About

We only partner with a few founders per year. But when we do, we’re fully committed.

Our portfolio

Companies

Read our investment rationales and market perspectives.

A team built to support and advise yours.

We invest behind insights.

View all research

Founders who aspire to build something bigger than themselves chose to partner with us at their most critical point.

Team

Scott was born and raised in East London, South Africa. He pursued an honors degree in Accounting at the University of Stellenbosch, where he first developed a keen interest in finance and investing. After completing his studies, Scott moved to London and joined Deloitte LLP in their international markets assurance division, where he qualified as a Chartered Accountant. Investing remained a central focus in Scott's career, leading him to a role at Credit-Suisse Equity Research. There, he analyzed the online retail and sporting goods sectors. Following his tenure at Credit-Suisse, Scott joined BNP Paribas Exane as a sell-side analyst within the top-ranked internet and media team, covering sectors such as video games, media agencies, music, and online classifieds.

After spending nearly a decade working in London, from broadening my experience at Deloitte to sharpening my skills as a sell-side analyst, I discovered a deep passion for investing. I found myself particularly drawn to the dynamic realms of IPOs, mergers, and buyouts. This fascination led me to immerse myself in the worlds of early-stage investments, private equity, and disruptive startups. At Activant, I've found a team that shares my enthusiasm for the startup landscape—a place where collaboration, in-depth research, and lively debate lie at the heart of everything we do.

We often mistakenly believe that AI will simply automate some jobs and augment others. However, the introduction of AI agents reveals a far more nuanced and transformative potential. This isn't just another technological advancement—AI agents represent the next evolution of outsourcing, offshoring, contracting, and technology replacing people, all with a focus on vertical integration. This shift marks a significant change in how we think about workforce management and job functions in the age of AI.

Is "Vertical/Function Specific" the Buzzword for AI Agents?

Between 2000 and 2005, SaaS began to gain traction with companies like Salesforce pioneering cloud-based CRM, initially offering generic functionalities. By the late 2000s, as the cloud market matured, businesses recognized the need for industry-specific solutions, leading to the emergence of vertical SaaS. The early 2010s saw significant growth in vertical SaaS, and by the late 2010s, there was a notable expansion and integration of these solutions.

While it took nearly two decades for applications in software to shift from horizontal to vertical, the integration of AI has accelerated this transition. AI-driven vertical solutions have proliferated rapidly due to their ability to leverage industry-specific data. This has led to a surge in demand not just for vertical solutions, but also for company-specific applications.

Much like SaaS, which evolved from horizontal to vertical applications, AI agents are following a similar trajectory. Initially, AI tools like ChatGPT offered broad, horizontal applications. Now, we see a growing trend towards niche AI applications tailored to specific sectors.

The evolution of the AI market since 2022 makes one thing clear: vertical AI agents are here to stay. However, the crucial question is whether legacy incumbents like Salesforce and Oracle can adapt quickly enough to meet the burgeoning demand for industry-specific AI applications. Or will startups, with their focus on single industries or functions, emerge as the true winners?

To understand the impact of AI agents on corporate structures, it's crucial to grasp the role of human managers and the limitations AI previously faced. Managers have two core job functions:

 Coordinating activities across various isolated workflows, integrating siloed data.

 Using these integrated workflows to make decisions and take actions toward achieving objectives.

In essence, managers excel at re-bundling workflows to drive business goals. Previously, AI could only assist with the first function, coordinating and providing insights from siloed data, as it lacked the ability to seek and achieve goals. AI agents have now overcome this limitation, enabling them to perform core managerial functions effectively.

The challenge with vertical AI agents is that not all verticals are equally suited for AI-driven opportunities. Effective management relies on two key factors:

: The skills and abilities required to complete tasks.

 Easy access to all necessary resources and siloed data.

For vertical AI to effectively serve a specific role akin to a human manager, it depends on:

 The advanced capabilities of the AI agent.

 The level of integration and open access to third-party resources within the domain.

Many industries lack either broad interoperability, sophisticated AI agents, or both. Consequently, some sectors and domains naturally favor existing incumbents, particularly where SaaS providers have already established strong hub positions. This inherent advantage makes it more challenging for vertical AI agents to disrupt these areas successfully.

