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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.

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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.

The first enterprise AI mandate was simple: 

. In the ensuing rush, enterprises have inadvertently built a digital Tower of Babel. Twelve months later, leaders are staring at a familiar mess: overlapping copilots, shadow keys, brittle automations, and invoices nobody can explain. This "AI sprawl" is now a direct threat to enterprise security and efficiency. Security teams can’t decipher what touched what. Finance can’t tie spend to outcomes. Operators are drowning in tabs. This isn’t a feature problem or a “pick a better LLM” problem. It’s a 

We believe the next mandate will be stricter: 

. The solution to this AI procurement mess is not another point solution, but a different architecture entirely. One emerging solution is the 

, the potential end state for enterprise adoption: a centralized "studio" for an enterprise's entire AI workforce.

These platforms are not just tools; they are marketplaces. We don't expect a winner-takes-all market. Instead, enterprises will rely on a few core platforms to consolidate the number of vendors they engage with. Their power lies in offering a comprehensive set of solutions that caters to the entire organization:

 who doesn't want to build: a marketplace to 

 a vetted, pre-built agent for their specific workflow.

 who wants to build: low-code and no-code frameworks to safely 

 and deploy agents that work precisely as desired.

 who must maintain a competitive edge: the full stack of tools to 

 unique, proprietary workflows, ensuring AI creates differentiation rather than parity.

This architecture functions as a unified control plane, transforming AI from a siloed feature into a governed, enterprise-wide system. Visually, this platform sits at the center of the enterprise:

This architecture represents the end state: a rational, secure, and observable AI ecosystem. To understand why this shift is urgent, we must first analyze the chaotic reality most enterprises face today.

The promise of enterprise software was to break down silos, yet the first wave of AI adoption achieved the exact opposite. Driven by departmental needs and a lack of central strategy, enterprises stumbled into a fragmented procurement process that created more complexity, not less. This chaotic approach results in what we call the "AI Frankenstack," a fragmented, department-led procurement process that creates more complexity than value.

As the diagram illustrates, this well-intentioned collage of tools multiplies risk and hides cost. It's the core driver of what the 2025 MIT AI Report calls the "GenAI Divide." Despite $30–$40 billion in enterprise investment, 95% of organizations report zero measurable profit and loss (P&L) impact from their AI pilots

of organizations report zero measurable impact from their AI pilots.

This "tool bloat" has become a major hidden risk. While precise numbers vary, the complexity is undeniable, with numerous organizations managing dozens of overlapping security solutions. Attackers are actively exploiting the blind spots. Organizations with complex, fragmented security stacks face significantly longer times to identify and contain breaches, often taking months longer than those with consolidated platforms, according to IBM's 2025 Cost of a Data Breach Report

The pain extends beyond security. This proliferation of vendors imposes a hidden tax on every AI initiative. Each new tool introduces unique APIs, forcing technical teams to spend months on custom integration instead of strategic development. In regulated industries, every new vendor requires a separate, costly cycle of security audits.

This decentralized buying process has created “automation islands,” a patchwork of AI tools managed in isolation. Instead of a unified system, enterprises end up with distinct, disconnected stacks for each function. The average enterprise stack has become a complex map of these automation islands.

The diagram provides a simplified illustration of this phenomenon. We consider this a bare minimum view; a mature organization typically has at least one vendor active in each of these subsectors, and often redundant tools within them. The true complexity far exceeds any simplified illustration. Real-world enterprise stacks encompass numerous additional subsectors and layers of technology, with significant variations in complexity from one organization to the next.

The functional siloing accelerates fragmentation:

, and Salesforce to score leads and automate CRM updates.

https://www.outreach.io/

https://www.gong.io/

 purchase a different set of tools, like 

, to generate content and personalize campaigns.

https://www.jasper.ai/

https://business.adobe.com/products/marketo.html

 implement yet another collection of solutions from vendors such as 

https://www.anaplan.com/

https://www.stampli.com/

for screening resumes and managing onboarding.

https://www.workable.com/

https://www.hirevue.com/

This departmental free-for-all has led to adoption fatigue. Some reports indicate the average enterprise uses hundreds of SaaS applications (Okta's 2023 report cited over 300), overwhelming knowledge workers

This friction has created the so-called "shadow AI economy". MIT confirms this, finding that while only 40% of firms provide official AI subscriptions, a staggering 90% of employees report using personal AI tools at work. This shadow usage delivers real productivity but operates completely outside of enterprise governance, creating security and compliance risks. It is a clear signal that employees are demanding capable tools, and the official procurement process is failing to keep up

The Frankenstack is a temporary and unstable equilibrium. Operational pain, security risks, and spiraling costs are creating market pull for a new operating model.

