What companies are attacking expensive incumbent SaaS by rebuilding the workflow from scratch with AI?

Yes. The interesting cohort is not “SaaS companies with an AI copilot.” It’s companies asking: if an AI agent is the primary user, why preserve the old screens, objects, workflows, and org structure at all?

A useful way to map the market in 2026:

The ERP/finance group is probably the clearest example of exactly what you're describing. Rillet, DualEntry, Campfire and Light are explicitly positioning themselves as AI-native financial systems against NetSuite/Sage/legacy ERP. An independent 2026 survey of the category describes these companies as rebuilding the architecture around AI rather than simply adding an AI assistant.ERP Research+1

The ones I'd pay particularly close attention to

1. Rillet / DualEntry / Campfire — “NetSuite, but the computer does the accounting”

This is perhaps the cleanest expression of the thesis. Instead of giving an accountant a better UI for the same ERP primitives, they're attempting to make the underlying ledger, reconciliation, close, reporting and revenue workflows AI-native.

Rillet, DualEntry and Campfire have each raised substantial capital, and their stated competitive target includes NetSuite/Sage/QuickBooks.ERP Research

2. Basis — “replace the accounting department's workflow, not QuickBooks”

Basis is a slightly different attack: rather than necessarily owning the entire financial system, it uses AI agents to perform accounting work across existing systems. That's potentially a more radical wedge because the thing being replaced is labor + workflow, not just software.

3. Glean — “what if the enterprise OS were the agent?”

Glean is interesting because it attacks the horizontal layer. Rather than asking employees to know whether information lives in Salesforce, Slack, Drive, Jira, etc., it builds a context layer across those systems and increasingly lets AI take action across them. TechCrunch describes its evolution from enterprise search toward the connective layer between models and enterprise systems.TechCrunch

Glean reported $300M ARR in May 2026, illustrating that this isn't merely an experimental startup category anymore.Glean

4. Day AI / Reevo — “CRM without CRM data entry”

This is one of the more conceptually interesting attacks on Salesforce.

The old model:

salesperson → Salesforce → manually create/update objects → run workflow The AI-native model:

salesperson communicates normally → AI observes the communication → AI constructs the customer record → AI determines next actions That distinction matters enormously. The product isn't really a prettier CRM; it's attempting to remove the human CRM operator entirely. Current AI-native CRM research identifies Day AI as particularly focused on generating the record from communications, while Reevo is pursuing a broader consolidation of the GTM stack.We The Flywheel

5. Sierra / Decagon — “customer support as a workforce”

Here the replacement target isn't really Zendesk. It's the human support organization plus its software stack.

Instead of:

customer → ticket → queue → agent → macros → CRM → escalation

the architecture becomes:

customer → AI agent → tools/APIs → resolution

That's an important distinction because the economic attack surface becomes much larger than the SaaS subscription.

6. Harvey / Legora — “legal software becomes legal labor”

The really interesting legal AI companies aren't just building better document search. They're trying to execute substantive legal workflows—research, drafting, review, analysis, diligence, etc.

That's another instance of the same pattern:

application SaaS → AI worker → outcome rather than:

application SaaS → human operator → outcome.

The bigger pattern

I think there are three increasingly aggressive levels of AI-native SaaS disruption:

Level 1 — AI-enhanced SaaS

Salesforce + AI Workday + AI ServiceNow + AI

The incumbent retains the database, objects, permissions and workflow model. AI is a new interface.

Level 2 — AI-native application

Rillet, DualEntry, Day AI, etc.

The underlying data model and workflows are redesigned assuming an AI can do most of the work.

Level 3 — AI replaces the application and the operator

This is the really interesting one:

Don't build better accounting software. Build an AI accountant. Don't build better CRM. Build an AI salesperson. Don't build better support software. Build an AI support organization. Don't build better legal software. Build an AI legal team. At Level 3, the incumbent SaaS category itself can become the wrong unit of analysis.