A 2023 study by Accenture on the transformative potential of generative AI in business identified the banking, insurance, software & platforms, and capital markets industries as having the highest potential for automation and augmentation.

Beyond the financial and insurance sectors, significant developments are also emerging in the legal, edtech and hiring industries. Currently, the AI agent market landscape is dominated by players who either focus on specific verticals or excel in horizontal capabilities. While we have evaluated several pioneering players in these fields, we recognize that this market environment is very dynamic. New industries and companies are continually evolving, demonstrating advanced capabilities. Notably, there is strong movement in heavily regulated industries, where advancements in AI-driven efficiency were unforeseen just a few years ago.

Vertical AI/Agents-Breaking Down the Verticals

This sector has seen significant funding rounds and acquisitions, including 

’s $100m Series C, as well as early-stage investments in AI legal platforms such as 

 ($9m Series A), as well as the $650m purchase of 

 by Thomson Reuters. While industry experts believe there is huge potential for AI to provide legal advice, approximately 95% think that having AI make final decisions in court or in matters of legal, tax, fraud, risk, and compliance would be “a step too 

 Thus, the human-in-the-loop model remains indispensable.

Use Case 1) Replacing human legal assistants

 offers a suite of AI products specifically tailored to lawyers and law firms, addressing all practice areas and workflows. Their custom-trained large language models (LLMs) execute legal tasks and track productivity based on industry-specific KPIs. The AI agent can draft texts, analyze documents, and answer questions in natural language, with a strong emphasis on correct citation and relevant sources. The success of Harvey's custom case law model highlights the significance of tailoring AI solutions to specific industries and use cases. The collaboration between Harvey, OpenAI, and the combined expertise of lawyers and AI researchers has produced a model that outperforms generic foundation models in terms of relevance, accuracy, and source citation. With their recent funding announcement, they plan to scale across other business functions.

In the insurance industry, AI agents range from chatbots assisting with customer service to autonomous systems managing claims and policy underwriting. These agents leverage adaptive learning, decision-making, and end-to-end process automation, embodying the concept of agentic automation. It is a rather mature field, seeing that incumbents such as 

 are offering insurance-specific chatbots and agents that are pre-trained with industry specific data.

Use case 1) Offering industry-specific customer service

 is a conversational AI tool specifically designed for the insurance industry, primarily focusing on customer support, sales, and operations. Use cases include chatbots for customer interaction, lead generation, and internal knowledge bases. They are going a step further with their “Personal Autonomous User Self-Service” AI agent. This agent integrates with the CRM system, allowing it to make changes to a customer's insurance plan or profile upon request.

Some companies that offer multi-industry AI agents, such as 

, also offering specialized AI agent solutions for the insurance industry. These solutions cover a range of service use cases, including claims processing and answering coverage questions. 

 also utilizes AI vision tools to detect license plates and analyze photos of damages for claims. Previously, claims processing involved manual tasks like analyzing unstructured data from various sources such as bills, invoices, and medical documents. AI now automates these repetitive tasks, extracting necessary data and categorizing it for efficient claims processing, significantly reducing the time and effort required from human agents.

Use Case 3) Replacing human insurance assistants

End-to-end solutions that combine previous efforts and technical capabilities in the sector are emerging, providing a solution that moves towards fully replacing agents in the insurance sector. Players like 

 offer a toolset of vertical-specific pretrained AI agents that can be integrated and connected to create workflows from claims management to AML/KYC automation.

When it comes to the role of AI Agents in education, their primary function is to supplement the learning experience and extend the support available to teachers. These Agents can aid in content delivery, provide instant feedback, facilitate communication, and enable personalized learning pathways. While the initial data used in education can often be standardized, the value here lies in the personalization of each individual’s unique learning speed and need for further depth in specific areas. AI can be beneficial in pattern recognition and can push the limits when utilizing this information to create individualized recommendations. Especially with children, the personalized exchange with an AI assistant can create a human-like feeling, required for adaptive learning similar to engaging with a teacher. As technology continues to evolve, so will the capabilities of AI Agents. In this vertical these could be advancements in natural language processing, adaptive learning algorithms, and the integration of virtual and augmented reality.

