Software Revaluation in the AI Agent Era: From Seat-Based Software to the Responsibility Runtime Layer

AI Agent Software & SaaS Trend-Focused Deep Research Report

Which company are you most bullish on in the AI agent era?

Pick the SaaS names most likely to benefit from agent-era revaluation. Results unlock after voting.

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ServiceNow (NOW) Intuit (INTU) Veeva (VEEV) Salesforce (CRM) Adobe (ADBE) Workday (WDAY) Snowflake (SNOW)

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1.1 | One-Page Decision Dashboard

1.1.1 | One-Sentence Thesis

AI agent will not simply destroy the software industry; instead, it will migrate software value from “interfaces operated by humans” to a deeper layer where systems understand state, execute actions, assume responsibility, attribute outcomes, and charge based on value.

This statement does not directly imply that “all strong SaaS companies will become stronger.” The companies that truly benefit must simultaneously have high-value state changes, context control, write authority, closed-loop governance, responsibility transfer, and budget migration capabilities. High historical gross margins, subscription revenue, legacy customers, multifunction UI, or large amounts of data alone are not enough to guarantee continued success in the AI agent era.

The core of this report is not to judge “which company has the most AI features,” but to judge:

Which company can push AI from the feature layer to the execution layer,
then from the execution layer to the responsibility layer and outcome layer,
and finally convert this control point into ARR, RPO, gross margin, FCF/share, and per-share value.

1.1.2 | Conclusion Map of This Report

Question	Current Conclusion	Implications for Investment Research
Will AI agent destroy SaaS?	It will neither destroy nor enhance evenly; it will drive a layered revaluation	Low-responsibility tool layers face price pressure, while high-responsibility runtime layers become stronger
Direction of value migration	From UI, features, and systems of record to state changes, write authority, responsibility governance, and outcome attribution	Research focus shifts from “whether there are AI features” to “whether there is action authority and a responsibility boundary”
Most worthy of deep research	ServiceNow, Intuit, Veeva	They respectively represent the horizontal workflow runtime layer, the vertical responsibility operating system, and high-responsibility industry cloud
Still needs validation	Salesforce, Adobe, Workday, Snowflake	They have important assets, but still need to prove whether they can move up into execution, governance, responsibility, or outcome layers
Biggest investment misjudgment	Treating demos, tokens, AI actions, and feature releases as investment-grade evidence	Real evidence must enter production, paid SKU, ARR / RPO, gross margin, and FCF/share
Biggest disconfirming evidence	Usage grows but revenue and cash flow do not; the runtime layer is abstracted by Microsoft / cloud vendors / model providers / customer-built systems; AI costs compress gross margin for a prolonged period	If this occurs, the AI beneficiary narrative needs to be revised downward
Positioning of this report	A trend-focused deep research report and research framework, not a buy rating	First establish the screening language, then move into company-specific deep research

1.1.3 | Current Company Ranking

Group	Company	Current Judgment	Research Action
Most worthy of deep research	ServiceNow	Closest to a horizontal workflow and agent governance runtime layer	Focus on AI control tower, large Now Assist ACV, RPO / cRPO, and gross margin
Most worthy of deep research	Intuit	Closest to a vertical responsibility operating system and outcome delivery	Focus on AI + expert cost structure, QBO attach purchases, and independent IES disclosures
Most worthy of deep research	Veeva	High-responsibility industry cloud, slow but deep	Focus on Vault CRM migration, top biopharma live / committed, and whether AI enters core workflows
To be validated	Salesforce	Strong distribution and strong record layer; execution runtime layer remains to be proven	Focus on Agentforce ARR, production accounts, Data 360 attach, and reacceleration in core clouds
To be validated	Adobe	Tool layer under pressure, with an option on enterprise content supply chains	Focus on AI-first ARR incrementality, GenStudio enterprise budgets, and Creative seat pressure
To be validated	Workday	Mature SoR + agent workforce governance option	Focus on whether agent actions convert into paid budgets and customer adoption
To be validated	Snowflake	Strong data context, but insufficient execution authority	Focus on paid Cortex / Intelligence workloads and whether AI increases consumption

This ranking is not a valuation conclusion, nor is it a buy rating. It only indicates each company’s industry position, control-point depth, and evidence strength in the AI agent era.

1.1.4 | Three Most Important Judgments

First, state change matters more than interface. The most valuable software in the future will not be the interface users open most often, but the software best able to change business state, write back to systems, complete actions, assume responsibility, and accept accountability. If a product can only generate, summarize, query, or recommend, it remains in the tool layer; only when it enters tasks, approvals, write-backs, audits, and rollbacks does it begin to approach the runtime layer.

