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Comparative Report: “AI agents” vs “Copilots” — Who’s Monetizing Faster, and How Are They Measuring It? Executive Summary

• Across the companies analyzed, copilots monetize fastest when they are sold as per‑seat add‑ons to large, existing user bases. Microsoft’s Copilot family exemplifies this with visible seat growth and ARPU uplift tied to M365/E5 and GitHub Copilot.
• AI agents are also monetizing rapidly, but typically via ARR, bookings/ACV, and consumption metrics rather than per‑seat pricing. Adobe, Five9, Verint, and Box report meaningful AI ARR, strong AI growth rates, and increasing AI mix—clear evidence of accelerating agentic monetization.
• Measurement frameworks differ: copilots emphasize seats, ARPU, and deployment scale; agents emphasize AI ARR, bookings mix, capacity/consumption, and platform usage—complicating apples‑to‑apples comparisons.
• Bottom line: In Microsoft’s ecosystem, copilots are monetizing faster and more transparently today. Across independent vendors focused on agentic automation (Adobe, Five9, Verint, Box), agents show strong, quantifiable monetization momentum with double‑digit to >40% growth in AI‑related revenue/ARR.

Definitions and Scope

• Copilots: Per‑user assistants embedded in productivity or developer suites (e.g., Microsoft 365 Copilot, GitHub Copilot), monetized primarily via per‑seat pricing and ARPU uplift.
• AI agents: Autonomous or semi‑autonomous systems performing tasks end‑to‑end across workflows (CX, marketing, document processes, data governance), monetized via ARR, consumption/capacity, or price‑tier uplifts.

Monetization “speed” is evaluated through: revenue/ARR growth rates, per‑seat uplifts, AI share of bookings/ACV, MAU/usage trajectories, and breadth of enterprise adoption.

Comparative Monetization Snapshot Company Analyses Microsoft: Copilots vs Agents

• Copilot monetization:
  • Large enterprise seat expansions: Barclays scaling to 100,000 employees (from 15,000); UBS expanding to all employees (from 55,000); Adobe, KPMG, Pfizer, Wells Fargo each purchased 25,000+ seats.
  • Paid M365 commercial seats grew 6% YoY; ARPU expansion driven by E5 and M365 Copilot.
  • Scale signals: 100M+ monthly active users across Copilot apps; 800M+ MAU across AI features. GitHub Copilot at 20M users; enterprise customers +75% QoQ; 90% of the Fortune 100 use it.
  • Measurement: per‑seat adds, ARPU uplift, MAU, suite mix (E5), and associated cloud revenue mix.
• Agent/platform monetization:
  • Strong platform adoption and usage signals: 3M agents created via SharePoint and Copilot Studio; Foundry Agent Service used by 14,000 customers; 80% of Fortune 500 use Foundry; >500 trillion tokens served; notable production use cases (e.g., Nasdaq board prep time reduced up to 25%).
  • Healthcare agent (Dragon Copilot) with 13M physician‑patient encounters in the quarter; measurable time savings at Mercyhealth.
  • Measurement: customers using agent services, agents created, tokens served, and production outcomes. Explicit revenue attribution for agents is not disclosed.
• Verdict: Copilots are monetizing faster and more transparently via per‑seat pricing and ARPU growth. Agents show robust adoption and usage that implies monetization potential, but with less direct revenue disclosure.

Adobe: Agentic Monetization at Scale

• Monetization signals:
  • AI‑influenced ARR surpassed $5B; ARR from new AI‑first products (Firefly, Acrobat AI Assistant, GenStudio for performance marketing) exceeded a $250M target.
  • Combined ARR for GenStudio, Workfront, Frame, AEM Assets, Firefly Services, and GenStudio for performance marketing > $1B, growing >25% YoY.
  • Adoption/consumption: 70% of eligible AEP customers use AI Assistant; Firefly usage at scale (29B generations; video generations +~40% QoQ; services consumption +32% QoQ; custom models +68% QoQ).
• Measurement: ARR milestones, product‑specific AI ARR, and usage/consumption metrics.
• Verdict: Rapid, clearly measured agentic monetization across Creative and Experience Cloud with disclosed ARR levels and growth.

Box: AI Agents via Enterprise Advanced

• Monetization signals:
  • Deals for Enterprise Advanced doubled sequentially; price per seat uplift of 20–40% vs Enterprise Plus.
  • Revenue +9% YoY; RPO $1.5B (+16% YoY); NRR 103%; churn 3%.
• Measurement: per‑seat price uplift, RPO/billings growth, NRR/churn, suite mix (suites = 63% of revenue).
• Verdict: Fast monetization through agent‑enabled suite tiering with tangible per‑seat ARPU uplift.

Five9: Agentic AI in CX

• Monetization signals:
  • Enterprise AI revenue +42% YoY; AI = 10% of Enterprise subscription revenue.
  • AI accounted for >20% of Enterprise new‑logo and net‑new ACV bookings; multiple multi‑million ARR wins and expansions.
  • Pricing: consumption‑based (Agent Assist, Workflow Automation) and capacity‑based (AI Agents); advanced agents ~25% higher ARPU than core AI agents.
• Measurement: AI revenue growth, AI mix of ACV/bookings, ARR by customer.
• Verdict: Strong and accelerating agentic monetization with clear pricing levers and mix shift.

Verint: AI ARR as the Core Metric

• Monetization signals:
  • AI ARR $354M (+24% YoY) and ~50% of subscription ARR; expected AI ARR growth >20% for the year vs ~8% overall ARR growth.
  • Large AI‑driven TCV deals ($13M, $14M); pipeline +30% YoY.
• Measurement: AI ARR (explicitly defined), TCV wins, pipeline growth, free‑cash‑flow linkage to ARR.
• Verdict: Measured, material agentic monetization embedded within subscription ARR.

AvePoint: Monetizing Governance for Copilots and Agents

• Monetization signals:
  • ARR $367.6M (+27% YoY); net new ARR +42% YoY; NRR 112% (FX‑adjusted); governance suite is fastest‑growing area.
  • Governance for agentic AI (e.g., Microsoft 365 Copilot) drives cross‑sell and multi‑cloud expansion.
• Measurement: ARR growth, NRR/GRR, channel contribution; commentary indicates spend ramps as customers move from experimentation to production in copilots/agents.
• Verdict: Indirect but accelerating monetization fueled by broader copilot/agent deployments; governance products capture the spend as adoption matures.

How Monetization Is Being Measured

• Copilots (seat‑centric):

  • Seats and ARPU: New seat adds, enterprise‑wide rollouts, seat tier mix (e.g., E5).
  • Usage/engagement: MAU across copilot apps; penetration within large enterprises.
  • Revenue linkage: Per‑seat pricing yields direct revenue attribution and faster recognition.