That is also why the current debate over the “SaaSpocalypse” is somewhat misleading. Some AI companies are indeed attacking software budgets, but others are attacking the labor and business process sitting on top of the software. Recent enterprise research describes this broader shift as AI moving from task automation toward restructuring entire operational processes.McKinsey & Company+1

And there's an important counterpoint: incumbents aren't standing still. Salesforce, for example, is now aggressively repositioning around agents and AI-native interfaces rather than merely bolting a chatbot onto CRM.TechRadar

If you're looking for the most interesting startup opportunities

I'd therefore look for incumbents where the existing SaaS product is essentially a database + workflow UI for a human employee.

Particularly juicy categories are:

• ERP/accounting
• customer support
• recruiting
• insurance operations
• legal
• procurement
• sales operations
• compliance
• healthcare administration
• logistics
• property management
• construction back office
• financial operations

The strongest pattern is where the customer isn't actually buying software—they're buying the completion of a repetitive business process, and today's SaaS merely helps a human perform it.

That's the seam AI-native companies can attack from underneath.

Yes. There’s a meaningful cohort of AI-native companies whose strategy is not “add a copilot to Salesforce/NetSuite/ServiceNow,” but “replace the application layer because the workflow itself can now be executed by agents.”

A useful way to map the space is:

The ones I’d watch particularly closely

1. Rillet — AI-native ERP

This is probably the cleanest example of the thesis. Rillet isn't positioning itself as “AI for NetSuite”; it explicitly describes itself as an ERP architected around automation from day one. It has reportedly reached 600+ customers and raised more than $200M.Rillet+1

The important idea is that the system of record and the agent doing the accounting become the same product. That's substantially more disruptive than an AI assistant sitting on top of an incumbent ERP.

2. Campfire — rebuilding NetSuite

Campfire is unusually explicit about attacking the incumbent. Its pitch is essentially: NetSuite is the old architecture; rebuild accounting around AI.

It replaces the combination of GL + reconciliation + close management + reporting with an AI-native ERP, and explicitly markets itself as a replacement for NetSuite, SAP and similar legacy systems.Campfire+1

This is exactly the “redo SaaS from scratch” pattern.

3. Decagon — rebuilding customer support

Decagon is interesting because the unit of software isn't really a “support ticket” anymore.

Its agents can interact over chat, email and voice, retrieve information from CRM/helpdesk systems, take actions, and escalate when necessary. It describes the product as moving customer experience away from traditional support infrastructure toward an AI concierge.Decagon+2

That's potentially a much bigger change than “AI makes Zendesk agents 30% faster.”

4. Harvey — rebuilding legal work

Harvey is doing something similar in legal. Rather than simply being legal research software, it is building a connected system around the matter: research, drafting, analysis, document review, institutional knowledge and agentic workflows.Harvey+1

Its current scale is also notable: Harvey says it has 2,400+ customers and 75+ AmLaw 100 firms, and announced a $550M raise at a $15.5B valuation in September 2026.Harvey+1

5. Tennr — vertical workflow replacement

Tennr is a particularly good example of where this gets interesting outside conventional SaaS categories.

Healthcare organizations have huge amounts of messy inbound documentation. Instead of making humans navigate another workflow application, Tennr's agents gather, extract and evaluate information, determine what's missing, assess coverage and decide what should happen next.Tennr

That's effectively turning an operational workflow into an agent.

6. EvenUp — legal operations

EvenUp is rebuilding the personal-injury case workflow rather than merely adding AI search to legal software. Its system can qualify cases, identify valuable cases, analyze treatment and documentation, and coordinate handoffs through the lifecycle of a case.EvenUp Law

The really interesting pattern

I think there are three levels of “AI SaaS disruption”, and only the third is what you're describing:

1. AI feature Salesforce + AI assistant → Same workflow, less typing.
2. AI layer Decagon connected to Zendesk/Salesforce → AI performs much more of the existing workflow.
3. AI-native application Rillet/Campfire → The underlying data model, workflow and UI are redesigned around an agent doing the work.