Second, seats will not disappear, but they will be downgraded. Seats will still carry identity, permissions, governance, supervision, and responsibility ownership; true incremental revenue will come more from AI workloads, quotas, tasks, usage, outcomes, and enterprise bundled packages. Pricing in the agent era does not jump in one step from seat to token; instead, it forms a hybrid stack of base seats + usage quotas + advanced workflows + governance modules + outcome-based fees.

Third, declining token costs are not automatically positive. Lower model costs are a necessary condition for gross margin recovery, not a sufficient condition. Who can retain the benefit of cost declines in its own income statement depends on pricing power, competitive intensity, customer bargaining power, model routing, caching, batch processing, productization capability, and outcome attribution. Low-responsibility tools may pass cost-saving benefits to customers; high-responsibility runtime layers are more likely to retain pricing power.

1.1.5 | Three Biggest Disconfirming Signals

Disconfirming Signal	Why It Matters	How to Respond If It Occurs
agent usage grows, but revenue, ARR, RPO, NRR, or FCF/share does not grow	Usage can be real demand, or it can simply be unmonetized cost	Do not capitalize AI usage; re-audit commercialization evidence
Customers place the agent runtime layer on Microsoft, OpenAI, Anthropic, AWS, Google, or self-built platforms	Existing SaaS may become a called back-end system of record	Revise downward the assessment of write authority, responsibility layer, and long-term pricing power
AI features become commoditized, customers are unwilling to pay extra, while inference, implementation, customer success, and governance costs rise	AI turns from a growth lever into gross margin pressure	Re-audit unit economics and the FCF/share path

1.1.6 | Reading Sequence for This Report

This report proceeds along an investment research chain:

Core mechanism
→ Winners and losers
→ Positioning of the seven companies
→ AI economics
→ Evidence of real agent transformation
→ Biggest counterexamples and abstraction risk
→ FCF/share and quarterly tracker

When reading, do not treat each chapter as parallel material. The true sequence of this report is: first judge where software value is migrating, then judge which companies own the control points, and then audit whether those control points have entered commercialization, gross margin, and per-share value.

1.1.7 | Next-Quarter Tracking Table

Tracking dimension	Most important metrics to watch	Explanation
AI commercialization	AI ARR, AI attach, production customers, AI large ACV	Distinguishes demos, pilots, and real budget
Order visibility	RPO, cRPO, NRR, subscription / product revenue growth	Whether AI is entering contracts and expansion
Write authority	whether the agent has action permissions, cross-system write-back, audit, and rollback	Distinguishes an assistant from the operating layer
Gross margin	gross margin, AI cost disclosure, services mix	Determines whether AI is leverage or cost
Per-share value	FCF/share, SBC, dilution, buyback efficiency	Whether the trend can become shareholder value
Abstraction risk	Microsoft / cloud vendors / model providers / customer in-house build progress	Determines whether the control point is being migrated

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1.2 | Glossary: Aligning Abbreviations and Definitions First

This article uses English explanations for core concepts wherever possible, but some abbreviations in software and investment research cannot be fully avoided. After this section, the article will use these abbreviations directly by default.

1.2.1 | Investment and Financial Definitions

Term	English explanation	Why it matters in this article
ARR	Annual recurring revenue	Evidence for determining whether AI has converted into renewable and expandable commercialization
ACV	Annual contract value	Determines whether AI is entering large-customer contracts and enterprise budgets
RPO	Remaining performance obligations	Determines future revenue visibility and order strength
cRPO	Current remaining performance obligations	Determines revenue visibility over the next 12 months
NRR	Net revenue retention	Determines whether existing customers are expanding their purchases
gross margin	Gross margin	Determines whether AI inference, implementation, and services costs are being absorbed by revenue
FCF	Free cash flow	Determines whether profit converts into cash
FCF/share	Free cash flow per share	Determines whether the trend is truly crystallizing into per-share shareholder value
SBC	Stock-based compensation expense	Determines whether free cash flow is diluted by equity cost
dilution	Dilution	Determines whether shareholders are actually sharing in cash flow growth
buyback	Buyback	Determines whether the company uses capital allocation to offset dilution and increase per-share value
SKU	Sellable product unit	Determines whether AI has moved from a feature into a chargeable product
attach	Add-on purchase / bundled purchase	Determines whether AI or a new module is being added by customers alongside the core product
production customer	Production customer	Distinguishes real deployment from demo / pilot

