Innovation Insights: A CTO’s Perspective

Is Agentic AI and Big Enterprise Software on a Collision Course?

Introduction

If you own the leading enterprise software vendors in your portfolios, it is highly likely that you are among those of us experiencing “a rollercoaster ride” on your holdings over the past 12 months and may be interested in what’s been driving the volatility as well as where the markets may be headed as it relates to enterprise software vendors (also often referred to as Software as a Service or SaaS vendors).

In this month’s CTO Newsletter, we explore how several of the enterprise software leaders are being impacted by AI and how these firms are evolving their individual business strategies (from both product portfolios and go -to-market approaches) to address growing client base interest in intelligent automation of their core business processes, often in a highly “tailored” manner specific to the unique characteristics of the industry and business processes.

What’s going on

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Last year, most enterprise software stocks peaked between July and October 2025 amid AI enthusiasm, with Oracle reaching the highest valuations. Since then, the S&P 500 Software & Services index has experienced one of its most severe corrections in recent history. Analysts still expect software and services earnings per share to expand around 13% in 2026, yet traders have driven the S&P 500 Software & Services Industry Group down by 20% year-to-date, with the drawdown now standing at 28% from the October high. Despite these price declines, analyst ratings remain mostly positive, with "Strong Buy" or "Buy" ratings across most leading SaaS enterprise software companies, suggesting that Wall Street views current prices as potential buying opportunities

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Software on a Collision Course?

From our perspective at Gideon, the enterprise software sector appears to be in a correction phase after excessive AI-driven optimism in mid-2025, with valuations resetting to more sustainable levels as the market demands clearer evidence of profitable AI revenue generation.

Today, the markets appear to be at an inflection point in enterprise software. Large Language Models (LLMs) and the rapid emergence of agentic AI are no longer experimental overlays or “bolt-on” capabilities integrated with APIs, they are now beginning to rewire how value is created, delivered, and monetized across the enterprise software stack. While both traditional workflow automation and agentic AI focus strongly on transactional processing efficiency, AI agents are also capable of intelligence, adaptability, and continuous learning. They can take autonomous, goal-directed actions and process and optimize workflows at an unprecedented rate, without the latency issues associated with more traditional methods. These AI-powered, interconnected agents embedded into workflows will even be able to adapt dynamically to changes in the environment, detecting and fixing issues independently and then moving to prevent them from happening again.

The recent market reactions to these new agentic AI capabilities reveal three interconnected fears about AI's impact on SaaS business models including: First, direct workflow replacement by Agentic AI, second, per-seat pricing model erosion, as firms move to less predictable usage-based pricing, outcome-based pricing, and platform fees rather than seat One recent selloff catalyst occurred with the announced release of a seemingly innocuous add-on from Anthropic, specifically their legal plugin for Claude Cowork. This plugin is aimed squarely at the legal workflows of an in-house counsel, including contract review, NDA triage, compliance checks, briefings, and templated responses, and is configurable to an organization's playbook and risk tolerances. The market immediately reacted, with the legal sector feeling the most acute pain. The London Stock Exchange Group fell 13%, Thomson Reuters plunged 16%, CS Disco sank 12%, and Legalzoom.com plummeted 20%. European legal publishing company RELX plunged 14%.

In our view, the biggest threat to the traditional enterprise software vendor’s revenue growth has been the rapid release of Agentic AI code development tools. These Agentic AI Coding tool suites have rapidly evolved from simple autocomplete tools in 2023-2024 to full agentic coding platforms in 2025, with all major AI companies now competing in this space.

These include Anthropic’s Claude Code and OpenAI’s Codex, both cloud-based software engineering agents that can work on multiple tasks in parallel. In addition, Microsoft has offered GitHub Copilot, originally developed as an autocomplete tool, has now evolved into an agentic AI partner with asynchronous coding agent capabilities integrated into the GitHub platform and Visual Studio 2026, an AI-native Integrated Development Environment (IDE) with integrated Azure MCP Server for agentic workflows. Google’s Antigravity features a dedicated "Agent Manager" interface for orchestrating multiple AI agents working asynchronously across editor, terminal, and browser. Other notable tools that while not from the largest AI companies are rapidly gaining traction in the agentic coding space include Cursor (by Anysphere), Windsurf (by Codeium), Augment Code which specializes in intelligent code refactoring and Amazon Q Developer, a purpose-built for cloud-native development with strong security controls.