• Agents (ARR/consumption‑centric):

  • AI ARR and AI‑influenced ARR: Discrete ARR for AI products (Adobe, Verint), or AI share of subscription revenue (Five9).
  • Bookings/ACV mix: AI share of new logo and net‑new ACV bookings (Five9); RPO and billings growth (Box).
  • Consumption/platform usage: Tokens served, generations, agents created, and capacity utilization (Microsoft Foundry, Adobe Firefly).
  • Tier uplifts: Price uplift for AI‑enabled suite tiers (Box 20–40%; Five9 advanced agents ~25% higher ARPU than core).

Synthesis: Who’s Monetizing Faster?

• Microsoft ecosystem: Copilots are monetizing faster today.

  • Evidence: Large, named seat deployments; M365 paid seats +6% YoY; ARPU uplift; 100M+ MAU across Copilot apps; GitHub Copilot scale and enterprise penetration.
  • Agents: Significant usage and customer adoption (3M agents created, 14,000 customers, 500T tokens), but less explicit revenue reporting.

• Broader agentic vendors (Adobe, Five9, Verint, Box): Agents are monetizing rapidly with clear ARR and growth metrics.

  • Adobe: AI‑influenced ARR > $5B; AI‑first ARR > $250M; >$1B ARR portfolio growing >25% YoY—substantial, disclosed monetization.
  • Five9: Enterprise AI revenue +42% YoY; AI >20% of Enterprise ACV; pricing leverage via advanced agents.
  • Verint: AI ARR $354M (+24% YoY), ~50% of subscription ARR—agentic AI is the growth engine.
  • Box: 20–40% per‑seat uplift for AI‑tier (Enterprise Advanced); deals doubling QoQ; RPO +16% YoY.

• Net takeaway:

  • Where copilots have direct per‑seat monetization paths into massive installed bases (Microsoft), they show faster and clearer monetization.
  • In vertical/functional domains where automation outcomes drive spend (CX, marketing, content supply chain), agentic AI shows rapid, quantifiable ARR growth and improving mix.

Practical Guidance for Comparing Monetization Going Forward

• For copilots:

  • Track: Per‑seat adoption by cohort, ARPU uplift, suite tier mix, MAU/engagement, enterprise rollouts.
  • Signals of acceleration: Cross‑suite upsells (e.g., E5), large named deployments, MAU conversion to paid seats.

• For agents:

  • Track: AI ARR and AI‑influenced ARR growth, AI share of bookings/ACV, RPO/billings trajectory, consumption/capacity utilization (tokens, generations), per‑tier ARPU uplifts.
  • Signals of acceleration: Rising AI mix in bookings and subscription revenue, large AI TCV deals, pipeline growth, measurable workflow outcomes (time saved, accuracy improvements).

Key point: Copilot monetization is often immediate and transparent through per‑seat pricing; agentic monetization is increasingly substantial and measured via ARR/consumption, but may require interpreting multiple metrics to gauge revenue impact.

Risks and Caveats

• Measurement asymmetry: Seats/ARPU (copilots) vs ARR/consumption (agents) complicates direct speed comparisons.
• Data readiness and ROI variability: Vendors (e.g., Five9, Verint) note data quality and deployment approach can affect realized ROI and pace of expansion.
• Disclosure gaps: Some platforms (e.g., Microsoft agents/Foundry) emphasize usage and adoption without explicit revenue breakdowns, making monetization inference directional rather than definitive.

Conclusion

• Copilots are monetizing faster in per‑seat, suite‑anchored models with massive installed bases—Microsoft provides the clearest evidence.
• AI agents are concurrently monetizing rapidly across CX, marketing, content, and governance, with strong AI ARR growth, rising AI bookings mix, consumption, and per‑tier ARPU uplifts—Adobe, Five9, Verint, and Box exemplify this trend.
• Expect convergence: copilots will adopt more task‑completion capabilities, while agents will gain user‑facing assistive features. The winning monetization model will blend per‑seat, ARR, and consumption with clear ROI proof points and governance.

Comparative Report: Can GenAI Investments Pay Back in Under 12 Months? Executive Summary

• Short answer to the query: Based on the provided disclosures, none of the companies—Amdocs (DOX), The Hackett Group (HCKT), Meta (META), or Oracle (ORCL)—explicitly commit to a sub-12-month revenue payback for GenAI investments. Meta explicitly signals that GenAI will not meaningfully drive revenue this year or next, implying payback is longer than 12 months. Oracle’s scale-up and capex profile point to multi-year returns. Amdocs frames 2025 as an exploration year with growing conversions but no stated payback horizon. The Hackett Group shows higher-margin GenAI engagements and >20% productivity gains, which could enable fast project-level ROI, but no company-level payback period is disclosed.
• Practical takeaway: Expect GenAI payback horizons to vary by business model. Asset-light, services-led firms (e.g., HCKT) can achieve faster project returns, while platform/infrastructure builders (Meta, Oracle) are pursuing multi-year payoff cycles. For telco software/services (Amdocs), commercialization is emerging, but data readiness and conversion cadence are gating factors.

Defining “Payback < 12 Months”

• What it means: The incremental GenAI investment (capex/opex) is recovered by incremental GenAI-driven revenue within one year.
• Why it varies:
  • Revenue model and deal cycles (project-based vs. multi-year subscription/consumption).
  • Capex intensity (infrastructure build-outs vs. services and software).
  • Data readiness and productionization speed for use cases.
  • Ability to price and capture GenAI value distinctly (bundled vs. stand-alone monetization).

Comparative Snapshot Company Analyses Amdocs (DOX)

• Current stance on payback: No explicit GenAI payback window. 2025 is positioned as an exploration year for GenAI and data services.
• Commercial traction:
  • Multiple POC-to-deal conversions, including expansion in the UAE with MACE and U.S. service provider wins.
  • Data readiness and the Data One platform are foundational; data services currently drive more revenue than GenAI-specific use cases.
• Financial context:
  • 12-month backlog of $4.15B (~90% of forward revenue) improves visibility.
  • FY2025 revenue growth guided at 2.4%–3.4% (pro forma CC); non-GAAP operating margin expected to expand to 21.1%–21.7%, aided by GenAI-related automation and exiting lower-margin work.
• Implications for <12-month payback:
  • Backlog and margin expansion suggest disciplined execution, but the emphasis on exploration and data groundwork points to a phased commercialization path.
  • Some discrete use cases could achieve quicker ROI, but management does not tie GenAI revenue to a sub-12-month payback target at the company level.

The Hackett Group (HCKT)

• Current stance on payback: No stated payback period, but economics are favorable.
• Commercial traction and margin signals:
  • GenAI engagements carry higher gross margins than traditional consulting.
  • Accelerator expected to deliver >20% productivity gains on Oracle/OneStream engagements.
  • AI XPLR and ZBrain streamline high-ROI GenAI solution design; LeewayHertz/ZBrain positioned for platform licensing via JV to build ARR.
• Financial context:
  • Q3 2025 guidance calls for adjusted EBITDA ~20.5%–21.5% of revenues; selective restructuring costs for GenAI pivot excluded from adjusted results.
• Implications for <12-month payback:
  • Project-level payback can be fast when productivity gains are captured and priced; platform licensing could further accelerate returns.
  • However, no explicit company-level GenAI payback timeline is disclosed; outcomes depend on channel scale-up and JV execution.