It is notable that 2021 saw a historic peak in VC investment in Edtech, reaching $20.8 billion, driven by the demand for digital and automated learning tools during the Covid-19 pandemic. Since then, investments have significantly declined, which is a natural progression. This decline can be attributed to the previous surge of companies in this sector and an increased desire for more personal and hybrid interactions following the 

 It remains to be seen if new AI assistants can replicate this human-like interaction and revitalize the industry's investment potential.

Use Case 1) Enhancing operational efficiency

 build a platform where one can monetize knowledge by creating educational content and building learning paths, enabled by AI. What is interesting in Heights’ product offering is the strong degree of automation that can be generated, eliminating annual tasks such as editing, community management and payment processing, so that educators can concentrate on their core skillset of creating educational content.

Use Case 2) Creating educational video content

, utilize AI in the field of content creation, reducing manual work by using AI Avatars to display content. The software is targeted at educators, assisting them increasing tutoring videos to build indefinitely accessible and scalable education channels. Going beyond simply creating the videos with text-to-speech tools, the avatars can answer questions and hold conversations on the education topics. The AI tutor can either be connected to an existing chatbot or a user can create a custom LLM based on the educational materials. Video templates showcase a first step into other functional sectors such as social media or marketing.

Use Case 3) Personalizing learning experiences (replacing teachers)

Taking one step further towards where we see the future of AI in Edtech leads to AI agents taking over the role of actual teachers or educators. This entails utilizing AI to optimize and individualize learning paths, combined with virtual teachers who communicate educational content. 

, for instance, demonstrates such features. It is an AI tutoring platform built for schools, leveraging both existing and individual data to provide personalized education. By offering individual feedback and customized learning cycles, the software extends the capabilities of teachers, enabling them to cover more than they could alone. Flint also integrates various AI features, including text-to-speech, speech-to-text, language translation, and image generation with DALL-E 3.

AI implementation in the finance sector has evolved considerably from rule-based systems used for automating routine tasks. With the rise of machine learning and deep learning, AI has entered a transformative phase. Nowadays, AI agents are adept at analyzing large datasets, identifying patterns, and making real-time decisions. The financial sector holds immense potential for AI agents, addressing issues like productivity loss from frequent task switching and high automation potential. It is predicted to be the industry most significantly transformed by generative AI in the coming years, with the highest potential for AI-led job 

Currently, we see most of AI being deployed in the finance sector in the field of predictive analytics. Solutions such as 

 are used by banks and investment firms to analyze market trends, assess risks, and evaluate economic indicators, aiding in portfolio optimization and strategic decision-making. These AI solutions go beyond mere data analysis by offering recommendations and forecasting trends, leveraging machine learning for enhanced insights.

Use Case 2) Engaging with employees and customers

 are adding the human touch by offering voice agents that act as knowledge providers. These agents deliver real-time market data and engage both internally and externally, offering financial guidance. With access to real-time stock data, market news, and insights, Finley AI serves as a productivity enhancement tool internally, providing financial data, detailed investment insights, and financial knowledge to support better decision-making. Externally, Finley AI functions as a customer service agent, keeping clients updated on financial data and offering personalized guidance on their financial decisions.

As one of the largest global industries, healthcare holds limitless potential for AI agents, especially as rising medical costs for government generates an inherent need for cost cutting. We have already started to see AI become integral across various sub-sectors, enhancing diagnostics in pathology, imaging, and radiology; enabling personalized medicine; accelerating drug discovery; providing virtual health assistance; and facilitating remote patient monitoring. Additionally, AI can streamline administrative tasks like medical documentation and billing and contributes to medical education and training. The entire healthcare value chain has numerous use cases for AI agents. With integration into healthcare IoT and support for ethical decision-making, AI has the potential to transform the entire sector.

Use Case 1) Automating inbound calls in clinics

AI agents are often associated with reducing administrative burdens through tasks like documentation, conversation intelligence, and appointment booking across sectors horizontally. Similarly, in healthcare, 

 specifically automate inbound calls. Additionally, companies like 

 use AI to transcribe and manage medical records.