Tools like Claude Code and Codex can plan and architect custom features from natural language descriptions, write, test, and debug multi-file implementations, handle complex refactors and architectural changes, all while ensuring that the new customizations follow your firm’s existing patterns and conventions. Claude Code can connect with a firm’s overall enterprise ecosystem and excels at automating repetitive enterprise customization tasks. This makes Agentic AI code development suites like Claude Code and Codex particularly effective for enterprises needing to rapidly develop and maintain custom CRM features, business logic, and integrations while maintaining code quality and security. This is followed by subscriptions, and third, an "Agentic Services" Future with less human and enterprise software involvement.

Software on a Collision Course?

Let’s take a deeper look at a few example business applications:

Workflow Orchestration in ERP/CRM Platforms: AI agents are auto-resolving IT service tickets, processing complex finance transactions, intelligently ordering expedited and/or rerouting inventory supplies in one location to cover inventory shortages in another with higher demand, all without the need for human input. Early adopters are seeing 20% to 45% faster workflow cycles and significant reductions in back-office costs.

Customer Service and Case Management: AI agents are handling insurance claims from end to end, including document validation, triage, and escalation or payout. As a result, claim handling time has been cut by 40% in some instances, and net promoter scores – a measure of how likely a customer is to refer an insurer to an acquaintance – have increased significantly, providing insurers with both higher retention and new client acquisition.

Sales and Marketing Automation: Agentic AI-driven campaign managers adapt and optimize consumer touchpoints in real time. Firms using these solutions have experienced a very significant increase in lead conversion after implementing agentic campaign routing.

Finance and Risk Monitoring: In both pilot and now initial production environments, AI agents are autonomously detecting anomalies, forecasting cash needs, and recommending reallocation across accounts. As a result, risk events will be reduced significantly.

These shifts are already visible in revenue growth profiles, margin structures, and most notably, public market valuations of incumbent platforms such as SAP, Oracle, Workday, Salesforce, and ServiceNow, among others.

In the near term, AI has served as a revenue accelerator for several leading enterprise vendors. AI copilots, embedded analytics, natural-language interfaces, and workflow automation are driving higher attach rates, seat expansion, and incremental subscription tiers. Salesforce’s Einstein, ServiceNow’s Now Assist, SAP’s Joule, and Workday’s AI-powered HR and finance features have all helped reinforce customer stickiness and supported modest re-acceleration in top-line growth. Importantly, these AI features are being monetized as premium add- ons, enabling vendors to defend pricing even in an otherwise budget-constrained IT spending environment.

However, the margin implications are more complex. While software historically benefited from near-zero marginal costs, AI inference, model training, and cloud computing introduce a materially different cost curve. Gross margins for AI-enhanced offerings are structurally lower than traditional SaaS modules, at least at current scale and efficiency levels. The leading vendors are absorbing these costs today, leveraging scale, long-term cloud contracts, and selective in-house model development. However, over time, margin expansion will depend on improved model efficiency, workload optimization, and successful repricing of AI value to customers.

Enterprise software vendor stock prices and valuations reflect this tension. Public markets are simultaneously rewarding AI “optionality” while discounting execution risk. Enterprise software leaders with credible AI roadmaps, deep proprietary data, and strong distribution are commanding premium multiples relative to slower- moving peers. At the same time, investors are increasingly scrutinizing whether AI-driven revenue is truly incremental or simply cannibalizing existing licenses under a new label. The dispersion in valuations across the sector is widening, signaling that AI is becoming a differentiator rather than a blanket tailwind.

Looking ahead, the collision course is less about AI replacing enterprise software and more about redefining it. Agentic AI, in particular, threatens to abstract away traditional application layers by automating outcomes rather than tasks (as Robotic Process Automation (RPA) does, automating existing business processes).

Software on a Collision Course?

This raises a strategic question for the incumbents: will they remain systems of record and workflow orchestration, or will they evolve into systems of intelligence and action? Those that successfully integrate agentic capabilities into their core platforms stand to deepen moats and extend their relevance to clients. Those that do not risk being disintermediated by the lighter-weight, AI-native competitors operating above or around their solution stacks.