Meta (META)

• Current stance on payback: Management indicates GenAI will not meaningfully drive revenue this year or next, implying payback >12 months.
• Strategy and monetization outlook:
  • Five opportunity areas: ad improvements, engaging experiences, business messaging, Meta AI, and AI devices—monetization expected in medium-to-long term.
• Investment profile:
  • Significant capex commitments: 2025 guidance $66–$72B, with further growth in 2026 to scale GenAI capacity; higher ongoing infra and talent costs expected.
• Implications for <12-month payback:
  • The scale and timing of infrastructure build-out, coupled with the stated monetization horizon, make sub-12-month payback unlikely.

Oracle (ORCL)

• Current stance on payback: No explicit <12-month payback disclosed; signals point to multi-year ramp as capacity comes online.
• Commercial traction and backlog:
  • RPO of ~$455B (up 359% YoY) with expectations for further growth; strong cloud momentum—OCI consumption +57%, cloud infrastructure +54%.
• Investment profile and AI strategy:
  • FY26 capex ~ $35B focused on revenue-generating data center equipment; aggressive expansion to ~71 multi-cloud data centers.
  • AI approach integrates vectorized private data with leading LLMs; AI capabilities embedded within app suites (not priced separately).
• Implications for <12-month payback:
  • Consumption-based revenue should grow as capacity deploys, but the magnitude of capex and integrated pricing indicate returns realized over a multi-year horizon rather than a sub-12-month payback.

Cross-Company Themes Affecting Payback

• Capex intensity vs. services leverage:
  • Heavy infrastructure builders (Meta, Oracle) face inherently longer payback cycles due to upfront capex and depreciation schedules.
  • Services-led or asset-light models (Hackett) can monetize immediately via higher-margin projects and productivity-linked pricing.
• Data readiness and integration:
  • Amdocs underscores that monetizing GenAI depends on data foundations; near-term revenue leans toward data services rather than pure GenAI.
• Monetization clarity:
  • Oracle embeds AI within applications rather than pricing it separately, which accelerates adoption but can obscure direct GenAI payback tracking.
  • Meta identifies monetization pillars but guides to medium-to-long term revenue impact.
• Conversion velocity:
  • POC-to-deal conversion (Amdocs) and channel/JV strategies (Hackett) are near-term levers for revenue realization and potential faster payback in discrete engagements.

Indicators to Monitor for Sub-12-Month Payback Potential

• Explicit payback disclosures or ROI benchmarks by product/workload.
• Proof-points of rapid POC-to-production conversion with measurable uplift in revenue per client.
• Pricing constructs that capture GenAI value distinctly (e.g., AI add-ons, usage-based fees).
• Mix shift toward high-margin GenAI services and ARR from orchestration platforms (e.g., Hackett’s ZBrain JV).
• Capacity readiness versus booked demand (e.g., Oracle’s conversion of RPO as new data centers go live).
• Reduction in delivery costs or cycle times attributable to GenAI automation (Amdocs margin expansion durability).

Conclusion

• Across the four companies, there is no disclosed commitment to a sub-12-month GenAI revenue payback. Meta explicitly indicates a longer runway; Oracle’s capex and backlog conversion point to multi-year returns; Amdocs is in commercialization build-out with no set payback timeline; Hackett shows the most favorable near-term unit economics at a project level but provides no company-wide payback period.
• If sub-12-month payback is the decision criterion, prioritize:
  • Asset-light, services-led opportunities where productivity gains are priced into deals (Hackett-like models).
  • Discrete, narrowly scoped GenAI use cases with clear data readiness and rapid deployment paths (select Amdocs engagements).
• For platform-scale and infrastructure-heavy strategies (Meta, Oracle), expect meaningful value creation—but over horizons exceeding 12 months.

Confluent's Strategy to Accelerate Cloud Consumption Growth Amid Optimization Trends

Confluent acknowledges ongoing consumption optimization by large customers, which has led to slower use case adoption and month-over-month growth rates notably below historical averages. Despite this, the company has outlined a multi-faceted approach to accelerate cloud consumption growth and expand its footprint:

1. Strengthening Field Execution and Account Ownership

• Improving coverage ratios between Account Executives (AEs), Sales Engineers (SEs), and post-sales roles to provide a higher-touch, integrated approach.
• This enhances account ownership and aligns more tightly with customers to drive use cases into production.
• Early results include a sequential increase of over 40% in late-stage pipeline progression.

2. Building Out DSP Specialist Team for Multiproduct Selling

• Expanding a dedicated team focused on Data Streaming Platform (DSP) sales plays, including pricing strategy, go-to-market messaging, and streamlined migration offerings.
• This team supports combined tooling and professional services to accelerate customer production go-lives.
• Early success is evident with customers accelerating DSP use cases, contributing to growth in Flink ARR (tripled in two quarters, nearing $10 million).

3. Doubling Down on Key Growth Areas

• Displacing Cloud Service Provider (CSP) Streaming Offerings:

  • Confluent has achieved win rates above 90% in replacing CSP streaming products with its own offerings like Freight Clusters, Enterprise Clusters, and WarpStream.
  • This strategy targets customers currently using CSP offerings, leveraging improved product capabilities and total cost of ownership (TCO).
  • WarpStream has shown positive trends, enabling customers to increase Confluent spend by 30% while reducing overall cloud infrastructure costs by roughly 50%.

• Expanding Partner Ecosystem:

  • Partners have sourced over 20% of Confluent's business in the past year.
  • New OEM programs and AI accelerator partnerships with leading AI vendors have been launched.
  • Deepened collaborations with major system integrators (e.g., Infosys, EY) to broaden market reach and co-innovate.

• Growing DSP and AI Use Cases:

  • Rapid growth in Flink and AI production workloads, with AI use cases expected to grow 10x across hundreds of customers.
  • AI-driven real-time data streaming is becoming a critical component of enterprise AI architectures.

4. Contractual Commitments and Long-Term Customer Relationships

• Despite short-term consumption headwinds, some customers have committed to larger multiyear deals, reflected in a 31% growth in Remaining Performance Obligations (RPO).
• These commitments indicate confidence in long-term growth and provide a foundation for future consumption expansion.

5. Sales Compensation Alignment

• Sales reps are compensated on incremental consumption, aligning incentives to drive cloud usage growth.
• Adjustments continue to optimize this model to unlock more use cases and DSP offerings.

Summary

Confluent is addressing the current cloud consumption optimization by large customers through operational enhancements in sales execution, focused multiproduct selling, aggressive displacement of CSP offerings, and leveraging a strong partner ecosystem. The company is also capitalizing on emerging growth in DSP and AI workloads, which are expected to drive future cloud consumption. While near-term consumption growth is expected to remain below historical levels, these strategic initiatives and customer commitments position Confluent to accelerate cloud consumption growth over the medium to long term.