AI's potential extends far beyond these applications. In imaging and radiology, 

) utilize AI to analyze medical images (X-rays, MRIs, CT scans). 

 has developed AI tools for pathology, aiding in the examination of tissue samples to identify cancer cells, with specific tools for prostate and breast cancer.

Use Case 3) Discovering and developing new drugs.

, which uses AI to revolutionize drug discovery.

The healthcare industry is undergoing a transformative shift with voice AI technology, especially for patient access calls. Voice AI solutions, like those provided by Talkie, are making it possible for hospitals and clinics to offer 24/7 access to essential services, from appointment scheduling, prescription refills through to call navigation and medical triage. By automating routine calls, we not only address critical issues like staff shortages and long wait times but also enhance the patient experience, providing immediate support when they need it most. This level of access and efficiency is critical for modern healthcare, where timely, reliable communication is paramount.”

One of the most developed functional use cases of AI agent deployment is in hiring. AI has been integrated into the hiring processes of major corporations like 

, the Big Four accounting firms, and large U.S. investment banks for several years. Given the numerous repetitive tasks involved, hiring was one of the first business applications of AI. The primary goals are to streamline the hiring process, reduce biases, and provide data-driven insights for better hiring decisions. Despite a mature market, numerous startups are innovating within various hiring tasks or offering end-to-end solutions.

Use Case 1) Automating application processes

 suggests unbiased language for job descriptions and communication, while 

 provides analytics to improve diversity in hiring. In candidate sourcing, AI agents scrape job boards, social media, and websites to identify potential candidates. 

 finds and engages passive candidates through data analysis, and 

 aggregates candidate information from multiple platforms. For automated resume screening, 

Use Case 2) Automating interview assessment and candidate quality assurance

For interview scheduling. candidate assessment is enhanced by AI agents conducting initial evaluations through chatbots, video interviews, and skill-based tests, with 

 applying various tools to assess candidates without HR presence. 

 uses neuroscience-based games with AI to assess cognitive and emotional traits.

 use AI to provide new hires with personalized information, training, and compliance documentation.

Regulations Remain a Stumbling Block for AI Agents

Regulations remain a significant hurdle in the broad implementation of AI agents, as various industries face unique yet similar challenges. The finance and insurance sectors are particularly heavily regulated, especially banks and institutions, where managing sensitive data elevates the importance of data security. Similarly, the legal sector faces significant regulatory constraints, particularly regarding privacy and data protection due to the sensitive nature of legal data. Additional concerns include algorithmic transparency, cybersecurity vulnerabilities, and the potential for unfairness, bias, and discrimination.

While the educational sector emerges as one of the leading sectors in the adoption of AI and AI agents, it faces challenges primarily related to the loss of interpersonal connection and emotional factors. Furthermore, the disparity between the potential of Edtech and its reality is shaped by limited budgets and inadequate technical infrastructure, particularly in underprivileged geographics.

In general, the most substantial constraints arise from ethical challenges and a fear of the loss of emotional, interpersonal exchanges, especially in industries with significant human interaction, such as healthcare and education.

Vertical Solutions Always Expand Horizontally Across Their Domains 

Vertical AI players inevitably evolve to integrate into broader ecosystems within their domains. This progression is driven by end user’s preferences to accomplish their tasks through one or two interfaces rather than switching between multiple specialized applications. Consequently, the long-term success of vertical solutions hinges on their ability to go horizontal within their specific domains, creating a flywheel effect that drives further integration and value creation.

To understand this transition, it helps to examine the rise of vertical software. Vertical software companies typically follow a similar playbook:

Gaining adoption with a focused use case:

 They start with a specific, well-defined problem.

: They expand by adding related features and functionalities that complement the initial use case.

 (starting with e-commerce and expanding into payments and shipping as well as Pos) and Slack (beginning with internal team communications and expanding into project management tools and customer support software), or 

 (initially offering customer support ticketing and expanding into a full product suite including sales automation, chat support, and customer engagement tools). These companies successfully scaled by first unbundling a specific service to solve a single pain point, embedding themselves in customer workflows, and then re-bundled it with additional capabilities to provide a comprehensive solution.