How AI Coding Tools Are Changing Build vs. Buy Dynamics in IT Departments

These Agentic AI code development tools are true game changers for the software development industry. Further, they are already significantly reshaping how Fortune 1000 IT departments approach traditional Build vs. Buy analysis for new enterprise application development. Here's our analysis of how this transformation is unfolding and its potential impact on enterprise software vendors:

The Shifting Economic Equation

In corporate IT departments, the traditional Build vs. Buy analysis has historically heavily favored purchasing enterprise software application suites because custom development was prohibitively expensive, slow, and risky in terms of successful outcomes. AI coding tools are fundamentally altering this calculation by reducing development costs and accelerating timelines, enabling the creation of truly differentiated capabilities. Companies can now build custom solutions in weeks rather than months, with smaller teams and at much lower total costs.

Key Factors Driving the Shift:

 Reduced Development Costs: AI tools slash the need for large development teams, making custom builds more affordable

 Accelerated Timelines: What previously took 6-12 months can now be accomplished in weeks

 Democratized Development: Low-code/no-code AI platforms enable non-technical users to build applications, allowing functional users to be directly involved in capability creation

 Lower Technical Debt: AI-assisted development produces cleaner, more maintainable code with automated testing

Let’s take a look at the BUY vs. BUILD decision process

When BUYING Enterprise Software is More Attractive

The experiences of three decades, working with well over a hundred clients selecting and implementing enterprise software solutions, have convinced me that most companies will continue to BUY leading Enterprise Software solutions for core business functions rather than build them from scratch.

Software on a Collision Course?

When BUYING Enterprise Software is More Attractive

The primary reasons to BUY Enterprise Software include:

 Speed to deployment and deployment of proven workflow capabilities, often within weeks or months.

 Lower Initial (upfront) capital requirements because subscription-based pricing models avoid large upfront development fees; recurring subscription fees are also easier to budget for than variable custom software maintenance costs.

 Commercial solutions reduce risk by shifting it to the vendor. McKinsey & Co. 6 notes that large custom- developed IT projects run about 45% over budget and 7% over schedule, while delivering 56% less value than predicted.

 Standardized business processes are a big benefit to enterprise solutions. If 80% of your needed functionality can be acquired in an “Out-of-the-Box” vendor solution, the decision scale should tilt heavily to Buy. Enterprise software most often incorporates “battle-tested” business process workflows proven by thousands of users across multiple industries.

 Vendor support & expertise is another benefit to purchasing enterprise software. Vendors typically provide implementation services, training, and ongoing support as part of the subscription fee. In addition, access to robust partner ecosystems for implementation and integration along with a continuous stream of updates and feature enhancements managed by the vendor are often very important to corporations working in constantly evolving regulatory and business environments.

 Proven Security capabilities are another benefit of enterprise software. Most vendors have certifications (SOC2, ISO, industry-specific) already in place with built-in audit trails, governance frameworks, and regulatory compliance features.

 Scalability & Future-Proofing are also a plus, as vendors continuously invest in R&D, keeping platforms current with emerging technologies

When BUILD Becomes More Attractive:

Fortune 1000 companies are increasingly likely to choose "BUILD" when they need solutions precisely tailored to proprietary workflows and when customization requirements are extensive. The BUILD option is also favored when software’s capabilities are needed to provide significant competitive differentiation that is not immediately available to market competitors. In addition, in some cases, CIOs and CFOs are frustrated with high ongoing SaaS subscription costs, inflation, and vendor lock-in. That said, you need to know what you’re going to build in terms of functional and performance requirements, and thus many firms will need to work with specialty development firms that can better leverage the agentic AI code development tools.

Software on a Collision Course?

The Hybrid Approach

Notably, the modern reframe for IT departments shows that the decision is no longer limited to just two options but now includes a third: Build, Buy, or Hybrid. Many CIOs are adopting a "BUY the commodity, BUILD the differentiator" strategy, in which core enterprise business functions are purchased from established vendors while custom development focuses on the 20% of capabilities that create competitive advantage. For most companies with significant legacy software already in place, API-first architectures allow flexible composition of bought and built components

The new reality is that while agentic AI tools lower the barriers to custom development, the message from enterprise business buyers is clear: “speed to value trumps perfect customization.”