The third category is where incumbent SaaS economics can get genuinely weird.

The old model is:

More work → more humans → more seats → more SaaS revenue. The AI-native model can become:

More work → more AI execution → fewer humans → potentially less software spend. That's why the most exposed categories tend to be workflow-heavy, document-heavy and seat-priced. A recent Redpoint analysis found especially high buyer openness to AI-native replacement in Salesforce automation, customer service management, ITSM, ERP and procurement.Forbes

And there's already evidence of the inverse phenomenon: companies are beginning to build internal AI systems specifically to replace expensive SaaS. Palantir, for example, said it replaced an expensive legacy CRM with an AI-first CRM built internally on AIP.Hudson Labs

My broader watchlist

If you're looking for startups pursuing the “kill the application, own the workflow” thesis, I'd put these on a research list:

• Rillet — ERP/accounting
• Campfire — ERP/accounting
• Decagon — customer support
• Sierra — customer-service agents
• Harvey — legal
• Legora — legal
• EvenUp — legal operations
• Tennr — healthcare operations
• Norm AI — compliance
• EliseAI — property-management operations
• Billow AI Labs — accounting/FP&A
• FullSeam — corporate accounting
• — AI-native accounting services

The especially interesting frontier is vertical SaaS where the customer isn't actually buying software—they're buying an outcome. Billow makes that unusually explicit: it says there is zero software for the customer to use; its agents connect to the existing systems and perform the accounting work.Y Combinator

That's arguably the logical endpoint of the trend: SaaS → agents → services, with the software disappearing from the customer's workflow entirely.

If you're thinking about this from a venture/investment perspective, I can also map the landscape as “$1B+ incumbent being attacked → startup → wedge → why AI changes the economics → evidence of traction” and identify the 20–30 most interesting companies.

And there's already evidence of the inverse phenomenon: companies are beginning to build internal AI systems specifically to replace expensive SaaS. Palantir, for example, said it replaced an expensive legacy CRM with an AI-first CRM built internally on AIP.

AI-native startups are disrupting traditional seat-based software by shifting the core enterprise software model from "selling access to a tool" to "delivering the actual work via autonomous agents." Instead of charging per human user login (seats) to manage clunky data entry, these new platforms ingest unstructured documents, parse rules, and execute complete business processes end-to-end.[av.vc](https://www.av.vc/blog/ai-is-eating-software-is-b2b-saas-dead)[[2](https://www.ledgerup.ai/solutions/saas-billing-automation)](https://www.ledgerup.ai/solutions/saas-billing-automation)

The market transition targets expensive, entrenched horizontal and vertical SaaS categories—such as finance, billing, customer support, and legal operations.

Key AI-Native Companies Rebuilding Incumbent Workflows

The Core Shifts in the Competitive Landscape

• Pricing Evolution: Moving away from per-seat subscriptions ($50–$150/user/month) toward usage-based or outcome-based pricing (paying per transaction, invoice parsed, or issue resolved).[av.vc](https://www.av.vc/blog/ai-is-eating-software-is-b2b-saas-dead)
• System of Action vs. System of Record: Incumbents like Salesforce (Agentforce ) and ServiceNow (Now Assist ) are fighting back by embedding agents into existing databases. However, AI-native challengers win by avoiding legacy database constraints, designing interfaces completely around conversational and agentic loops rather than forms and tables.[av.vc](https://www.linkedin.com/posts/ronnizehavi_long-live-saas-and-maybe-its-time-activity-7494274864435015680-MiJg)

If you want to focus on a specific sector, let me know which industry you'd like to explore next:

• Finance & Accounting (Billing, AR/AP, Expense management)
• Customer Operations (Support, Success, Sales development)
• Back-office & Legal (Procurement, Compliance, Contract management)