1.2.2 | Software and AI agent Definitions

Term	English explanation	Why it matters in this article
SaaS	Software as a service	The traditional subscription software business model; this article discusses its layered revaluation in the agent era
UI	User interface	Historically an important software entry point, but interface value may decline in the agent era
SoR	System of Record, the system-of-record layer	Stores customer, employee, financial, and business facts, and serves as the source of truth called by agents
agent	Agent / intelligent execution entity	Not merely answering questions, but a software capability that can understand context, call tools, and execute tasks
copilot	Copilot-style assistant	Usually assists people in completing tasks, but may not have write authority or defined accountability boundaries
write-back	Write-back	The agent writes action results back to systems such as CRM, HCM, ERP, and ITSM, which is an important marker of the operating layer
workflow	Workflow	The way tasks, approvals, tickets, and status transitions are organized; the core object of agent execution
governance	Governance	Permissions, audit, rollback, compliance, and accountability boundaries; the foundation of high-responsibility agents
context	Context	The data, objects, history, and business semantics an agent needs to make judgments
semantic layer	Semantic layer	Translates enterprise data into business-understandable objects, which is key for data companies moving up into the action layer
token	Unit of measurement for model input or output	Affects AI inference cost, but lower token prices do not automatically mean better software company gross margins
consumption	Consumption / usage	One of the core revenue drivers for usage-based companies such as Snowflake
AI actions	Number of AI actions	A useful usage signal, but not equivalent to revenue quality

1.2.3 | Custom Frameworks Used in This Article

Framework	Meaning	How to use it
L0-L6	Software value hierarchy, from point tools to outcome delivery	Determines whether a company sits at the tool layer, record layer, workflow layer, accountability layer, or outcome layer
E0-E5	Evidence levels, from launch-event demo to FCF/share improvement	Prevents product launches, usage, and investment-grade commercialization evidence from being conflated
D0-D5	Write depth, from display-only to closed-loop cross-system execution	Determines whether an agent has truly gained action authority and accountability boundaries
Gross margin J-curve	AI pressures gross margin early on, productization reduces costs in the middle stage, and accountability pricing releases profit later	Determines whether AI cost pressure is a temporary investment or a long-term structural issue
Abstraction risk	The operating layer, entry point, or orchestration right is taken by Microsoft, cloud vendors, model providers, or customer-built platforms	Determines whether a SaaS company is turning from a control point into a back-end interface
Budget migration	Migration from seat budgets to workload, labor, services, risk, or outcome budgets	Determines whether AI is truly expanding the capturable market for software companies

1.2.4 | Company and Product Abbreviations

Abbreviation / Name	Refers to	Focus in this article
NOW	ServiceNow	Horizontal workflow operating layer and agent governance control tower
INTU	Intuit	Vertical responsibility operating system and AI + expert outcome delivery
VEEV	Veeva	High-responsibility life sciences industry cloud and Vault CRM migration
ADBE	Adobe	Pressure on the creative tools layer and enterprise content supply chain option value
CRM	Salesforce	Strong system-of-record layer, with Agentforce execution layer still to be validated
SNOW	Snowflake	Strong data context, but execution authority and action layer still to be validated
WDAY	Workday	HR / Finance SoR and agent workforce governance option value
QBO	QuickBooks Online	Core asset of Intuit's small-business financial system
IES	Intuit Enterprise Suite	Intuit's AI-native ERP / operating system direction for mid-market enterprises
Vault CRM	Veeva's CRM for the life sciences industry	Veeva's key second control point after migrating away from Salesforce
Agentforce	Salesforce's agent product system	The key question is whether usage can convert into ARR, production customers, and core reacceleration
Data 360	Salesforce data platform	Assessing whether Agentforce has enough context
Cortex / Intelligence	Snowflake's AI / intelligent application direction	Assessing whether Snowflake can move up from the data layer into the action layer
AI-first ARR	The AI-related annual recurring revenue metric disclosed by Adobe	Incrementality needs to be assessed, rather than looking only at the growth multiple

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2.1 | Core Mechanism: Software Value Is Migrating from UI to the Responsibility Operating Layer

2.1.1 | Why AI agent Is Not an Ordinary Feature Update

The core assumption of traditional SaaS is that humans remain the execution subject: people log into software, review data, make judgments, and take actions, while software is responsible for records, collaboration, reporting, and workflows. The goal of AI agent is to hand part of the judgment and execution actions to the system itself. Software is no longer merely a "tool operated by people"; it increasingly resembles an "operating layer authorized to complete tasks."