The Impact on Enterprise Software Vendors

Revenue Growth Pressures: Despite the fact that Enterprise Software will likely not go away any time soon, the data suggests enterprise software vendors are already feeling pressure, though the impact is nuanced. Looking at current enterprise software revenue trends, we note that:

• Salesforce revenue growth has decelerated from 11% (FY2024) to 8-9% (FY2025-2026)

• Growth rates are slowing even as companies invest heavily in AI features like Agentforce

• The industry faces what analysts call an “inflection point” where enterprises especially those focused on deploying next-generation capabilities, become convinced that custom solutions better serve their needs

Leading Enterprise Software companies looking to the future are adapting through several strategies:

1. Embedding AI Deeply: Salesforce, SAP, and HubSpot are integrating AI agents and automation throughout their platforms to increase stickiness

2. Shifting to “Malleable Software”: Moving beyond rigid off-the-shelf solutions to platforms that customers can easily customize using AI, creating hybrid build-buy models

3. New Pricing Models: Introduction of “Agentic Enterprise License Agreements” to provide predictable pricing as AI reduces seat-based revenue

4. Platform Strategies: Positioning themselves as foundations for custom development rather than complete solutions

Software on a Collision Course?

The Likely Outcome: A Blended Future

Research indicates that rather than a wholesale shift to building, we'll see a hybrid approach:

• 67% success rate for purchased AI solutions vs. only 33% for purely internal builds • Most Fortune 1000 companies will adopt a "build WITH buy" strategy—using vendor platforms as foundations while customizing the last mile • Core capabilities will remain purchased, while differentiating features are built in-house

For enterprise software vendors, the financial implications are:

1. Revenue Model Transformation: Shift from selling complete solutions to selling platforms and foundations 2. Margin Pressure: More competition from custom-built alternatives will constrain pricing power 3. Market Segmentation: Commodity features become harder to monetize; vendors must focus on true differentiation 4. Consolidation Risk: Smaller, single-purpose vendors face the greatest threat as companies build those capabilities themselves

The estimated 70-80% of enterprise software currently purchased off-the-shelf could shift significantly, perhaps to 50-60% over the next 3-5 years. This represents a potential multi-billion-dollar revenue headwind for traditional enterprise software vendors, particularly for those offering features that AI tools can replicate quickly.

However, vendors that successfully pivot to become AI-powered platforms that enable customization, rather than just sell fixed solutions, are likely to expand their addressable market by serving as the foundation layer and platform for enterprise custom development.

Gideon Conclusions

From a portfolio perspective, we continue to favor enterprise software companies that combine three attributes: (1) mission-critical data assets, (2) distribution at global enterprise scale, and (3) disciplined capital allocation as AI investment ramps. AI is not destroying enterprise software economics, but it is reshaping them. The winners will be those who can translate technological leadership into durable, profitable growth driven by new value-driven pricing models that recover component costs with respectable margins, rather than temporary narrative-driven valuation uplift.

This disruption is going to take a while to shake out; however, the large installed base leaders like Microsoft, Oracle, SAP, Salesforce, ServiceNow, etc., can leverage their cash flows and deep client business knowledge to build and or acquire/integrate emerging AI-native solutions into their suites.

Software on a Collision Course?

Strategic Investment Framework for AI-Driven Enterprise Software

Below is a framework that highlights some key investment criteria for analyzing the current AI-infused enterprise software landscape:

Appendix 1: Enterprise Software Vendor AI Capabilities & Integration

The following is a summary analysis of key leading firms that examines how specific enterprise software categories are being impacted and disrupted by AI, focusing on capabilities, valuation shifts, and strategic actions around LLM and agentic AI integration.

Sector Deep-Dive: AI’s Impact on Leading Enterprise Software Providers

Enterprise Desktop & Resource Planning (ERP) SaaS Vendors

Microsoft Capabilities & Integration: Microsoft is deeply integrating agentic and generative AI across its enterprise suite primarily through Microsoft Copilot, which is powered by OpenAI's GPT models (Microsoft's strategic partner and major investment). In Microsoft 365 (Office, Outlook, Teams, etc.), Copilot is an AI assistant that can draft emails, summarize meeting transcripts, generate PowerPoint presentations from prompts, analyze Excel data, and create Word documents—all via natural language commands. In Dynamics 365, Microsoft has embedded Copilot capabilities for sales (automating email responses, meeting summaries, and CRM data entry), customer service (generating support responses and case summaries), and supply chain management. These implementations use the Azure OpenAI Service as the underlying infrastructure, giving enterprises access to GPT-4 and other models with Microsoft's enterprise-grade security and compliance features.

Beyond Copilot's chat-based assistance, Microsoft is building more autonomous agentic AI capabilities through Copilot Studio and autonomous agents that can execute multi-step workflows independently. These agents can be configured to monitor business processes, take actions based on triggers (like automatically processing invoices, managing inventory, or handling customer inquiries), and integrate with Microsoft's Dataverse and Power Platform for workflow automation. Microsoft also leverages its own models, like Phi (smaller language models), for on-device and specialized tasks, alongside partnerships with OpenAI for frontier models and Meta for open-source models.