The reason this strategy works is that while disruption often starts with unbundling established services, most venture returns are realized through the subsequent capabilities re-bundling process. By integrating and expanding their offerings, vertical AI players can capture greater value and build more sustainable, scalable businesses.

Vertical AI/Agents Opportunities

We believe as seen in the exhibit above that Healthcare, Financial Services, and Legal Tech represent the industries with the greatest potential for disruption by Vertical AI Agents. These sectors are characterized by massive market sizes, a wealth of available data, stringent regulatory requirements, and historically slow technology adoption. Additionally, they rely heavily on human input for repetitive, data-driven, and highly specialized tasks—creating a perfect storm for vertical AI solutions.

Organizations have long used offshoring, outsourcing, and contract work to access external human resources. However, the introduction of AI agents is changing this landscape, enabling technology to perform tasks previously managed by humans. Unlike external human resources, AI agents can easily be directly embedded into organizations.

This shift marks the beginning of a new phase in technology, where AI agents form structured networks that align with organizational objectives. These AI agent fleets will interact and require new communication protocols for efficient collaboration. We are witnessing AI agents becoming specialized in various verticals, adopting specific industry operations. The growing market of autonomous AI agents will lead to the creation of marketplaces, where hiring an AI agent will become as straightforward as hiring a freelancer on platforms like 

As AI agents become more widespread, compliance and liability issues will emerge as significant challenges and opportunities. Organizations that effectively manage these aspects will gain a competitive advantage, particularly as AI agents operate independently.

 (RevOps), we explored how the advanced capabilities of AI agents can pave the way towards Virtual AI Employees and highlighted the significance of cross-functional data alignment. Combined with the capabilities of vertical AI, the greatest value emerges when these players expand their reach horizontally within the vertical specific domain, gradually integrating additional business functions to form an AI agent workforce rather than relying solely on standalone point solution agents. Companies like 

 are taking significant steps in this direction, starting with a virtual outbound SDR and expanding into other areas such as RevOps and call centers. We believe that advanced vertical players, such as 

, and others who successfully extend their capabilities horizontally, will be the winners in this market.

The information contained herein is provided for informational purposes only and should not be construed as investment advice. The opinions, views, forecasts, performance, estimates, etc. expressed herein are subject to change without notice. Certain statements contained herein reflect the subjective views and opinions of Activant. Past performance is not indicative of future results. No representation is made that any investment will or is likely to achieve its objectives. All investments involve risk and may result in loss. This newsletter does not constitute an offer to sell or a solicitation of an offer to buy any security. Activant does not provide tax or legal advice and you are encouraged to seek the advice of a tax or legal professional regarding your individual circumstances.

This content may not under any circumstances be relied upon when making a decision to invest in any fund or investment, including those managed by Activant. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Activant. While taken from sources believed to be reliable, Activant has not independently verified such information and makes no representations about the current or enduring accuracy of the information or its appropriateness for a given situation.

Activant does not solicit or make its services available to the public. The content provided herein may include information regarding past and/or present portfolio companies or investments managed by Activant, its affiliates and/or personnel. References to specific companies are for illustrative purposes only and do not necessarily reflect Activant investments. It should not be assumed that investments made in the future will have similar characteristics. Please see “full list of investments” at https://activantcapital.com/companies/ for a full list of investments. Any portfolio companies discussed herein should not be assumed to have been profitable. Certain information herein constitutes “forward-looking statements.” All forward-looking statements represent only the intent and belief of Activant as of the date such statements were made. None of Activant or any of its affiliates (i) assumes any responsibility for the accuracy and completeness of any forward-looking statements or (ii) undertakes any obligation to disseminate any updates or revisions to any forward-looking statement contained herein to reflect any change in their expectation with regard thereto or any change in events, conditions or circumstances on which any such statement is based. Due to various risks and uncertainties, actual events or results may differ materially from those reflected or contemplated in such forward-looking statements.