MIT’s report identifies the core problem as a learning gap: tools fail because they 

"do not retain feedback, adapt to context, or improve over time"

. This is precisely why the solution is not another point solution, but a platform.

This is where the strategy pivots. The goal is no longer to simply 

 a cohesive, intelligent workforce. This new platform ecosystem integrates two components: the Agent Studio (the "build" solution) and the AI Marketplace (the "buy" solution).

The clearest parallel is Apple's iOS ecosystem: Xcode is the "Agent Studio" where developers build custom applications, while the App Store is the "AI Marketplace" where users acquire pre-built, vetted solutions. You don't choose one or the other; the power comes from having both within a single, governed environment.

The point isn’t to choose build or buy; it’s to do both without fragmenting governance. This platform-based approach fundamentally resolves the traditional build vs. buy dilemma. Instead of forcing a binary choice between slow, custom-built tools and rigid, off-the-shelf products, it provides a hybrid model that captures the benefits of both.

This combination of a "build" studio and a "buy" marketplace is what defines the new control plane. But to be effective, that studio component must be more than just a wrapper; it must be a production-grade factory.

An Agent Studio is not another notebook or a clever wrapper. It is a production-grade, opinionated factory that enforces the non-negotiable components required for production AI:

Agents never borrow human credentials, running under least-privilege service accounts with clear boundaries.

Geography rules, PII handling, model choices, and allow/deny lists are enforced automatically — not socially.

 Redaction is a capability in the runtime, not a policy document.

 Agents remember only what they should, closing the “forgetful tools” gap.

 Every tool is signed, rate-limited, and approval-gated, with explorable logs so you can replay the 

 Assemble multi-step, multi-agent workflows on a canvas.

 Flows are versioned, reproducible, and friendly to continuous integration (CI) and continuous delivery/deployment (CD).

 First-class eval loops, sandboxes, and a reasoning paper trail measure accuracy, safety, and cost prior to GA.

 Every call is traceable, replayable, and priced. Tag spend to workflows so you can defend it in a budget review.

These principles are not just a checklist; they must be architected into the agent's core workflow. A production-grade agent's lifecycle is a sophisticated, multi-stage process of security, policy, and governance checks from the moment a prompt is received to the moment an output is delivered.

This level of built-in governance is what makes an AI agent trustworthy enough for the enterprise. However, even the most secure and well designed AI will fail if employees refuse to use it. This brings us to the next barrier to adoption: the user experience.

The barrier to successful AI adoption is simple but lethal: enterprises don’t need one more tool; they need AI that lives invisibly within the tools employees already use. "Tool fatigue" means any new standalone platform is often perceived as an extra burden, leading to low adoption.

This fatigue is measurable. MIT found that enterprise-grade systems are being “quietly rejected”: while 60% of organizations evaluated such tools, only 20% reached pilots and a mere 5% made it to production, most failing due to brittle workflows and poor integration with daily operations. The lesson is simple: if AI isn’t embedded where work already happens, it won’t be adopted

Work happens in collaboration suites. Microsoft Teams reported 360 million monthly active users (MAU) as of June 2025, while Slack boasts an estimated 47 million daily active users (DAU) in 2025

Embedding into existing surfaces outperforms launching a new tab.

Successful platforms will prioritize deep, native integrations:

 Employees trigger agents directly from chat, eliminating context switching.

 A sales agent operates directly inside Salesforce. A finance agent reconciles invoices inside SAP.

 An HR agent appears as an "approve/reject suggestion" inside Workday, not as a separate portal.

The new generation of agent builders is taking this principle to heart. 

 with Model Context Protocol (MCP) support, allowing teams to promote no-code flows into tool-using agents that trigger from Slack. It’s a subtle but important shift: automation becomes ambient, living inside existing tools rather than beside them.

https://www.gumloop.com/

Making AI invisible removes the primary barrier to adoption and unlocks the productivity gains missing from most pilot-stage projects. The enterprise systems that succeed won’t feel like software upgrades at all; they’ll feel like work simply got easier.