AI Security 2025–2026: Market Landscape and Comparative Company Analysis Executive Summary

• AI security is entering a platformization phase, driven by explosive GenAI adoption, enterprise risk exposure, and sovereign AI agendas.
• Two vectors dominate:
  • Securing AI use (GenAI controls, AI-native SOC/SIEM, browser/SASE, DLP, CNAPP, secure SDLC).
  • Securing AI infrastructure (sovereign AI buildouts, compliance and export controls, energy-efficient AI factories).
• SentinelOne and Palo Alto Networks are converging data, cloud, endpoint, and SOC into AI-native platforms. NVIDIA anchors sovereign AI infrastructure with geopolitical and energy-security implications. Meta foregrounds governance and safety for frontier AI. Grid Dynamics operationalizes secure AI development and deployment.

Market Themes and Forces

• GenAI exposure is scaling faster than controls:

  • Palo Alto Networks reports +890% YoY GenAI traffic and more than double the GenAI-related security incidents, catalyzing demand for end‑to‑end AI security.
  • Enterprises prioritize visibility, data protection, and policy enforcement for AI apps, agents, and models.

• AI-native SOC and data convergence:

  • SentinelOne’s AI-native SIEM and Purple AI, alongside Palo Alto Networks’ XSIAM/Cortex Cloud, show the SOC shifting to AI-driven detection, hyperautomation, and rapid containment.
  • Data platforms and CNAPP are becoming first-class citizens in SOC workflows.

• GenAI governance becomes table stakes:

  • SentinelOne’s Prompt Security acquisition targets runtime GenAI security (DLP, prompt injection defenses) across endpoints, browsers, and APIs.
  • Palo Alto Networks’ Prisma AIRS plus Protect AI address model/app/data governance with integrated DLP and AI firewalling.

• Sovereign AI, regulation, and compliance shape infrastructure choices:

  • NVIDIA expects over $20B sovereign AI revenue this year; export controls and licensing (e.g., Blackwell/H20 into China) are pivotal to market access and timing.
  • Meta underscores safety governance and EU regulatory headwinds (DMA/LPA), balancing openness with responsible deployment.

• Control points are shifting:

  • The browser is emerging as a dominant control plane for AI and cloud access (Palo Alto Networks’ Prisma Access Browser with >3M licenses in Q4; large enterprise wins).
  • Endpoint-to-cloud DLP and agentless+agent-based CNAPP are converging into unified policies (SentinelOne).

Comparative Landscape by Archetype Platform Security Vendors: SentinelOne vs. Palo Alto Networks

• Convergence strategy:
  • Both are unifying endpoint, cloud (CNAPP/CSPM), data, and SOC with AI-native analytics and automation.
• Differentiation:
  • SentinelOne emphasizes AI-native SIEM with real-time autonomous response, and deep GenAI runtime controls via Prompt Security, extending DLP from the endpoint outward. Strong AWS Marketplace and MSSP motions, plus Flex licensing to accelerate land-and-expand.
  • Palo Alto Networks positions Prisma AIRS as an end-to-end AI security platform integrated with XSIAM/Cortex Cloud, and extends controls into the browser/SASE fabric. Large multi-platform deals and high AI ARR momentum underscore scale.

Infrastructure and Sovereign AI: NVIDIA

• Security is embedded in access, sovereignty, and resilience:
  • Export controls, licensing, and geopolitics directly gate who can train and deploy frontier AI at scale.
  • Energy efficiency and networking (Rubin/Blackwell, NVLink, Spectrum XGS) are tied to secure, performant AI factories. Sovereign AI investments (> $20B revenue this year) elevate data residency, compliance, and national resilience requirements.

Frontier AI Builder and Governance: Meta

• Emphasis on safety and governance:
  • Meta integrates safety concerns (including superintelligence) into R&D and product management, while tempering open-source release strategies on safety grounds.
  • EU DMA/LPA introduce operational risks and potential product modifications during appeals, reinforcing the governance-first approach.

Services and Secure SDLC: Grid Dynamics

• Operationalizing AI security:
  • Deploys AI expert agents for code quality and security reviews at a Tier 1 investment bank.
  • Hermetic C++ toolchains for ML portfolios deliver reproducible builds (10x reliability, 25% OpEx reduction).
  • An AI-native SDLC model (GAIN) embeds security-by-design, enabling scalable, secure AI delivery.

Capability Comparison Go-to-Market and Economics

• Platformization and cross-sell:

  • SentinelOne’s Flex licensing and AWS Marketplace listing speed procurement and expand multi-product adoption (endpoints, data, CNAPP, Purple AI).
  • Palo Alto Networks leverages breadth (SASE, netsec, SecOps, cloud, AIRS) to secure large, multi-year, multi-platform wins.

• Scale and profitability:

  • SentinelOne: strong ARR momentum (> $1B), rising non-endpoint mix (~half of bookings), gross margin ~78.5–79%, FY26 operating margin ~3%.
  • Palo Alto Networks: software mix expanding (56% of Q4 product revenue), operating margin 29.2–29.7%, adjusted FCF margin 38–39%.
  • NVIDIA: outsized growth linked to sovereign AI; China licensing can swing shipments ($2–$5B potential in Q3 if issues persist), highlighting geopolitical sensitivity.

• Distribution control points:

  • Browser as the new OS for AI: Palo Alto Networks’ Prisma Access Browser at scale (3M+ licenses) adds a high-leverage enforcement plane for AI access and data egress.
  • Endpoint-to-cloud DLP: SentinelOne’s Prompt accelerates GenAI policy enforcement across user and workload edges.

Risk, Regulation, and Governance

• Export controls and sovereignty (NVIDIA):

  • Licensing for advanced platforms (Blackwell/H20) affects delivery timing and revenue recognition.
  • Sovereign AI deployments prioritize data residency, national compliance, and infrastructure self-reliance.

• Platform policy and EU regulation (Meta):

  • DMA/LPA findings and appeals could require product changes during litigation, impacting AI features and data flows.
  • Meta signals selective openness in releasing models to manage safety risks at scale.

• Enterprise GenAI risks (SentinelOne, Palo Alto Networks):

  • Visibility gaps, data leakage, and prompt-injection threats are top of mind.
  • Runtime governance (Prompt Security), DLP integration, and AI firewalls address immediate control needs.

Customer Outcomes and Evidence

• Faster, cheaper, safer operations:

  • SentinelOne Purple AI: IDC notes 55% faster threat remediation, 60% lower likelihood of major incidents, 338% ROI over three years.
  • Palo Alto Networks XSIAM: ~400 deployments with mean time to contain under 10 minutes in 60% of cases; AI ARR ~$545M, up 2.5x YoY.
  • Grid Dynamics: Hermetic toolchains deliver 10x build reliability and 25% OpEx reduction for ML builds; AI expert agents embedded in SDLC at a Tier 1 investment bank.