Vertical AI/Agents - Breaking down the Verticals

Rewiring Organizations

AI Agents-Breaking Down the Verticals

The Revenue Operations (RevOps) function, popularized by 

in the 2000s, gained widespread adoption by the late 2010s as businesses recognized the benefits of a cohesive strategy for managing revenue operations. Initially, RevOps faced skepticism from traditional business leaders who viewed sales, marketing, and customer success as separate functions with distinct goals. Executives were hesitant to invest in a new function without a clear track record, perceiving it as an unproven theory rather than a practical solution. This skepticism was heightened by the challenge of demonstrating immediate value as many benefits of RevOps, such as improved alignment and optimized revenue processes, materialize over time.

However, as early adopters reported substantial improvements in revenue growth, forecasting accuracy, and operational efficiency, perceptions began to shift. The advent of RevOps tools equipped with AI capabilities has revolutionized the function, accelerating the transition of capabilities like prospecting, conversational and emotional intelligence, guided selling, deal collaboration, and forecast capture from near-core to core competencies. Customer demand and expectations for these platform capabilities underscore the sector's competitive nature. This is further evidenced by the active acquisition landscape, where companies are acquiring others to protect strategic positions, expand capabilities, or gain market share, as seen with 

 acquisition of Wingman. These deals highlight the vibrant acquisition market as companies enhance their offerings and capabilities.

In today's rapidly evolving business landscape, implementing RevOps tools is a necessity, not a luxury. Integrating RevOps tools drives growth, improves profitability, and streamlines operations across departments. The urgency to adopt these tools is underscored by recent data, trends, and positive customer feedback on companies such as 

, highlighting the transformative impact on organizations.

Strong evidence from improved customer feedback metrics, along with third-party research from 

, and the Harvard Business Review, underscores the critical importance of implementing a revenue operations platform. These sources consistently show that adopting a RevOps approach enhances organizational efficiency, aligns sales, marketing, and customer success teams, and drives revenue growth. Highlights from this research are summarized in the table on the next page.

AI market trends have emphasized the critical need for AI implementation. Activant's article 

 concludes that leveraging RevOps platforms is the most effective approach for scaling AI. This perspective is supported by industry leaders, with 

 reporting a 3000% increase in AI mentions on Russell 3000 earnings calls since 2010, with significant growth in 2022.

Although implementing RevOps platforms and AI is imperative, companies must navigate several key challenges to successfully integrate these systems into their business processes.

In calls with industry experts, we identified seven major challenges to adoption that companies typically face:

 Platforms must seamlessly integrate with existing systems like CRM, email, calendars, and other sales tools. Integration complexity is a significant barrier, especially for companies with entrenched legacy systems or multiple disparate systems. According to MuleSoft’s 2024 

, 95% of leaders cite integration as the top blocker for AI implementation.

 Many organizations struggle with data that is siloed across different departments. Consolidating this data in a way that is meaningful and useful for a RevOps platform can be challenging. Additionally, data quality issues can undermine the effectiveness of RevOps tools, leading to poor adoption and lack of trust in the system.

 Implementing a RevOps platform often requires significant operational and collaborative changes. Staff resistance, especially if benefits are unclear, can hinder adoption. According to

 McKinsey & Company report on Organizational Transformation

, 70% of change management initiatives fail due to employee resistance and lack of management support.

 Platforms require technical and analytical skills that are often lacking in sales or marketing teams. Training and upskilling are necessary to ensure effective use. Salesforce reports that 62% of respondents feel their organizations are ill-equipped to harmonize data systems and leverage 

The cost of implementing a new RevOps platform can be substantial, including software, training, and change management. Companies need a clear path to ROI, which can be difficult to project, especially early on.

 The rapid pace of technological change requires RevOps platforms to continually evolve and adopt new tools like AI and machine learning, necessitating ongoing investment. 

, 89% of UK executives believe that with rapid tech advancement, innovation with purpose is more critical than ever.

Companies often have tools covering various sales, marketing, and customer service functions. A new RevOps platform might overlap with these tools, causing redundancy and confusion about which tool to use.

Overcoming obstacles to implementation and adoption typically requires a clear and detailed understanding of the expected benefits and capabilities that new systems will deliver. RevOps is no different.