While the move to governed Agent Studios is not yet widespread, the 

, driven by market forces and tangible economic proof points:

 MIT notes that while over 80% of organizations have piloted tools like ChatGPT, these primarily boost individual productivity, not P&L performance

 The biggest EBIT gains only show up when core workflows are rewired, precisely what Agent Studios are designed to enable.

 90% of employees use unapproved AI tools, proving that "ban and block" doesn't work. The only answer is a governed path that’s fast enough to win behavior

 Data breach costs remain stubbornly high, with the global average declining slightly in 2025 but the US average hitting a record $10 million per incident. AI adds new supply-chain edges and attack surfaces (e.g. Prompt Injection, Model Poisoning) requiring first-class governance

 The EU AI Act is in force, with general application starting August 2026 and full effect by 2027. Improvisation accrues liability; a single control plane accrues value

 At production scale, governed agents have collapsed cycle times. Klarna's customer-service agent handled roughly two-thirds of chats, cut resolution from 11 minutes to less than 2 minutes, and was projected to add $40M in annual profit, though this figure stems from earlier 2024 reports. This is a rare example of the 5% of successful pilots cited by MIT that deliver tangible, multi-million-dollar value

 Forecasts show global AI spend potentially doubling by 2028. As compliance costs rise, buyers will trade dozens of experiments for a few governed platforms that pass audits and scale

Most AI efforts stall in the gap between demo and deployment.

MIT calls this the "pilot-to-production chasm". The 5% of organizations that successfully cross it do so by demanding deep customization and holding vendors accountable to business metrics, not just buying 

The bridge between the two is built from a few straightforward ideas:

 Not “help sales,” but “produce a client-ready proposal in three minutes with <2% hallucination rate.” If you can’t measure it, you can’t improve it.

 Choose your p50 and p95 latency budgets and decide what happens when they aren't met. No SLO, no promotion.

 Treat evals like CI. Every model or prompt change runs the full suite. Failing agents don’t ship.

: Start with tightly scoped identity and tool access. Expand only as reliability and confidence grows

 Tokens are trivia. Time, quality, and dollars per accepted result are the scoreboard.

The opportunity presented by AI agent platforms is not a niche play but a chance to capture a significant share of one of the fastest-growing technology markets in history.

 The broadest view is the global AI market. Grand View Research projects this market to grow from 

 A more focused measure is the Enterprise AI market, estimated by Mordor Intelligence at 

 Within this, the market specifically for AI Agents is projected to grow from 

$5.1 billion in 2024 to $47.1 billion by 2030

 This validates our thesis: the agentic paradigm is where new enterprise spending is concentrating. Assuming a winning platform captures 5-10% of the AI Agents market, this translates to a serviceable obtainable market of approximately 

$2.3 billion to $4.7 billion annually by 2030

The Arena: A Field Guide to the Platform Wars

This is an ecosystem race, not a single-vendor sweep. The race to become the dominant platform for the agentic enterprise is intensifying, pitting the scale of tech giants against the focus of new startups. This market is not a single category but a complex, multi-layered stack. To navigate it, we first need a map of the key domains, from the foundational developer frameworks to the application layer where users and agents will interact.

As the map illustrates, this is a complex, multi-layered ecosystem. The race to own it is being fought between two primary forces: the established incumbents, who are leveraging their massive scale and the new, focused challengers.

The incumbents are not standing still; they are aggressively defending their turf by attacking from different angles:

, their agent runtime (Cortex Agents, Mosaic AI Agent Framework) has a first look at your workloads

 Suites with deep hooks into collaboration (

 Apigee) can make agents feel native (Azure AI Foundry, Vertex AI Agent Builder).

 Some platforms lead on runtime security and non-human identity (

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.