• Platform-scale validation:

  • Palo Alto Networks: multi-platform mega deals ($100M consulting; $60M+ European bank; $33M U.S. insurer) spanning AI access security, SASE, SecOps, and cloud.
  • SentinelOne: large enterprise adoption in data visibility/management, cloud security wins (Fortune 50), and hyperscaler/channel momentum.

Outlook: What’s Next in AI Security

• AI-native SOC becomes mainstream:

  • Expect rapid adoption of AI SIEM/XDR with autonomous response, driven by measurable containment gains and ROI.

• GenAI runtime governance standardizes:

  • DLP, prompt-injection defenses, and policy enforcement across endpoints, browsers, SaaS, and APIs will consolidate into unified control planes.

• Cloud and data platforms merge into security operations:

  • CNAPP telemetry and data platforms will feed AI-driven SOC pipelines, improving prevention, detection, and response.

• Sovereign AI tightens infra-security linkages:

  • Regulatory and energy constraints shape where and how AI runs; nations and regulated sectors will require sovereign-by-design stacks.

• Browser and developer toolchains emerge as frontline controls:

  • Secure browsers plus hermetic, reproducible builds will be foundational for governing human and machine development workflows.

Practical Recommendations

• Adopt a platform-first posture:

  • Consolidate around AI-native SOC/SIEM integrated with CNAPP and DLP. Evaluate SentinelOne (AI-native SIEM + Prompt) and Palo Alto Networks (Prisma AIRS + XSIAM) for breadth and automation depth.

• Establish GenAI runtime guardrails:

  • Enforce DLP and prompt-injection controls at endpoints, browsers, and APIs. Ensure policy alignment across employee and application/service agents.

• Treat the browser as a strategic enforcement plane:

  • Deploy secure enterprise browsers with fine-grained AI access controls, session isolation, and data exfiltration policies.

• Modernize SDLC for AI:

  • Use hermetic toolchains, signed/reproducible builds, and AI code-review agents to reduce supply-chain and model risk (Grid Dynamics-style patterns).

• Plan for sovereignty and resilience:

  • Map workloads to jurisdictions, energy availability, and export-control portability. For large-scale model work, anticipate NVIDIA-driven cycles and licensing dependencies.

• Measure outcomes, not just controls:

  • Track mean time to contain, incident likelihood reduction, ROI, and AI traffic risk posture to prioritize investments and validate platform choices.

Appendix: Thematic Cross-Company Highlights

• Convergence of data, AI, and security is the defining inflection: both SentinelOne and Palo Alto Networks emphasize AI-driven operations with integrated data visibility and cloud security.
• Sovereign AI is now a revenue line, not a concept: NVIDIA’s >$20B forecast and hyperscaler CapEx underscore durable demand with security and compliance embedded.
• Governance is shifting left and right: from Meta’s safety-first frontier AI stance to Grid Dynamics’ secure SDLC and enterprises’ browser/endpoint enforcement, controls span the full lifecycle.

Analysis of "AI Security" in Palo Alto Networks, Inc. Q4 2025 Earnings Transcript

Palo Alto Networks places significant strategic emphasis on AI security as a critical and rapidly evolving area within its cybersecurity portfolio. The discussion highlights both the business opportunity and the company’s proactive approach to addressing the emerging risks associated with widespread AI adoption.

Context and Strategic Positioning

• Rapid AI Adoption and Emerging Risks:
  Management underscores the explosive growth in generative AI (GenAI) usage, citing an internal study showing a 890% increase in GenAI traffic in 2024 and a doubling of related data security incidents. This sets the stage for AI security as a pressing customer need driven by a new and complex attack surface.

• Acquisition and Product Innovation:
  The company completed the acquisition of Protect AI in the quarter, which complements its existing AI firewall capabilities. This acquisition is positioned as a timely move to strengthen their AI security offerings.

• Launch of Prisma AIRS:
  Palo Alto Networks introduced Prisma AIRS, described as the "industry's most comprehensive AI security platform." This unified platform aims to provide end-to-end security across AI applications, agents, models, and data sets, addressing blind spots and ensuring compliance. This product launch signals a major step in the company’s AI security strategy.

• Dual-Focus Strategy:
  The company emphasizes a two-sided approach to AI security:

  1. Securing AI infrastructure and AI models that organizations build and deploy.
  2. Securing how employees use third-party AI services, with granular visibility and control.

  This comprehensive vision includes integrated Data Loss Prevention (DLP) controls to protect sensitive data, highlighting a holistic security framework.

• Customer Conviction and Market Leadership:
  The transcript references a strong pipeline for AI security offerings and growing customer conviction in Palo Alto Networks’ strategy. The company frames its AI security initiatives as part of a broader pattern of anticipating and leading technology shifts, citing past leadership in next-generation firewalls, cloud security, SASE, and proactive SOX-powered SIEM (XSIAM).

Business Implications and Forward-Looking Statements

• Market Opportunity:
  The rapid increase in AI usage and associated security incidents creates a large and urgent market opportunity for AI security solutions. Palo Alto Networks is positioning itself as a first-mover and innovator in this space.

• Platform Evolution:
  AI security is integrated into the company’s broader platformization strategy, reinforcing its competitive differentiation through integrated, evolving platforms that deliver superior security outcomes.

• Customer Promise:
  The company commits to staying ahead of the threat landscape, promising customers continuous innovation and protection against emerging AI-related threats.

Relevant Transcript Excerpts

"Our timely acquisition of Protect AI, which we completed this quarter coupled with our native abilities around our AI firewalls are driving conversations across many platform customers and prospects around securing the AI infrastructure."

"In early Q4, we introduced Prisma AIRS, industry's most comprehensive AI security platform. AIRS is a unified platform that empowers organizations to deploy AI bravely, by providing end-to-end security across every AI app, agent model and data set."

"We are unique in providing a strategy for both sides of the landscape, each with integrated DLP controls to protect our customers' bill sensitive data."

"The chart you see here tells a powerful story. It's not just a graph of numbers going up. It's a visual representation of our customers' growing conviction in our strategy."

Summary

Palo Alto Networks views AI security as a critical growth area driven by the rapid adoption of generative AI and the associated increase in security risks. The company is actively expanding its capabilities through acquisitions (Protect AI), new product launches (Prisma AIRS), and a comprehensive dual-sided security strategy that covers both AI infrastructure and employee use of third-party AI tools. This approach is integrated into their broader platform vision, reinforcing their market leadership and commitment to innovation in cybersecurity. The company’s messaging conveys confidence in capturing the expanding AI security market and maintaining a competitive edge through continuous evolution and customer-centric solutions.

Arlo Technologies, Inc. highlights "AI security" as a key driver in its subscription and services business growth, positioning it as a strategic pillar for revenue expansion and customer value enhancement.

Context and Discussion of AI Security

The term "AI security" appears in the context of the company’s subscription service platform, specifically referenced as Arlo Secure 6, described as a "robust AI security service platform." This platform is credited with helping to increase the average revenue per user (ARPU) and the lifetime value (LTV) of subscribers:

"The launch of Arlo Secure 6, our robust AI security service platform, helped drive retail and direct subscriber monthly ARPU to over $15 in the quarter, growing our subscriber LTV to $840."