As the revenue intelligence market has continued to grow and expand its capabilities, vendors have started to consolidate around a set of core and near-core capabilities. These tools process sales force automation (SFA), market automation, sales engagement, sales enablement, customer success, customer, and external data through AI/ML models. Based on our understanding of the space and the gracious feedback of experts we consulted, we have schematically shown the capabilities of third party RevOps tools, along with the core and near-core capabilities that are common.

We believe that the core capabilities of RevOps platforms should include:

 Tracks, monitors, and provides insight from other platform such as email, calendars, Zoom, Teams, Slack.

 Provides guidance to sellers on what actions to take next, including setting up calls, drafting follow-up messages, providing conversational guidance, and forecasting potential for success.

 Uses activity information, combined with AI insights on the deal health and potential steps, to improve health as well as determine which deals need more focus and attention than others.

Provides sales managers with a view of their team’s pipeline through a combination of AI, activity intelligence, and sales history.

Provides AI-driven forecasts and recommendations at multiple stages of the sales cycle.

As RevOps vendors continuously seek to differentiate themselves by offering a range of additional features, we expect these near-core capabilities to become entrenched in the market which will shift them to required core capabilities over time.

 Logs accounts, contacts, and activities in SFA and associates them with an account, contact, lead, and opportunity.

Provides marketing and sales reps with lead data, AI scoring and guidance on nurturing connections.

 AI analysis of conversations between the buyer and seller, providing active insight to improve interaction quality such as script prompts and guidance as well as insights on the health of the conversations.

 Uses emotional cues from text, audio, or video interactions to provide insight on the health of customer relationships.

 Provides sales with a list of tasks based off AI analysis of deal signals and health.

Enables deal teams to collaborate as they engage a customer through providing one source of truth that funnels customer interaction into one place for all members to see.

Allows managers and leaders to easily submit a bottom-up sales forecast for multiple levels of the sales hierarchy.

As always, companies face the decision to build or buy additional capabilities.

The intense competition in the RevOps sector has fostered a dynamic acquisition environment. Companies are continually seeking to maintain their market share, enhance their current capabilities, or expand their offerings into near-core areas, thus driving healthy acquisition activity within the industry. 

opportunity health assessment/sales readiness

 in 2021 for a record $27.7 billion. Similarly, 

 acquired TopOpps in 2020 to strengthen their 

 purchased Wingman in 2022 to develop their 

Using CrunchBase data, we analyzed acquisitions by major RevOps players since 2018. Key CRM companies like Salesforce, Oracle, and 

 have consistently acquired smaller RevOps platforms to enhance their offerings. Smaller pure-play RevOps companies such as 

 have also made strategic acquisitions to expand their capabilities and stay competitive. This trend underscores the intense competition and ongoing consolidation within the RevOps market, reflecting the sector's dynamic nature as both established and emerging players aggressively enhance their technological capabilities through acquisitions. We expect this activity to continue.

While each company on our market map is impressive, we are particularly excited about the growth and potential buyouts of the best-in-class players and segments highlighted in red. However, our market map is not exhaustive and does not include all companies in the RevOps and Sales Development space. We acknowledge that many companies span multiple segments and continuously add new features as they grow and expand.

With that said, we are excited about companies represented in Conversational Intelligence/Marketing, Automated Lead Management and Sales Readiness.

Vendors in this space often start with a narrow focus and then broaden their scope. 

, a leader in the space, initially focused on conversation intelligence but has since expanded into sales enablement and engagement. Similarly, some vendors are acquired to enhance larger companies' offerings. 

 and Wingman (now Clari Copilot) were acquired by 

: Provides real-time call guidance and conversational analytics. It uses AI to guide sales reps during calls and ensure they follow best practices.

: Uses AI to provide real-time assistance and insights during sales calls. It focuses on improving sales conversations and providing actionable intelligence to sales teams.

Acquisitions are inevitable

Building RevOps Capabilities

Identifying the next generation of businesses poised to transform the world

Office

Hometown

Residence

Scott
Watson

Research Associate

Office

Hometown

Residence

Research

Relevant Experiences

Office

Hometown

Residence

Scott Watson

Research Associate

Office

Hometown

Residence

Research

Relevant Experiences

Scott
Watson