The companies you’re describing are usually called AI-native vertical software, agentic applications, or SaaS challengers: startups that do not add a chatbot/copilot to an old workflow, but instead replace the workflow itself (often replacing multiple seats of an incumbent SaaS product with an AI operator). The most exposed incumbents tend to be expensive, seat-based systems where humans spend time moving information around, filling forms, reviewing documents, or coordinating tasks.XYZBytes+1

A few notable examples:

LeadPrysm SaaS Mag## Categories where this pattern is most active

1. Customer support → “AI employee”

Instead of selling 100 support seats, the new model is an agent that resolves tickets, updates systems, and escalates exceptions.

Examples:

• Sierra
• Decagon
• Intercom Fin AI

The incumbent question changes from “how do we make agents more productive?” to “how many human-agent workflows still need to exist?”Deloitte

2. Legal → “AI associate”

Legal is attractive because much of the work is document-heavy and language-based.

Examples:

• Harvey
• Legora
• EvenUp

These companies are attacking workflows traditionally supported by expensive lawyers, paralegals, research platforms, and document tools.SaaS Mag

3. Back office finance/accounting

This is one of the biggest targets because many processes are repetitive but expensive.

Targets:

• AP/AR automation
• bookkeeping
• procurement
• expense review
• financial close

Potential incumbents:

• Workday
• SAP
• Oracle
• Coupa Software

4. Recruiting and HR operations

The thesis: replace recruiter workflows, not just add AI search.

Targets:

• applicant tracking systems
• sourcing tools
• interview coordination
• screening workflows

5. Software engineering

The new product is not “a better IDE”; it is an AI engineer that owns tickets end-to-end.

Examples:

• Cognition
• Factory
• Cursor

The pattern investors are watching

The strongest candidates tend to have:

1. High labor cost

• A workflow currently requiring many people.

2. High software spend

• A company paying $50k–$5M/year for SaaS.

3. Structured inputs

• Documents, tickets, calls, forms, transactions.

4. Measurable outcomes

• Cases closed, claims processed, tickets resolved, revenue collected.

5. Weak system-of-record moat

• The incumbent stores data but does not own the actual business outcome.

The big strategic question is whether AI companies become a new layer on top of SaaS or whether they become the new system of action, with old SaaS becoming a backend database. Analysts and operators disagree on how broadly this will happen; some argue incumbents will successfully embed agents, while others expect more workflow replacement.TechRadar+1

If you’re looking from a startup/founder angle, the most interesting “white spaces” are probably the ugly $100M–$10B SaaS categories that have lots of human operators, compliance burden, and document workflows (insurance, healthcare admin, construction, logistics, procurement, tax, and regulated finance).LeadPrysm+1

The enterprise software landscape is undergoing a massive structural shift . Traditional Software-as-a-Service (SaaS)—built on the premise of giving humans a digital dashboard/tool to do work and charging them per seat—is being challenged by AI-native "systems of action" and agentic workflows . Instead of human users clicking through menus, logging entries, and generating reports inside an expensive legacy tool, AI-native platforms treat the LLM/agent as the primary user or worker, automating the end-to-end outcome rather than just assisting with a task.[reddit.com](https://www.reddit.com/r/SaaS/comments/1rg1dlf/in_2025_ai_agents_were_cute_2026_is_about_ai/)[[2](https://news.crunchbase.com/saas/growing-agentic-ai-market-desilva-lateral)](https://news.crunchbase.com/saas/growing-agentic-ai-market-desilva-lateral/)

Startups and scale-ups attacking expensive incumbents are focusing on several key operational domains:

• Customer Support & Service Operations: Instead of traditional ticketing systems like Zendesk or ServiceNow where human agents manually read, tag, route, and reply to tickets, AI-native alternatives (like Sierra , co-founded by Bret Taylor) are built from the ground up to autonomously converse, troubleshoot back-end database systems, and resolve entire customer workflows end-to-end.
• Sales, CRM & Revenue Operations: Traditional CRMs like Salesforce or HubSpot are databases meant for humans to manually input call notes, manage pipelines, and update deal stages. AI-first challengers (like Attio or emerging autonomous outbound and pipeline agents) rebuild the CRM to auto-capture telemetry, emails, and calendar metadata, treating the CRM as a living, autonomous system rather than a digital filing cabinet.
• Finance, Accounting & Billing: Incumbent ERP and billing tools (like NetSuite or legacy spend-management systems) require armies of accountants to reconcile invoices, process expenses, and run checks. AI-native financial platforms (such as Numeric for automated accounting close or various agentic AP/AR automation tools) ingest raw banking, invoice, and receipt data to execute the accounting workflow itself rather than just providing fields to type numbers into.
• Legal, Compliance & Contract Review: Traditional contract lifecycle management (CLM) software just stored PDFs and tracked signatures. AI-native legal platforms (like Disclo, Harvey , or specialized compliance agents) analyze, negotiate, redline, and risk-assess documents natively through legal-domain models, bypassing the slow, human-heavy workflows of legacy tools.
• Software Engineering & IT Ticketing: Traditional bug-tracking and project management tools (like Jira ) are being pressured by autonomous coding and QA agents (such as Devin by Cognition Labs or specialized engineering workflow platforms) that take a natural-language product requirement, write the code, run tests, and open the pull request directly, bypassing standard backlog-grooming workflows.

The New Playbook: Why Incumbents are Vulnerable

1. The Death of Per-Seat Pricing: Traditional SaaS thrives when a company buys 5,000 seats. AI-native competitors charge by the outcome, resolution, or token/compute usage , completely decoupling revenue from human headcount. If an AI agent does the work of 10 people, a per-seat model collapses the incumbent's revenue.[reddit.com](https://news.crunchbase.com/saas/growing-agentic-ai-market-desilva-lateral/)
2. From System of Record to System of Action: Incumbents are mostly systems of record (where data sits passively). AI challengers build systems of action that actively ingest context, make API calls, trigger multi-step logic, and execute the business process without human data entry.[reddit.com](https://www.youtube.com/watch?v=qzazpuD-Zpw)
3. Data Layer Re-Architecture: True AI-native competitors don't just "bolt on a chatbot feature" to an old database schema; they rebuild the underlying data structures specifically to feed continuous context to autonomous agents.[reddit.com](https://www.saasmag.com/ai-native-vs-ai-bolted-on/)

Are you looking at this shift from an investment/market perspective , or are you trying to find specific AI-native alternatives to replace a bloated tool in your own tech stack? Let me know the category and I can narrow down the exact players.

Yes. The interesting cohort is not simply “AI SaaS startups.” It’s companies using AI to delete an incumbent’s workflow, often replacing a seat-based software product and some of the labor around it.

A useful framing is Bessemer’s “AI systems of action”: AI is weakening the traditional system-of-record moat around products like SAP, Salesforce, NetSuite and Workday because an agent can increasingly execute the workflow without a human operating the old UI.Bessemer Venture Partners

Here are the companies I’d put on the shortlist:

The ones I think are most interesting

1. Sierra → customer support

This is probably the cleanest example of the thesis. Instead of selling another helpdesk UI, Sierra sells an agent that resolves the customer's issue. That's a fundamental change from “software that helps a support rep work” to “software that does the support work.”

Sierra reportedly reached $100M ARR in 2025 and was being used by companies including WeightWatchers, SiriusXM, Sonos and Redfin.Sacra+1

2. Harvey → legal

Harvey is attacking a particularly juicy combination: extremely expensive software + extremely expensive human labor. Rather than making lawyers marginally faster inside traditional legal software, it is automating research, drafting, analysis and other legal workflows.

That's why I consider Harvey more important than “AI legal research startup.” The ambition is closer to rebuilding the operating system for legal work.