Build & Buy in One Governed Stack

The Agent Control Plane

Conversational AI and the Future of Customer Service

n early 2024, the market thesis was centered on realism: Could generative AI replicate natural, multi-turn dialogue well enough to move beyond clumsy Interactive Voice Response (IVR) systems and high-turnover human call centers? At the time, we focused on how AI would disrupt the $314 billion call center market by achieving human parity in sound and 

 Almost two years later, that feels outdated. Infrastructure improvements have pushed response latencies below 300 milliseconds, aligning AI interaction with natural human speech. The standard for realism itself has elevated significantly and what seemed ambitious is now table stakes.

What began as a customer service tool is developing into a cross-industry infrastructure shift. Industries including healthcare, finance, retail, gaming, translation, education, and automotive are integrating voice capabilities. Even Wimbledon has adopted electronic line-calling technology that uses voice systems to call “out” or “fault” on the 

 However, a critical tension has surfaced: while consumer-facing AI applications proliferate, enterprise adoption for core processes remains measured and cautious. The reason for this gap will define the next phase as the competitive focus migrates from “sounds human” to “thinks human.”

Differentiation no longer comes from voice quality alone; it comes from memory, context-awareness, and verifiable governance. In this report, we revisit our initial assumptions and explore where true, defensible value is accruing.

A note on terminology: While often called "chatbots," that term fails to capture the complexity of systems integrated into enterprise workflows. We will use "voice agents" to describe AI that handles complex tasks, memory, and actions.

Revisiting Our 2024 Thesis—Hype vs. Enterprise Reality

Our initial analysis predicted consolidation and easier technological adoption. The market, however, proved more nuanced. Enterprise buyers, wary of brand risk and integration complexity, have prioritized control over speed, reshaping go-to-market strategies and technology moats.

Thesis Revisited: Adoption Pacing and the Wedge Strategy

 Full automation would rapidly displace human agents in customer-facing roles.

 Augmentation has taken precedence over full automation. The vision of fully autonomous agents ran into the high stakes of brand reputation. McDonald’s, for example, faced a public relations headache when viral videos exposed incorrect orders from its automated 

Enterprises have learned to separate ambition from deployment reality. A 2024 Gartner survey revealed significant consumer apprehension, with many fearing AI would make it harder to reach a 

 Consequently, leaders are implementing Voice AI through a 

: targeting high-volume, lower-risk use cases first. This typically includes after-hours support, overflow call routing, and internal agent assistance (e.g. summaries and recommendations). This strategy works because it delivers immediate ROI without jeopardizing core operations. For instance, startups like 

 target automotive dealerships where 56% of leads arrive after 

 By capturing and qualifying these leads instead of letting them go to voicemail, the voice agent demonstrates clear value, building internal trust for future expansion. Since most enterprise adoption paths are multi-modal, the wedge strategy provides an entry point that expands into omni-channel automation and ultimately positions the voice agent as the system of record.

Yet despite these gains, board-level concerns persist. In July 2025, the World Economic Forum reported that as AI becomes more agentic in enterprise operations, issues of trust, explainability, data control, and regulatory compliance continue to delay 

Thesis Revisited: Market Consolidation and Switching Costs

 The market would consolidate rapidly, with category leaders emerging and as many as half the players on our initial market map disappearing within a year.

 Consolidation has been slower than anticipated. According to CB Insights, most Voice AI companies are still iterating on product rather than achieving true 

 The field remains fragmented, buoyed by broad definitions of the category and steady VC funding (Voice AI companies raised $371M in equity funding by July, matching the total for all of 2024 within just the first seven months of the year

The primary barrier to consolidation is not a lack of M&A interest, but high switching costs. Our initial thesis assumed stack modularity would simplify migration. However, 

enterprise adoption continues to move at the speed of IT, not the speed of innovation.

 Activant’s expert network notes that while an SMB might switch vendors in weeks, large enterprise migrations stretch from eight to twelve months. In regulated sectors like healthcare, compliance verification can add another two to four months.

These barriers are less contractual than technical and operational. Systems are deeply coupled with provider-specific IDs and workflow logic. Re-implementation requires re-training staff and sacrificing operational knowledge built around a specific vendor’s quirks. As one product head noted in an expert call, "The longer you stay with one vendor, the more cost-effective it becomes to continue."

The bottom line: enterprises are moving cautiously, redefining defensibility and pointing us toward the next phase of value creation in Voice AI.