This indicates that Arlo is leveraging AI capabilities within its security services to enhance the value proposition for customers, which in turn supports higher recurring revenue and profitability.

Strategic Positioning and Business Implications

• Revenue Growth and Profitability: The AI security platform is directly linked to strong financial performance in the subscription segment, which now constitutes over 60% of total revenue. The company reports a 30% year-over-year increase in service revenue and a record 85% non-GAAP service gross margin, underscoring the profitability of this AI-driven service model.

• Customer Demand and Market Expansion: The AI security service is part of a broader strategy to expand the subscriber base and increase ARPU. The company added 218,000 paid subscriptions in the quarter, reaching 5.1 million total subscribers, with expectations for continued ARPU expansion as more annual subscribers migrate to the AI-enhanced service plans.

• Product and Channel Strategy: The AI security platform launch coincides with the largest product release in Arlo’s history, including over 100 new SKUs across multiple product segments. This suggests a coordinated approach to integrate AI security features with new hardware offerings, enhancing shelf presence and consumer appeal.

• Partnerships and Technology Integration: The company also announced a strategic partnership with ADT, North America’s largest security company, focusing on key technology integrations. While details are forthcoming, this partnership likely leverages Arlo’s AI security capabilities to strengthen market reach and competitive positioning.

Summary

Arlo Technologies treats "AI security" not just as a product feature but as a core element of its subscription service growth strategy. The AI-driven Arlo Secure 6 platform is a significant contributor to increased ARPU, subscriber LTV, and overall service revenue growth. This focus on AI security supports Arlo’s ambition to be a leading SaaS player in the security market, driving both top-line growth and margin expansion. The integration of AI security with new product launches and strategic partnerships further underscores its central role in Arlo’s competitive and financial outlook.

Key quote:

"The launch of Arlo Secure 6, our robust AI security service platform, helped drive retail and direct subscriber monthly ARPU to over $15 in the quarter, growing our subscriber LTV to $840."

Analysis of "AI Security" in Zscaler, Inc.'s Q4 2025 Earnings Transcript

Zscaler places AI security as a core strategic growth pillar alongside Zero Trust Everywhere and Data Security Everywhere. The company highlights AI security as a rapidly expanding market opportunity driven by the explosive adoption of AI/ML technologies and the new security challenges they introduce.

1. Strategic Positioning and Market Opportunity

• Zscaler views AI security as a billion-dollar growth vector and a key part of its platform innovation roadmap heading into fiscal 2026.
• The company emphasizes the transformational impact of AI on enterprises, noting a 3500% increase in AI/ML traffic on its cloud in the past year.
• AI adoption is creating new cyber risks such as model jailbreaking, prompt injection, and model poisoning, which Zscaler aims to address with innovative security solutions.

"We have entered an era of omnipresent AI, which is fundamentally transforming enterprises and is leading to an explosive growth of AIML traffic... The adoption of AI at this breakneck pace is creating new security challenges, such as model jailbreaking, prompt injection, model poisoning, and more."

• AI security is integrated into Zscaler’s broader platform, which already serves a significant portion of the Global 2000 and Fortune 500 companies, underscoring strong market validation.

2. Product Innovation and Solution Offerings

• Zscaler has launched AI Guard and GenAI security offerings to secure AI applications and workloads.
• The company is innovating in two primary AI security areas:
  1. Security for AI applications: Protecting AI apps and access from both traditional cyber threats and emerging AI-specific attacks.
  2. Agentic AI security: Securing agent-to-agent and agent-to-application communication using Zero Trust principles, extending their proven platform to new AI-driven communication models.

"To address these emerging security challenges, we are innovating in two primary areas. First, security for AI applications... we recently launched Zscaler AI Guard, which is being tested by a significant number of large customers... Our second area of AI innovations is agentic, which includes AgenTeq SecOps and AgenTeq ITOps."

• The company is also developing an AI-powered SOC (Security Operations Center) solution aimed at simplifying operations, reducing alert fatigue, automating threat hunting, and predicting breaches, which will help customers reduce costs and complexity.

3. Customer Adoption and Go-to-Market Approach

• AI security solutions are primarily being adopted through upsell within the existing large customer base, leveraging strong CIO and CSO relationships.
• Zscaler also uses design partnerships with customers to co-develop AI security solutions, indicating a collaborative approach to product innovation.
• While upsell is the main growth driver, AI security is also opening doors for new logo acquisition.

"Most of the new stuff we bring in majority of that will come from upsell opportunity but there are many solutions that are opening doors for new logos as well."

• Customers initially focus on securing public AI services (e.g., Google Gemini), then move to securing private AI models and applications in data centers or public clouds.
• The company recently launched AI guardrails to secure private AI models, which is generating strong early interest.

"Our customers first start to have secure use of public AI such as Gemini of the world... The next piece became how about secure my private applications, my private models... From there, we recently launched an offering We call it AI guardrails. It's early stage, but it is having tremendous interest."

4. Financial Impact and Growth Outlook

• AI security is part of the platform segments that collectively surpassed $1 billion in ARR in Q4.
• The company expects AI security to grow at a faster pace than some other segments, though it is currently smaller in scale compared to Zero Trust and Data Security.

"AI security is somewhat smaller, but we expect it to grow at a faster pace as well."

• The strong customer demand for AI security solutions contributed to Zscaler’s 22% year-over-year ARR growth, surpassing $3 billion.
• The company is confident that AI security, combined with Zero Trust and Data Security, will continue to drive strong growth and platform adoption in fiscal 2026.

Summary

Zscaler treats AI security as a strategic growth pillar and a critical component of its evolving cybersecurity platform. The company is actively innovating to address the unique security challenges posed by AI adoption, including securing AI applications, agentic AI communications, and AI-powered security operations. With strong customer interest, early deployments of AI Guard and AI guardrails, and integration into a broader Zero Trust platform, Zscaler is positioning itself to capitalize on the rapidly expanding AI security market. The company’s messaging underscores confidence in AI security as a high-growth, high-potential segment that complements its existing strengths and drives both upsell and new customer acquisition.

Selected Quotes

"We are seeing significant customer interest in our powerful AI security solutions, including our new AI Guard and GenAI security offerings."

"The adoption of AI at this breakneck pace is creating new security challenges... To address these emerging security challenges, we are innovating in two primary areas."

"We recently launched Zscaler AI Guard, which is being tested by a significant number of large customers."

"Our customers first start to have secure use of public AI such as Gemini of the world... From there, we recently launched an offering We call it AI guardrails. It's early stage, but it is having tremendous interest."

"AI security is somewhat smaller, but we expect it to grow at a faster pace as well."

This analysis reflects Zscaler’s clear strategic focus on AI security as a key driver of future growth and innovation within its cybersecurity platform.