3. Rogo → investment banking

Rogo is another strong example because banking has historically had enormous amounts of analyst labor wrapped around information systems. Its product handles things like presentations and financial models, and it is expanding from investment banking toward wealth management.The Wall Street Journal

The interesting question isn't “Can Rogo beat Bloomberg?” It's how much of the workflow that caused a bank to buy Bloomberg, Excel, presentation software, research databases, etc. can disappear when the agent does the work?

4. Decagon → support

Decagon is slightly different from Sierra but follows the same thesis. The key metric isn't seats replaced; it's tickets/interactions resolved. One report says more than half of Decagon's customers replacing existing IVR, ticketing or CRM-based support systems moved to it.Sai SuperAI

That's the tell: the customer isn't merely adding AI to the stack. They're removing the old stack.

5. LayerX → back office

LayerX is particularly interesting because it's going after ugly, entrenched workflows—expenses, invoices, procurement, tax and HR. Its Bakuraku product competes with products including SAP Concur and other expense-management systems while using AI to automate the underlying processing.TechCrunch

This is exactly the kind of market where an AI-native company can ask:

Why does a human need to fill out this form at all? rather than:

How can we make the form slightly nicer?

---

The pattern I'd watch

The highest-potential targets have roughly this structure:

Expensive SaaS + lots of human labor + repetitive documents/data + terrible UX + per-seat pricing.

That's why legal, customer support, finance, recruiting, sales ops, compliance, insurance and healthcare administration are so interesting.

The startup doesn't necessarily need to replace the incumbent's database. It can initially replace the human interaction layer sitting on top of it.

That's the crucial distinction:

Old SaaS

Human → UI → workflow → database → outcome

AI-native

Human → intent → agent → outcome

The UI and much of the workflow become optional.

A 2026 BCG analysis similarly finds AI-native startups capturing disproportionate share in vertical and departmental software: startups account for 88% of the cited vertical-AI category and 60% of departmental AI spend.BCG Global

And there's an even more aggressive version

The companies I would watch most closely are moving from:

“AI software replaces incumbent software”

to:

“AI service replaces the people who operate the software.”

That's the service-as-software model. Sierra is the canonical example: instead of selling customer-service software for $X per agent, the company can effectively sell resolved customer interactions. The software is becoming a delivery mechanism for labor.Coommit

That creates a much larger TAM because the startup isn't competing for the customer's $50k SaaS budget. It's competing for the $5M labor budget attached to the workflow.

So if you're looking for companies that could produce the next generation of $10B–$100B application businesses, I'd focus less on “AI features” and more on companies where the pitch sounds like:

“You don't need this software anymore. Tell us what you want done, and we'll do it.” That's the real attack on incumbent SaaS.

The software landscape is experiencing a massive shift from traditional "system of record" SaaS (where humans do the heavy lifting inside bloated, expensive dashboards) to AI-native agentic workflows (where autonomous digital workers execute the end-to-end task).11x.ai [11x.ai](https://www.11x.ai/worker/alice)[[2](https://seekingalpha.com/news/4490510-ai-is-disrupting-every-industry-these-58-companies-are-at-the-top-of-the-list)](https://seekingalpha.com/news/4490510-ai-is-disrupting-every-industry-these-58-companies-are-at-the-top-of-the-list)

Rather than just adding a generic chatbot "copilot" to an existing product, these startups are completely bypassing the legacy user-interface and pricing model. Instead of charging per seat, they charge by outcome or task completion.hubspot.com [11x.ai](https://www.hubspot.com/startups/ai/aisummit-ai-agents-disrupt-saas)

Prominent companies attacking expensive incumbent SaaS by rebuilding workflows from scratch include:

• 11x builds autonomous digital workers (like "Alice" for outbound sales development) that replace the traditional human SDR workflow. Instead of buying expensive sequence tools (like Outreach or SalesLoft) and paying multiple human seats to research, personalize, and email leads, 11x automates the entire funnel from raw market signals to booked meetings.11x.ai [11x.ai](https://www.11x.ai/)[[2](https://seekingalpha.com/news/4490510-ai-is-disrupting-every-industry-these-58-companies-are-at-the-top-of-the-list)](https://www.11x.ai/worker/alice)[[3](https://www.hubspot.com/startups/ai/aisummit-ai-agents-disrupt-saas)](https://seekingalpha.com/news/4490510-ai-is-disrupting-every-industry-these-58-companies-are-at-the-top-of-the-list)

Would you like to explore how these AI startups are shifting pricing models away from traditional per-seat SaaS, or look into how large legacy incumbents (like Salesforce and Workday) are trying to defend their moats?

The shift from traditional SaaS (where software is a tool humans log into to fill out forms, click buttons, and build dashboards) to AI-native workflow systems (where autonomous agents handle the execution and humans simply supervise) is redefining enterprise tech.linkedin.com [linkedin.com](https://www.linkedin.com/pulse/ai-saas-2026-building-products-think-weyhc)[[2](https://www.wsj.com/tech/ai/saas-software-as-a-service-apocalypse-ai-b9b6da99)](https://www.wsj.com/tech/ai/saas-software-as-a-service-apocalypse-ai-b9b6da99)

Instead of legacy incumbents trying to "bolt on" a chatbot feature, a new wave of AI-native disruptors is rewriting the underlying mechanics of customer support, legal ops, sales development, coding, and finance.linkedin.com [linkedin.com](https://www.linkedin.com/pulse/ai-saas-2026-building-products-think-weyhc)

Here is how startups and emerging platforms are attacking expensive incumbent categories by rebuilding workflows from scratch:

• Customer Support & Helpdesks
  • The Incumbents: Zendesk, ServiceNow, Salesforce Service Cloud.
  • The AI-Native Attack: Traditional helpdesks are priced "per seat," charging companies more the more human agents they hire. AI-native alternatives like Sierra (founded by Bret Taylor and Clay Bavor) and autonomous support platforms (e.g., Decagon, Kapa.ai ) don't just route tickets to humans or suggest canned macros—they resolve end-to-end multi-step issues across backend databases, handle authentication, and process refunds autonomously. The economic model shifts from charging per human seat to paying per resolution, attacking the core unit economics of legacy helpdesks.enjo.ai [linkedin.com](https://www.enjo.ai/post/best-ai-customer-service-agents)
• Sales Prospecting & Outbound Development
  • The Incumbents: Outreach, Salesloft, ZoomInfo, HubSpot.
  • Traditional sales engagement platforms rely on humans building static sequences, manual list filtering, and template blasting. AI-native disruptors like (building "Alice," an autonomous digital worker for sales development) and Artisan (building "Ava") build the workflow around recursive, context-aware execution. They research prospects across the web in real-time, tailor hyper-personalized messaging based on recent company news or SEC filings, handle email replies natively, and book meetings without a human ever touching a sequence builder.

Would you like to explore how the pricing models (per-seat vs. outcome-based) are shifting between these AI disruptors and legacy vendors, or do you want to look at a specific industry workflow (like HR or marketing)?

Yes. The interesting cohort isn’t “SaaS companies adding an AI assistant.” It’s AI-native companies trying to make the old SaaS category itself unnecessary—often by replacing a sequence of screens, humans, and workflows with an agent that simply gets the job done.

Redpoint calls the broader shift “SaaS → Service-as-Software”: instead of selling software that helps a person perform work, sell software that performs the work and charge for usage/outcomes. www.slideshare.net Gartner estimates that as much as $234B of enterprise-application spending could be exposed to this “agentic arbitrage” by 2030.Gartner

Here are the companies I'd put on the watchlist:

The ones I'd pay particular attention to

1. Sierra / Decagon — customer support

This may be the cleanest example of the thesis. Instead of selling a better Zendesk interface, they sell an AI agent that resolves the customer's problem. Redpoint specifically identifies Sierra and Decagon as leaders in this new support model.www.slideshare.net

The strategic difference is huge:

Old SaaS: “Give every support agent a seat.” AI-native: “Pay us for the customer issues we resolve.” That attacks both the product architecture and the pricing model.