This will rewrite the most important control points for software companies. In the past, strong software typically controlled the interface, data objects, team collaboration, and organizational training. In the future, stronger software will control business state changes, next actions, permission boundaries, cross-system writebacks, audit records, accountability, and outcome attribution.

Therefore, the research question must shift from "Does the company have AI agent?" to:

What state was the customer originally in?
What state does the software move the customer into?
Is the cost of error in that state change high?
Does the software have context and write authority?
When something goes wrong, can it be audited, rolled back, and assigned accountability?
Is the customer willing to migrate budget for this outcome?

2.1.2 | L0-L6 Software Value Layers

Layer	Name	Typical old form	Form in the AI agent era	Value direction
L0	Point tool layer	Generate, edit, summarize, query	Point copilot / assistant	Most easily price-compressed by models and platforms
L1	Collaboration interface layer	User login, multi-user collaboration, comment-based approvals	Conversational entry points, multimodal entry points, collaboration front end	UI value declines, but organizational embedding remains
L2	System-of-record layer	CRM, HCM, ERP, industry systems of record	Source of facts called by agent	Still important, but actions may flow elsewhere
L3	Data context layer	Data warehouse, data cloud, semantic layer	governed context, retrieval, semantic layer	Value depends on whether it can enter action
L4	Workflow execution layer	Tickets, approvals, tasks, state transitions	agent orchestration, cross-system execution, writeback	Moat rises
L5	Responsibility governance layer	Compliance, audit, permissions, risk, rollback	An authorizable, auditable, and rollback-capable agent control tower	High-responsibility moat
L6	Outcome delivery layer	Software helps people deliver outcomes	Software directly completes business outcomes or replaces human services	Largest room for budget migration

The most dangerous investment misjudgment is treating L0 / L1 AI features as L4 / L5 / L6 control points. A product's ability to generate text, images, query results, or summaries does not mean it can write back to systems, assume responsibility, or capture outcome budgets.

2.1.3 | Why L4 / L5 / L6 Are More Valuable

The L4 workflow execution layer is more valuable because it controls business state changes. A ticket moving from unresolved to resolved, a customer case moving from open to closed, a finance task moving from pending to completed, or an employee request moving from submitted to approved are all measurable state changes.

The L5 responsibility governance layer is more valuable because once agent obtains action authority, what customers care about most is no longer "Can it answer?" but "Can it be authorized, constrained, audited, and rolled back?" Enterprises will not easily hand high-responsibility processes to software that lacks a governance layer.

The L6 outcome delivery layer is more valuable because it may move from software budget into labor budget, services budget, BPO budget, expert budget, risk budget, and revenue growth budget. What customers buy is no longer software access, but task completion, error reduction, lower risk, labor savings, or revenue creation.

2.1.4 | Where the Seven Companies Currently Sit in the Layers

Company	Current primary layer	Target layer in the AI era	Key question
ServiceNow	L4 / L5	L5 / L6 enterprise operating layer	Can it become the enterprise agent control tower, rather than a workflow backend being called by others?
Intuit	L2 / L4 / L6	L6 vertical outcome delivery	Can AI + expert deliver tax, accounting, and SMB operating outcomes at scale?
Veeva	L2 / L5	L5 / L6 industry responsibility cloud	Will the Vault CRM migration and industry AI enter core compliance workflows?
Adobe	L0 / L1 / L3	L4 / L5 content supply chain	Can it move up from creative tools to enterprise content production, approval, publishing, and attribution?
Salesforce	L2 / L4	L4 / L5 agent execution layer	Can Agentforce move from a CRM value-added module into a cross-system execution layer?
Snowflake	L3	L3 / L4 governed context	Can it move from data context into actions, semantics, and agent workloads?
Workday	L2 / L5	L5 agent workforce governance layer	Will Agent System of Record become a real budget pool?

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2.2 | Who Wins, Who Loses

2.2.1 | Three Types of Winners

First type of winner: high-responsibility vertical operating systems.

Representative companies are Intuit, Veeva, and parts of Workday, NICE, and industry clouds. Their shared traits are high cost of error, specialized business language, slow customer replacement, data that can trigger actions, and relatively attributable outcomes. In these scenarios, AI is not an ordinary assistant, but the productization of expert capability, compliance workflows, financial operations, industry approvals, and risk control.