Okta, Inc. positions AI security as a critical and strategic frontier in its identity security offerings, emphasizing innovation, market leadership, and future growth opportunities. The discussion around AI security in the Q2 2026 earnings transcript highlights several key themes:

Strategic Positioning and Innovation

Okta frames AI security as the "next frontier" in identity management and security, underscoring its role in evolving the industry architecture to be both more valuable and more secure. The company introduces a new open standard called cross-app access, which is designed to control AI agents' access to data and systems securely:

"Securing AI is the next frontier. Our introduction of a new open standard called cross-app access is a key part of the solution. This is an important innovation that helps control what AI agents can access, allowing us to help make our customers and ISVs more secure and providing better end-user experience."

This innovation enables AI agents to operate within the identity security fabric safely and flexibly, supporting integration with other technologies. The mention of strong partner interest—including AWS, Boomi, Box, Ryder, and Zoom—signals early ecosystem adoption and validation.

Market Leadership and Competitive Landscape

Okta emphasizes its pioneering role in the modern identity market, particularly in securing a broad range of identities, including AI agents:

"Okta has pioneered the modern identity market... To this day, we remain the only modern, comprehensive, cloud-native solution built to secure every identity, from employees to customers to nonhuman machine identities to AI agents without locking customers in."

The company contrasts its independent and neutral platform approach with recent market moves by competitors, suggesting that Okta’s flexibility and comprehensive coverage position it well for continued leadership.

Customer Engagement and Industry Influence

Okta highlights strong engagement around AI security, noting over 1,100 attendees at a recent identity summit focused on the topic and promoting its upcoming Oktane Conference as a major event for sharing how organizations can build, deploy, and manage AI agents securely at scale:

"At our Oktane Conference next month, we will share how we are enabling every organization to build, deploy, and manage AI agents safely, securely, and at scale."

This demonstrates Okta’s commitment to thought leadership and community building around AI security.

Business Implications and Outlook

The company links AI security directly to its growth narrative and product portfolio expansion:

"We're pleased with our Q2 results and we're excited about the future with our growing portfolio of modern identity solutions and how Okta secures AI."

Okta sees AI security as a driver of demand for its identity security fabric, which simplifies control and strengthens protection across organizations. This suggests that AI security is not only a technological focus but also a key element in Okta’s long-term profitable growth strategy.

Summary

Okta’s discussion of AI security in the Q2 2026 earnings transcript reveals a clear strategic emphasis on:

• Innovating with new standards (cross-app access) to securely manage AI agents.
• Leveraging its unique position as a cloud-native, comprehensive identity platform.
• Engaging partners and customers actively through events and ecosystem collaboration.
• Positioning AI security as a growth catalyst within its broader identity security portfolio.

This approach underscores Okta’s confidence in maintaining leadership in identity security amid the rising importance of AI technologies in enterprise environments.

Okta, Inc. positions AI security as a critical and strategic frontier in its identity security offerings, emphasizing innovation, market leadership, and future growth opportunities. The discussion around AI security in the Q2 2026 earnings transcript highlights several key themes:

Strategic Positioning and Innovation

Okta frames AI security as the "next frontier" in identity management and security, underscoring its role in evolving the industry architecture to be both more valuable and more secure. The company introduces a new open standard called cross-app access, which is designed to control AI agents' access to data and systems securely:

"Securing AI is the next frontier. Our introduction of a new open standard called cross-app access is a key part of the solution. This is an important innovation that helps control what AI agents can access, allowing us to help make our customers and ISVs more secure and providing better end-user experience."

This innovation enables AI agents to operate within the identity security fabric safely and flexibly, supporting integration with other technologies. The mention of strong partner interest—including AWS, Boomi, Box, Ryder, and Zoom—signals early ecosystem adoption and validation.

Market Leadership and Competitive Landscape

Okta emphasizes its pioneering role in the modern identity market, particularly in securing a broad range of identities, including AI agents:

"Okta has pioneered the modern identity market... To this day, we remain the only modern, comprehensive, cloud-native solution built to secure every identity, from employees to customers to nonhuman machine identities to AI agents without locking customers in."

The company contrasts its independent and neutral platform approach with recent market moves by competitors, suggesting that Okta’s flexibility and comprehensive coverage position it well for continued leadership.

Customer Engagement and Industry Influence

Okta highlights strong engagement around AI security, noting over 1,100 attendees at a recent identity summit focused on the topic and promoting its upcoming Oktane Conference as a major event for sharing how organizations can build, deploy, and manage AI agents securely at scale:

"At our Oktane Conference next month, we will share how we are enabling every organization to build, deploy, and manage AI agents safely, securely, and at scale."

This demonstrates Okta’s commitment to thought leadership and community building around AI security.

Business Implications and Outlook

The company links AI security directly to its growth narrative and product portfolio expansion:

"We're pleased with our Q2 results and we're excited about the future with our growing portfolio of modern identity solutions and how Okta secures AI."

Okta sees AI security as a driver of demand for its identity security fabric, which simplifies control and strengthens protection across organizations. This suggests that AI security is not only a technological focus but also a key element in Okta’s long-term profitable growth strategy.

Summary

Okta’s discussion of AI security in the Q2 2026 earnings transcript reveals a clear strategic emphasis on:

• Innovating with new standards (cross-app access) to securely manage AI agents.
• Leveraging its unique position as a cloud-native, comprehensive identity platform.
• Engaging partners and customers actively through events and ecosystem collaboration.
• Positioning AI security as a growth catalyst within its broader identity security portfolio.

This approach underscores Okta’s confidence in maintaining leadership in identity security amid the rising importance of AI technologies in enterprise environments.

Who Uses Adobe: A Comparative User-Segment Report (Q3 2025) Executive Summary

Adobe’s user base spans large enterprises and global brands, marketing and advertising agencies, public sector institutions, and education. Adoption is anchored by Creative Cloud, Express, Acrobat (including the AI Assistant), Firefly, GenStudio, and Adobe Experience Platform (AEP) with its AI Assistant. Momentum indicators—such as 14,000+ organizations added to Express in Q3, 70% AI Assistant use among eligible AEP customers, and >30% QoQ MAU growth for Firefly—suggest broad-based, AI-led expansion across segments.