Software on a Collision Course?

These agents can be configured to monitor business processes, take actions based on triggers (like automatically processing invoices, managing inventory, or handling customer inquiries), and integrate with Microsoft's Dataverse and Power Platform for workflow automation. Microsoft also leverages its own models, like Phi (smaller language models), for on-device and specialized tasks, alongside partnerships with OpenAI for frontier models and Meta for open-source models.

Market Perception & Competitive Positioning: Microsoft's AI initiatives have significantly boosted its market position, with the company becoming one of the most valuable publicly traded companies globally (often trading places with Apple and Nvidia for the top spot). The Azure OpenAI Service and Copilot offerings have driven substantial growth in Azure cloud revenue and positioned Microsoft as the enterprise leader in AI adoption, with analysts crediting AI for adding hundreds of billions to its market capitalization. However, the massive capital expenditure required for AI infrastructure (tens of billions annually in data centers and chips) and questions about Copilot monetization timelines have created some investor scrutiny around return on investment, even as the strategic positioning strengthens Microsoft's competitive moat against rivals like Google and Amazon.

SAP Capabilities & Integration: SAP has embedded generative AI and agentic features across its cloud suite, including Joule Everywhere and SAP Business Data Cloud, enabling conversational access to real-time enterprise data and customizable AI agents across finance, logistics, HR, and field services. Integration with partner platforms like Databricks strengthens data unification and positions SAP to leverage enterprise process context for AI outcomes.

Market Perception & Competitive Positioning: AI has been a tailwind for SAP cloud adoption, helping drive revenue growth in cloud offerings and indirectly lifting investor sentiment given cloud’s higher recurring margins. While SAP’s share price has experienced periods of volatility, early market commentary points to AI integration and cloud transformation as central drivers of renewed valuation support.

Oracle Capabilities & Integration: Oracle’s AI strategy spans embedded AI agents within its Fusion Cloud applications that offer predictive forecasting, human capital automation, and supply chain reliability. Oracle’s AI Agent Studio and multi-LLM orchestration tools aim to enable predictive decision support and end-to-end automation without requiring separate AI tooling. At SuiteWorld 2025, Oracle announced NetSuite Next which they claim is designed to be collaborative, insightful, adaptive, intuitive, and trustworthy, building in powerful and practical AI capabilities, including embedded conversational intelligence, agentic workflows, and natural language search capabilities. The platform will roll out in North America starting mid-2026, with other regions following through 2026-2027.

Market Perception & Competitive Positioning: Oracle has benefited from strong demand for AI-enhanced applications and infrastructure, with markets rewarding its vertical integration across database, middleware, and application layers thus creating a bundled revenue stack. Analyst commentary highlights Oracle’s AI momentum as a differentiator among legacy ERP suites.

Software on a Collision Course?

Workday Capabilities & Integration: Workday is incorporating machine learning and early agentic automation to support HR and financial function workflows. Collaborative efforts, such as Workday’s federated data efforts with Salesforce, are intended to reduce data fragmentation and accelerate AI adoption across HR, finance, and CRM systems.

Market Perception & Competitive Positioning: Workday’s stock performance has been more muted relative to some peers, reflecting cautious investor expectations on monetizing AI features and the longer sales cycles typical of HCM and ERP deals with deep process integration.

Customer Relationship Management (CRM) SaaS Vendors

Salesforce Capabilities & Integration: Salesforce has been among the most proactive in embedding LLMs and agentic AI. Its Agentforce 360 and Einstein AI extend autonomous task execution across sales, service, and marketing workflows. Salesforce reports that Agentforce and Data 360 AI products have generated significant annual recurring revenue, reportedly approaching $1.4 billion, and have materially contributed to the raised fiscal 2026 revenue forecasts.

Salesforce also claims broad AI workload usage within its own operations and emphasizes AI’s role in reducing repetitive tasks while enabling deeper predictive insights for customers.

Market Perception & Competitive Positioning: Markets have responded positively to Salesforce’s AI monetization milestones, including raised guidance and expanding AI ARR. The company’s emphasis on embedded AI differentiates it from peers and contributes to a premium valuation multiple relative to slower adopters.