The New Moats—Architecture, Memory, and Pricing

The market’s cautious pace and fragmentation point to a new set of requirements. Value has adapted from off-the-shelf model performance to full-stack control and contextual intelligence. In enterprise contexts, voice quality is the customer experience, and small improvements translate into measurable business value.

From Commodity to Technical Moat: The Return of Architecture

In 2024, many Voice AI vendors appeared interchangeable, leveraging similar underlying foundation models from hyperscalers. From the outside looking in, our internal feature analysis revealed limited visible differentiation, making it difficult to identify clear technical advantages. Today, however, the market is rewarding companies that own more of their stack to control performance and cost.

A crucial distinction has since emerged. For basic applications, "good enough" voice realism is quickly becoming commoditized. But for enterprise-grade use-cases, voice quality remains a moat.

 The difference between a neutral but robotic voice and one that conveys emotional nuance can drive a 20% increase in call 

 At scale, that improvement reduces call transfers, improves Customer Satisfaction (CSAT) and Net Promoter Scores (NPS), and unlocks millions in cost 

 A premium tier of proprietary models offers demonstrably superior emotional nuance and prosody. This elite quality tier is far from a commodity and serves as a differentiator for market leaders capable of developing their own core speech synthesis technology.

 illustrates this shift. It wasn’t just buying a library of realistic voices; it was securing proprietary technology that offered full control over the speech generation process and guaranteed sub-100ms 

 Voice synthesis is evolving from a feature add-on into core infrastructure, becoming as valuable as foundation models 

 provides Meta with scalable text-to-speech, while 

 brings advances in emotional intelligence and its pursuit of the “Speech Turing Test,” which measures whether listeners can tell human and synthetic speech 

 adds enterprise agents that think, adapt, and act independently to deliver human-like service to their Contact Center as a Service (CCaaS) 

 These deals signal a race toward deeper technical capability, where full-stack ownership delivers reliable and scalable enterprise products, differentiating them from vendors who are merely "wrappers" on third-party APIs.

This leads to the fundamental architectural choice facing developers today: cascading pipelines versus speech-native models.

Traditionally, Voice AI has followed a cascading three-step process, with each stage often powered by different vendors. Speech-to-text (STT) engines like 

 first transcribe audio into text. Large language models such as OpenAI or Gemini then generate a response, which is finally converted back into voice through text-to-speech (TTS) providers like 

. The voice stack is typically stitched together by orchestration platforms such as 

, combining STT, LLM, and TTS components with telephony integration, observability, compliance, and security, while also adding conversational polish through features 

 Detects precisely when a speaker has finished talking

: Lets users cut in without breaking the conversation flow

 Blocks background speech from TVs, echoes, or other voices

 Inserts natural affirmations like “yeah” or “got it” at the right moments

 Reads user tone and passes emotional context to the LLM

 Adds natural pauses and fillers (“um,” “like,” “so”) to make responses more human

Cascading pipelines remain enterprise standard because each step creates a control surface for validation and logging. However, they come with three major obstacles:

 The cumulative delay often exceeds 500 milliseconds, breaking the flow of natural conversation (where pauses over 200ms feel unnatural) and frustrating 

 Converting speech to text strips away crucial paralinguistic signals such as tone, hesitation, and emphasis. These signals carry up to 38% of emotional meaning according to foundational communication 

Office

Hometown

Residence

Scott
Watson

Research Associate

Office

Hometown

Residence

Research

The Agent Control Plane

Voice Agents 2.0

The Playbook for Winning in Market Consolidation

Click. Buy. Drive.

The AI Pricing Cliff No One Wants To Talk About

Deregulated, Not Unregulated

The Future of Transfer Pricing

AI Agents-Breaking Down the Verticals

Building RevOps Capabilities

RevOps - The Road to Virtual Employees

Relevant Experiences

Office

Hometown

Residence

Scott Watson

Research Associate

Office

Hometown

Residence

Research

The Agent Control Plane

Voice Agents 2.0

The Playbook for Winning in Market Consolidation

Click. Buy. Drive.

The AI Pricing Cliff No One Wants To Talk About

Deregulated, Not Unregulated

The Future of Transfer Pricing

AI Agents-Breaking Down the Verticals

Building RevOps Capabilities

RevOps - The Road to Virtual Employees

Relevant Experiences

Scott
Watson