2. Rillet / DualEntry / Campfire — ERP

This is probably the most interesting “incumbent SaaS replacement” battlefield because the incumbent products are extraordinarily expensive and painful to implement.

Rillet, DualEntry and Campfire are building AI-native accounting/ERP systems targeting NetSuite, Sage Intacct and QuickBooks. DualEntry's particularly interesting wedge is AI-assisted migration from legacy financial systems, attacking one of the biggest reasons customers don't switch.ERP Research

Rillet is especially compelling because it isn't merely “AI bookkeeping”; it's trying to become the financial system of record.

3. Harvey — legal

Harvey is further along than most of these companies in turning AI from “software for lawyers” into software doing lawyer work. Redpoint highlights Harvey alongside Legora as examples of AI-native legal systems moving beyond traditional legal software toward actual work execution.www.slideshare.net

This is a particularly attractive market because the incumbent isn't just software—it's also hundreds of billions of dollars of human labor.

4. Clay — GTM

Clay is an interesting version where the target isn't one incumbent. It's attacking the stack of Salesforce + ZoomInfo + Clearbit + Apollo + Outreach + spreadsheets + human SDR research.

Its insight is essentially: why maintain six databases and make a salesperson orchestrate them when an agent can research the account, enrich it, generate personalization and execute the workflow?

Redpoint specifically calls out Clay and Unify GTM as examples of this new sales/marketing architecture.www.slideshare.net

5. Cursor — developer software

Cursor is slightly different because it attacks GitHub/Copilot-style tooling rather than classic business SaaS. But strategically it's perhaps the purest example of the phenomenon.

The old workflow was:

IDE → write code → search docs → Stack Overflow → run tests → debug → repeat

The new abstraction is increasingly:

“Build this.” → agent operates across the repository.

That's what makes AI-native applications dangerous to incumbents: they don't necessarily need to reproduce the incumbent's feature set. They can delete the workflow that made those features necessary.

The second-order opportunity is even more interesting

I think there are three tiers of this phenomenon:

1. AI feature — Salesforce adds an AI agent.
2. AI-native application — a startup rebuilds Salesforce around agents.
3. AI-native service — the customer doesn't buy CRM software at all; they buy the sales outcome.

The third is where things get really disruptive.

For example:

• Don't sell customer-support software → sell resolved tickets
• Don't sell accounting software → sell a closed, reconciled set of books
• Don't sell legal software → sell completed legal work
• Don't sell sales-engagement software → sell qualified pipeline
• Don't sell recruiting software → sell hires
• Don't sell insurance software → sell processed claims
• Don't sell AP software → sell invoices paid correctly

Redpoint's research explicitly describes this movement from SaaS toward “Service-as-Software”, with companies increasingly charging by usage or outcomes rather than seats.www.slideshare.net

And that's why I wouldn't define the opportunity as simply “AI-native SaaS.” The more interesting thesis is:

Take an expensive software category where the customer is really paying for a human workflow, then use AI to collapse the software + labor stack into an outcome. The strongest current examples, in my view, are Sierra/Decagon, Harvey, Rillet/DualEntry, Clay, Cursor, and Doss.

One caveat: this is still very much an emerging market. Incumbents have enormous integration/data advantages, and Gartner currently cautions that replacing core ERP wholesale with AI remains immature. Gartner So the most vulnerable categories are generally workflow-heavy applications, not deeply entrenched systems of record.

If you're thinking about this from a startup/investment angle, I can also map out 50+ incumbent SaaS categories and the specific AI-native startups attacking each one, ranked by how vulnerable the incumbent is.