HubSpot Capabilities & Integration: HubSpot has integrated generative AI capabilities into marketing, sales, and service workflows to automate content generation, lead scoring, and triage. AI advances are widening its appeal among mid - market customers seeking intelligent automation without extensive customization.

Market Perception & Competitive Positioning: While not at the scale of Salesforce, HubSpot’s valuation has reflected growing confidence in its AI strategy as a competitive differentiator in CRM—seen especially in markets where small and medium enterprises prioritize ease of use paired with embedded intelligence.

Software on a Collision Course?

Service Management (SM)

ServiceNow Capabilities & Integration: ServiceNow is integrating generative AI and agentic automation across its IT Service Management (ITSM), Customer Service Management (CSM), HR, and security workflows. Recent partnerships (e.g., with OpenAI) aim to embed advanced LLM and multimodal AI into autonomous task execution within enterprise workflows. According to a recent BCG study, ServiceNow's AI agents and Now Assist capabilities are automating IT, HR, and operational processes, reducing manual workloads by up to 60%.

In addition, ServiceNow, who’s costly $2.85 billion (cash and stock) acquisition of Moveworks was completed in December 2025, is integrating Moveworks' leading conversational AI assistant and enterprise search with ServiceNow's AI platform to create a powerful, agentic AI front door for automating enterprise workflows.

Market Perception & Competitive Positioning: Despite strong AI revenue growth forecasts and expanded use cases (e.g., Now Assist and Pro Plus bundles), ServiceNow’s stock performance has at times lagged broader AI optimism, as investor expectations calibrate with the pace of AI monetization and execution risk on high valuation acquisitions.

ServiceNow’s AI stack competes with Salesforce’s CRM-oriented intelligent workflows and Oracle’s integrated operational AI, with market discourse emphasizing that AI will increasingly blur historical software category boundaries.

Financial Services Software SaaS Vendors

Bloomberg Capabilities & Integration: Bloomberg integrates NLP and AI tools across terminals and analytics platforms to accelerate research, signal detection, and automated insight generation from complex financial datasets. AI-enabled workflows assist portfolio analysis, risk modeling, and data synthesis—redefining user expectations for real-time decision support across banking and asset management.

Market Perception & Competitive Positioning: As a privately held entity, traditional stock valuation metrics don’t apply; however, Bloomberg’s sustained investment in AI enhances its competitive moat against newer data- intensive financial platforms and reflects its strategic orientation toward AI-augmented workflows.

SS&C Technologies Capabilities & Integration: SS&C provides the financial industry software for fund administration, risk, and operations. SS&C has been embedding AI to automate reconciliation, compliance checks, and reporting tasks. Advanced automation and intelligent process orchestration reduce manual work in financial operations at scale.

Market Perception & Competitive Positioning: SS&C’s valuation has reflected investor interest in financial SaaS providers with recurring revenue and automation capabilities. We believe AI integration strengthens SS&C recurring revenue predictability and operational efficiency, thus reinforcing valuation support. Markets have tended to reward firms demonstrating quantifiable productivity and cost savings from AI implementations.

Software on a Collision Course?

Appendix 2: The New Breed – AI-Native Innovators Most of the newer generation of AI-native companies are still privately owned and looking to disintermediate, augment and/or replace aspects (or subsets) of the traditional ERP platform capabilities that lack true AI- native, intelligent process automation capabilities. Key Functional Capabilities Across AI-native Platforms currently available in the market include:

• Workflow orchestration where AI agents are auto-resolving IT service tickets, rerouting supplies to cover inventory shortages, and triggering procurement flows without human input • Automated financial close, revenue recognition, multi-entity consolidation • Real-time forecasting and predictive maintenance • Cloud cost management and unit economics tracking • Automated data entry and enrichment (eliminating 90+% of manual journal entries in many cases) • AI-powered prospecting, lead scoring, and pipeline management • Conversation intelligence and deal coaching • Automated email sequences and personalized outreach • Real-time forecasting with risk identification

These capabilities are designed to provide several competitive advantages over today’s legacy systems inclusive of:

• Speed of solution implementation trending to days to weeks vs. 12-18 months for traditional ERP systems

• Cost reduction from both lower tool stack costs, solution implementation costs as well as the previously noted efficiency gains (~25-40% reduction in manual work)

• Adaptability based on modular architectures allowing rapid customization without IT involvement

• AI-native Architectures that are developed from scratch for AI vs. retrofitting AI onto decades-old code

• Natural Language Interfaces that allow business users to configure workflows without coding

Illustrative companies in the AI-Native category include:

Everest Systems (Private, $140M funding)

• Founded by: Former SAP executives Franz Färber and Sandeep Chopra • Core Capabilities:

Full end-to-end, AI-native ERP for software businesses that emerged from four years of stealth in

November Complex revenue recognition, multi-entity consolidation, international operations Live Sandbox for real-time experimentation with AI agents Automated financial analysis and routine data processing Targets mid-market software companies initially, expanding to inventory and project-based

businesses

Software on a Collision Course?