Intuit's tax, accounting, small-business accounting, payroll, payments, credit, marketing, and cash flow management are naturally close to outcome delivery. Veeva's life sciences processes are slower, but the responsibility is deeper. Clinical, quality, commercial compliance, regulatory materials, and industry CRM are not low-responsibility tools that customers are willing to replace lightly.

Second type of winner: cross-system workflow operating layers.

The representative company is ServiceNow. ServiceNow's advantage is that it naturally handles state transitions within the enterprise: requests, tickets, approvals, incidents, services, assets, risk, and security. For an agent to execute safely in the enterprise, it needs context, permissions, tasks, auditability, rollback, and governance. If ServiceNow can combine Now Assist, the AI control tower, autonomous workforce, and workflow platform, it has a chance to become the operating layer for enterprise agents.

Third category of winners: companies with strong data context and the ability to move up into execution.

Representative companies include Snowflake's potential path, Salesforce's potential path, and Adobe's enterprise content potential path. These companies all have assets, but they still need to prove whether those assets can move up the stack.

Snowflake has data context, governance, and a model ecosystem, but data context is not the same as the right to act. Salesforce has customer records, sales and service processes, Data 360, Slack, and enterprise distribution, but whether Agentforce becomes an execution layer remains to be validated. Adobe has creative assets, documents, brand assets, and Marketing Cloud, but it must prove that it can migrate from creative tools to the enterprise content supply chain.

2.2.2 | Three Types of Losers

First category of losers: low-responsibility single-point tools.

These tools only solve low-responsibility tasks such as generating copy, generating images, summarizing meetings, auto-filling forms, and simple customer-service bots. Without proprietary context, write access, auditability, responsibility, distribution, and outcome attribution, they will be rapidly repriced downward by improvements in model capabilities, built-in platform features, and open source.

Second category of losers: software that has data but no right to act.

Data matters, but data is not the endpoint. Enterprises will not pay indefinitely for "more data"; they pay for better judgment and action. Companies that remain only in storage, cleaning, reporting, query, and analysis, and cannot enter the chain of business actions, will be abstracted away by upper-layer agents and business systems.

Third category of losers: software that bears AI costs but cannot charge for them.

Some software will be forced to add AI features because customers expect them to exist. But if AI cannot be charged for separately, legacy seats are compressed by efficiency gains, inference costs rise, competitors give it away for free, and customers also see AI as a basic feature, then AI is not a growth engine but a source of margin pressure.

2.2.3 | Three Fates of Tool-Type Software

Tool-type software will not all disappear, but it will split into three paths.

Fate	Characteristics	Investment Implication	Representative Assessment
Commoditized by models	Single-point generation, editing, summarization, and query, with weak context and weak responsibility	Pricing power declines	Most dangerous
Becomes a platform feature layer	Features are built into platforms such as Microsoft, Google, Adobe, Salesforce, and ServiceNow	May have distribution, but independent value declines	Neutral to somewhat weak
Moves up to the outcome operating layer	Enters brand, compliance, approval, publishing, attribution, code deployment, financial execution, or legal processes	Pricing power may be rebuilt	Most worth researching

Adobe is the most typical example. Low-end creative generation is being repriced downward, but if the enterprise content supply chain can connect brand assets, copyright, approvals, publishing, distribution, and performance measurement, it may move up from the tool layer to the operating layer.

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3.1 | Positioning Map of the Seven Companies

3.1.1 | Evidence Level Definitions

Level	Meaning	Investment Interpretation
E0	Launch event, demo, product concept	Can only be recorded, not upgraded
E1	Feature available, customer pilots, early usage	Weak signal, still needs productionization
E2	Entered customer production environments	Moderate signal, indicating this is not just a demo
E3	Paid SKU, attach, usage-based revenue	Strong signal, starting to enter budgets
E4	ARR, RPO, cRPO, NRR, and large-customer expansion are visible	Very strong, entering contracts and growth metrics
E5	Gross margin, FCF/share, or per-share value improvement	Investment-grade evidence, showing the trend is entering shareholder value

3.1.2 | Write-Depth Definitions

Level	Meaning	Investment Interpretation
D0	Only displays or reads	Weak control point
D1	Generate / edit / summarize	Tool layer, easily replaced
D2	Suggests the next step	Valuable but does not control the outcome
D3	Generates a draft and writes it into the workflow, awaiting approval	Begins to form process embedding
D4	Executes actions within the scope of permissions and writes back to the system	Close to the operating layer
D5	Executes a closed loop across systems and assumes auditability, rollback, compliance, and responsibility	High-responsibility moat

3.1.3 | Company Evidence Summary Table