Adobe User Landscape: Segments and Named Customers

• Enterprises and global brands
  • Named: Disney, FedEx, Home Depot, Meta, MetLife, Ulta, Volkswagen, Allegis Group, DSV, Intuit, KKR, Lloyds Bank, Lockheed Martin, Vivendi, Amgen, Australia Post, Dick’s Sporting Goods.
  • What they use: Creative Cloud, Express, Firefly, GenStudio for content production; Acrobat + AI Assistant for document workflows; AEP + AI Assistant for data-driven experiences.
• Global marketing and advertising agencies
  • Named: Dentsu (brands include Carat, iProspect, dentsu X, Dentsu Creative, Tag, Merkle); Stagwell.
  • What they use: Creative Cloud and Express within GenStudio for scaled creative operations; AEP where customer data activation and personalization are critical.
  • Notable scale: Enterprise-wide rollout across approximately 68,000 employees at a global marketing group, with standardized workflows across multiple agency brands.
• Public sector
  • Named: Ontario (Provincial Government), Virginia State Government; California partnership delivering AI-enabled Express, Acrobat AI Assistant, and Firefly.
  • What they use: Acrobat and AI Assistant for document-heavy workflows; Express and Firefly to modernize content and communication; AEP where constituent engagement requires data integration and governance.
• Education (K–12 and Higher Ed)
  • Characteristics: Broad access for students, faculty, and staff; strong adoption of Express and Acrobat for learning content, collaboration, and paperwork.
  • What they use: Express for multimodal creation; Acrobat + AI Assistant for reading, summarizing, and authoring support; Firefly for safe, commercially viable generative content.
• Internal and cross-enterprise rollouts
  • Pattern: Large organizations standardizing on Adobe to unify creative-to-document workflows and accelerate go-to-market content, often tying creative assets to downstream experience activation via AEP.

Comparative View of Who Uses What Product Adoption Signals by Segment

• Express
  • 14,000+ organizations added in Q3; 4x quarter-over-quarter growth versus a year ago.
  • Pull strongest in education and marketing-centric enterprises; accelerates social, short-form, and campaign content.
• Acrobat, Acrobat AI Assistant, and Acrobat Studio
  • Acrobat Studio launched (PDF Spaces + AI Assistant + Express), bundling creation and collaboration.
  • Particularly resonant in public sector, education, and enterprise back-office functions with heavy document workloads.
• Firefly and GenStudio
  • Firefly App MAU up 30% QoQ; downloads and subscriptions growing.
  • Safe, commercially viable generative content for brands; embedded in Creative Cloud and GenStudio for production-scale creation.
• Adobe Experience Platform (AEP) + AI Assistant
  • 70% of eligible AEP customers leverage AI Assistant.
  • Indicates strong enterprise and agency adoption where data activation and personalization are key.
• AI-led growth
  • AI-first and AI-infused products driving high ARR; AI-influenced ARR surpassed $5B, signaling cross-segment monetization.

Segment-by-Segment Analysis Enterprises & Global Brands

• Why Adobe: Need for brand-safe, large-scale content production and governance; measurable personalization tied to customer data; AI to compress content and document cycle times.
• How they use it:
  • Creative Cloud + GenStudio + Express for production pipelines and omnichannel asset adaptation.
  • Firefly for compliant, generative scaling of imagery and variations.
  • Acrobat + AI Assistant to streamline contract, policy, and operational documentation.
  • AEP + AI Assistant to unify data, build audiences, and automate insights and activations.
• Outcome: Faster campaign velocity, consistent brand execution, and improved marketing ROI via data-driven targeting.

Global Marketing Agencies

• Why Adobe: Need to industrialize creative operations across many brands and markets; ensure interoperability across studios and accounts.
• How they use it:
  • Standardized creative stack (Creative Cloud + Express in GenStudio) across multiple agency brands for repeatable, governed creation.
  • AEP to connect creative with audience strategy and performance activation.
• Outcome: Network-wide workflow consistency, talent productivity gains, and tighter loop from creative to media activation.

Public Sector

• Why Adobe: Modernize services, reduce manual document burden, and broaden accessibility.
• How they use it:
  • Acrobat + AI Assistant for summarization, drafting, and document collaboration.
  • Express and Firefly for accessible public communications and visual storytelling.
  • Select AEP usage where constituent experience and data governance intersect.
• Outcome: Faster service delivery, better citizen communications, and reduced paperwork friction.

Education

• Why Adobe: Democratize creative tools for students and faculty; support AI-assisted literacy and creation with safe models.
• How they use it:
  • Express for classroom multimedia projects and faculty content.
  • Acrobat + AI Assistant for studying, summarization, and collaborative review.
  • Firefly for safe generative exploration and asset creation.
• Outcome: Scaled creative literacy and productivity across campuses with accessible, AI-enabled workflows.

Internal/Cross-Enterprise Rollouts

• Why Adobe: Consolidate toolchains, enable cross-team collaboration, and embed AI in everyday work.
• How they use it:
  • Creative Cloud Pro and Express to standardize content creation.
  • Acrobat Studio to integrate documents, AI assistance, and lightweight design.
  • AEP where customer data coordination is strategic.
• Outcome: Unified governance, lower tool fragmentation, and measurable productivity uplifts.

Comparative Insights: Similarities and Differences

• Common threads
  • AI as the adoption catalyst across all segments (content generation, summarization, assistance).
  • Desire for safe, commercially usable generative outputs (Firefly) and trusted document intelligence (Acrobat AI Assistant).
  • Need to connect creative work with activation and measurement (GenStudio and AEP).
• Key differences
  • Enterprises and agencies emphasize scale, brand governance, and data activation; public sector and education emphasize accessibility, document modernization, and safe AI.
  • Agencies prioritize cross-brand standardization and speed-to-asset; enterprises lean into end-to-end integration from creative to customer experience.
  • Education focuses on broad enablement and learning outcomes, with lighter governance compared to regulated enterprises and governments.

Case Snapshots

• Global marketing group rollout
  • Approximately 68,000 employees working across agency brands (Carat, iProspect, dentsu X, Dentsu Creative, Tag, Merkle) standardize on Creative Cloud and share via Express within GenStudio—elevating reuse and governance while accelerating content throughput.
• State-level education partnership (California)
  • Provisioning AI-enabled Express, Acrobat AI Assistant, and Firefly to students, faculty, and staff—expanding access to creative and document AI while maintaining safety and compliance.
• Public sector modernization
  • Governments such as Ontario and Virginia scale Acrobat and Express to streamline documentation and improve constituent communications.

Implications for Adobe’s Go-To-Market and Product Strategy

• Double down on AI co-pilots across creation, documents, and data activation, given strong uptake (70% AEP AI Assistant use among eligible customers).
• Package value for large networks and enterprises (e.g., Acrobat Studio, Creative Cloud Pro, GenStudio) to encourage standardized, cross-brand rollouts.
• Continue education and public-sector partnerships where Express/Acrobat adoption is strong and generative safety is paramount.
• Leverage Firefly growth to drive upsell from Express and Creative Cloud into GenStudio and AEP-driven activation.

Key Takeaways

• Who uses Adobe: Large enterprises and global brands; marketing and advertising agencies; public sector institutions; and educational organizations—along with large internal rollouts within customer organizations.
• What they use: A connected stack—Creative Cloud, Express, Firefly, GenStudio, Acrobat + AI Assistant (and Acrobat Studio), and AEP + AI Assistant.
• Why they use it: To scale content creation safely, modernize document workflows, and connect creative output to data-driven customer experiences.
• Adoption is accelerating: Express added 14,000+ organizations in Q3 (4x YoY QoQ growth), Firefly MAU rose 30% QoQ, 70% of eligible AEP customers use AI Assistant, and AI-influenced ARR surpassed $5B.