Doss (Private, $18M Series A from Theory Ventures)

• Founded: 2022 by Wiley Jones • Core Capabilities:

Adaptive Resource Platform (ARP) with novel ERP architecture designed for both humans and LLMs Implementation in as fast as two days versus years for incumbent systems Natural language interfaces for workflow and UI customization Unified inventory management, order processing, accounting, and production 30+ prebuilt integrations with composable, modular architecture Serves food & beverage, consumer goods, construction, distribution industries

In the area of CRM, new AI-native players include:

Aurasell (Private, $30M seed funding)

• Founded by: Jason Eubanks (former CRO at Harness) and Sri Bandi (ex-SVP at Harness) • Investors: N47, Menlo Ventures, Unusual Ventures • Core Capabilities:

First AI-native CRM platform consolidating 15+ disjointed tools into a single intelligent system Agentic workflows and custom AI agents enabling seamless human-agent collaboration, reducing license fees

by up to 50% Unified data model integrating ideal customer profiles, buyer personas, forecasting, and conversation

intelligence Territory planning, prospecting workflows, deal coaching, forecasting Built-in CPQ (Configure, Price, Quote) functionality Targets mid-market B2B companies at $20,000+ annual subscriptions

Creatio (Private, $1.2B valuation, $200M funding)

• Founded: 2014 by CEO Katherine Kostereva • Investors: Sapphire Ventures, StepStone Group, Volition Capital, Horizon Capital • Core Capabilities:

AI-native CRM that embeds AI directly into its platform, transforming CRM from a static data management

tool into an intelligent system that anticipates needs and automates workflows No-code platform for building custom AI agents ("Digital Talent") Composable architecture with unified, actionable, and composable design principles Integration across Microsoft Outlook, Zoom, and Microsoft Teams Serves major enterprises, including Colgate-Palmolive, AMD, and MetLife Starting at $25 per user per month

1. Anthropic https://www.anthropic.com/webinars/future-of-ai-at-work-introducing-cowork

2. Bain & Company https://www.bain.com/insights/is-agentic-ai-the-inflection-point-for-scaling-ERP-transformations/

3. Bessemer Venture Partners https://www.bvp.com/atlas/lessons-from-building-an-ai-powered-star-system-erp-with-everest- systems

4. BCG https://www.bcg.com/publications/2025/how-agentic-ai-is-transforming-enterprise-platforms

5. CIO Magazine https://www.cio.com/article/4068993/netsuite-unveils-netsuite-next-expands-suitecloud-platform-adding- ai.html

6. Fein International: https://www.feinternational.com/blog/saas-metrics-value-saas-business

7. Foundation Capital: https://foundationcapital.com/where-ai-is-headed-in-2026/

8. Fortune https://fortune.com/2026/02/06/anthropic-claude-opus-4-6-stock-selloff-new-upgrade/

9. Goldman Sachs: https://www.goldmansachs.com/insights/articles/why-ai-companies-may-invest-more-than-500-billion-in- 2026

10. IEEE: https://techblog.comsoc.org/2025/12/22/hyperscaler-capex-600-bn-in-2026-a-36-increase-over-2025-while-global- spending-on-cloud-infrastructure-services-skyrockets/

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1. Legal IT Insider https://legaltechnology.com/2026/02/03/anthropic-unveils-claude-legal-plugin-and-causes-market-meltdown/

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2. Tech Funding News https://techfundingnews.com/aurasell-raises-30m-seed-for-ai-native-crm-platform/

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3. Trading View- InVezz: https://www.tradingview.com/news/invezz:751717ae0094b:0-looking-ahead-to-2026-why-hyperscalers- can-t-slow-spending-without-losing-the-ai-war/

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4. Venture Beat https://venturebeat.com/ai/crm-provider-creatio-launches-first-ai-native-platform-with-agentic-digital-talent-built- in

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